23 research outputs found
Fast Point Spread Function Modeling with Deep Learning
Modeling the Point Spread Function (PSF) of wide-field surveys is vital for
many astrophysical applications and cosmological probes including weak
gravitational lensing. The PSF smears the image of any recorded object and
therefore needs to be taken into account when inferring properties of galaxies
from astronomical images. In the case of cosmic shear, the PSF is one of the
dominant sources of systematic errors and must be treated carefully to avoid
biases in cosmological parameters. Recently, forward modeling approaches to
calibrate shear measurements within the Monte-Carlo Control Loops ()
framework have been developed. These methods typically require simulating a
large amount of wide-field images, thus, the simulations need to be very fast
yet have realistic properties in key features such as the PSF pattern. Hence,
such forward modeling approaches require a very flexible PSF model, which is
quick to evaluate and whose parameters can be estimated reliably from survey
data. We present a PSF model that meets these requirements based on a fast
deep-learning method to estimate its free parameters. We demonstrate our
approach on publicly available SDSS data. We extract the most important
features of the SDSS sample via principal component analysis. Next, we
construct our model based on perturbations of a fixed base profile, ensuring
that it captures these features. We then train a Convolutional Neural Network
to estimate the free parameters of the model from noisy images of the PSF. This
allows us to render a model image of each star, which we compare to the SDSS
stars to evaluate the performance of our method. We find that our approach is
able to accurately reproduce the SDSS PSF at the pixel level, which, due to the
speed of both the model evaluation and the parameter estimation, offers good
prospects for incorporating our method into the framework.Comment: 25 pages, 8 figures, 1 tabl
Spectro-Imaging Forward Model of Red and Blue Galaxies
For the next generation of spectroscopic galaxy surveys, it is important to
forecast their performances and to accurately interpret their large data sets.
For this purpose, it is necessary to consistently simulate different
populations of galaxies, in particular Emission Line Galaxies (ELGs), less used
in the past for cosmological purposes. In this work, we further the forward
modeling approach presented in Fagioli et al. 2018, by extending the spectra
simulator Uspec to model galaxies of different kinds with improved parameters
from Tortorelli et al. 2020. Furthermore, we improve the modeling of the
selection function by using the image simulator Ufig. We apply this to the
Sloan Digital Sky Survey (SDSS), and simulate multi-band images.
We pre-process and analyse them to apply cuts for target selection, and finally
simulate SDSS/BOSS DR14 galaxy spectra. We compute photometric, astrometric and
spectroscopic properties for red and blue, real and simulated galaxies, finding
very good agreement. We compare the statistical properties of the samples by
decomposing them with Principal Component Analysis (PCA). We find very good
agreement for red galaxies and a good, but less pronounced one, for blue
galaxies, as expected given the known difficulty of simulating those. Finally,
we derive stellar population properties, mass-to-light ratios, ages and
metallicities, for all samples, finding again very good agreement. This shows
how this method can be used not only to forecast cosmology surveys, but it is
also able to provide insights into studies of galaxy formation and evolution.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}
Forward Modeling of Spectroscopic Galaxy Surveys: Application to SDSS
Galaxy spectra are essential to probe the spatial distribution of galaxies in
our Universe. To better interpret current and future spectroscopic galaxy
redshift surveys, it is important to be able to simulate these data sets. We
describe Uspec, a forward modeling tool to generate galaxy spectra taking into
account some intrinsic galaxy properties as well as instrumental responses of a
given telescope. The model for the intrinsic properties of the galaxy
population, i.e., the luminosity functions, and size and spectral coefficients
distribu- tions, was developed in an earlier work for broad-band imaging
surveys [1], and we now aim to test the model further using spectroscopic data.
We apply Uspec to the SDSS/CMASS sample of Luminous Red Galaxies (LRGs). We
construct selection cuts that match those used to build this LRG sample, which
we then apply to data and simulations in the same way. The resulting real and
simulated average spectra show a good statistical agreement overall, with
residual differences likely coming from a bluer galaxy population of the
simulated sam- ple. We also do not explore the impact of non-solar element
ratios in our simulations. For a quantitative comparison, we perform Principal
Component Analysis (PCA) of the sets of spectra. By comparing the PCs
constructed from simulations and data, we find good agree- ment for all
components. The distributions of the eigencoefficients also show an appreciable
overlap. We are therefore able to properly simulate the LRG sample taking into
account the SDSS/BOSS instrumental responses. The differences between the two
samples can be ascribed to the intrinsic properties of the simulated galaxy
population, which can be reduced by further improvements of our modelling
method in the future. We discuss how these results can be useful for the
forward modeling of upcoming large spectroscopic surveys.Comment: 32 pages, 14 figures, accepted by JCA
The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging
Weak gravitational lensing is a powerful probe of the dark sector, once
measurement systematic errors can be controlled. In Refregier & Amara (2014), a
calibration method based on forward modeling, called MCCL, was proposed. This
relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture
the key features of galaxy populations and measurement effects. The MCCL
approach has been used in Herbel et al. (2017) to determine the redshift
distribution of cosmological galaxy samples and, in the process, the authors
derived a model for the galaxy population mainly based on broad-band
photometry. Here, we test this model by forward modeling the 40 narrow-band
photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the
same forced photometric pipeline on data and simulations using Source Extractor
(Bertin & Arnouts 1996). The image simulation scheme performance is assessed at
the image and at the catalogues level. We find good agreement for the
distribution of pixel values, the magnitudes, in the magnitude-size relation
and the interband correlations. A principal component analysis is then
performed, in order to derive a global comparison of the narrow-band photometry
between the data and the simulations. We use a `mixing' matrix to quantify the
agreement between the observed and simulated sets of Principal Components
(PCs). We find good agreement, especially for the first three most significant
PCs. We also compare the coefficients of the PCs decomposition. While there are
slight differences for some coefficients, we find that the distributions are in
good agreement. Together, our results show that the galaxy population model
derived from broad-band photometry is in good overall agreement with the PAUS
data. This offers good prospect for incorporating spectral information to the
galaxy model by adjusting it to the PAUS narrow-band data using forward
modeling.Comment: Submitted to JCAP, 28 pages, 15 figures, 3 appendice
A forward-modeling approach to cosmic shear
The current cosmological concordance model, ΛCDM, is very successful at describing the statistical properties of the Universe and its evolution with cosmic time at both low and high redshifts. However, two major ingredients of ΛCDM, cold dark matter (CDM) and dark energy (Λ), are only phenomenologically motivated and cosmologists lacks deeper understanding of their origins. Therefore, investigating the physical nature of this dark sector of the ΛCDM model is one of the most pressing issues in modern cosmology and multiple major observational programs aimed at investigating the dark components of ΛCDM are either on the way or already in operation.
At low redshifts, three major wide-field surveys, the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES) and the Hyper Suprime-Cam (HSC) survey, have recently published updated cosmology constraints. They all rely on cosmic shear, the weak gravitational lensing by large-scale structures, as a powerful probe of both the expansion history of the Universe and the growth of structures. While cosmic shear has great potential to shine light on the dark sector of ΛCDM, the effect is challenging to measure and prone to systematic effects. Therefore, Refregier & Amara (2014, DOI: 10.1016/j.dark.2014.01.002) proposed the Monte-Carlo Control Loops (MCCL) framework. This method employs large amounts of forward simulations to quantify the systematic uncertainty of cosmic shear measurements and propagate it through the analysis in a probabilistic way.
In this thesis, we develop methods for measuring cosmology with cosmic shear based on the MCCL framework. We first implement and test a forward-modeling approach to measuring the redshift distribution n(z) of typical weak lensing samples. To this end, we devise an empirical model of the intrinsic galaxy population based on redshift-dependent luminosity functions. We then use Approximate Bayesian Computation (ABC) to adjust our simulations to survey data in a Bayesian framework. This yields a family of likely posterior n(z) curves which quantifies the uncertainty of the measurement. Moreover, we develop a method for fast point spread function (PSF) estimation and modeling based on Deep Learning, specifically a convolutional neural network (CNN). Once trained, the computational speed of this algorithm allows it to be used within the MCCL framework to analyze large volumes of synthetic data.
Based on the methods described above, we next present the first end-to-end application of the MCCL framework to survey data. In a non-tomographic setup, we constrain cosmology with cosmic shear using the DES Year (Y1) data. The core of our method is the joint measurement of the shear 2-point function and the associated redshift distribution. By simulating the full survey footprint numerous times, we quantify the systematic uncertainty of our analysis and are furthermore able to disentangle statistical and systematic errors. Building on this achievement, we implement a tomographic shear pipeline for the DES Year 3 (Y3) data. We classify galaxies into redshift bins with a machine-learning approach which enables us to measure tomographic shear 2-point functions along with the redshift distributions. The current results with this pipeline offer great prospects for applying the MCCL framework to current and future tomographic weak lensing datasets
Depth-sensitive time-of-flight small-angle neutron scattering
Block copolymers are quiet imported in industry. A detailed knowledge of the solid-liquid boundary conditions for surface effects in their aqueous solutions supports the development of smart coatings or the understanding of the folding of proteins in the vicinity of cell membranes. In this work, data collected from small-angle neutron scattering (SANS) experiments will be evaluated. The probed material is a 20\% (in weight) solution of the polymer Pluronic F127, which forms micelles inside the solution. The structures built by these micelles at different temperatures and different surface energies are the main topic of this work. It will be shown that SANS is an appropriate technique to probe solid-liquid boundaries depth-sensitively where the latter is a result of the use of a whole wavelengths spectrum instead of running the experiment at one fixed wavelength. The main result is that the micelles form a polycristalline fcc structure in case of an attractive surface potential. A repulsive potential suppresses crystallization up to a certain distance from the surface
Depth-sensitive time-of-flight small-angle neutron scattering
Block copolymers are quiet imported in industry. A detailed knowledge of the solid-liquid boundary conditions for surface effects in their aqueous solutions supports the development of smart coatings or the understanding of the folding of proteins in the vicinity of cell membranes. In this work, data collected from small-angle neutron scattering (SANS) experiments will be evaluated. The probed material is a 20\% (in weight) solution of the polymer Pluronic F127, which forms micelles inside the solution. The structures built by these micelles at different temperatures and different surface energies are the main topic of this work. It will be shown that SANS is an appropriate technique to probe solid-liquid boundaries depth-sensitively where the latter is a result of the use of a whole wavelengths spectrum instead of running the experiment at one fixed wavelength. The main result is that the micelles form a polycristalline fcc structure in case of an attractive surface potential. A repulsive potential suppresses crystallization up to a certain distance from the surface
Depth-sensitive time-of-flight small-angle neutron scattering
Block copolymers are quiet imported in industry. A detailed knowledge of the solid-liquid boundary conditions for surface effects in their aqueous solutions supports the development of smart coatings or the understanding of the folding of proteins in the vicinity of cell membranes. In this work, data collected from small-angle neutron scattering (SANS) experiments will be evaluated. The probed material is a 20\% (in weight) solution of the polymer Pluronic F127, which forms micelles inside the solution. The structures built by these micelles at different temperatures and different surface energies are the main topic of this work. It will be shown that SANS is an appropriate technique to probe solid-liquid boundaries depth-sensitively where the latter is a result of the use of a whole wavelengths spectrum instead of running the experiment at one fixed wavelength. The main result is that the micelles form a polycristalline fcc structure in case of an attractive surface potential. A repulsive potential suppresses crystallization up to a certain distance from the surface