The document discusses different Level 1 trigger selections for fat jets that could be alternatives to the baseline single jet trigger. It analyzes selections based on the sum of energy of closeby jets, HT constructed from low-energy jets, and multijet triggers. HT(C)200, which requires the scalar sum of transverse energies of jets with ET > 20 GeV and |η| < 2.5(3.2) to be above 200 GeV, seems to have the best overall performance compared to the other selections for the models considered. Combining selections could help recover some inefficiencies at lower event filter thresholds.
This document discusses optical pulse measurement techniques. It begins with definitions of ultrafast pulses and challenges in directly measuring them. It then covers several correlation-based measurement methods, including field autocorrelation, cross-correlation, and intensity autocorrelation. Field autocorrelation provides intensity but no phase information, so it cannot distinguish transform-limited from chirped pulses. Cross-correlation with a short reference pulse can retrieve the signal pulse spectrum. Intensity autocorrelation also measures intensity but provides additional fringe information about the pulse shape.
Impact of Auto-tuning of Kernel Loop Transformation by using ppOpen-ATTakahiro Katagiri
SPNS2013, December 5th -6th, 2013, Conference Room, 3F, Bldg.1, Earthquake Research Institute (ERI), The University of Tokyo, December 6th, 2013, ppOpen-HPC and Automatic Tuning (Chair: Hideyuki Jitsumoto), 1330-1400
First results from the full-scale prototype for the Fluorescence detector Arr...Toshihiro FUJII
The Fluorescence detector Array of Single-pixel Telescopes (FAST) is a design concept for the next generation of ultrahigh-energy cosmic ray (UHECR) observatories, addressing the requirements for a large-area, low-cost detector suitable for measuring the properties of the highest energy cosmic rays. In the FAST design, a large field of view is covered by a few pixels at the focal plane of a mirror or Fresnel lens. Motivated by the successful detection of UHECRs using a prototype comprised of a single 200 mm photomultiplier-tube and a 1 m2 Fresnel lens system [Astropart.Phys. 74 (2016) 64-72], we have developed a new full-scale prototype consisting of four 200 mm photomultiplier-tubes at the focus of a segmented mirror of 1.6 m in diameter. In October 2016 we installed the full-scale prototype at the Telescope Array site in central Utah, USA, and began steady data taking. We report on first results of the full-scale FAST prototype, including measurements of artificial light sources, distant ultraviolet lasers, and UHECRs.
35th International Cosmic Ray Conference — ICRC2017 18th July, 2017
Bexco, Busan, Korea
IRJET - Optical Emission Technique for Understanding the Spark Gap Discharge ...IRJET Journal
This document presents a study on using optical emission spectroscopy (OES) to understand spark gap discharge properties in argon, nitrogen, and helium gases at pressures ranging from 0-2 kg/cm2. Emission spectra were obtained during spark gap discharge in the different gases. Plasma temperature and electron density were estimated from emission line intensities and lifetime profiles. Results showed that electron temperature was higher in argon compared to nitrogen. Electron density ranged from 108-1015 cm-3 over emission time. The study provides insight into spark gap discharge mechanisms and properties under different gas conditions.
The document discusses quantum chemistry calculations and high performance computing. It provides details on methods like Hartree-Fock and density functional theory. Hartree-Fock calculations scale cubically with system size, while density functional theory scales linearly by using techniques like order-N methods. The document also mentions several quantum chemistry software packages that can perform these calculations in parallel using MPI and OpenMP on high performance computing clusters.
The document summarizes the FAST (Fluorescence detector Array of Single-pixel Telescopes) project, which aims to detect ultra-high energy cosmic rays and neutral particles. FAST proposes an array of single-pixel telescopes with simple optics and cameras to cover a large target area more cost-effectively than existing fluorescence detectors. Each FAST telescope would have a 1m2 mirror, four photomultiplier tubes, and a 30°x30° field of view. An array of 500 stations with 12 telescopes each could achieve coverage of 150,000 km2. Initial observations with three FAST telescopes have detected air shower signals in coincidence with an existing fluorescence detector.
The document describes the Fluorescence detector Array of Single-pixel Telescopes (FAST) project. Some key points:
- FAST will consist of an array of single-pixel telescopes to detect ultra-high energy cosmic rays via fluorescence technique.
- A prototype was constructed and tested in 2015-2017. Data was collected at the Telescope Array site over 21 km and compared to simulations.
- The project aims to build a larger array with more telescopes that could achieve 4 times the exposure of the Telescope Array or 10 times that of the Pierre Auger Observatory. This would allow studies of cosmic rays above 10^19.5 eV.
- An update on the project's progress in design
4 matched filters and ambiguity functions for radar signalsSolo Hermelin
Matched filters (Part 1 of 2) maximizes the output signal-to-noise ratio for a known radar signal at a predefined time.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
This document discusses optical pulse measurement techniques. It begins with definitions of ultrafast pulses and challenges in directly measuring them. It then covers several correlation-based measurement methods, including field autocorrelation, cross-correlation, and intensity autocorrelation. Field autocorrelation provides intensity but no phase information, so it cannot distinguish transform-limited from chirped pulses. Cross-correlation with a short reference pulse can retrieve the signal pulse spectrum. Intensity autocorrelation also measures intensity but provides additional fringe information about the pulse shape.
Impact of Auto-tuning of Kernel Loop Transformation by using ppOpen-ATTakahiro Katagiri
SPNS2013, December 5th -6th, 2013, Conference Room, 3F, Bldg.1, Earthquake Research Institute (ERI), The University of Tokyo, December 6th, 2013, ppOpen-HPC and Automatic Tuning (Chair: Hideyuki Jitsumoto), 1330-1400
First results from the full-scale prototype for the Fluorescence detector Arr...Toshihiro FUJII
The Fluorescence detector Array of Single-pixel Telescopes (FAST) is a design concept for the next generation of ultrahigh-energy cosmic ray (UHECR) observatories, addressing the requirements for a large-area, low-cost detector suitable for measuring the properties of the highest energy cosmic rays. In the FAST design, a large field of view is covered by a few pixels at the focal plane of a mirror or Fresnel lens. Motivated by the successful detection of UHECRs using a prototype comprised of a single 200 mm photomultiplier-tube and a 1 m2 Fresnel lens system [Astropart.Phys. 74 (2016) 64-72], we have developed a new full-scale prototype consisting of four 200 mm photomultiplier-tubes at the focus of a segmented mirror of 1.6 m in diameter. In October 2016 we installed the full-scale prototype at the Telescope Array site in central Utah, USA, and began steady data taking. We report on first results of the full-scale FAST prototype, including measurements of artificial light sources, distant ultraviolet lasers, and UHECRs.
35th International Cosmic Ray Conference — ICRC2017 18th July, 2017
Bexco, Busan, Korea
IRJET - Optical Emission Technique for Understanding the Spark Gap Discharge ...IRJET Journal
This document presents a study on using optical emission spectroscopy (OES) to understand spark gap discharge properties in argon, nitrogen, and helium gases at pressures ranging from 0-2 kg/cm2. Emission spectra were obtained during spark gap discharge in the different gases. Plasma temperature and electron density were estimated from emission line intensities and lifetime profiles. Results showed that electron temperature was higher in argon compared to nitrogen. Electron density ranged from 108-1015 cm-3 over emission time. The study provides insight into spark gap discharge mechanisms and properties under different gas conditions.
The document discusses quantum chemistry calculations and high performance computing. It provides details on methods like Hartree-Fock and density functional theory. Hartree-Fock calculations scale cubically with system size, while density functional theory scales linearly by using techniques like order-N methods. The document also mentions several quantum chemistry software packages that can perform these calculations in parallel using MPI and OpenMP on high performance computing clusters.
The document summarizes the FAST (Fluorescence detector Array of Single-pixel Telescopes) project, which aims to detect ultra-high energy cosmic rays and neutral particles. FAST proposes an array of single-pixel telescopes with simple optics and cameras to cover a large target area more cost-effectively than existing fluorescence detectors. Each FAST telescope would have a 1m2 mirror, four photomultiplier tubes, and a 30°x30° field of view. An array of 500 stations with 12 telescopes each could achieve coverage of 150,000 km2. Initial observations with three FAST telescopes have detected air shower signals in coincidence with an existing fluorescence detector.
The document describes the Fluorescence detector Array of Single-pixel Telescopes (FAST) project. Some key points:
- FAST will consist of an array of single-pixel telescopes to detect ultra-high energy cosmic rays via fluorescence technique.
- A prototype was constructed and tested in 2015-2017. Data was collected at the Telescope Array site over 21 km and compared to simulations.
- The project aims to build a larger array with more telescopes that could achieve 4 times the exposure of the Telescope Array or 10 times that of the Pierre Auger Observatory. This would allow studies of cosmic rays above 10^19.5 eV.
- An update on the project's progress in design
4 matched filters and ambiguity functions for radar signalsSolo Hermelin
Matched filters (Part 1 of 2) maximizes the output signal-to-noise ratio for a known radar signal at a predefined time.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
The document discusses constraints on Higgs-portal models of weakly interacting massive particles (WIMPs) from Large Hadron Collider (LHC) data. It analyzes limits from various LHC search channels, including vector boson fusion (VBF), mono-jet, and mono-Z, on heavy Higgs-portal WIMPs with masses between 100 GeV to 350 GeV. The VBF channel provides the strongest constraints, excluding coupling strengths greater than approximately 0.5 for vector WIMPs in this mass range. LHC searches can probe WIMPs with very small predicted relic abundances from thermal freeze-out.
Quantum networks with superconducting circuits and optomechanical transducersOndrej Cernotik
Connecting distant chips in a quantum network is one of biggest challenges for superconducting quantum computers. Superconducting systems operate at microwave frequencies; transmission of microwave signals through room-temperature quantum channels is impossible due to the omnipresent thermal noise. I will show how two well-known experimental techniques—parity measurements on superconducting systems and optomechanical force sensing—can be combined to generate entanglement between two superconducting qubits through a room-temperature environment. An optomechanical transducer acting as a force sensor can be used to determine the state of a superconducting qubit. A joint readout of two qubits and postselection can lead to entanglement between the qubits. From a conceptual perspective, the transducer senses force exerted by a quantum object, entering a new paradigm in force sensing. In a typical scenario, the force sensed by an optomechanical system is classical. I will argue that the coherence between different states of the qubit (which give rise to different values of the force) can be preserved during the measurement, making it an important resource for quantum communication.
Observing ultra-high energy cosmic rays with prototypes of the Fluorescence d...Toshihiro FUJII
1. The document describes observations of ultra-high energy cosmic rays using prototypes of the Fluorescence detector Array of Single-pixel Telescopes (FAST) project in both hemispheres.
2. FAST aims to observe cosmic rays with energies over 10^20 eV using an array of low-cost telescopes to cover a large ground area.
3. Initial results are presented from FAST prototypes installed at the Telescope Array site, including coincident observations of air showers with the Telescope Array fluorescence detector and reconstruction of shower parameters from FAST data.
This doctoral thesis models pulse propagation in optical fibers using finite-difference methods. It summarizes the numerical model, which solves the nonlinear Schrödinger equation describing pulse propagation using Crank-Nicholson and split-step Fourier methods. It tests the model's accuracy by comparing results to analytic solutions and a commercial simulation program. Effects like dispersion, loss, self-phase modulation and polarization mode dispersion are modeled. Additional models are presented for optical amplifiers and filters used in the fiber links.
This document provides a summary of signal analysis and Fourier series. It begins by defining periodic functions and using examples to determine the period of periodic signals. It then introduces Fourier series and decomposes periodic signals into a sum of sines and cosines. It describes how these sine and cosine functions form an orthogonal basis and can be used to represent any periodic signal. The document also presents the Fourier series in complex exponential form and uses an example of a square wave to illustrate the decomposition. It defines harmonics and discusses how to determine the amplitude and phase of each harmonic component from the Fourier series coefficients.
Stochastic Processes describe the system derived by noise.
Level of graduate students in mathematics and engineering.
Probability Theory is a prerequisite.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
White light emission from graphene quantum dotsTufan Ghosh
We report herein the synthesis and characterization of unmodified graphene oxide quantum dots (GOQDs) with white-light-emitting properties, upon photoexcitation at 340 nm. The Commission International de l’Éclairage (CIE) 1931 chromaticity coordinates for GOQDs (x = 0.29, y = 0.34) suggest that highly pure white-light emission was achieved. A detailed mechanistic study was carried out utilizing UV–visible absorption, steady-state and time-resolved fluorescence spectroscopy, and dynamic light scattering (DLS) techniques to understand the origin of the white-light emission. The results taken together suggest that GOQDs could self-assemble in solution and thus transform the luminescence behavior. Furthermore, the results indicate that the pH of the medium also plays a crucial role in assisting the aggregation to generate the white-light emission. The concentration-dependent DLS measurements support a cooperative mechanism for the aggregation kinetics in the system. More importantly, the study suggests that white-light emission can be generated from unmodified graphene oxide quantum dots by tuning their nanoscopic aggregation properties.
Graphene synthesis: Rate and Mechanistic Investigation of Eu(OTf)2-Mediated R...Tufan Ghosh
We describe a fast, efficient, and mild approach to prepare chemically reduced graphene oxide (rGO) at room temperature using divalent europium triflate {Eu(OTf)2}. The characterization of solution-processable reduced graphene oxide has been carried out by various spectroscopic (FT-IR, UV–visible absorption, and Raman), microscopic (TEM and AFM), and powder X-ray diffraction (XRD) techniques. Kinetic study indicates that the bimolecular rate constants for the reduction of graphene oxide are 13.7 ± 0.7 and 5.3 ± 0.1 M–1 s–1 in tetrahydrofuran (THF)–water and acetonitrile (ACN)–water mixtures, respectively. The reduction rate constants are two orders of magnitude higher compared to the values obtained in the case of commonly used reducing agents such as the hydrazine derivative, sodium borohydride, and a glucose–ammonia mixture. The present work introduces a feasible reduction process for preparing reduced graphene oxide at ambient conditions, which is important for bulk production of GO. More importantly, the study explores the possibilities of utilizing the unique chemistry of divalent lanthanide complexes for chemical modifications of graphene oxide.
This document discusses methods for estimating the remaining useful life (RUL) of lithium-ion batteries using data-driven prognostics. It presents a bilinear kernel regression model that uses capacity fade data from batteries to predict RUL while accounting for noise in the training data. The model transforms the data using a kernel and employs LASSO regularization to provide sparse predictions and prevent overfitting. An experiment applies the model to capacity data from 8 test batteries and shows it can accurately estimate RUL even in the presence of noise in the training and test data.
The document discusses Fourier transforms and their applications in signal processing. It provides examples of Fourier transforms of common signals like sine waves, delta functions, Gaussians, and square waves. It also examines how changing parameters of sampled signals like sampling rate and duration affect the Fourier transform and frequency resolution. The document demonstrates measuring multiple frequencies within a signal and discusses the discrete Fourier transform and fast Fourier transform which are used to analyze digital signals and images.
Quantum force sensing with optomechanical transducersOndrej Cernotik
Optomechanical force sensing is an established measurement technique that can reach remarkable precision. In most applications, the system exerting the force on the mechanical oscillator is treated classically and we are not interested in any coherence between states of the system that give rise to different forces. A full quantum treatment, however, enables richer physics since measuring more such systems can lead to interference effects.
In this talk, I will show that the coherence can survive the measurement and can be used for quantum-technological applications. I will consider a model example of spin readout in superconducting qubits. Coupling two transmon qubits to mechanical oscillators and reading out the mechanical positions using a single beam of light provides information on the total spin of the qubits. It is thus possible to conditionally generate entanglement between the two qubits. The system represents a basic quantum network with superconducting circuits. The scheme has modest requirements on the system parameters; it does not require ground-state cooling or resolved-sideband regime and can work with quantum cooperativity moderately larger than unity.
Afterwards, I will consider another scheme, namely nondestructive detection of a single photon using an optomechanical transducer. The basic idea is similar to spin readout; the photon exerts a force on a mechanical oscillator and the the force is measured optically. I will argue that such a measurement is subject to a quantum limit due to backaction of the transducer on the dynamics of the photon and that this result also applies to other techniques of nondestructive photon detection, such as methods using Kerr interaction between the single photon and a meter beam. Finally, I will show numerically that measurement backaction can be evaded when the measurement rate is suitably modulated.
Pressemitteilung 'Fascination of Plants Day' Schweizwd4u
Pressemitteilung - 13. Mai 2013
Internationaler Tag der Pflanze - 18. Mai 2013
Am kommenden Samstag, 18. Mai 2013, findet zum zweiten Mal der Internationale Tag der Pflanze statt.
This document describes a PSpice simulation of a 3-input comparator circuit. It includes the equivalent circuit diagram showing resistors and three voltage inputs. It also includes behavioral voltage sources representing the comparator functionality of outputting 5V if the positive input is greater than either of the negative inputs by over 0.01V. The simulation input and output waveforms are shown.
The document summarizes India's coal industry and reserves. It notes that bulk of India's coal reserves are located in certain southeastern states. While India has substantial coal reserves, production has been insufficient and controversies like coal allocation scams have arisen. Coal remains a key energy source for India in the coming decades. The document also reviews India's regulatory framework for the coal industry and the major players like Coal India. It discusses issues like the coal blocks allocation scam where the CAG estimated a loss of 1.86 lakh crore to the exchequer.
Nacional Financiera (NAFIN) es un banco estatal mexicano encargado de apoyar a pequeñas y medianas empresas a través de financiamiento, capacitación y asistencia técnica. NAFIN publica boletines y mantiene un portal web para que las empresas accedan a sus programas y recursos. La institución puede servir como fuente de consulta secundaria para investigaciones de mercado ya que sus publicaciones contienen informes sobre industrias, empresas y tendencias económicas.
This document describes the equivalent circuit and simulation of an output drive using PSpice. The circuit includes transistors, diodes, and resistors to model the output drive. The simulation examines the output when a pulse signal is applied to the input over a 10 microsecond period with a 5 volt high signal and 0 volt low signal.
This document discusses the Research Data Switchboard project, which aims to enable cross-platform discovery between research data services by automating the process of connecting identifiers like DOIs, ORCIDs, and grants. It provides examples of how machines could search the research graph to link related research outputs and data across platforms using identifiers and shortest path algorithms.
El término Web 2.0 comprende aquellos sitios web que facilitan el compartir información, la interoperabilidad, el diseño centrado en el usuario y la colaboración en la World Wide Web.
El documento describe tres parejas formadas por jóvenes donde la mujer se siente atraída por otro hombre que la hace sufrir, dejando al hombre original que la ama desconsolado. Leonel ama a Gaby pero ella prefiere a Tomás, un "cabrón"; Paul se esfuerza por Paulina pero ella elige a Samuel; y Román quiere a Alexia pero ella termina con Abraham.
The document discusses constraints on Higgs-portal models of weakly interacting massive particles (WIMPs) from Large Hadron Collider (LHC) data. It analyzes limits from various LHC search channels, including vector boson fusion (VBF), mono-jet, and mono-Z, on heavy Higgs-portal WIMPs with masses between 100 GeV to 350 GeV. The VBF channel provides the strongest constraints, excluding coupling strengths greater than approximately 0.5 for vector WIMPs in this mass range. LHC searches can probe WIMPs with very small predicted relic abundances from thermal freeze-out.
Quantum networks with superconducting circuits and optomechanical transducersOndrej Cernotik
Connecting distant chips in a quantum network is one of biggest challenges for superconducting quantum computers. Superconducting systems operate at microwave frequencies; transmission of microwave signals through room-temperature quantum channels is impossible due to the omnipresent thermal noise. I will show how two well-known experimental techniques—parity measurements on superconducting systems and optomechanical force sensing—can be combined to generate entanglement between two superconducting qubits through a room-temperature environment. An optomechanical transducer acting as a force sensor can be used to determine the state of a superconducting qubit. A joint readout of two qubits and postselection can lead to entanglement between the qubits. From a conceptual perspective, the transducer senses force exerted by a quantum object, entering a new paradigm in force sensing. In a typical scenario, the force sensed by an optomechanical system is classical. I will argue that the coherence between different states of the qubit (which give rise to different values of the force) can be preserved during the measurement, making it an important resource for quantum communication.
Observing ultra-high energy cosmic rays with prototypes of the Fluorescence d...Toshihiro FUJII
1. The document describes observations of ultra-high energy cosmic rays using prototypes of the Fluorescence detector Array of Single-pixel Telescopes (FAST) project in both hemispheres.
2. FAST aims to observe cosmic rays with energies over 10^20 eV using an array of low-cost telescopes to cover a large ground area.
3. Initial results are presented from FAST prototypes installed at the Telescope Array site, including coincident observations of air showers with the Telescope Array fluorescence detector and reconstruction of shower parameters from FAST data.
This doctoral thesis models pulse propagation in optical fibers using finite-difference methods. It summarizes the numerical model, which solves the nonlinear Schrödinger equation describing pulse propagation using Crank-Nicholson and split-step Fourier methods. It tests the model's accuracy by comparing results to analytic solutions and a commercial simulation program. Effects like dispersion, loss, self-phase modulation and polarization mode dispersion are modeled. Additional models are presented for optical amplifiers and filters used in the fiber links.
This document provides a summary of signal analysis and Fourier series. It begins by defining periodic functions and using examples to determine the period of periodic signals. It then introduces Fourier series and decomposes periodic signals into a sum of sines and cosines. It describes how these sine and cosine functions form an orthogonal basis and can be used to represent any periodic signal. The document also presents the Fourier series in complex exponential form and uses an example of a square wave to illustrate the decomposition. It defines harmonics and discusses how to determine the amplitude and phase of each harmonic component from the Fourier series coefficients.
Stochastic Processes describe the system derived by noise.
Level of graduate students in mathematics and engineering.
Probability Theory is a prerequisite.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
White light emission from graphene quantum dotsTufan Ghosh
We report herein the synthesis and characterization of unmodified graphene oxide quantum dots (GOQDs) with white-light-emitting properties, upon photoexcitation at 340 nm. The Commission International de l’Éclairage (CIE) 1931 chromaticity coordinates for GOQDs (x = 0.29, y = 0.34) suggest that highly pure white-light emission was achieved. A detailed mechanistic study was carried out utilizing UV–visible absorption, steady-state and time-resolved fluorescence spectroscopy, and dynamic light scattering (DLS) techniques to understand the origin of the white-light emission. The results taken together suggest that GOQDs could self-assemble in solution and thus transform the luminescence behavior. Furthermore, the results indicate that the pH of the medium also plays a crucial role in assisting the aggregation to generate the white-light emission. The concentration-dependent DLS measurements support a cooperative mechanism for the aggregation kinetics in the system. More importantly, the study suggests that white-light emission can be generated from unmodified graphene oxide quantum dots by tuning their nanoscopic aggregation properties.
Graphene synthesis: Rate and Mechanistic Investigation of Eu(OTf)2-Mediated R...Tufan Ghosh
We describe a fast, efficient, and mild approach to prepare chemically reduced graphene oxide (rGO) at room temperature using divalent europium triflate {Eu(OTf)2}. The characterization of solution-processable reduced graphene oxide has been carried out by various spectroscopic (FT-IR, UV–visible absorption, and Raman), microscopic (TEM and AFM), and powder X-ray diffraction (XRD) techniques. Kinetic study indicates that the bimolecular rate constants for the reduction of graphene oxide are 13.7 ± 0.7 and 5.3 ± 0.1 M–1 s–1 in tetrahydrofuran (THF)–water and acetonitrile (ACN)–water mixtures, respectively. The reduction rate constants are two orders of magnitude higher compared to the values obtained in the case of commonly used reducing agents such as the hydrazine derivative, sodium borohydride, and a glucose–ammonia mixture. The present work introduces a feasible reduction process for preparing reduced graphene oxide at ambient conditions, which is important for bulk production of GO. More importantly, the study explores the possibilities of utilizing the unique chemistry of divalent lanthanide complexes for chemical modifications of graphene oxide.
This document discusses methods for estimating the remaining useful life (RUL) of lithium-ion batteries using data-driven prognostics. It presents a bilinear kernel regression model that uses capacity fade data from batteries to predict RUL while accounting for noise in the training data. The model transforms the data using a kernel and employs LASSO regularization to provide sparse predictions and prevent overfitting. An experiment applies the model to capacity data from 8 test batteries and shows it can accurately estimate RUL even in the presence of noise in the training and test data.
The document discusses Fourier transforms and their applications in signal processing. It provides examples of Fourier transforms of common signals like sine waves, delta functions, Gaussians, and square waves. It also examines how changing parameters of sampled signals like sampling rate and duration affect the Fourier transform and frequency resolution. The document demonstrates measuring multiple frequencies within a signal and discusses the discrete Fourier transform and fast Fourier transform which are used to analyze digital signals and images.
Quantum force sensing with optomechanical transducersOndrej Cernotik
Optomechanical force sensing is an established measurement technique that can reach remarkable precision. In most applications, the system exerting the force on the mechanical oscillator is treated classically and we are not interested in any coherence between states of the system that give rise to different forces. A full quantum treatment, however, enables richer physics since measuring more such systems can lead to interference effects.
In this talk, I will show that the coherence can survive the measurement and can be used for quantum-technological applications. I will consider a model example of spin readout in superconducting qubits. Coupling two transmon qubits to mechanical oscillators and reading out the mechanical positions using a single beam of light provides information on the total spin of the qubits. It is thus possible to conditionally generate entanglement between the two qubits. The system represents a basic quantum network with superconducting circuits. The scheme has modest requirements on the system parameters; it does not require ground-state cooling or resolved-sideband regime and can work with quantum cooperativity moderately larger than unity.
Afterwards, I will consider another scheme, namely nondestructive detection of a single photon using an optomechanical transducer. The basic idea is similar to spin readout; the photon exerts a force on a mechanical oscillator and the the force is measured optically. I will argue that such a measurement is subject to a quantum limit due to backaction of the transducer on the dynamics of the photon and that this result also applies to other techniques of nondestructive photon detection, such as methods using Kerr interaction between the single photon and a meter beam. Finally, I will show numerically that measurement backaction can be evaded when the measurement rate is suitably modulated.
Pressemitteilung 'Fascination of Plants Day' Schweizwd4u
Pressemitteilung - 13. Mai 2013
Internationaler Tag der Pflanze - 18. Mai 2013
Am kommenden Samstag, 18. Mai 2013, findet zum zweiten Mal der Internationale Tag der Pflanze statt.
This document describes a PSpice simulation of a 3-input comparator circuit. It includes the equivalent circuit diagram showing resistors and three voltage inputs. It also includes behavioral voltage sources representing the comparator functionality of outputting 5V if the positive input is greater than either of the negative inputs by over 0.01V. The simulation input and output waveforms are shown.
The document summarizes India's coal industry and reserves. It notes that bulk of India's coal reserves are located in certain southeastern states. While India has substantial coal reserves, production has been insufficient and controversies like coal allocation scams have arisen. Coal remains a key energy source for India in the coming decades. The document also reviews India's regulatory framework for the coal industry and the major players like Coal India. It discusses issues like the coal blocks allocation scam where the CAG estimated a loss of 1.86 lakh crore to the exchequer.
Nacional Financiera (NAFIN) es un banco estatal mexicano encargado de apoyar a pequeñas y medianas empresas a través de financiamiento, capacitación y asistencia técnica. NAFIN publica boletines y mantiene un portal web para que las empresas accedan a sus programas y recursos. La institución puede servir como fuente de consulta secundaria para investigaciones de mercado ya que sus publicaciones contienen informes sobre industrias, empresas y tendencias económicas.
This document describes the equivalent circuit and simulation of an output drive using PSpice. The circuit includes transistors, diodes, and resistors to model the output drive. The simulation examines the output when a pulse signal is applied to the input over a 10 microsecond period with a 5 volt high signal and 0 volt low signal.
This document discusses the Research Data Switchboard project, which aims to enable cross-platform discovery between research data services by automating the process of connecting identifiers like DOIs, ORCIDs, and grants. It provides examples of how machines could search the research graph to link related research outputs and data across platforms using identifiers and shortest path algorithms.
El término Web 2.0 comprende aquellos sitios web que facilitan el compartir información, la interoperabilidad, el diseño centrado en el usuario y la colaboración en la World Wide Web.
El documento describe tres parejas formadas por jóvenes donde la mujer se siente atraída por otro hombre que la hace sufrir, dejando al hombre original que la ama desconsolado. Leonel ama a Gaby pero ella prefiere a Tomás, un "cabrón"; Paul se esfuerza por Paulina pero ella elige a Samuel; y Román quiere a Alexia pero ella termina con Abraham.
Computers can be classified based on their speed, power, and intended use. Personal computers (PCs) are designed for individual use and have moderate power. Workstations are similar to PCs but more powerful and intended for engineering, publishing, and software development. Mini computers support up to 250 users simultaneously while mainframes support hundreds to thousands of users and execute many programs concurrently. Supercomputers are extremely fast and capable of hundreds of millions of instructions per second, used for specialized applications requiring immense calculations.
Trends in Future CommunicationsInternational Workshop - Renato RabeloCPqD
This document summarizes research on electro-optic tunable sparse gratings in lithium niobate waveguides for applications in dense wavelength division multiplexing (DWDM) communication systems. It describes the fabrication of titanium diffused lithium niobate waveguides with silicon dioxide strain gratings to achieve mode conversion between the transverse electric and transverse magnetic modes. Test results show over 96% polarization conversion efficiency across the C-band with a free spectral range of 9.131 GHz. The device operates by applying a voltage to integrated electrodes to tune the polarization response across a wide wavelength range.
This document provides specifications for a standalone rotary axis, including:
- Dimensions such as outside diameter, stage height, and inside diameter.
- Load and torque capabilities.
- Dynamic performance values like maximum speed and acceleration.
- Accuracy and repeatability specifications.
- Electrical specifications for the motor such as torque and back EMF constants.
- Encoder characteristics such as type, resolution, and operating temperature range.
- Materials and environmental compatibility details.
Estimation of misalignment in bearing shaft by signal processing of acoustic ...IAEME Publication
This document presents research on estimating misalignment in a bearing shaft using signal processing of acoustic signals. The researchers recorded acoustic signals from a test bearing under different levels of induced misalignment. They analyzed the signals using fast Fourier transformation (FFT) and wavelet decomposition. FFT results showed peaks corresponding to rotational frequency harmonics. As misalignment increased, the energy value of the fourth peak also increased. Wavelet decomposition allowed visualization of ball impacts, and analysis of these impacts over time allowed calculation of shaft rotation speed. Overall, the analyses demonstrated that signal processing techniques can effectively estimate misalignment levels in a bearing shaft by examining changes in acoustic signals.
Demonstrating Quantum Speed-Up with a Two-Transmon Quantum Processor Ph.D. d...Andreas Dewes
1. Andreas Dewes demonstrated quantum speed-up using a two-transmon quantum processor.
2. The processor realized a universal set of gates including single-qubit rotations and a two-qubit entangling gate through tunable coupling between transmon qubits.
3. Quantum algorithms like Grover's search were implemented on the processor, exhibiting quantum speed-up over classical algorithms for the same tasks.
Design and Implementation of Low Ripple Low Power Digital Phase-Locked LoopCSCJournals
We propose a phase-locked loop (PLL) architecture, which reduces the double frequency ripple without increasing the order of loop filter. Proposed architecture uses quadrature numerically–controlled oscillator (NCO) to provide two output signals with phase difference of π/2. One of them is subtracted from the input signal before multiplying with the other output of NCO. The system also provides stability in case the input signal has noise in amplitude or phase. The proposed structure is implemented using field programmable gate array (FPGA), which dissipates 15.44mw and works at clock frequency of 155.8 MHz.
Comparison of Estimated Torques Using Low Pass Filter and Extended Kalman Fil...IAES-IJPEDS
Torque calculation process is one of the major concerns for controlling induction motors in industry, which requires very accurate state estimation of unmeasurable variables of nonlinear models. This can be solved if the variables used for torque calculation is accurately estimated. This paper presents a torque calculation based on a voltage model represented with a low-pass filter (LPF), and an extended Kalman filter (EKF). The experimental results showed that the estimated torque at low speed based on EKF is more accurate in the expense of more complicated and larger computational time.
This is my previous work (a decade ago) regarding modeling, simulation and design of single-axis CMOS MEMS Gyroscope. Hope it helps those who are still working in this field.
A Novel Extended Adaptive Thresholding for Industrial Alarm SystemsKoorosh Aslansefat
Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system’s performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.
Actuation and Control Design for Safe Wearable Robotic ArmsValerio Salvucci
The document discusses actuation and control design for safe wearable robotic arms. It proposes using redundant biarticular actuators and an infinity norm approach to actuator redundancy resolution. This allows the controllable force at the end effector to match the maximum human force, improving safety. Experimental validation with a bi-articularly actuated and wire-driven robot arm shows the infinity norm approach increases the controllable output force compared to the conventional pseudo-inverse matrix 2-norm approach.
This document provides specifications for the Nippon Pulse PFL35T-48 stepper motor. It includes dimensions, performance characteristics like force and torque curves, electrical properties and operating parameters. The motor has a 48 step resolution and operates at 12V. It can provide between 30-35N of force at 200 pulses per second depending on the thread pitch and excitation type as either unipolar or bipolar.
17.pmsm speed sensor less direct torque control based on ekfMouli Reddy
This document presents a speed sensorless direct torque control method for permanent magnet synchronous motors (PMSM) using an Extended Kalman Filter (EKF). The EKF is used to estimate the stator flux linkage and rotor speed without needing a mechanical speed sensor. This overcomes issues with traditional direct torque control methods like large current and flux ripples. Simulation results show the EKF method maintains the fast torque response of direct torque control while improving dynamic and static performance and robustness to parameter and load variations compared to traditional methods.
Model reduction design for continuous systems with finite frequency specificationsIJECEIAES
This paper is concerned with the problem of model reduction design for continuous systems in Takagi-Sugeno fuzzy model. Through the defined FF H gain performance, sufficient conditions are derived to design model reduction and to assure the fuzzy error system to be asymptotically stable with a FF H gain performance index. The explicit conditions of fuzzy model reduction are developed by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method. 1 1
A Simple Design to Mitigate Problems of Conventional Digital Phase Locked LoopCSCJournals
This paper presents a method which can estimate frequency, phase and power of received signal corrupted with additive white Gaussian noise (AWGN) in large frequency offset environment. Proposed method consists of two loops, each loop is similar to a phase–locked loop (PLL) structure. The proposed structure solves the problems of conventional PLL such as limited estimation range, long settling time, overshoot, high frequency ripples and instability. Traditional inability of PLL to synchronize signals with large frequency offset is also removed in this method. Furthermore, proposed architecture along with providing stability, ensures fast tracking of any changes in input frequency. Proposed method is also implemented using field programmable gate array (FPGA), it consumes 201 mW and works at 197 MHz.
Julio Bravo's Master Graduation ProjectJulio Bravo
The document describes applying an optimal control tracking problem to a wind power system to track desired trajectories of system states. It presents the nonlinear mathematical model of a wind power system and linearizes it around operating points. It then formulates the optimal control problem to minimize errors from desired trajectories, subject to system dynamics. The solution provides control inputs to drive the system states to follow the desired trajectories over time.
Presentacion en ATLAS Calorimetry Calibration Workshop,"Clustering of very lo...CARMEN IGLESIAS
The document summarizes studies on clustering very low energy particles using the ATLAS calorimeter. It discusses using topoclusters with different seed and neighbor cell energy thresholds to better reconstruct particles below 10 GeV. Preliminary conclusions found that a seed threshold of 4 and neighbor threshold of 2 provided the best energy resolution and efficiency for pions, photons, and neutrons compared to other clustering algorithms. Further studies examined the impact of overlapping nearby particles on cluster reconstruction and found the new splitter algorithm in release 8.2.0 did not significantly improve resolution over not using splitting for particles separated by 0.1 or more in deltaR or below 0.1.
Vedran Peric's PhD Defense Presentation: Non-intrusive Methods for Mode Estimation in Power Systems using Synchrophasors
Thesis available at:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-182134
Abstract [en]
Real-time monitoring of electromechanical oscillations is of great significance for power system operators; to this aim, software solutions (algorithms) that use synchrophasor measurements have been developed for this purpose. This thesis investigates different approaches for improving mode estimation process by offering new methods and deepening the understanding of different stages in the mode estimation process.
One of the problems tackled in this thesis is the selection of synchrophasor signals used as the input for mode estimation. The proposed selection is performed using a quantitative criterion that is based on the variance of the critical mode estimate. The proposed criterion and associated selection method, offer a systematic and quantitative approach for PMU signal selection. The thesis also analyzes methods for model order selection used in mode estimation. Further, negative effects of forced oscillations and non-white noise load random changes on mode estimation results have been addressed by exploiting the intrinsic power system property that the characteristics of electromechanical modes are predominately determined by the power generation and transmission network.
An improved accuracy of the mode estimation process can be obtained by intentionally injecting a probing disturbance. The thesis presents an optimization method that finds the optimal spectrum of the probing signals. In addition, the probing signal with the optimal spectrum is generated considering arbitrary time domain signal constraints that can be imposed by various probing signal generating devices.
Finally, the thesis provides a comprehensive description of a practical implementation of a real-time mode estimation tool. This includes description of the hardware, software architecture, graphical user interface, as well as details of the most important components such as the Statnett’s SDK that allows easy access to synchrophasor data streams.
The document summarizes the heavy-ion physics program using the Large Hadron Collider (LHC) detectors. It discusses probing novel regimes of high density saturated gluon distributions and qualitatively new physics. Key observables include jet quenching, quarkonia suppression, and heavy flavor modification to study the quark-gluon plasma produced in Pb-Pb collisions. The ALICE, ATLAS and CMS experiments are well-suited to measure bulk properties and select hard probes over a wide momentum range.
Experimental Investigation of Faults in Roller Element Bearing Using Vibratio...IRJET Journal
This study investigated faults in roller element bearings using vibration analysis. An experimental test rig was constructed to simulate distributed bearing faults. Vibration data was collected from bearings using an accelerometer and analyzed using an FFT analyzer. Both localized and distributed faults were introduced to bearings and their vibration signatures were compared to healthy bearings. Analytical models of defect frequencies were compared to experimental results. The study provides a method to estimate bearing life after introduction of a fault by monitoring vibration over time.
Similar to Trigger Workshop material CERN Anton Osika (20)
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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1. L1 Trigger selections for Fat Jets
Contribution to the Hadronic Calibration 2013 Workshop
(Jet Substructure and tagging)
Anton Osika, Anna Sfyrla, Zachary Marshall
CERN
osika@kth.se
September 16, 2013
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 1 / 51
2. Overview
1 L1 Trigger selections for Fat Jets
Introduction
Results
Summary
2 Detailed Material
Introduction - more details
Online/Offline Correlations
Trigger Efficiencies
Event properties per sample
Summary
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 2 / 51
3. Table of Contents
1 L1 Trigger selections for Fat Jets
Introduction
Results
Summary
2 Detailed Material
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 3 / 51
4. Introduction & Motivation
Events with fat jets and no leptons are typically triggered using fat jet
triggers.
These require a fat jet at the HLT
i.e. significant energy deposits in cones of ∆R = 1.0.
They are seeded at L1 by a ‘standard’ (narrow) single jet item, as L1
uses exclusively cones of 0.8 × 0.8 in the η − φ space.
We are seeking answers in the two following questions:
How is the L1 seed affecting the fat jet trigger efficiencies?
What is the best alternative to a single jet L1 seed?
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 4 / 51
5. Trigger Selections
Investigated variables to cut at:
Selection Threshold example Acronym
ET of leading single jet - Default L1 selection 100 [ GeV ] J100
4 jets w/ ET above theshold - L1 multijet selection 20 [ GeV ] 4J20
2/3 jets w/ ET > 20, all closer than ∆R 1.0 2J20DR12
Sum of ET for jets w/ ET > 20 200 [ GeV ] HT200
As HT, for jets w/ |η| < 2.5 200 [ GeV ] HTC200
Sum of ET for (up to) 2 jets closer than ∆R = 1.0 100 [ GeV ]
P
ET(2)100
Sum of ET for (up to) 3 jets closer than ∆R = 1.0 100 [ GeV ]
P
ET(3)100
We have also considered additional variables, proven not that interesting in
the end: Invariant mass of two closeby jets and requirements close-by taus,
in combination to single jet, multijet or HT selections.
These will not be shown in the following slides.
Reminders:
run1 lowest unprescaled single jet L1 item: L1 J75
lowest unprescaled single jet L1 item planned for run2: L1 J100
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 5 / 51
6. Results 1 - efficiency(fat jet pT)
The baseline L1 J100 is compared to L1 HTC200 and two other selections that
include close-by jet requirements; OR-ing this selection to the baseline recovers
inefficiencies of the individual selections.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 6 / 51
7. Results 2 - efficiency(fat jet pT)
The baseline L1 J100 is compared to L1 4J20, HT200, HTC200 and the ET sum
of up to two close-by jets. The 4-jet selection leads to large inefficiencies in
events without a large jet multiplicity.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , zprime1000 (20000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , Ttbar (50000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL900 (10000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL1 (49999)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 7 / 51
8. Summary
Summary:
We have looked at L1 seeds for fat jet triggers; more specifically, at
alternatives to the baseline L1 single jet trigger items:
a selection of L1 jet close-by pairs with high ET , ORed to the
baseline selection;
HT constructed from 20GeV L1 jets with |η| < 3.2 or < 2.5,
multijet triggers,
the two above, including requirements on the distance between jets.
HT(C)200 seems to have the best overall performance in the models we
have considered and for the assumed threshold of 360GeV for the EF
selection.
Other selections can recover inefficiencies if lower EF thresholds can be
allowed; e.g. if EF fat jet selections are made more robust to pile-up.
Further possible steps:
Investigate the pile-up robustness for the various selections;
Investigate L1 processing limitations when searching for combinations of
close-by jets.
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 8 / 51
9. Table of Contents
1 L1 Trigger selections for Fat Jets
2 Detailed Material
Introduction - more details
Online/Offline Correlations
Trigger Efficiencies
Event properties per sample
Summary
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 9 / 51
10. Introduction - more details
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 10 / 51
11. L1(Topo) after LS1
After LS1, at L1 there will be provided new topological selection capability;
Selections will be possible on angles and kinematic compbinations of
objects found in L1Calo, and with limited information from L1Muon.
This will be critical for physics channels with multiple objects in final state
that so far relied on inclusive (high rate) L1 triggers.
Proposed (hadronic) selections include HT, MHT, ∆η, ∆φ, ∆R (e.g.
between jets or jets and MET), dijet invariant mass, ...
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 11 / 51
12. Processes
For the studies presented in these slides we used the following three sample
categories (resulting in four samples)
t¯t production;
Z → t¯t, with the Z massed fixed at 1TeV;
˜g → t¯tχ0
1, pair produced. Two samples are used from this model; both have
the ˜g mass fixed at 1.4TeV; one has the χ0
1 mass fixed at 1GeV and the
other has the χ0
1 mass fixed at 900GeV.
These four samples give a variety of fat jet multiplicity and pT spectrum in the
final state, thus ensuring a good coverage.
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 12 / 51
13. Rates
The threshold for a possible variable is primarily decided by its estimated
rate;
The efficiency decides if the trigger is desirable or not;
The table below shows rates for various triggers at 14TeV, pile-up 54 for
25ns and the 12 first BCIDs vetoes. Lumi considered here: 2e34. Source:
https://twiki.cern.ch/twiki/bin/viewauth/Atlas/RateEstimator
Trigger Rate Unique rate w.r.t. J100
J100 5.8 ± 0.7 0
HT200 4.8 ± 0.7 1.2 ± 0.3
HTC200 3.8 ± 1.0 1.0 ± 0.4
HT250 2.1 ± 0.4 0.7 ± 0.3
4J20 4.5 ±0.7 4.0 ± 0.7
∆R < 1.0
P
ET (2) > 120 0.5 ± 0.1 0.1 ± 0.1
∆R < 1.0
P
ET (2) > 100 0.8 ± 0.3 0.3 ± 0.3
∆R < 1.2
P
ET (2) > 120 0.5 ± 0.2 0.2 ± 0.2
∆R < 1.2
P
ET (2) > 100 1.1 ± 0.4 0.5 ± 0.3
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 13 / 51
14. Event Filter and Offline selection
“EF selection” always imposes a fat jet requirement (R = 1.0) of ET > 360
GeV and |η| < 3.2; EF jets are ‘AntiKt10 lctopo’;
“Offline fat jet selection” pre-requires pT> 50 GeV and |η| < 2.0
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 14 / 51
16. L1 Jet ET vs Fat Jet pT
0
20
40
60
80
100
120
140
160
180
200
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineleadingjetET
0
100
200
300
400
500
600
700
800
Leading online ET vs Offline leading pT, a10, zprime1000 (20000)
Correlation Factor: 0.86
Leading online ET vs Offline leading pT, a10, zprime1000 (20000)
0
500
1000
1500
2000
2500
3000
3500
4000
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineleadingjetET
0
100
200
300
400
500
600
700
800
Leading online ET vs Offline leading pT, a10, Ttbar (50000)
Correlation Factor: 0.9
Leading online ET vs Offline leading pT, a10, Ttbar (50000)
0
20
40
60
80
100
120
140
160
180
200
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineleadingjetET
0
100
200
300
400
500
600
700
800
Leading online ET vs Offline leading pT, a10, GttL900 (10000)
Correlation Factor: 0.77
Leading online ET vs Offline leading pT, a10, GttL900 (10000)
0
20
40
60
80
100
120
140
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineleadingjetET
0
100
200
300
400
500
600
700
800
Leading online ET vs Offline leading pT, a10, GttL1 (49999)
Correlation Factor: 0.73
Leading online ET vs Offline leading pT, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 16 / 51
17. L1 ET (2) vs Fat Jet pT
0
50
100
150
200
250
300
350
400
450
Offline fat jet pT
0 100 200 300 400 500 600 700 800
sumclosejetsT
OnlineE
0
100
200
300
400
500
600
700
800
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, zprime1000 (20000)
Correlation Factor: 0.92
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, zprime1000 (20000)
0
1000
2000
3000
4000
5000
6000
7000
8000
Offline fat jet pT
0 100 200 300 400 500 600 700 800
sumclosejetsT
OnlineE
0
100
200
300
400
500
600
700
800
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, Ttbar (50000)
Correlation Factor: 0.94
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, Ttbar (50000)
0
50
100
150
200
250
300
350
Offline fat jet pT
0 100 200 300 400 500 600 700 800
sumclosejetsT
OnlineE
0
100
200
300
400
500
600
700
800
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, GttL900 (10000)
Correlation Factor: 0.87
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, GttL900 (10000)
0
50
100
150
200
250
300
350
400
Offline fat jet pT
0 100 200 300 400 500 600 700 800
sumclosejetsT
OnlineE
0
100
200
300
400
500
600
700
800
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, GttL1 (49999)
Correlation Factor: 0.82
Online sum of ET for, up to 3, close jets vs Offline leading pT, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 17 / 51
21. L1 HT triggers & EF Fat Jet selection
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
HTC200 HT250 J100 comparison, zprime1000 (20000)
Efficency for L1 multijet selection
Applying EF
J100 + EF
HT250 + EF
HTC200 + EF
HT200 + EF
HTC200 HT250 J100 comparison, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
HTC200 HT250 J100 comparison, Ttbar (50000)
Efficency for L1 multijet selection
Applying EF
J100 + EF
HT250 + EF
HTC200 + EF
HT200 + EF
HTC200 HT250 J100 comparison, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
HTC200 HT250 J100 comparison, GttL900 (10000)
Efficency for L1 multijet selection
Applying EF
J100 + EF
HT250 + EF
HTC200 + EF
HT200 + EF
HTC200 HT250 J100 comparison, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
HTC200 HT250 J100 comparison, GttL1 (49999)
Efficency for L1 multijet selection
Applying EF
J100 + EF
HT250 + EF
HTC200 + EF
HT200 + EF
HTC200 HT250 J100 comparison, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 21 / 51
22. L1 ET > 100, J100
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum ET 2 close jets DR < 1.0, zprime1000 (20000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum ET 2 close jets DR < 1.0, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum ET 2 close jets DR < 1.0, Ttbar (50000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum ET 2 close jets DR < 1.0, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum ET 2 close jets DR < 1.0, GttL900 (10000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum ET 2 close jets DR < 1.0, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum ET 2 close jets DR < 1.0, GttL1 (49999)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum ET 2 close jets DR < 1.0, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 22 / 51
23. L1 ET > 100, J100 & EF Fat Jet selection
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum E_T 2 close jets DR < 1.0, zprime1000 (20000)
Efficiency for ET sum cut for close jets + EF
(EF only)
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum E_T 2 close jets DR < 1.0, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum E_T 2 close jets DR < 1.0, Ttbar (50000)
Efficiency for ET sum cut for close jets + EF
(EF only)
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum E_T 2 close jets DR < 1.0, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum E_T 2 close jets DR < 1.0, GttL900 (10000)
Efficiency for ET sum cut for close jets + EF
(EF only)
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum E_T 2 close jets DR < 1.0, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
sum E_T 2 close jets DR < 1.0, GttL1 (49999)
Efficiency for ET sum cut for close jets + EF
(EF only)
HTC200
J100
> 100
T
E∑2 close jets
> 100
T
E∑3 close jets
sum E_T 2 close jets DR < 1.0, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 23 / 51
24. L1 ET > 100, ET > 120
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 24 / 51
25. L1 ET > 100, ET > 120 & EF Fat Jet selection
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
Efficiency for selecting close jets, cutting on summed ET + EF
(EF only)
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
Efficiency for selecting close jets, cutting on summed E
(EF only)
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
Efficiency for selecting close jets, cutting on summed ET + EF
(EF only)
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Efficiency for selecting close jets, cutting on summed E
(EF only)
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 25 / 51
26. Summary 1 - efficiency(fat jet pT)
The baseline L1 J100 is compared to L1 HTC200 and two other selections that
include close-by jet requirements; OR-ing this selection to the baseline recovers
inefficiencies of the individual selections.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Efficiency for close jet selection
Signal distribution
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 26 / 51
27. Summary 1 - including EF Fat Jet selection
“Dominant” inefficiencies after a EF j360 a10tclcw selection.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
Efficiency for close jet selection + EF
(EF Only)
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
Efficiency for close jet selection + EF
(EF Only)
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
Efficiency for close jet selection + EF
(EF Only)
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Efficiency for close jet selection + EF
(EF Only)
J100
HT200
2 close & HT200
2 close & HT200 OR J100
> 100
T
E∑2 close jets
L1 close jet AND HT selection ORed with J100, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 27 / 51
28. Summary 2 - efficiency(fat jet pT)
The baseline L1 J100 is compared to L1 4J20, HT200, HTC200 and the ET sum
of up to two close-by jets. The 4-jet selection leads to large inefficiencies in
events without a large jet multiplicity.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , zprime1000 (20000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , Ttbar (50000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL900 (10000)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL1 (49999)
Efficency for L1 multijet selection
Signal distribution
J100
4J20
HT200
HTC200
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 28 / 51
29. Summary 2 - including EF Fat Jet selection
“Dominant” inefficiencies after a EF j360 a10tclcw selection.
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , zprime1000 (20000)
Efficency for L1 multijet selection
(EF only)
J100 + EF
4J20 + EF
HT200 + EF
HTC200 + EF
> 100
T
E∑2 close jets
L1 Summarizing Selections , zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , Ttbar (50000)
Efficency for L1 multijet selection
(EF only)
J100 + EF
4J20 + EF
HT200 + EF
HTC200 + EF
> 100
T
E∑2 close jets
L1 Summarizing Selections , Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL900 (10000)
Efficency for L1 multijet selection
(EF only)
J100 + EF
4J20 + EF
HT200 + EF
HTC200 + EF
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 Summarizing Selections , GttL1 (49999)
Efficency for L1 multijet selection
(EF only)
J100 + EF
4J20 + EF
HT200 + EF
HTC200 + EF
> 100
T
E∑2 close jets
L1 Summarizing Selections , GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 29 / 51
30. Event properties per sample
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 30 / 51
31. Total Offline HT histogram
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
200
400
600
800
1000
1200
distribution of Offline HT, zprime1000distribution of Offline HT, zprime1000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
1000
2000
3000
4000
5000
6000
7000
distribution of Offline HT, Ttbardistribution of Offline HT, Ttbar
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
100
200
300
400
500
600
700
800
distribution of Offline HT, GttL900distribution of Offline HT, GttL900
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
distribution of Offline HT, GttL1distribution of Offline HT, GttL1
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 31 / 51
32. offline number of jets
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
Number of jets on Offline, zprime1000Number of jets on Offline, zprime1000
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
6000
Number of jets on Offline, TtbarNumber of jets on Offline, Ttbar
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
6000
7000
Number of jets on Offline, GttL900Number of jets on Offline, GttL900
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Number of jets on Offline, GttL1Number of jets on Offline, GttL1
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 32 / 51
33. offline number of fat jets
0 2 4 6 8 10 12 14 16 18 20
0
2000
4000
6000
8000
10000
12000
Number of fat jets on Offline, zprime1000Number of fat jets on Offline, zprime1000
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
6000
7000
8000
Number of fat jets on Offline, TtbarNumber of fat jets on Offline, Ttbar
0 2 4 6 8 10 12 14 16 18 20
0
1000
2000
3000
4000
5000
6000
7000
8000
Number of fat jets on Offline, GttL900Number of fat jets on Offline, GttL900
0 2 4 6 8 10 12 14 16 18 20
0
2000
4000
6000
8000
10000
Number of fat jets on Offline, GttL1Number of fat jets on Offline, GttL1
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 33 / 51
34. Eta histograms
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
200
400
600
800
1000
1200
1400
jet eta, zprime1000jet eta, zprime1000
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
500
1000
1500
2000
2500
3000
jet eta, Ttbarjet eta, Ttbar
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
200
400
600
800
1000
1200
jet eta, GttL900jet eta, GttL900
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
1000
2000
3000
4000
5000
6000
jet eta, GttL1jet eta, GttL1
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 34 / 51
35. Jet orientations on different trigger levels, Gtt;L900
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
eta
-3 -2 -1 0 1 2 3
phi
-3
-2
-1
0
1
2
3
eta vs phi for an event, 3R10: 0, 2R06: 0
eta-phi1.pdf
Entries 8
Mean x -0.1
Mean y -0.1473
RMS x 0.5916
RMS y 2.047
eta phi for jets in 1 event
L1
Offline jets
Offline Fat jets
eta vs phi for an event, 3R10: 0, 2R06: 0
Triggerbit for 2/3 close jets in title
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 35 / 51
36. Jet orientations on different trigger levels, Gtt;L900
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
eta
-3 -2 -1 0 1 2 3
phi
-3
-2
-1
0
1
2
3
eta vs phi for an event, 3R10: 0, 2R06: 0
eta-phi2.pdf
Entries 5
Mean x -0.48
Mean y -0.4712
RMS x 0.6765
RMS y 1.65
eta phi for jets in 1 event
L1
Offline jets
Offline Fat jets
eta vs phi for an event, 3R10: 0, 2R06: 0
Triggerbit for 2/3 close jets in title
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 36 / 51
37. Jet orientations on different trigger levels, Gtt;L900
eta
-3 -2 -1 0 1 2 3
phi
-3
-2
-1
0
1
2
3
eta-phi3.pdf
Entries 7
Mean x -0.6571
Mean y 0.3085
RMS x 0.798
RMS y 1.886
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
eta-phi3.pdf
Entries 7
Mean x -0.6571
Mean y 0.3085
RMS x 0.798
RMS y 1.886
eta vs phi for an event, 3R10: 0, 2R06: 1
eta phi for jets in 1 event
L1
Offline jets
Offline Fat jets
Triggerbit for 2/3 close jets in title
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 37 / 51
38. Jet orientations on different trigger levels, Gtt;L900
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
eta
-3 -2 -1 0 1 2 3
phi
-3
-2
-1
0
1
2
3
eta vs phi for an event, 3R10: 1, 2R06: 0
eta-phi4.pdf
Entries 6
Mean x -0.12
Mean y 0.1571
RMS x 0.5154
RMS y 2.216
eta phi for jets in 1 event
L1
Offline jets
Offline Fat jets
eta vs phi for an event, 3R10: 1, 2R06: 0
Triggerbit for 2/3 close jets in title
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 38 / 51
39. Jet orientations on different trigger levels, Gtt;L900
eta
-3 -2 -1 0 1 2 3
phi
-3
-2
-1
0
1
2
3
eta-phi5.pdf
Entries 6
Mean x 0.6333
Mean y -0.589
RMS x 0.725
RMS y 2.109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
eta-phi5.pdf
Entries 6
Mean x 0.6333
Mean y -0.589
RMS x 0.725
RMS y 2.109
eta vs phi for an event, 3R10: 0, 2R06: 0
eta phi for jets in 1 event
L1
Offline jets
Offline Fat jets
Triggerbit for 2/3 close jets in title
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 39 / 51
40. Summary
Summary:
We have looked at L1 seeds for fat jet triggers; more specifically, at
alternatives to the baseline L1 single jet trigger items:
a selection of L1 jet close-by pairs with high ET , ORed to the
baseline selection;
HT constructed from 20GeV L1 jets with |η| < 3.2 or < 2.5,
multijet triggers,
the two above, including requirements on the distance between jets.
HT(C)200 seems to have the best overall performance in the models we
have considered and for the assumed threshold of 360GeV for the EF
selection.
Other selections can recover inefficiencies if lower EF thresholds can be
allowed; e.g. if EF fat jet selections are made more robust to pile-up.
Further possible steps:
Investigate the pile-up robustness for the various selections;
Investigate L1 processing limitations when searching for combinations of
close-by jets.
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 40 / 51
41. Backup
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 41 / 51
42. L1 ET > 100, ET > 120∆R = 1.0
Comparison of cutting 1.2 or 1.0
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
HTC200
L1 cuts on max ET sum of 1, 2 DR<1.0 and 3 DR<1.0 jets, ET > 20, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 42 / 51
43. L1 ET > 100, ET > 120∆R = 1.2
Comparison of cutting 1.2 or 1.0
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, zprime1000 (20000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
applying HTC200
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, Ttbar (50000)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
applying HTC200
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, GttL900 (10000)
Efficiency for selecting close jets, cutting on summed ET + EF
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
applying HTC200
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, GttL1 (49999)
Efficiency for selecting close jets, cutting on summed E
Signal distribution
> 120
T
E∑3 close jets
> 100
T
E∑3 close jets
> 120
T
E∑2 close jets
> 100
T
E∑2 close jets
applying HTC200
L1 cuts on max ET sum of 1, 2 DR<1.2 and 3 DR<1.2 jets, ET > 20, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 43 / 51
44. Correlation between L1 L2 EF, GttL900
0
20
40
60
80
100
pT names L2
0 100 200 300 400 500 600 700 800
pTnamesL1
0
100
200
300
400
500
600
700
800
Scatter of leading ET no selection for 5000 events, L1 vs L2
0.97523591288
Scatter of leading ET no selection for 5000 events, L1 vs L2
0
2
4
6
8
10
12
14
16
18
pT names EF
0 100 200 300 400 500 600 700 800
pTnamesL2
0
100
200
300
400
500
600
700
800
Scatter of leading ET no selection for 5000 events, L2 vs EF
0.666858029371
Scatter of leading ET no selection for 5000 events, L2 vs EF
0
10
20
30
40
50
pT names OL
0 100 200 300 400 500 600 700 800
pTnamesEF
0
100
200
300
400
500
600
700
800
Scatter of leading ET no selection for 5000 events, EF vs OL
0.983869758058
Scatter of leading ET no selection for 5000 events, EF vs OL
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 44 / 51
45. Online/Offline Comparison; L1 Inv. mass vs Fat Jet pT
0
50
100
150
200
250
300
350
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineInvariantMass
0
100
200
300
400
500
600
700
800
Online invariant mass of 2 close by jets vs Offline leading pT, a10, zprime1000 (20000)
Correlation Factor: 0.58
Online invariant mass of 2 close by jets vs Offline leading pT, a10, zprime1000 (20000)
0
1000
2000
3000
4000
5000
6000
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineInvariantMass
0
100
200
300
400
500
600
700
800
Online invariant mass of 2 close by jets vs Offline leading pT, a10, Ttbar (50000)
Correlation Factor: 0.67
Online invariant mass of 2 close by jets vs Offline leading pT, a10, Ttbar (50000)
0
50
100
150
200
250
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineInvariantMass
0
100
200
300
400
500
600
700
800
Online invariant mass of 2 close by jets vs Offline leading pT, a10, GttL900 (10000)
Correlation Factor: 0.6
Online invariant mass of 2 close by jets vs Offline leading pT, a10, GttL900 (10000)
0
50
100
150
200
250
300
Offline fat jet pT
0 100 200 300 400 500 600 700 800
OnlineInvariantMass
0
100
200
300
400
500
600
700
800
Online invariant mass of 2 close by jets vs Offline leading pT, a10, GttL1 (49999)
Correlation Factor: 0.41
Online invariant mass of 2 close by jets vs Offline leading pT, a10, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 45 / 51
47. L1 ET > 120, J120
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
cluster 2 close jets DR < 1.0, zprime1000 (20000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
cluster 2 close jets DR < 1.0, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
cluster 2 close jets DR < 1.0, Ttbar (50000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
cluster 2 close jets DR < 1.0, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
cluster 2 close jets DR < 1.0, GttL900 (10000)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
cluster 2 close jets DR < 1.0, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
cluster 2 close jets DR < 1.0, GttL1 (49999)
Efficiency for ET sum cut for close jets
Signal distribution
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
cluster 2 close jets DR < 1.0, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 47 / 51
48. L1 ET > 120, J120 & EF Fat Jet selection
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
2 close jets DR < 1.0, zprime1000 (20000)
Efficiency for ET sum cut for close jets
(EF only)
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
2 close jets DR < 1.0, zprime1000 (20000)
jet pT (GeV)
0 100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
2 close jets DR < 1.0, Ttbar (50000)
Efficiency for ET sum cut for close jets
(EF only)
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
2 close jets DR < 1.0, Ttbar (50000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
2 close jets DR < 1.0, GttL900 (10000)
Efficiency for ET sum cut for close jets
(EF only)
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
2 close jets DR < 1.0, GttL900 (10000)
jet pT (GeV)
100 200 300 400 500 600
Efficency
0
0.2
0.4
0.6
0.8
1
2 close jets DR < 1.0, GttL1 (49999)
Efficiency for ET sum cut for close jets
(EF only)
HTC200
J120
> 120
T
E∑2 close jets
> 120
T
E∑3 close jets
2 close jets DR < 1.0, GttL1 (49999)
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 48 / 51
49. Multijet Rates
Trigger 8 TeV Data 8 TeV MC 14 TeV MC MC Scaling Data scaled to 14 TeV
4J20 0.8+- 0.0 0.7+- 0.1 4.2+- 0.3 (5.7+-0.9) 4.5+- 0.7
Unique 1.5+- 0.0 0.4+- 0.1 3.0+- 0.3 (7.4+-1.6) 3.8+- 0.8
HTC200 1.2+- 0.0 0.9+- 0.1 2.8+- 0.3 (3.3+-0.5) 3.8+- 0.6
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
HT200 1.5+- 0.0 1.2+- 0.1 4.0+- 0.3 (3.3+-0.5) 4.8+- 0.7
Unique 0.2+- 0.0 0.2+- 0.1 1.0+- 0.2 (3.9+-1.2) 0.9+- 0.3
========================================================================================
Total 2.0+- 0.0 1.6+- 0.2 7.1+- 0.4 (4.3+-0.5) 8.6+- 1.0
Trigger 8 TeV Data 8 TeV MC 14 TeV MC MC Scaling Data scaled to 14 TeV
1J100 1.7+- 0.0 1.4+- 0.1 4.6+- 0.3 (3.3+-0.4) 5.8+- 0.7
Unique 0.6+- 0.0 0.4+- 0.1 1.7+- 0.2 (3.9+-0.9) 2.2+- 0.5
4J20 0.8+- 0.0 0.7+- 0.1 4.2+- 0.3 (5.7+-0.9) 4.5+- 0.7
Unique 0.5+- 0.0 0.4+- 0.1 3.0+- 0.3 (7.4+-1.6) 3.7+- 0.8
HT200 1.5+- 0.0 1.2+- 0.1 4.0+- 0.3 (3.3+-0.5) 4.8+- 0.7
Unique 0.2+- 0.0 0.1+- 0.0 0.6+- 0.1 (5.0+-2.1) 0.8+- 0.3
HT300 0.3+- 0.0 0.3+- 0.1 1.1+- 0.2 (3.6+-1.0) 1.0+- 0.3
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
=======================================================================================
Total 2.5+- 0.0 2.1+- 0.2 8.8+- 0.5 (4.3+-0.4) 10.7+- 1.1
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 49 / 51
50. Estimation for ET rate
Trigger 8 TeV Data 8 TeV MC 14 TeV MC MC Scaling Data scaled to 14 TeV
2J55:DR10-J55-J55 0.0+- 0.0 0.0+- 0.0 0.1+- 0.1 (8.7+-9.5) 0.4+- 0.4
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
J65_2J45:DR10-J65-J45 0.1+- 0.0 0.0+- 0.0 0.2+- 0.1 (6.1+-4.9) 0.5+- 0.4
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
J75_2J35:DR10-J75-J35 0.1+- 0.0 0.1+- 0.0 0.2+- 0.1 (3.1+-1.9) 0.3+- 0.2
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
J85_2J25:DR10-J85-J25 0.1+- 0.0 0.1+- 0.0 0.2+- 0.1 (2.6+-1.4) 0.3+- 0.1
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
J95_2J20:DR10-J95-J20 0.1+- 0.0 0.1+- 0.0 0.2+- 0.1 (2.8+-1.6) 0.2+- 0.1
Unique 0.0+- 0.0 0.0+- 0.0 0.0+- 0.0 (0.0+-0.0) 0.0+- 0.0
=======================================================================================================
Total 0.2+- 0.0 0.1+- 0.0 0.3+- 0.1 (2.9+-1.5) 0.5+- 0.2
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 50 / 51
51. The End
Anton Osika, Anna Sfyrla, Zachary Marshall (CERN)L1 Trigger selections for Fat Jets September 16, 2013 51 / 51