13 research outputs found
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. These
networks have mostly been developed for regular Euclidean domains such as those
supporting images, audio, or video. Because of their success, CNN-based methods
are becoming increasingly popular in Cosmology. Cosmological data often comes
as spherical maps, which make the use of the traditional CNNs more complicated.
The commonly used pixelization scheme for spherical maps is the Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for
analysis of full and partial HEALPix maps, which we call DeepSphere. The
spherical CNN is constructed by representing the sphere as a graph. Graphs are
versatile data structures that can act as a discrete representation of a
continuous manifold. Using the graph-based representation, we define many of
the standard CNN operations, such as convolution and pooling. With filters
restricted to being radial, our convolutions are equivariant to rotation on the
sphere, and DeepSphere can be made invariant or equivariant to rotation. This
way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix
sampling of the sphere. This approach is computationally more efficient than
using spherical harmonics to perform convolutions. We demonstrate the method on
a classification problem of weak lensing mass maps from two cosmological models
and compare the performance of the CNN with that of two baseline classifiers.
The results show that the performance of DeepSphere is always superior or equal
to both of these baselines. For high noise levels and for data covering only a
smaller fraction of the sphere, DeepSphere achieves typically 10% better
classification accuracy than those baselines. Finally, we show how learned
filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other
author
Effects of baryons on weak lensing peak statistics
Upcoming weak-lensing surveys have the potential to become leading
cosmological probes provided all systematic effects are under control.
Recently, the ejection of gas due to feedback energy from active galactic
nuclei (AGN) has been identified as major source of uncertainty, challenging
the success of future weak-lensing probes in terms of cosmology. In this paper
we investigate the effects of baryons on the number of weak-lensing peaks in
the convergence field. Our analysis is based on full-sky convergence maps
constructed via light-cones from -body simulations, and we rely on the
baryonic correction model of Schneider et al. (2019) to model the baryonic
effects on the density field. As a result we find that the baryonic effects
strongly depend on the Gaussian smoothing applied to the convergence map. For a
DES-like survey setup, a smoothing of arcmin is sufficient
to keep the baryon signal below the expected statistical error. Smaller
smoothing scales lead to a significant suppression of high peaks (with
signal-to-noise above 2), while lower peaks are not affected. The situation is
more severe for a Euclid-like setup, where a smoothing of
arcmin is required to keep the baryonic suppression signal below the
statistical error. Smaller smoothing scales require a full modelling of
baryonic effects since both low and high peaks are strongly affected by
baryonic feedback.Comment: 22 pages, 11 figures, JCAP accepte
Weak lensing peak statistics in the era of large scale cosmological surveys
Weak lensing peak counts are a powerful statistical tool for constraining
cosmological parameters. So far, this method has been applied only to surveys
with relatively small areas, up to several hundred square degrees. As future
surveys will provide weak lensing datasets with size of thousands of square
degrees, the demand on the theoretical prediction of the peak statistics will
become heightened. In particular, large simulations of increased cosmological
volume are required. In this work, we investigate the possibility of using
simulations generated with the fast Comoving-Lagrangian acceleration (COLA)
method, coupled to the convergence map generator Ufalcon, for predicting the
peak counts. We examine the systematics introduced by the COLA method by
comparing it with a full TreePM code. We find that for a 2000 deg survey,
the systematic error is much smaller than the statistical error. This suggests
that the COLA method is able to generate promising theoretical predictions for
weak lensing peaks. We also examine the constraining power of various
configurations of data vectors, exploring the influence of splitting the sample
into tomographic bins and combining different smoothing scales. We find the
combination of smoothing scales to have the most constraining power, improving
the constraints on the amplitude parameter by at least 40% compared to a
single smoothing scale, with tomography brining only limited increase in
measurement precision.Comment: 17 pages, 9 figure
Cosmological Forecast for non-Gaussian Statistics in large-scale weak Lensing Surveys
Cosmic shear data contains a large amount of cosmological information
encapsulated in the non-Gaussian features of the weak lensing mass maps. This
information can be extracted using non-Gaussian statistics. We compare the
constraining power in the plane of three
map-based non-Gaussian statistics with the angular power spectrum, namely;
peak/minimum counts and Minkowski functionals. We further analyze the impact of
tomography and systematic effects originating from galaxy intrinsic alignments,
multiplicative shear bias and photometric redshift systematics. We forecast the
performance of the statistics for a stage-3-like weak lensing survey and
restrict ourselves to scales 10 arcmin. We find, that in our setup, the
considered non-Gaussian statistics provide tighter constraints than the angular
power spectrum. The peak counts show the greatest potential, increasing the
Figure-of-Merit (FoM) in the plane by a factor
of about 4. A combined analysis using all non-Gaussian statistics in addition
to the power spectrum increases the FoM by a factor of 5 and reduces the error
on by 25\%. We find that the importance of tomography is
diminished when combining non-Gaussian statistics with the angular power
spectrum. The non-Gaussian statistics indeed profit less from tomography and
the minimum counts and Minkowski functionals add some robustness against galaxy
intrinsic alignment in a non-tomographic setting. We further find that a
combination of the angular power spectrum and the non-Gaussian statistics
allows us to apply conservative scale cuts in the analysis, thus helping to
minimize the impact of baryonic and relativistic effects, while conserving the
cosmological constraining power. We make the code that was used to conduct this
analysis publicly available
Rapid Simulations of Halo and Subhalo Clustering
The analysis of cosmological galaxy surveys requires realistic simulations
for their interpretation. Forward modelling is a powerful method to simulate
galaxy clustering without the need for an underlying complex model. This
approach requires fast cosmological simulations with a high resolution and
large volume, to resolve small dark matter halos associated to single galaxies.
In this work, we present fast halo and subhalo clustering simulations based on
the Lagrangian perturbation theory code PINOCCHIO, which generates halos and
merger trees. The subhalo progenitors are extracted from the merger history and
the survival of subhalos is modelled. We introduce a new fitting function for
the subhalo merger time, which includes a redshift dependence of the fitting
parameters. The spatial distribution of subhalos within their hosts is modelled
using a number density profile. We compare our simulations with the halo finder
ROCKSTAR applied to the full N-body code GADGET-2. The subhalo velocity
function and the correlation function of halos and subhalos are in good
agreement. We investigate the effect of the chosen number density profile on
the resulting subhalo clustering. Our simulation is approximate yet realistic
and significantly faster compared to a full N-body simulation combined with a
halo finder. The fast halo and subhalo clustering simulations offer good
prospects for galaxy forward models using subhalo abundance matching.Comment: 28 pages, 10 figures, Accepted for publication in JCA
Fast Lightcones for Combined Cosmological Probes
The combination of different cosmological probes offers stringent tests of
the CDM model and enhanced control of systematics. For this purpose,
we present an extension of the lightcone generator UFalcon first introduced in
Sgier et al. 2019 (arXiv:1801.05745), enabling the simulation of a
self-consistent set of maps for different cosmological probes. Each realization
is generated from the same underlying simulated density field, and contains
full-sky maps of different probes, namely weak lensing shear, galaxy
overdensity including RSD, CMB lensing, and CMB temperature anisotropies from
the ISW effect. The lightcone generation performed by UFalcon is parallelized
and based on the replication of a large periodic volume simulated with the
GPU-accelerated -Body code PkdGrav3. The post-processing to construct the
lightcones requires only a runtime of about 1 walltime-hour corresponding to
about 100 CPU-hours. We use a randomization procedure to increase the number of
quasi-independent full-sky UFalcon map-realizations, which enables us to
compute an accurate multi-probe covariance matrix. Using this framework, we
forecast cosmological parameter constraints by performing a multi-probe
likelihood analysis for a combination of simulated future stage-IV-like
surveys. We find that the inclusion of the cross-correlations between the
probes significantly increases the information gain in the parameter
constraints. We also find that the use of a non-Gaussian covariance matrix is
increasingly important, as more probes and cross-correlation power spectra are
included. A version of the UFalcon package currently including weak
gravitational lensing is publicly available.Comment: 49 pages, 24 pictures, The UFalcon weak lensing package is available
here:
$\href{https://cosmology.ethz.ch/research/software-lab/UFalcon.html}{https://cosmology.ethz.ch/research/software-lab/UFalcon.html}
Symbolic Implementation of Extensions of the Boltzmann Solver
is a Python-based framework for the fast computation of
cosmological model predictions. One of its core features is the symbolic
representation of the Einstein-Boltzmann system of equations. Efficient
code is generated from the symbolic
expressions making use of the package. This enables easy
extensions of the equation system for the implementation of new cosmological
models. We illustrate this with three extensions of the
Boltzmann solver to include a dark energy component with a constant equation of
state, massive neutrinos and a radiation streaming approximation. We describe
the framework, highlighting new features, and the symbolic
implementation of the new models. We compare the predictions
for the CDM model extensions with , both in terms of
accuracy and computational speed. We find a good agreement, to better than 0.1%
when using high-precision settings and a comparable computational speed. Links
to the Python Package Index (PyPI) page of the code release and to the PyCosmo
Hub, an online platform where the package is installed, are available at:
https://cosmology.ethz.ch/research/software-lab/PyCosmo.html.Comment: 35 pages including 5 figures and 3 tables. Link to
package: https://cosmology.ethz.ch/research/software-lab/PyCosmo.htm