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IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

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arXiv 2025年

作者： Dia, Noé Yantovski-Barth, M.J. Adam, Alexandre Bowles, Micah Perreault-Levasseur, Laurence Hezaveh, Yashar Scaife, Anna Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Department of Astrophysics University of Oxford Oxford United Kingdom Flatiron Institute Center for Computational Astrophysics New York United States Perimeter Institute Waterloo Canada Trottier Space Institute McGill University Montréal Canada University of Manchester Manchester United Kingdom The Alan Turing Institute United Kingdom

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples. Our code is open source and freely available at https://***/EnceladeCandy/IRIS. © 2025, CC BY.

关键词： Gaussian distribution

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference machines

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arXiv 2023年

作者： Adam, Alexandre Perreault-Levasseur, Laurence Hezaveh, Yashar Welling, Max Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Microsoft Research AI4Science

Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the Recurrent Inference machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

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arXiv 2024年

作者： Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to "clean" non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations;alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade. © 2024, CC BY.

关键词： Background noise

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

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arXiv 2023年

作者： Payot, Nicolas Pasquato, Mario Trani, Alessandro Alberto Hezaveh, Yashar Perreault-Levasseur, Laurence Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Niels Bohr Institute Copenhagen Denmark Research Center for the Early Universe School of Science The University of Tokyo Tokyo Japan Okinawa Institute of Science and Technology Okinawa Japan

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem’s decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology. © 2023, CC BY.

关键词： Fractals

learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

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arXiv 2023年

作者： Payot, Nicolas Lemos, Pablo Perreault-Levasseur, Laurence Cuesta-Lazaro, Carolina Modi, Chirag Hezaveh, Yashar Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Center for Computational Astrophysics Flatiron Institute NY United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA United States Center for Astrophysics Harvard & Smithsonian CambridgeMA United States Center for Computational Mathematics Flatiron Institute NY United States

Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities. © 2023, CC BY.

关键词： Cosmology

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

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arXiv 2024年

作者： Stone, Connor Adam, Alexandre Coogan, Adam Yantovski-Barth, M.J. Filipp, Andreas Setiawan, Landung Core, Cordero Legin, Ronan Wilson, Charles Barco, Gabriel Missael Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela Institute Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila Québec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States eScience Institute Scientific Software Engineering Center 1410 NE Campus Pkwy SeattleWA98195 United States

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ∼ 1 arcsecond for galaxy-galaxy lensing, or 10’s of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe’s expansion rate (e.g. K. C. Wong et al., 2020), dark matter (e.g. Hezaveh et al., 2016;Vegetti & Vogelsberger, 2014), supernovae (e.g. Rodney et al., 2021), quasars (e.g. Peng et al., 2006), and the first stars (e.g. Welch et al., 2022) among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present caustics, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems. © 2024, CC BY.

关键词： Python

The Nearly Universal Disk Galaxy Rotation Curve

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arXiv 2024年

作者： Patel, Raj Arora, Nikhil Courteau, Stephane Stone, Connor Frosst, Matthew Widrow, Lawrence Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada Department of Physics Université de Montréal MontréalQC Canada Mila Québec Artificial Intelligence Institute MontréalQC Canada Ciela Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada ICRAR M468 University of Western Australia CrawleyWA6009 Australia

The Universal Rotation Curve (URC) of disk galaxies was originally proposed to predict the shape and amplitude of any rotation curve (RC) based solely on photometric data. Here, the URC is investigated with an extensive set of spatially-resolved rotation curvesdrawn from the PROBES-I, PROBES-II, and MaNGA data bases with matching multi-band surface brightness profiles from the DESI-LIS and WISE surveys for 3,846 disk galaxies. Common URC formulations fail to achieve an adequate level of accuracy to qualify as truly universal over fully sampled RCs. We develop neural network (NN) equivalents for the proposed URCs which predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather non-optimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN equivalents trained on our sub-sample of 579 disk galaxies with symmetric RCs perform similarly to the URC/NN trained on the complete data sample. We conclude that a URC with an acceptable level of accuracy (∆Vcirc 15 per cent) at all radii would require a detailed modelling of a galaxy’s central regions and outskirts (e.g., for baryonic effects leading to contraction or expansion of any dark-matter-only halo). © 2024, CC BY.

关键词： Galaxies

The re-markable 21-cm power spectrum I: Probing the Hi distribution in the post-reionization era using marked statistics

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arXiv 2024年

作者： Kamran, Mohd Sahlén, Martin Sarkar, Debanjan Majumdar, Suman Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Department of Physics Trottier Space Institute McGill University QCH3A 2T8 Canada Ciela-Montreal Institute for Astrophysical Data Analysis and Machine Learning QCH2V 0B3 Canada Department of Physics Ben-Gurion University of the Negev Be'er Sheva84105 Israel Department of Astronomy Astrophysics & Space Engineering Indian Institute of Technology Indore Indore453552 India Department of Physics Blackett Laboratory Imperial College LondonSW7 2AZ United Kingdom

The neutral hydrogen (Hi) power spectrum, measured from intensity fluctuations in the 21-cm background, offers insights into the large-scale structures (LSS) of our Universe in the post-reionization era (redshift z −1) we expectedly find that the Hi evolves slowly at early times, but more rapidly at late times. This evolution is not well-captured by the power spectrum of the standard (unmarked) Hi field. We also study how the evolution of the Hi field depends on the chosen smoothing scale for the mark, and how this affects the marked power spectrum. We conclude that the information about the Hi content at low and intermediate densities is important for a correct and consistent analysis of Hi content and evolution based on the 21-cm background. The marked power spectrum can thus provide a less biased statistic for parameter constraints than the normal power spectrum. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models

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Monthly Notices of the Royal Astronomical Society: Letters 2024年第1期527卷 L173-L178页

作者： Legin, Ronan Ho, Matthew Lemos, Pablo Perreault-Levasseur, Laurence Ho, Shirley Hezaveh, Yashar Wandelt, Benjamin Department of Physics Universite de Montreal Montreal H2V 0B3 Canada Mila - Quebec Artificial Intelligence Institute Montreal H2S 3H1 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montreal H2V 0B3 Canada Sorbonne Universite CNRS UMR 7095 Institut d'Astrophysique de Paris 98 bis bd Arago Paris 75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York 10010 NY United States Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 ON Canada

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods. © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

关键词： early Universe large-scale structure of Universe methods: statistical