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Detectability of Neutrinos from Failed Supernovae and Black Hole-Neutron Star Mergers

Detectability of Neutrinos from Failed Supernovae and Black Hole-Neutron Star Mergers

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  • SIGNIFICANCEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE
  • We are going to change fluxPut minipictures of fluxes
  • Talk about what flux isMake one plot for each parameterization
  • Livermore = standardCaba… = bhnsDefinefluence
  • We are going to change fluxPut minipictures of fluxes
  • Water cherenkovContinuous lineBigger legendEnergy before timePut BH-NS on threshold plot
  • Additional types of detectorsPut vertical lines for milky way, etc
  • Additional types of detectorsPut vertical lines for milky way, etc
  • Additional types of detectorsPut vertical lines for milky way, etc
  • SIGNIFICANCEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

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  • Detectability of Neutrinos fromFailed Supernovae and Black Hole-Neutron Star Mergers Halston Lim and Jason Liang North Carolina School of Science and Mathematics 1
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Neutrinos and their Detection • Fundamental particles – Three flavors (νe, νμ, ντ) – Mainly interact through weak force • Can propagate through matter – Useful when astrophysical phenomena are opaque to light – Detectors use secondary particles to The fundamental particles of the Standard Model determine if event has occurred • Detectors – Water Cherenkov (Super-Kamiokande) – Liquid argon (LBNE) 2Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Neutrino Emission • Neutrinos emitted by astrophysical phenomena • Core collapse supernova (SN) – stellar collapse and explosion • Emits neutrinos (99% of binding energy) Supernova 1987A• Analyzed important events different from typical SN - Failed supernovae (fSN) - Black hole-neutron star mergers (BHNSM) 3Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion fSN and BHNSM • fSN - Very high-mass star - Site of nucleosynthesis - Would allow for first observation of BH Artist’s conception of a fSN formation • BHNSM - Thought to be linked with short- period gamma-ray bursts - Very luminous events - Can be used to study the evolution of early universe Artist’s conception of a BHNSM 4Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Research Goals 1. Determine observability of neutrinos from fSN and BHNSM in current and proposed detectors 2. Compare detector signals from our events with signals from typical SN 3. Investigate how well the parameters of the original flux distribution can be determined from interaction rates Schematic of Super-Kamiokande 5Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion General Methods Use theoretical After finding the models of fSN neutrinos emitted, Determine the and BHNSM to use SNOwGLoBES observability of calculate the to calculate the neutrinos neutrino what detectors on emission Earth observe Consider existing astrophysical models of fSN and BHNSM and how observations will confirm/reject these Neutrino event generated models with Superscan event display program 6Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion SNOwGLoBES (SuperNova Observatories with General Long Baseline Experiment Simulator)1• Interaction rates calculator that we used to simulate neutrino events on Earth• We calculated the neutrino flux from fSN and BHNSM and integrated real detectors parameters (cross sections, smearing, efficiencies)[1] K. Scholberg, in APS April Meeting 2011 (2011), p. 1. 7Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Fluence Calculation[2] H. Minakata et al., Journal of Cosmology and Astroparticle Physics 2008, 006 (2008).[3] K. Sumiyoshi, S. Yamada, and H. Suzuki, The Astrophysical Journal 667, 32 (2007).[4] O. L. Caballero and G. C. McLaughlin, Physical Review D 80, 123004 (2009). 8Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Examples of Flux Parameterizations Garching Parameterization 3 Fermi-Dirac Parameterization 3 Best to model fSN Best to model BHNSM (non-thermal emission) (thermal emission) 9Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Flux Comparisons with Typical SN SH fSN BHNSM 5 4 5 • fSN and BHNSM have higher neutrino energies[5] G. Shen, arXiv Preprint arXiv:1202.5791 1–20 (2012). 10Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion 4 Event Rate Calculation Flux Interaction Threshold Response x Cross Section x Energy Resolution Secondary Particle Distribution • Calculated the flux – Applied intrinsic interaction and detection inputs for various detectors in SNOwGLoBES6 • Main focus on liquid argon (LBNE) and water Cherenkov (Super-K) detectors[6] K. Scholberg, arXiv Preprint arXiv:1205.6003 1–19 (2012). 11Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusiona. Neutrino Detector Signal b. 7LEFT: The total number of events as a function of threshold energy is plotted for typical SN, fSN, andBHNSM models.RIGHT: The total number of events is shown as a function of time for typical SN (Livermore, Basel) and fSNmodels (SH and LS nuclear equation of states. All events are calculated in Super-Kamiokande at 10 kpc.[7] J. M. Lattimer and F. D. Swesty, Nuclear Physics A 535, 331 (1991). 12Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion a. Observability b. SH fSN BHNSM The total number of events in various current (Super-Kamiokande, HALO, LVD, Borexino) and proposed (Hyper-Kamiokande, LENA, GLACIER, LBNE) detectors as a function of distance. Water Cherenkov (blue), liquid scintillator (red), and liquid argon (green) detectors are shown. Viewing neutrinos from our neighbor Andromeda (700 kpc) is feasible with new detectors. 13Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Observed Neutrino Events Center of Milky Way (10 kpc) Andromeda (700 kpc) fSN (32 kt Super-K) 37400 events 8 events fSN (560 kt Hyper-K) 654000 events 134 events BHNSM (32 kt Super-K) 9300 events 2 events BHNSM (560 kt Hyper-K) 162000 events 33 events • Supernova 1987A, which exploded in the Large Magellanic Cloud 50 kpc away, only produced 20 neutrinos that were detected • Only confirmed observation of astrophysical neutrinos to date • The next supernova event would give many more neutrinos! 14Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time EvolutionParameter Determination Conclusion Neutronization Burst fSN neutrino luminosities3 Neutronization burst 15Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time EvolutionParameter Determination Conclusion Neutronization Burst Visibility Water Cherenkov (Super-K) Liquid Argon (LBNE) 16Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Nucleosynthesis 17Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion 18Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Nucleosynthesis Potential GR parameterization for fSN FD parameterization for BHNSM Temperature 19Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Results • Showed differences between typical SN detector signal and fSN/BHNSM detector signals • Calculated the number of observed events from fSN and BHNSM in current and proposed detectors • Determined the potential for nucleosynthesis to occur in fSN and BHNSM 20Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Future Work • Incorporate systematic uncertainties in parameter determinations • Apply flavor dependent flux parameterizations to improve fits • Use time-dependent models of BHNSM • Incorporate neutrino oscillation 21Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013
  • Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion Credits Dr. Kate Scholberg, Duke University Dr. Josh Albert, Duke University Dr. Alex Himmel, Duke University Dr. Jonathan Bennett, NCSSM Poison Bear 22Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013