Talk given at the workshop "Multiphase turbulent flows in the atmosphere and ocean", National Centre for Atmospheric REsearch, Boulder CO, August 15 2012
Talk given at the workshop "Multiphase turbulent flows in the atmosphere and ocean", National Centre for Atmospheric REsearch, Boulder CO, August 15 2012
Instantaneous Gelation in Smoluchwski's Coagulation Equation Revisited, Confe...Colm Connaughton
Invited talk given at "Boltzmann equation:
mathematics, modeling and simulations
In memory of Carlo Cercignani", Institut Henri Poincare, Paris, February 11, 2011.
Dynamic stiffness and eigenvalues of nonlocal nano beams - new methods for dynamic analysis of nano-scale structures. This lecture gives a review and proposed new techniques.
Chemical dynamics and rare events in soft matter physicsBoris Fackovec
Talk for the Trinity Math Society Symposium. First summarises the approximations leading from Dirac equation to molecular description and then the synthesis towards non-equilibrium statistical mechanics. The relaxation approach to projection of a molecular system to a Markov jump process is discussed.
Reference: R. K. Vierck, Vibration Analysis, 2nd Edition, Harper & Row Publishers, New York, NY, 1979, sec. 5-8.
Solver(s):
ANSYS Mechanical
Analysis Type(s): Flexible Dynamic Analysis
Element Type(s): Solid and Spring
The distribution and_annihilation_of_dark_matter_around_black_holesSérgio Sacani
Uma nova simulação computacional feita pela NASA mostra que as partículas da matéria escura colidindo na extrema gravidade de um buraco negro pode produzir uma luz de raios-gamma forte e potencialmente observável. Detectando essa emissão forneceria aos astrônomos com uma nova ferramenta para entender tanto os buracos negros como a natureza da matéria escura, uma elusiva substância responsável pela maior parte da massa do universo que nem reflete, absorve ou emite luz.
Final parsec problem of black hole mergers and ultralight dark matterSérgio Sacani
When two galaxies merge, they often produce a supermassive black hole binary (SMBHB) at
their center. Numerical simulations with cold dark matter show that SMBHBs typically stall out
at a distance of a few parsecs apart, and take billions of years to coalesce. This is known as the
final parsec problem. We suggest that ultralight dark matter (ULDM) halos around SMBHBs can
generate dark matter waves due to gravitational cooling. These waves can effectively carry away
orbital energy from the black holes, rapidly driving them together. To test this hypothesis, we
performed numerical simulations of black hole binaries inside ULDM halos. Our results imply that
ULDM waves can lead to the rapid orbital decay of black hole binaries.
Instantaneous Gelation in Smoluchwski's Coagulation Equation Revisited, Confe...Colm Connaughton
Invited talk given at "Boltzmann equation:
mathematics, modeling and simulations
In memory of Carlo Cercignani", Institut Henri Poincare, Paris, February 11, 2011.
Dynamic stiffness and eigenvalues of nonlocal nano beams - new methods for dynamic analysis of nano-scale structures. This lecture gives a review and proposed new techniques.
Chemical dynamics and rare events in soft matter physicsBoris Fackovec
Talk for the Trinity Math Society Symposium. First summarises the approximations leading from Dirac equation to molecular description and then the synthesis towards non-equilibrium statistical mechanics. The relaxation approach to projection of a molecular system to a Markov jump process is discussed.
Reference: R. K. Vierck, Vibration Analysis, 2nd Edition, Harper & Row Publishers, New York, NY, 1979, sec. 5-8.
Solver(s):
ANSYS Mechanical
Analysis Type(s): Flexible Dynamic Analysis
Element Type(s): Solid and Spring
The distribution and_annihilation_of_dark_matter_around_black_holesSérgio Sacani
Uma nova simulação computacional feita pela NASA mostra que as partículas da matéria escura colidindo na extrema gravidade de um buraco negro pode produzir uma luz de raios-gamma forte e potencialmente observável. Detectando essa emissão forneceria aos astrônomos com uma nova ferramenta para entender tanto os buracos negros como a natureza da matéria escura, uma elusiva substância responsável pela maior parte da massa do universo que nem reflete, absorve ou emite luz.
Final parsec problem of black hole mergers and ultralight dark matterSérgio Sacani
When two galaxies merge, they often produce a supermassive black hole binary (SMBHB) at
their center. Numerical simulations with cold dark matter show that SMBHBs typically stall out
at a distance of a few parsecs apart, and take billions of years to coalesce. This is known as the
final parsec problem. We suggest that ultralight dark matter (ULDM) halos around SMBHBs can
generate dark matter waves due to gravitational cooling. These waves can effectively carry away
orbital energy from the black holes, rapidly driving them together. To test this hypothesis, we
performed numerical simulations of black hole binaries inside ULDM halos. Our results imply that
ULDM waves can lead to the rapid orbital decay of black hole binaries.
I am Baddie K. I am a Magnetic Materials Assignment Expert at eduassignmenthelp.com. I hold a Masters's Degree in Electro-Magnetics, from The University of Malaya, Malaysia. I have been helping students with their assignments for the past 12 years. I solve assignments related to Magnetic Materials.
Visit eduassignmenthelp.com or email info@eduassignmenthelp.com. You can also call on +1 678 648 4277 for any assistance with Magnetic Materials Assignments.
Lecture by prof. dr Neven Bilic from the Ruđer Bošković Institute (Zagreb, Croatia) at the Faculty of Science and Mathematics (Niš, Serbia) on October 29, 2014.
The visit took place in the frame of the ICTP – SEENET-MTP project PRJ-09 “Cosmology and Strings”.
As recent and future galaxy surveys map the large-scale structure of the universe with unprecedented pace and precision, it is worthwhile to consider innovative data analysis methods beyond traditional Gaussian 2-point statistics to extract more cosmological information from those datasets. Such efforts are often plagued by substantially increased complexity of the analysis. Hoping to improve this, I will present simple, nearly optimal methods to measure 3-point statistics as easily as 2-point statistics, by cross-correlating the mass density with specific quadratic fields [arXiv:1411.6595]. Inspired by these results, I will argue that BAO reconstructions already combine 2-point statistics with certain 3- and 4-point functions automatically [arXiv:1508.06972]. I will present several new Eulerian and Lagrangian reconstruction algorithms and discuss their performance in simulations.
The electronic band parameters calculated by the Triangular potential model f...IOSR Journals
This work reports on theoretical investigation of superlattices based on Cd1-xZnxS quantum dots
embedded in an insulating material. This system, assumed to a series of flattened cylindrical quantum dots with
a finite barrier at the boundary, is studied using the triangular potential. The electronic states and the effective
mass of 1 Γ miniband have been computed as a function of inter-quantum dot separation for different zinc
compositions. Calculations have been made for electrons, heavy holes and light holes. Results are discussed and
compared with those of the Kronig-Penney and sinusoidal potentials
Neil Lambert - From D-branes to M-branesSEENET-MTP
Lecture by prof. dr Neil Lambert (Department of Mathematics, King’s College, London, UK & CERN, Geneva, Switzerland) on October 22, 2010 at the Faculty of Science and Mathematics, Nis, Serbia.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Sergey Sibiryakov "Galactic rotation curves vs. ultra-light dark matter: Implications of the soliton—host halo relation"
1. Sergey Sibiryakov
Galactic rotation curves vs.
ultra-light dark matter:
Implications of the soliton — host halo
relation
BW2018, Niš, Serbia
with Nitsan Bar, Diego Blas, Kfir Blum, arXiv:1805.00122
10. The simplest DM model: free massive scalar
0
e.o.m. in expanding Universe:
L =
1
2
( µ )2
m2 2
¨ + 3H ˙ + m2
= 0
11. The simplest DM model: free massive scalar
0
e.o.m. in expanding Universe:
• = const
L =
1
2
( µ )2
m2 2
¨ + 3H ˙ + m2
= 0
H > m
12. The simplest DM model: free massive scalar
p = 0
density:
pressure:
0
e.o.m. in expanding Universe:
• = const
•
L =
1
2
( µ )2
m2 2
¨ + 3H ˙ + m2
= 0
p = cos(2mt)
=
m2 2
0
2
= 0 cos(mt)
H > m
H < m
13. The simplest DM model: free massive scalar
p = 0
density:
pressure:
0
e.o.m. in expanding Universe:
• = const
•
behaves as DM on times longer than
L =
1
2
( µ )2
m2 2
¨ + 3H ˙ + m2
= 0
p = cos(2mt)
=
m2 2
0
2
= 0 cos(mt)
m 1
H > m
H < m
14. • Theoretical pedigree: pseudo-Goldstone boson
e.g. global periodic variable with period 2 fU(1)
15. • Theoretical pedigree: pseudo-Goldstone boson
non-zero mass can be generated by non-perturbative
effects
examples: QCD axion, string theory ALP’s, relaxion,
quasi-dilaton, ....
e.g. global periodic variable with period 2 fU(1)
m exp( Sinst)
V ( ) = m2
f2
1 cos( /f)
16. • Theoretical pedigree: pseudo-Goldstone boson
non-zero mass can be generated by non-perturbative
effects
examples: QCD axion, string theory ALP’s, relaxion,
quasi-dilaton, ....
e.g. global periodic variable with period 2 fU(1)
after inflation
• Density fraction:
f
m exp( Sinst)
V ( ) = m2
f2
1 cos( /f)
0.05
f
1017GeV
2 m
10 22eV
1/2
17. • from CMB and LSS : otherwise too much
suppression of structure
Ly forest: based on complicated modelling
Kobayashi et al. (2017)
m 10 21
eV
m 10 23
eV
Observational probes
18. • from CMB and LSS : otherwise too much
suppression of structure
• affects structures on small scales, could address
the problems of particle CDM (?) Hu, Barkana, Gruzinov (2000)
Hui et al. (2016)
Ly forest: based on complicated modelling
Kobayashi et al. (2017)
m 10 22
eV
m 10 21
eV
m 10 23
eV
Observational probes
19. • from CMB and LSS : otherwise too much
suppression of structure
• affects structures on small scales, could address
the problems of particle CDM (?) Hu, Barkana, Gruzinov (2000)
Hui et al. (2016)
Ly forest: based on complicated modelling
• leads to black-hole superradiance:
probed by black-hole spins, gravitational waves
Arvanitaki, Dubovsky (2011)
Arvanitaki et al. (2016)
Kobayashi et al. (2017)
m 10 14
÷ 10 10
eV
m 10 22
eV
m 10 21
eV
m 10 23
eV
can probably be extended down to m 10 18
eV
Observational probes
20. • from CMB and LSS : otherwise too much
suppression of structure
• affects structures on small scales, could address
the problems of particle CDM (?) Hu, Barkana, Gruzinov (2000)
Hui et al. (2016)
focus of this talk:
NB. Can be axion-like particle, but not QCD axion
Ly forest: based on complicated modelling
• leads to black-hole superradiance:
probed by black-hole spins, gravitational waves
Arvanitaki, Dubovsky (2011)
Arvanitaki et al. (2016)
Kobayashi et al. (2017)
m 10 14
÷ 10 10
eV
m 10 22
eV
m 10 21
eV
m 10 23
eV
can probably be extended down to m 10 18
eV
m 10 22
÷ 10 18
eV
Observational probes
21. Challenges to particle CDM at sub-kpc scales ?
14 Oh et al.
Fig. 5.— Upper-left panel: The (DM only) rotation curves (small dots) of the 21 LITTLE THINGS (including 3 THINGS galaxies)
for which Spitzer 3.6µm image is available. These are all scaled with respect to the rotation velocity V0.3 at R0.3 where the logarithmic
slope of the rotation curve is dlogV/dlogR = 0.3 as described in Hayashi & Navarro (2006). The ‘⇥’ symbol represents the median values of
the rotation curves in each 0.1R/R0.3 bin. The error bars show the 1 scatter. Lower-left panel: The scaled rotation curves of the seven
THINGS, and the two simulated dwarf galaxies (DG1 and DG2 in Governato et al. 2010) which are overplotted to the median values of
the LITTLE THINGS rotation curves. The grey solid and black solid lines with small dots indicate the CDM NFW dark matter rotation
curves with V200 which is > 90 km s 1 and < 90 km s 1, respectively. Right panels: The corresponding dark matter density profiles
derived using the scaled rotation curves in the left panels. The grey (V200 > 90 km s 1) and black solid lines with small dots (V200 < 90
km s 1) represent the CDM NFW models with the inner density slope ↵⇠ 1.0. See Section 4 for more details.
profiles. This is much like the THINGS dwarf galaxies,
and the simulated dwarfs (DG1 and DG2) with baryonic
feedback processes as shown in the lower-right panel of
Fig. A.3), we perform a least squares fit (dotted lines) to
the inner data points (grey dots) within a ‘break radius’.
As described in de Blok & Bosma (2002; see also Oh
- cores vs. cusps
- missing satellites
- too big to fail
from Oh et al., arXiv:1502.01281
perhaps are explained by baryonic physics
22. Dynamics of ULDM in the Newtonian limit
slowly varying amplitude
}Schroedinger --
Poisson
system
leads to suppression of fluctuations at short
scale --- “quantum pressure”
= (x, t)e imt
+ h.c.
i ˙
2
2m
+ m (x, t) = 0
2
= 2Gm2
| |2
24. ULDM in the halo
Figure 2: A slice of density field of ψDM simulation on various scales at zzz === 000...111. This scaled sequence
(each of thickness 60 pc) shows how quantum interference patterns can be clearly seen everywhere from
the large-scale filaments, tangential fringes near the virial boundaries, to the granular structure inside the
haloes. Distinct solitonic cores with radius ∼ 0.3 − 1.6 kpc are found within each collapsed halo. The
density shown here spans over nine orders of magnitude, from 10−1 to 108 (normalized to the cosmic mean
density). The color map scales logarithmically, with cyan corresponding to density 10.
graphic processing unit acceleration, improving per-
formance by almost two orders of magnitude21 (see
Supplementary Section 1 for details).
Fig. 1 demonstrates that despite the completely
different calculations employed, the pattern of fil-
aments and voids generated by a conventional N-
body particle ΛCDM simulation is remarkably in-
distinguishable from the wavelike ΛψDM for the
same linear power spectrum (see Supplementary Fig.
S2). Here Λ represents the cosmological constant.
This agreement is desirable given the success of stan-
dard ΛCDM in describing the statistics of large scale
structure. To examine the wave nature that distin-
guishes ψDM from CDM on small scales, we res-
imulate with a very high maximum resolution of
60 pc for a 2 Mpc comoving box, so that the dens-
est objects formed of 300 pc size are well re-
solved with ∼ 103 grids. A slice through this box
is shown in Fig. 2, revealing fine interference fringes
the boundaries of virialized objects, where the de
Broglie wavelengths depend on the local velocity of
matter. An unexpected feature of our ψDM simula-
tions is the generation of prominent dense coherent
standing waves of dark matter in the center of every
gravitational bound object, forming a flat core with
a sharp boundary (Figs. 2 and 3). These dark matter
cores grow as material is accreted and are surrounded
by virialized haloes of material with fine-scale, large-
amplitude cellular interference, which continuously
fluctuates in density and velocity generating quan-
tum and turbulent pressure support against gravity.
The central density profiles of all our collapsed
cores fit well with the stable soliton solution of the
Schr¨odinger-Poisson equation, as shown in Fig. 3
(see also Supplementary Section 2 and Fig. S3). On
the other hand, except for the lightest halo which
has just formed and is not yet virialized, the outer
profiles of other haloes possess a steepening loga-
Schive, Chiueh,
Broadhurst,
arXiv: 1406.6586
3
Schive, Chiueh, Boardhurst,
arXiv:1407.7762
soliton
25. Properties of the soliton
ok for a quasi-stationary phase-coherent solution,
bed by the ansatz3
(x, t) =
✓
mMpl
p
4⇡
◆
e i mt
(x). (5)
LDM mass density is
⇢ =
(mMpl)
2
4⇡
2
(6)
⇡ 4.1 ⇥ 1014
⇣ m
10 22 eV
⌘2
2
M /pc3
.
arameter is proportional to the ULDM energy
it mass Validity of the non-relativistic regime re-
| | ⌧ 1, and since we are looking for gravitation-
und configurations, < 0.
uming spherical symmetry and defining r = mx,
equations for and are given by
2
-1.5
-1.0
-0.5
Φ1
FIG. 1. Profile of the
solid). We also show
(orange dashed) and
green).
Other solutions
1(r), 1(r) by a sc
tions (r), (r),
by
m
22 eV
⌘2
2
M /pc3
.
rtional to the ULDM energy
he non-relativistic regime re-
we are looking for gravitation-
< 0.
metry and defining r = mx,
are given by
2r ( ) , (7)
r 2
. (8)
solution amounts to solving
! 0) = const, (r ! 1) = 0,
nitial value of at r ! 0, the
ue value of .
olve Eqs. (7-8) with the initial
us call this auxiliary solution
al calculation gives [4, 5, 8]
green).
Other solutions of Eqs. (7-8) can b
1(r), 1(r) by a scale transformation.
tions (r), (r), together with the eig
by
(r) = 2
1( r),
(r) = 2
1( r),
= 2
1,
also satisfy Eqs. (7-8) with correct bou
for any > 0. The soliton mass and c
are
M = M1,
xc = 1
xc1.
A mnemonic for the numerical value of
looking for gravitation-
.
and defining r = mx,
e given by
) , (7)
(8)
ion amounts to solving
= const, (r ! 1) = 0,
value of at r ! 0, the
lue of .
qs. (7-8) with the initial
l this auxiliary solution
ulation gives [4, 5, 8]
9, (9)
g. 1. The mass of the 1
tions (r), (r), together with the eige
by
(r) = 2
1( r),
(r) = 2
1( r),
= 2
1,
also satisfy Eqs. (7-8) with correct boun
for any > 0. The soliton mass and co
are
M = M1,
xc = 1
xc1.
A mnemonic for the numerical value of
= 3.6 ⇥ 10 4
⇣ m
10 22 eV
⌘ ✓
10
The product of the soliton mass and cor
) , (7)
(8)
amounts to solving
nst, (r ! 1) = 0,
ue of at r ! 0, the
of .
(7-8) with the initial
is auxiliary solution
ion gives [4, 5, 8]
(9)
The mass of the 1
(10)
(r) = 1( r),
= 2
1,
also satisfy Eqs. (7-8) with correct bo
for any > 0. The soliton mass and
are
M = M1,
xc = 1
xc1.
A mnemonic for the numerical value o
= 3.6 ⇥ 10 4
⇣ m
10 22 eV
⌘ ✓
1
The product of the soliton mass and c
pendent of ,
8
⇣ m ⌘
3
r
2 4 6 8 10
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
χ1
Φ1
Vcirc,1
.
rc1
FIG. 1. Profile of the “standard” 1 soliton with = 1 (blue
solid). We also show the corresponding gravitational potential
/pc3
.
ULDM energy
istic regime re-
or gravitation-
fining r = mx,
y
(7)
(8)
unts to solving
(r ! 1) = 0,
at r ! 0, the
Other solutions of Eqs. (7-8) can
1(r), 1(r) by a scale transformation
tions (r), (r), together with the e
by
(r) = 2
1( r),
(r) = 2
1( r),
= 2
1,
also satisfy Eqs. (7-8) with correct b
for any > 0. The soliton mass and
are
c = 4
c1
26. Soliton - host halo relationwhere Mh is the virial mass of the host halo. As noted
in [6–8], Eqs. (29-30) are an excellent numerical fit for a
soliton . The mass of this soliton is
M ⇡ 1.4 ⇥ 109
⇣ m
10 22 eV
⌘ 1
✓
Mh
1012 M
◆1
3
M , (31)
so its parameter is
⇡ 4.9 ⇥ 10 4
✓
Mh
1012 M
◆1
3
. (32)
Note that Eq. (31) is applicable only as long as the halo
exceeds a minimal mass,
Mh,min ⇠ 5.2 ⇥ 107
✓
m
10 22eV
◆ 3/2
M . (33)
Smaller mass halos would be dominated by the soliton.
Ref. [7] showed that Eq. (31) is consistent with the
relation,
✓
|Eh|
◆1
2 M2
pl
However, this is just
ergy Eh by the energ
density profile found
approximation, it mu
of the halo in the sim
the central soliton c
likely to hold for rea
Mh significantly abov
How could this hav
in the simulations of
which were then allow
initial conditions wer
state soliton – the so
to absorb the entire
from Ref. [7] that con
tical initial solitons, t
N solitons with a ran
dius. Such distribut
soliton energy becau
tial condition set-up
the most massive init
Schive, Chiueh, Boardhurst, arXiv:1407.7762
Mh,min ⇠ 5.2 ⇥ 107
✓
m
10 22eV
◆ 3/2
M . (33)
Smaller mass halos would be dominated by the soliton.
Ref. [7] showed that Eq. (31) is consistent with the
relation,
Mc ⇡ ↵
✓
|Eh|
Mh
◆1
2 M2
pl
m
, (34)
where Mc is the core mass (mass within x < xc); Mh, Eh
are the virial mass and energy of the simulated halo; and
↵ = 1 provides a good fit to the data. Ref. [7] gave
a heuristic argument, pointing out that Eq. (34) iden-
tifies the soliton scale radius (chosen as the core radius
xc in [7]) with the inverse velocity dispersion in the host
in [7]) with the inverse velocity dispersion in the hos
lo, in qualitative agreement with a wave-like “uncer
nty principle”.
However, there is another way to express Eq. (34). Th
re mass of a soliton is related to its total mass vi
c ⇡ 0.236M . Thus, using Eq. (25) we have an an
ytic relation Mc ⇡ 1.02
⇣
|E |
M
⌘1
2 M2
pl
m . This allows u
rephrase the empirical Eq. (34) by a more intuitiv
hough equally empirical) expression:
E
M soliton
⇡
E
M halo
. (35
herefore, the soliton–host halo relation in the simula
ns of Ref. [6, 7] can be summarised by the statemen
27. Exercise for NFW halo
ss this result quantitatively.
er a halo with an NFW density profile
⇢NF W (x) =
⇢c c
x
Rs
⇣
1 + x
Rs
⌘2 , (37)
z) =
3H2
(z)
8⇡G
, c =
200
3
c3
ln(1 + c) c
1+c
. (38)
le has two parameters: the radius Rs and the
tion parameter c = R200/Rs, where R200 is the
ere the average density of the halo equals 200
cosmological critical density, roughly indicating
radius of the halo. The gravitational potential
o is
NF W (x) =
4⇡G⇢c cR3
s
x
ln
✓
1 +
x
Rs
◆
. (39)
with
f(c) = 0.54
v
u
u
t
Eq. (45) depends w
rameter, via the
0.9 ÷ 1.1 for c = 5
the simulation resu
pendence, which w
agrees quantitative
to account for the
mass Mh, used in [
ison in App. A.
Consider the ro
galaxy satisfying E
given by
peak rotation velocity in the outskirts of a halo should
approximately repeat itself in the deep inner region. We
now discuss this result quantitatively.
Consider a halo with an NFW density profile
⇢NF W (x) =
⇢c c
x
Rs
⇣
1 + x
Rs
⌘2 , (37)
where
⇢c(z) =
3H2
(z)
8⇡G
, c =
200
3
c3
ln(1 + c) c
1+c
. (38)
The profile has two parameters: the radius Rs and the
concentration parameter c = R200/Rs, where R200 is the
adius where the average density of the halo equals 200
imes the cosmological critical density, roughly indicating
he virial radius of the halo. The gravitational potential
of the halo is
NF W (x) =
4⇡G⇢c cR3
s
x
ln
✓
1 +
x
Rs
◆
. (39)
Near the origin, x ⌧ Rs, NF W is approximately con-
tant, NF W (x ⌧ Rs) ⇡ h, and is related to the mass
of the halo, M200 = 200⇢c
4⇡
3 c3
R3
s, via
⇥
✓
H(z)
H0
◆3
✓
with
f(c) = 0.54
v
u
u
t
✓
c
1 + c
Eq. (45) depends weakly on
rameter, via the factor f(
0.9 ÷ 1.1 for c = 5 ÷ 30. I
the simulation result, Eq. (3
pendence, which we have su
agrees quantitatively to abo
to account for the slightly d
mass Mh, used in [7], and ou
ison in App. A.
Consider the rotation v
galaxy satisfying Eq. (35).
given by
V 2
circ,h(x)
V 2
circ,h(Rs)
=
2(1 + ⇠) l
⇠(1 + ⇠
scale radius
concentration
c 5 ÷ 30
value
maxVcirc,h ⇡ 1.37 ⇥ 105
( h)
1
2 km/s. (47)
e other hand, in the inner galaxy x ⌧ Rs, the circu-
locity due to the soliton peaks to a local maximum
maxVcirc, ⇡ 1.51 ⇥ 105
✓
˜c
0.4
◆1
2
( h)
1
2 km/s, (48)
we used Eq. (44) to fix and Eq. (28) to relate it
xVcirc, .
anticipated in the beginning of this section, Eq. (35)
cts approximately equal peak circular velocities for
ner soliton component and for the host halo,
maxVcirc,
maxVcirc,h
⇡ 1.1
✓
˜c
0.4
◆1
2
, (49)
endent of the particle mass m, independent of the
mass M200, and only weakly dependent on the de-
of the halo via the factor (˜c/0.4)
1
2 . Eq. (49) is plot-
Fig. 3 as function of the concentration parameter.
the soliton bump in the rotation c
smaller x according to Eq. (50), bu
height.
c = 25
c =
Vcirc [km/s]
10 100 1000 10450
100
150
200
250
300
c = 25
c = 15
Vcirc [km/s]
75
100
125
150
where we used Eq. (44) to fix and Eq. (28) to relate it
to maxVcirc, .
As anticipated in the beginning of this section, Eq. (35)
predicts approximately equal peak circular velocities for
the inner soliton component and for the host halo,
maxVcirc,
maxVcirc,h
⇡ 1.1
✓
˜c
0.4
◆1
2
, (49)
independent of the particle mass m, independent of the
halo mass M200, and only weakly dependent on the de-
tails of the halo via the factor (˜c/0.4)
1
2 . Eq. (49) is plot-
ted in Fig. 3 as function of the concentration parameter.
5 10 15 20 25 30
1.05
1.10
1.15
1.20
1.25
1.30
c
maxVcirc,
maxVcirc,h
28. bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
FIG. 4. Rotation curves for the ULDM soliton+halo system,
predictions vs data
m = 10 22
eV
bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
29. bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
FIG. 4. Rotation curves for the ULDM soliton+halo system,
predictions vs data
m = 10 22
eV
bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
tive to the details of this mass cut. Of the 175 galaxies
in [28], 162 pass the Mgal cut for m = 10 22
eV, and all
175 pass it for m = 10 21
eV.
Vcirc [km/s]
0 1 2 3 4 5 6
0
20
40
60
80
UGC 1281
m = 10 22
eV
x [kpc]
Vcirc [km/s]
0 1 2 3 4 5 6
0
20
40
60
80
UGC 1281
m = 10 21
eV
x [kpc]
30. bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
FIG. 4. Rotation curves for the ULDM soliton+halo system,
predictions vs data
m = 10 22
eV
bottom panels, respectively. For larger m > 10 22
eV,
the soliton bump in the rotation curve would shift to
smaller x according to Eq. (50), but would maintain its
height.
M200 = 1012
M
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
10 100 1000 104
105
10650
100
150
200
250
300
c = 25
c = 15
c = 10
Vcirc [km/s]
x [pc]
M200 = 5 ⇥ 1010
M
10 100 1000 104
105
25
50
75
100
125
150
tive to the details of this mass cut. Of the 175 galaxies
in [28], 162 pass the Mgal cut for m = 10 22
eV, and all
175 pass it for m = 10 21
eV.
Vcirc [km/s]
0 1 2 3 4 5 6
0
20
40
60
80
UGC 1281
m = 10 22
eV
x [kpc]
Vcirc [km/s]
0 1 2 3 4 5 6
0
20
40
60
80
UGC 1281
m = 10 21
eV
x [kpc]
Our analysis is as follows. For each SPARC galaxy, we
ake a crude estimate of the halo mass contained within
e observed rotation curve profile by Mgal ⇠ RV 2
/G,
here R is the radial distance of the last (highest dis-
nce) data point in the rotation curve, and V the cor-
sponding velocity. We keep only galaxies with Mgal >
9
m/10 22
eV
3/2
M . We do this in order to limit
urselves to galaxy masses that are comfortably above
e minimal halo mass (33). Our results are not sensi-
ve to the details of this mass cut. Of the 175 galaxies
[28], 162 pass the Mgal cut for m = 10 22
eV, and all
5 pass it for m = 10 21
eV.
Vcirc [km/s]
0 1 2 3 4 5 6
0
20
40
60
80
UGC 1281
m = 10 22
eV
x [kpc]
Vcirc [km/s]
60
80
UGC 1281
ing the halo peak with a soliton peak, which would
bias our analysis. Defining the halo cut anywhere at
& 1 m/10 22
eV
1
kpc guarantees that such confusion
is avoided.
Our first pass on the data includes only galaxies for
which the predicted soliton is resolved, namely, xpeak,
from Eq. (50), with maxVcirc, = maxVcirc,h, lies within
the rotation curve data. For these galaxies, we compute
from data the ratio
Vcirc, obs(xpeak, )
maxVcirc,h
. (51)
Here, Vcirc, obs(xpeak, ) is the measured velocity at the
expected soliton peak position.
The results of this first pass on the data are shown in
Fig. 11. A total of 46 galaxies pass the resolved soliton
Vcirc [km/s]
m = 10 22
eV
0 1 2 3 4 5
0
20
40
60
80
100
120
140
UGC 4325
x [kpc]
Vcirc [km/s]
31. Analyzing SPARC data
175 high resolution rotation curves
cuts:
fbar2DM =
V
(bar)
circ,h
V
(DM)
circ,h
5 108 m
10 22eV
3
2
M < Mhalo < 5 1011
M
nresolved soliton, that can be added to the sample of
g. 11. No galaxy is added for fbar2DM < 0.33. For m =
0 21
eV, 16 and 5 galaxies are added with fbar2DM <
0.5, and none for fbar2DM < 0.33.
In Figs. 11-12, vertical dashed line indicates the
liton–host halo prediction. The shaded region shows
e range of the prediction, modifying the RHS of
q. (49) between 0.5 1.5, consistent with the scatter
en in the simulations.
We conclude that the four galaxies in Figs. 7-10 are not
utliers: they are representative of a systematic discrep-
ncy, that would be di cult to attribute to the scatter
en in the simulations. If the soliton-host halo rela-
on of [6, 7] is correct, then ULDM in the mass range
soliton resolved
m=10-22
eV
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
5
10
15
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
data.
We have limited our attention to the range m
(10 22
÷ 10 21
) eV, for which we believe the result
clear. We leave a detailed study of the precise exclu
range to future work. We note that for lower pa
mass, m . 10 23
eV, the soliton contribution ext
over much of the velocity profile of many of the SP
galaxies, leaving little room for a host halo. This l
where the galaxies are essentially composed of a s
giant soliton, was considered in other works. We do
pursue it further, one reason being that this ran
small m is in significant tension with Ly-↵ data [18
For higher particle mass, m & 10 21
eV, the so
peak is pushed deep into the inner 100 pc of the rot
curve. Although high resolution data (e.g. NGC 1
Fig. 9) is sensitive to and disfavours this situation, a
careful analysis would be needed to draw a definitive
clusion. Note that in this range of m, ULDM ceas
o↵er a solution to the small-scale puzzles of ⇤CDM
e.g., review in [12]).
soliton
m=10
-22
eV
allow unresolved10
15
20galaxies
soliton resolved
m=10-22
eV
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
5
10
15
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
m=10
-21
eV
soliton resolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
1
2
3
4
5
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
45
26
5
< 1 < 0.55 < 0.33
4
32. Analyzing SPARC data cntd.
if soliton peaks inside the innermost point
m = 10 22
eV
0 1 2 3 4 5
0
20
40
60
x [kpc]
Vcirc [km/s]
m = 10 21
eV
0 1 2 3 4 5
0
20
40
60
80
100
120
140
UGC 4325
x [kpc]
FIG. 8. Same as Fig. 7 for UGC 4325.
-to-light ratio ⌥⇤ = 0.5 M /L . Set-
DM),2
rc,h = V
(obs),2
circ,h , we calculate the ratio
/V
(DM)
circ,h . We present results when cut-
values of fbar2DM < 1, 0.5, 0.33.
on the data includes only galaxies for
ted soliton is resolved, namely, xpeak,
th maxVcirc, = maxV
(DM)
circ,h , lies within
e data. For these galaxies, we compute
io
Vcirc, obs(xpeak, )
maxV
(DM)
circ,h
. (51)
eak, ) is the measured velocity at the
peak position.
his first pass on the data are shown in
e, and green histograms show the result
bar2DM < 1, 0.5, 0.33, respectively. For
e find 45, 26, and 5 galaxies that pass
C 1560
model, as seen from the lower panel of Fig. 8. To over-
come this without complicating the analysis, we perform
a second pass on the data. Here, we allow galaxies with
unresolved soliton, as long as the innermost data point is
located not farther than 3 ⇥ xpeak, . We need to correct
for the fact that the soliton peak velocity is outside of
the measurement resolution. To do this, we modify our
observable as
Vcirc, obs(xpeak, )
maxV
(DM)
circ,h
!
Vcirc, obs(xmin,data)
maxV
(DM)
circ,h
⇥
r
xmin,data
xpeak,
,
(52)
where xmin,data is the radius of the first data point. This
correction is conservative, because it takes the fall-o↵ of
the soliton gravitational porential at at x > xpeak, to
be the same as for a point mass. In reality, the potential
decays slower and the soliton-induced velocity decreases
slower. Keeping this caveat in mind, Fig. 12 presents our
results including unresolved solitons. For m = 10 22
eV,
Vcirc [km/s]
80
100
120
NGC 100
mass, m . 10 23
eV, the soliton contribution extends
ver much of the velocity profile of many of the SPARC
alaxies, leaving little room for a host halo. This limit,
where the galaxies are essentially composed of a single
iant soliton, was considered in other works. We do not
ursue it further, one reason being that this range of
mall m is in significant tension with Ly-↵ data [18, 19].
For higher particle mass, m & 10 21
eV, the soliton
eak is pushed deep into the inner 100 pc of the rotation
urve. Although high resolution data (e.g. NGC 1560,
ig. 9) is sensitive to and disfavours this situation, a more
areful analysis would be needed to draw a definitive con-
lusion. Note that in this range of m, ULDM ceases to
↵er a solution to the small-scale puzzles of ⇤CDM (see,
.g., review in [12]).
soliton
m=10-22
eV
allow unresolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
5
10
15
20
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
soliton resolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
5
10#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
m=10-21
eV
soliton resolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
1
2
3
4
5
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
G. 11. Distribution of SPARC galaxies [28] with respect
the ratio of observed circular velocity at the soliton peak
the maximal circular velocity of the halo. The vertical
shed line shows the prediction for the mean implied by the
iton-host halo relation and the shaded region accounts for
e intrinsic scatter in this relation. The ULDM mass is m =
22 21
e.g., review in [12]).
soliton
m=10
-22
eV
allow unresolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
5
10
15
20
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
soliton
m=10-21
eV
allow unresolved
0 0.5 1 1.5 2
(velocity at soliton peak)/(max halo velocity)
0
2
4
6
8
10
#ofgalaxies
Vcirc, obs(xpeak, )/maxVcirc,h
5
93
42
20
5
33. Conclusion:
ULDM with
is disfavoured by rotation curves of disk galaxies
m (10 22
÷ 10 21
)eV
cannot play a role in solving small-scale
problems of LambdaCDM
35. Caveats:
• Baryonic effects
- stars tend to increase the soliton mass (J.H.H.Chan et al.
(2017)); their potential can be taken into account self-
consistently
- baryonic feedback unlikely to destroy the soliton:
in the inner partMsol > 10 Mbaryons
36. Caveats:
• Baryonic effects
- stars tend to increase the soliton mass (J.H.H.Chan et al.
(2017)); their potential can be taken into account self-
consistently
- baryonic feedback unlikely to destroy the soliton:
in the inner part
• Self-interaction and direct interaction with baryons
- negligible for minimal models
Msol > 10 Mbaryons
37. Caveats:
• Baryonic effects
- stars tend to increase the soliton mass (J.H.H.Chan et al.
(2017)); their potential can be taken into account self-
consistently
- baryonic feedback unlikely to destroy the soliton:
in the inner part
• Self-interaction and direct interaction with baryons
- negligible for minimal models
• Accretion on supermassive black hole
- negligible for and ,
but quickly increases with the mass of the field
Msol > 10 Mbaryons
m 10 21
eV MSMBH < 1010
M
38. Future: probing higher masses
computed holding their mass fixed by Eq. (31), but in-
cluding the e↵ects of baryons as we discuss below. An
NFW profile, fitted in Ref. [36] to r & 10 kpc SDSS data,
is shown in dashed black.
10
-4
10
-2
10
0
10
2
10
4
r [pc]
10
5
10
6
10
7
10
8
10
9
10
10
10
11
10
12
enclosedmass[M]
m=10-19
eV
m=10-20
eV
m=10-21
eV
m=10-22
eV
Ghez 2003
McGinn 1989
Fritz 2016
Lindqvist 1992
Schodel 2014
Sofue 2009
Sofue 2012
Sofue 2013
Chatzopoulos 2015
Deguchi 2004
Oh 2009
Trippe 2008
Gilessen 2008
Nuclear Bulge (disc+star cluster)
from photometry, Launhardt (2002)
NFW fit,
Piffl (2015)
to the re
The p
superim
the phot
ties due
and due
we learn
that sta
ically in
Assum
toy mod
an ULD
of the N
radially
the NB
in [55],
300 pc.
we calcu
Fig. 1
paramet
the unp
• Inner dynamics of the Milky Way
40. Summary
ULDM is a simple (perhaps, the simplest) option for dark
matter with interesting phenomenology. Theoretically
motivated
41. Summary
ULDM is a simple (perhaps, the simplest) option for dark
matter with interesting phenomenology. Theoretically
motivated
If soliton — host halo relation holds for real halos,
is disfavoured by galactic rotation curves
(also Ly )
m 10 21
eV
42. Summary
ULDM is a simple (perhaps, the simplest) option for dark
matter with interesting phenomenology. Theoretically
motivated
Further understanding of structure formation with ULDM
(baryonic effects, supermassive black hole)
More probes: Inner Milky Way, 21 cm, pulsar timing (see
talk by Diego Blas)
If soliton — host halo relation holds for real halos,
is disfavoured by galactic rotation curves
(also Ly )
Outlook
m 10 21
eV