Encoding large-scale cosmological structure with generative adversarial networks

Ullmo, Marion; Decelle, Aurélien; Aghanim, N.

doi:10.1051/0004-6361/202039866

Cited by 14 publications

(2 citation statements)

References 32 publications

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“…Their SGAN model has a similar architecture to DCGAN and can be used to generate synthetic images in astrophysics and other domain. Ullmo et al [127] used GANs to generate cosmological images to bypass simulations which generally require lots of computing resources and are quite expensive. Dia et al [128] showed that GANs can replace expensive model-driven approaches to generate astronomical images.…”

Section: E Astronomymentioning

confidence: 99%

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

Dash,

Ye,

Wang

2024

IEEE Access

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We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zerosum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different field INDEX TERMS deep learning, generative adversarial networks, computer vision, time series, applications

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Section: E Astronomymentioning

confidence: 99%

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

Dash,

Ye,

Wang

2024

IEEE Access

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“…In fact, the nature of DM still represents an open question in physics and cosmology, and many efforts have been devoted to understand its nature via the application of novel ML techniques in several related fields 1 (e.g. Bertone et al (2018); Morice-Atkinson et al (2018); Agarwal et al (2018); Ullmo et al (2021); Feickert & Nachman (2021)).…”

Section: Introductionmentioning

confidence: 99%

A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning

Gammaldi¹,

Zaldívar²,

Coronado-Blázquez³

2022

Preprint

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Around one third of the point-like sources in the Fermi-LAT catalogs remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma rays from WIMPs annihilation. We propose a new approach to solve the standard, Machine Learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. Concretely, we artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93% performance. Applying the NN to the unIDs sample, we find that the degeneracy between some astrophysical and DM sources can be partially solved within this methodology. Nonetheless, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.

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Large-scale dark matter simulations

Angulo

Hahn

2022

Living Rev Comput Astrophys

111

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We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then recap the relevant numerical approaches: discretization of the phase-space distribution (focusing on N-body but including alternatives, e.g., Lagrangian submanifold and Schrödinger–Poisson) and the respective techniques for their time evolution and force calculation (direct summation, mesh techniques, and hierarchical tree methods). We pay attention to the creation of initial conditions and the connection with Lagrangian Perturbation Theory. We then discuss the possible alternatives in terms of the micro-physical properties of dark matter (e.g., neutralinos, warm dark matter, QCD axions, Bose–Einstein condensates, and primordial black holes), and extensions to account for multiple fluids (baryons and neutrinos), primordial non-Gaussianity and modified gravity. We continue by discussing challenges involved in achieving highly accurate predictions. A key aspect of cosmological simulations is the connection to cosmological observables, we discuss various techniques in this regard: structure finding, galaxy formation and baryonic modelling, the creation of emulators and light-cones, and the role of machine learning. We finalise with a recount of state-of-the-art large-scale simulations and conclude with an outlook for the next decade.

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Encoding large-scale cosmological structure with generative adversarial networks

Cited by 14 publications

References 32 publications

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning

Large-scale dark matter simulations

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