The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of ...galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.
Here, we present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the ...accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large-scale forces efficiently on distributed platforms. We explore the scaling of the simulation on large-scale supercomputers and compare it with corresponding Python based PM code, finding on an average 10x speed-up in terms of wallclock time. We also demonstrate how this novel tool can be used for efficiently solving large scale cosmological inference problems, in particular reconstruction of cosmological fields in a forward model Bayesian framework with hybrid PM and neural network forward model. We provide skeleton code for these examples and the entire code is publicly available at https://github.com/modichirag/flowpmFormula presented.
ABSTRACT
We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the ...density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a maximum a posteriori estimate with a different model for the prior probability of the map: Kaiser–Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic ΛCDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date.
We compare the efficiency with which 2D and 3D weak lensing mass mapping techniques are able to detect clusters of galaxies using two state-of-the-art mass reconstruction techniques: MRLens in 2D and ...GLIMPSE in 3D. We simulate otherwise-empty cluster fields for 96 different virial mass–redshift combinations spanning the ranges 3 × 1013 h
−1 M⊙ ≤ M
vir ≤ 1015 h
−1 M⊙ and 0.05 ≤ z
cl ≤ 0.75, and for each generate 1000 realizations of noisy shear data in 2D and 3D. For each field, we then compute the cluster (false) detection rate as the mean number of cluster (false) detections per reconstruction over the sample of 1000 reconstructions. We show that both MRLens and GLIMPSE are effective tools for the detection of clusters from weak lensing measurements, and provide comparable quality reconstructions at low redshift. At high redshift, GLIMPSE reconstructions offer increased sensitivity in the detection of clusters, yielding cluster detection rates up to a factor of ∼10 × that seen in 2D reconstructions using MRLens. We conclude that 3D mass mapping techniques are more efficient for the detection of clusters of galaxies in weak lensing surveys than 2D methods, particularly since 3D reconstructions yield unbiased estimators of both the mass and redshift of the detected clusters directly.
Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science ...results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser–Squires (KS), Wiener filter, and Glimpse. Kaiser–Squires is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. Glimpse uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and Glimpse offer substantial improvements over smoothed Kaiser–Squires with a range of metrics. Both the Wiener filter and Glimpse convergence reconstructions show a 12 percent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods’ abilities to find mass peaks, we measure the difference between peak counts from simulated ΛCDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for Glimpse. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals’ concentration is improved 17 percent by Glimpse and 18 percent by the Wiener filter. The correlationbetween reconstructions from data and foreground redMaPPer clusters is increased 18 percent by the Wiener filter and 32 percent by Glimpse.
Euclid preparation Huertas-Company, M.; Lanusse, F.; Jullo, E. ...
Astronomy and astrophysics (Berlin),
01/2022, Volume:
657
Journal Article
Peer reviewed
Open access
We present a machine learning framework to simulate realistic galaxies for the
Euclid
Survey, producing more complex and realistic galaxies than the analytical simulations currently used in
Euclid
. ...The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real
Hubble
Space Telescope observations by deep generative models. We simulate a galaxy field of 0.4 deg
2
as it will be seen by the
Euclid
visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sérsic profiles. Based on these simulations, we estimate that the
Euclid
Wide Survey (EWS) will be able to resolve the internal morphological structure of galaxies down to a surface brightness of 22.5 mag arcsec
−2
, and the
Euclid
Deep Survey (EDS) down to 24.9 mag arcsec
−2
. This corresponds to approximately 250 million galaxies at the end of the mission and a 50% complete sample for stellar masses above 10
10.6
M
⊙
(resp. 10
9.6
M
⊙
) at a redshift
z
∼ 0.5 for the EWS (resp. EDS). The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies.
Spherical 3D isotropic wavelets Lanusse, F; Rassat, A; Starck, J-L
Astronomy and astrophysics (Berlin),
04/2012, Volume:
540
Journal Article
Peer reviewed
Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D spherical Fourier-Bessel analysis in spherical coordinates is natural. Wavelets are ...particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, ie one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. The authors describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform described in Starck et al. They also present a new fast discrete spherical Fourier-Bessel transform based on both a discrete Bessel transform (DSFBT) and the HEALPIX angular pixelisation scheme. They have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel DSFBT.
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