24 research outputs found
Cosmological constraints from HSC Y1 lensing convergence PDF
We utilize the probability distribution function (PDF) of normalized
convergence maps reconstructed from the Subaru Hyper Suprime-Cam (HSC) Y1 shear
catalogue, in combination with the power spectrum, to measure the matter
clustering amplitude . The large-scale
structure's statistical properties are incompletely described by the
traditional two-point statistics, motivating our investigation of the PDF -- a
complementary higher-order statistic. By defining the PDF over the standard
deviation-normalized convergence map we are able to isolate the non-Gaussian
information. We use tailored simulations to compress the data vector and
construct a likelihood approximation. We mitigate the impact of survey and
astrophysical systematics with cuts on smoothing scales, redshift bins, and
data vectors. We find from the PDF alone and
from the combination of PDF and power spectrum
(68% CL). The PDF improves the power spectrum-only constraint by about 10%.Comment: 6+4 pages, 4+4 figures; PRD accepted versio
An exploration of the properties of cluster profiles for the thermal and kinetic Sunyaev-Zel'dovich effects
With the advent of high-resolution, low-noise CMB measurements, the ability
to extract cosmological information from thermal Sunyaev-Zel'dovich effect and
kinetic Sunyaev-Zel'dovich effect will be limited not by statistical
uncertainties but rather by systematic and theoretical uncertainties. The
theoretical uncertainty is driven by the lack of knowledge about the electron
pressure and density. Thus we explore the electron pressure and density
distributions in the IllustrisTNG hydrodynamical simulations, and we
demonstrate that the cluster properties exhibit a strong dependence on the halo
concentration -- providing some of the first evidence of cluster assembly bias
in the electron pressure and density. Further, our work shows evidence for a
broken power-law mass dependence, with lower pressure in lower mass halos than
previous work and a strong evolution with mass of the radial correlations in
the electron density and pressure. Both of these effects highlight the
differing impact of active galactic nuclei and supernova feedback on the gas in
galaxy groups compared to massive clusters. We verified that we see
qualitatively similar features in the SIMBA hydro-dynamical simulations,
suggesting these effects could be generic features. Finally, we provide a
parametric formula for the electron pressure and density profile as a function
of dark matter halo mass, halo concentration, and redshift. These fitting
formulae can reproduce the distribution of density and pressure of clusters and
will be useful in extracting cosmological information from upcoming CMB
surveys.Comment: 18 pages, 7 figure
Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps
The thermal Sunyaev-Zel'dovich (tSZ) and the kinematic Sunyaev-Zel'dovich
(kSZ) effects trace the distribution of electron pressure and momentum in the
hot Universe. These observables depend on rich multi-scale physics, thus,
simulated maps should ideally be based on calculations that capture baryonic
feedback effects such as cooling, star formation, and other complex processes.
In this paper, we train deep convolutional neural networks with a U-Net
architecture to map from the three-dimensional distribution of dark matter to
electron density, momentum and pressure at ~ 100 kpc resolution. These networks
are trained on a combination of the TNG300 volume and a set of cluster zoom-in
simulations from the IllustrisTNG project. The neural nets are able to
reproduce the power spectrum, one-point probability distribution function,
bispectrum, and cross-correlation coefficients of the simulations more
accurately than the state-of-the-art semi-analytical models. Our approach
offers a route to capture the richness of a full cosmological hydrodynamical
simulation of galaxy formation with the speed of an analytical calculation.Comment: 21 pages, 18 figure
Cosmology from weak lensing peaks and minima with Subaru Hyper Suprime-Cam survey first-year data
We present cosmological constraints derived from peak counts, minimum counts,
and the angular power spectrum of the Subaru Hyper Suprime-Cam first-year (HSC
Y1) weak lensing shear catalog. Weak lensing peak and minimum counts contain
non-Gaussian information and hence are complementary to the conventional
two-point statistics in constraining cosmology. In this work, we forward-model
the three summary statistics and their dependence on cosmology, using a suite
of -body simulations tailored to the HSC Y1 data. We investigate systematic
and astrophysical effects including intrinsic alignments, baryon feedback,
multiplicative bias, and photometric redshift uncertainties. We mitigate the
impact of these systematics by applying cuts on angular scales, smoothing
scales, statistic bins, and tomographic redshift bins. By combining peaks,
minima, and the power spectrum, assuming a flat-CDM model, we obtain
, a 35\%
tighter constraint than that obtained from the angular power spectrum alone.
Our results are in agreement with other studies using HSC weak lensing shear
data, as well as with Planck 2018 cosmology and recent CMB lensing constraints
from the Atacama Cosmology Telescope and the South Pole Telescope
Predicting the impact of feedback on matter clustering with machine learning in CAMELS
Extracting information from the total matter power spectrum with the
precision needed for upcoming cosmological surveys requires unraveling the
complex effects of galaxy formation processes on the distribution of matter. We
investigate the impact of baryonic physics on matter clustering at using
a library of power spectra from the Cosmology and Astrophysics with MachinE
Learning Simulations (CAMELS) project, containing thousands of volume realizations with varying cosmology, initial random field,
stellar and AGN feedback strength and sub-grid model implementation methods. We
show that baryonic physics affects matter clustering on scales and the magnitude of this effect is dependent on the
details of the galaxy formation implementation and variations of cosmological
and astrophysical parameters. Increasing AGN feedback strength decreases halo
baryon fractions and yields stronger suppression of power relative to N-body
simulations, while stronger stellar feedback often results in weaker effects by
suppressing black hole growth and therefore the impact of AGN feedback. We find
a broad correlation between mean baryon fraction of massive halos (\,\Msun) and suppression of matter clustering but with
significant scatter compared to previous work owing to wider exploration of
feedback parameters and cosmic variance effects. We show that a random forest
regressor trained on the baryon content and abundance of halos across the full
mass range \Msun can predict the
effect of galaxy formation on the matter power spectrum on scales --20.0\,
The SZ flux-mass (-) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Feedback from active galactic nuclei (AGN) and supernovae can affect
measurements of integrated SZ flux of halos () from CMB surveys,
and cause its relation with the halo mass () to deviate from
the self-similar power-law prediction of the virial theorem. We perform a
comprehensive study of such deviations using CAMELS, a suite of hydrodynamic
simulations with extensive variations in feedback prescriptions. We use a
combination of two machine learning tools (random forest and symbolic
regression) to search for analogues of the relation which are more robust
to feedback processes for low masses ();
we find that simply replacing in the
relation makes it remarkably self-similar. This could serve as a robust
multiwavelength mass proxy for low-mass clusters and galaxy groups. Our
methodology can also be generally useful to improve the domain of validity of
other astrophysical scaling relations.
We also forecast that measurements of the relation could provide
percent-level constraints on certain combinations of feedback parameters and/or
rule out a major part of the parameter space of supernova and AGN feedback
models used in current state-of-the-art hydrodynamic simulations. Our results
can be useful for using upcoming SZ surveys (e.g., SO, CMB-S4) and galaxy
surveys (e.g., DESI and Rubin) to constrain the nature of baryonic feedback.
Finally, we find that the an alternative relation, , provides
complementary information on feedback than Comment: Version appearing in MNRAS. Minor change to Fig.6 and added Fig. A5
compared to the previous version. 7+5 pages. The code and data associated
with this paper are available at
https://github.com/JayWadekar/ScalingRelations_M
The CAMELS Project: Public Data Release
The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lyα spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io