We present an update of the CLUMPY code for the calculation of the astrophysical J-factors (from dark matter annihilation/decay) for any Galactic or extragalactic dark matter halo including ...substructures: halo-to-halo concentration scatter may now be enabled, boost factors can include several levels of substructures, and triaxiality is a new option for dark matter haloes. This new version takes advantage of the cfitsio and HEALPix libraries to propose fits output maps using the HEALPix pixelisation scheme. Skymaps for γ-ray and ν signals from generic annihilation/decay spectra are now direct outputs of CLUMPY. Making use of HEALPix routines, smoothing by a user-defined instrumental Gaussian beam and computing the angular power spectrum of the maps are now possible. In addition to these improvements, the main novelty is the implementation of a Jeans analysis module, to obtain dark matter density profiles from kinematic data in relaxed spherical systems (e.g., dwarf spheroidal galaxies). The code is also interfaced with the GreAT toolkit designed for Markov Chain Monte Carlo analyses, from which probability density functions and credible intervals can be obtained for velocity dispersions, dark matter profiles, and J-factors.
Program title:CLUMPY
Catalogue identifier: AEKS_v2_0
Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKS_v2_0.html
Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 494335
No. of bytes in distributed program, including test data, etc.: 24425968
Distribution format: tar.gz
Programming language: C/C++.
Computer: PC and Mac.
Operating system: UNIX(Linux), MacOS X.
RAM: Between 500MB and 1GB depending on the size of the requested skymap
Catalogue identifier of previous version: AEKS_v1_0
Journal reference of previous version: Comput. Phys. Comm. 183(2012)656
Classification: 1.1, 1.7, 1.9.
External routines: CERN ROOT (http://root.cern.ch), GSL (http://www.gnu.org/software/gsl), cfitsio (http://heasarc.gsfc.nasa.gov/fitsio/fitsio.html), HEALPix C++ and F90 (http://healpix.sourceforge.net/index.php), GreAT (http://lpsc.in2p3.fr/great) (for MCMC analyses only), and Doxygen (http://www.doxygen.org) (optional)
Does the new version supersede the previous version?: Yes
Nature of problem: Calculation of dark matter profile from kinematic data, γ-ray and ν signals from dark matter annihilation/decay.
Solution method: Solve the integro-differential Jeans equation (optimised for speed) for several generic distributions (dark matter profile, light profile, velocity anisotropy). Integration of the DM density (squared) along a line-of-sight for generic dark matter haloes with substructures (spatial, mass, concentration distributions). Draw full skymaps of γ-ray and ν emission from dark matter structures, smoothed by an instrument PSF using HEALPix tools.
Reasons for new version: Many more functionalities and options have been added to the code.
Summary of revisions: Inclusion of the PPPC4DMID spectra for gamma-ray and neutrino fluxes; HEALPix pixelisation for skymaps and angular power spectrum; DM profile triaxiality enabled; More mass–concentration options; Multi-level substructure boost; Jeans analysis module to compute dark matter profiles for stellar kinematic data; Improved ROOT and FITS output.
Restrictions: The diffuse extragalactic contribution to the signal (and γ-ray attenuation) as well as secondary radiation from dark matter remains to be included in order to provide a comprehensive description of the expected signal.
Running time: This is highly dependent of the user-defined choices of DM profiles, precision ε and integration angle αint: •∼1 hour for a full skymap (including substructures) with αint=0.1° and ε=0.01;•∼=1 mn for a 5°×5° skymap (including substructures) with αint=0.1° and ε=0.01;•∼5 mn for a typical Jeans/MCMC analysis (on a ‘ultrafaint’-like dwarf spheroidal galaxy) using a constant anisotropy profile.
We present the first public code for semi-analytical calculation of the
γ-ray flux astrophysical
J-factor from dark matter annihilation/decay in the Galaxy, including dark matter substructures. The ...core of the code is the calculation of the line of sight integral of the dark matter density squared (for annihilations) or density (for decaying dark matter). The code can be used in three modes: i) to draw skymaps from the Galactic smooth component and/or the substructure contributions, ii) to calculate the flux from a specific halo (that is not the Galactic halo, e.g. dwarf spheroidal galaxies) or iii) to perform simple statistical operations from a list of allowed DM profiles for a given object. Extragalactic contributions and other tracers of DM annihilation (e.g. positrons, anti-protons) will be included in a second release.
Program title: CLUMPY
Catalogue identifier: AEKS_v1_0
Program summary URL:
http://cpc.cs.qub.ac.uk/summaries/AEEF_v1_0.html
Program obtainable from: CPC Program Library, Queenʼs University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence,
http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 207 466
No. of bytes in distributed program, including test data, etc.: 6 342 889
Distribution format: tar.gz
Programming language: C/C++
Computer: PC and Mac
Operating system: UNIX(Linux), MacOS X
RAM: Depends on the requested size of skymaps (
∼
40
Mb
for a
500
×
500
map)
Classification: 1.1, 1.9
External routines: CERN ROOT library (
http://root.cern.ch/drupal/), Doxygen (
http://www.doxygen.org) (optional)
Nature of problem: Calculation of
γ-ray signal from dark matter annihilation (resp. decay). This involves a particle physics term and an astrophysical one. The focus here is on the latter.
Solution method: Integration of the DM density squared (resp. density) along a line of sight. The code is optimised to deal with the DM density peaks encountered along the line of sight (DM substructures). A semi-analytical approach (calibrated on N-body simulations) is used for the spatial and mass distributions of the dark matter substructures in the Galaxy.
Restrictions: Some generic dark matter annihilation spectra are provided but are not included in the calculation so far as it is assumed that the particle physics is independent of the astrophysics of the problem.
Running time: This is highly dependent on the DM profiles considered, the requested precision
ε and integration angle
α
int
:
•
about 60 mn for a
5
°
×
5
°
map towards the Galactic centre, with
α
int
=
0.01
°
, NFW dark matter profiles and
ε
=
10
−
2
;
•
about 2 h for the same set-up towards the anti-centre;
•
0.1 to 10 DM models per second, depending on integration angle and DM profile.
► Gamma-ray annihilation from dark matter structures. ► Public code for skymaps and statistical analyses. ► Full documentation and examples.
We derived constraints on cosmological parameters using weak lensing peak statistics measured on the ∼ 130 deg2 of the Canada–France–Hawaii Telescope Stripe 82 Survey. This analysis demonstrates the ...feasibility of using peak statistics in cosmological studies. For our measurements, we considered peaks with signal-to-noise ratio in the range of ν = 3, 6. For a flat Λ cold dark matter model with only (Ωm, σ8) as free parameters, we constrained the parameters of the following relation Σ8 = σ8(Ωm/0.27)α to be Σ8 = 0.82 ± 0.03 and α = 0.43 ± 0.02. The α value found is considerably smaller than the one measured in two-point and three-point cosmic shear correlation analyses, showing a significant complement of peak statistics to standard weak lensing cosmological studies. The derived constraints on (Ωm, σ8) are fully consistent with the ones from either WMAP9 or Planck. From the weak lensing peak abundances alone, we obtained marginalized mean values of
$\Omega _{\rm m}=0.38^{+0.27}_{-0.24}$
and σ8 = 0.81 ± 0.26. Finally, we also explored the potential of using weak lensing peak statistics to constrain the mass–concentration relation of dark matter haloes simultaneously with cosmological parameters.
Abstract
We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the changes in ...the scatter and outlier fraction of photometric redshifts when galaxy size, ellipticity, Sérsic index, and surface brightness are included in training on galaxy samples from the SDSS and the CFHT Stripe-82 Survey (CS82). We show that by adding galaxy morphological parameters to full ugriz photometry, only mild improvements are obtained, while the gains are substantial in cases where fewer passbands are available. For instance, the combination of grz photometry and morphological parameters almost fully recovers the metrics of 5-band photometric redshifts. We demonstrate that with morphology it is possible to determine useful redshift distribution N(z) of galaxy samples without any colour information. We also find that the inclusion of quasar redshifts and associated object sizes in training improves the quality of photometric redshift catalogues, compensating for the lack of a good star-galaxy separator. We further show that morphological information can mitigate biases and scatter due to bad photometry. As an application, we derive both point estimates and posterior distributions of redshifts for the official CS82 catalogue, training on morphology and SDSS Stripe-82 ugriz bands when available. Our redshifts yield a 68th percentile error of 0.058(1 + z), and a outlier fraction of 5.2 per cent. We further include a deep extension trained on morphology and single i-band CS82 photometry.
We present a new measurement of the mass-concentration relation and the stellar-to-halo mass ratio over the halo-mass range 5 × 1012 to 2 × 1014 M . To achieve this, we use weak lensing measurements ...from the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82), combined with the central galaxies from the redMaPPer cluster catalog and the LOWZ/CMASS galaxy sample of the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Tenth Data Release. The stacked lensing signals around these samples are modeled as a sum of contributions from the central galaxy, its dark matter halo, and the neighboring halos, as well as a term for possible centering errors. We measure the mass-concentration relation: with A = 5.24 1.24, B = −0.13 0.10 for 0.2 < z < 0.4, and A = 6.61 0.75, B = −0.15 0.05 for 0.4 < z < 0.6. These amplitudes and slopes are completely consistent with predictions from recent simulations. We also measure the stellar-to-halo mass ratio for our samples, and find results consistent with previous measurements from lensing and other techniques.
We derived constraints on cosmological parameters using weak lensing peak statistics measured on the ... of the Canada-France-Hawaii Telescope Stripe 82 Survey. This analysis demonstrates the ...feasibility of using peak statistics in cosmological studies. For our measurements, we considered peaks with signal-to-noise ratio in the range of alpha = 3, 6. For a flat ... cold dark matter model with only (...) as free parameters, we constrained the parameters of the following relation ... to be ... and a = 0.43 plus or minus 0.02. The a value found is considerably smaller than the one measured in two-point and three-point cosmic shear correlation analyses, showing a significant complement of peak statistics to standard weak lensing cosmological studies. The derived constraints on (...) are fully consistent with the ones from either WMAP9 or Planck. From the weak lensing peak abundances alone, we obtained marginalized mean values of ... and ... = 0.81 plus or minus 0.26. Finally, we also explored the potential of using weak lensing peak statistics to constrain the mass-concentration relation of dark matter haloes simultaneously with cosmological parameters. (ProQuest: ... denotes formulae/symbols omitted.)
We present a weak lensing mass map covering ∼124 deg2 of the Canada–France–Hawaii Telescope Stripe 82 Survey (CS82). We study the statistics of rare peaks in the map, including peak abundance, the ...peak–peak correlation functions and the tangential-shear profiles around peaks. We find that the abundance of peaks detected in CS82 is consistent with predictions from a Λ cold dark matter cosmological model, once noise effects are properly included. The correlation functions of peaks with different signal-to-noise ratio (SNR) are well described by power laws, and there is a clear cross-correlation between the Sloan Digital Sky Survey III/Constant Mass galaxies and high SNR peaks. The tangential-shear profiles around peaks increase with peak SNR. We fit analytical models to the tangential-shear profiles, including a projected singular isothermal sphere (SIS) model and a projected Navarro, Frenk & White (NFW) model, plus a two-halo term. For the high SNR peaks, the SIS model is rejected at ∼3σ. The NFW model plus a two-halo term gives more acceptable fits to the data. Some peaks match the positions of optically detected clusters, while others are relatively dark. Comparing dark and matched peaks, we find a difference in lensing signal of a factor of 2, suggesting that about half of the dark peaks are false detections.
We present an update of the CLUMPY code for the calculation of the astrophysical -factors (from dark matter annihilation/decay) for any Galactic or extragalactic dark matter halo including ...substructures: halo-to-halo concentration scatter may now be enabled, boost factors can include several levels of substructures, and triaxiality is a new option for dark matter haloes. This new version takes advantage of the cfitsio and HEALPix libraries to propose fits output maps using the HEALPix pixelisation scheme. Skymaps for -ray and signals from generic annihilation/decay spectra are now direct outputs of CLUMPY. Making use of HEALPix routines, smoothing by a user-defined instrumental Gaussian beam and computing the angular power spectrum of the maps are now possible. In addition to these improvements, the main novelty is the implementation of a Jeans analysis module, to obtain dark matter density profiles from kinematic data in relaxed spherical systems (e.g., dwarf spheroidal galaxies). The code is also interfaced with the GreAT toolkit designed for Markov Chain Monte Carlo analyses, from which probability density functions and credible intervals can be obtained for velocity dispersions, dark matter profiles, and -factors. Program summary Program title: CLUMPY Catalogue identifier: AEKS_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEKS_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 494335 No. of bytes in distributed program, including test data, etc.: 24425968 Distribution format: tar.gz Programming language: C/C++. Computer: PC and Mac. Operating system: UNIX(Linux), MacOS X. RAM: Between 500MB and 1GB depending on the size of the requested skymap Catalogue identifier of previous version: AEKS_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183(2012)656 Classification: 1.1, 1.7, 1.9. External routines: CERN ROOT (http://root.cern.ch), GSL (http://www.gnu.org/software/gsl), cfitsio (http://heasarc.gsfc.nasa.gov/fitsio/fitsio.html), HEALPix C++ and F90 (http://healpix.sourceforge.net/index.php), GreAT (http://lpsc.in2p3.fr/great) (for MCMC analyses only), and Doxygen (http://www.doxygen.org) (optional) Does the new version supersede the previous version?: Yes Nature of problem: Calculation of dark matter profile from kinematic data, -ray and signals from dark matter annihilation/decay. Solution method: Solve the integro-differential Jeans equation (optimised for speed) for several generic distributions (dark matter profile, light profile, velocity anisotropy). Integration of the DM density (squared) along a line-of-sight for generic dark matter haloes with substructures (spatial, mass, concentration distributions). Draw full skymaps of -ray and emission from dark matter structures, smoothed by an instrument PSF using HEALPix tools. Reasons for new version: Many more functionalities and options have been added to the code. Summary of revisions: Inclusion of the PPPC4DMID spectra for gamma-ray and neutrino fluxes; HEALPix pixelisation for skymaps and angular power spectrum; DM profile triaxiality enabled; More mass-concentration options; Multi-level substructure boost; Jeans analysis module to compute dark matter profiles for stellar kinematic data; Improved ROOT and FITS output. Restrictions: The diffuse extragalactic contribution to the signal (and -ray attenuation) as well as secondary radiation from dark matter remains to be included in order to provide a comprehensive description of the expected signal. Running time: This is highly dependent of the user-defined choices of DM profiles, precision and integration angle : hour for a full skymap (including substructures) with and ; mn for a skymap (including substructures) with and ; mn for a typical Jeans/MCMC analysis (on a 'ultrafaint'-like dwarf spheroidal galaxy) using a constant anisotropy profile.
Abstract
We set out to quantify the number density of quiescent massive compact galaxies at intermediate redshifts. We determine structural parameters based on i-band imaging using the ...Canada–France–Hawaii Telescope (CFHT) equatorial Sloan Digital Sky Survey (SDSS) Stripe 82 (CS82) survey (∼170 deg2) taking advantage of an exquisite median seeing of ∼0.6 arcsec. We select compact massive (M
⋆ > 5 × 1010 M⊙) galaxies within the redshift range of 0.2 < z < 0.6. The large volume sampled allows to decrease the effect of cosmic variance that has hampered the calculation of the number density for this enigmatic population in many previous studies. We undertake an exhaustive analysis in an effort to untangle the various findings inherent to the diverse definition of compactness present in the literature. We find that the absolute number of compact galaxies is very dependent on the adopted definition and can change up to a factor of >10. We systematically measure a factor of ∼5 more compacts at the same redshift than what was previously reported on smaller fields with Hubble Space Telescope (HST) imaging, which are more affected by cosmic variance. This means that the decrease in number density from z ∼ 1.5 to z ∼ 0.2 might be only of a factor of ∼2–5, significantly smaller than what was previously reported. This supports progenitor bias as the main contributor to the size evolution. This milder decrease is roughly compatible with the predictions from recent numerical simulations. Only the most extreme compact galaxies, with R
eff < 1.5 × (M
⋆/1011 M⊙)0.75 and M
⋆ > 1010.7 M⊙, appear to drop in number by a factor of ∼20 and hence likely experience a noticeable size evolution.
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