Ronan Legin

2025-11-23

ArXiv (prépublication)

Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images

Gabriel Missael Barco

We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF charact… (voir plus)erization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly expressive generative diffusion model prior, trained on a library of HST ePSF templates. Compared to traditional methods (parametric Moffat, ePSF template-based, and regularized likelihood), we demonstrate that our PSF models achieve orders of magnitude higher likelihood and residuals consistent with noise, all while remaining visually realistic. Further, the method applies even for faint and heavily masked point sources, merely producing a broader posterior. By recovering a realistic, pixel-level posterior distribution, our technique enables the first meaningful propagation of detailed PSF morphological uncertainty in downstream analysis. An implementation of our posterior sampling procedure is available on GitHub.

2025-11-23

ArXiv (prépublication)

Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models

Gabriel Missael Barco

Connor Stone

2025-10-31

arXiv (publié)

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

Maximiliano Isi

Kaze W. K. Wong

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from t… (voir plus)his 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.

2025-05-28

The Astrophysical Journal Letters (publié)

The CASTOR mission

Patrick Côté

T. Woods

John Hutchings

J. Rhodes

R. Sánchez-Janssen

Alan D. Scott

J. Pazder

Melissa Amenouche

Michael Balogh

Simon Blouin

Alain Cournoyer

M. Drout

Nick Kuzmin

Katherine J. Mack

Laura Ferrarese

Wesley C. Fraser

S. Gallagher

Frederic J. Grandmont

Daryl Haggard

P. Harrison … (voir 160 de plus)

V. Hénault-Brunet

J. Kavelaars

V. Khatu

J. Roediger

J. Rowe

Marcin Sawicki

Jesper Skottfelt

Matt Taylor

L. van Waerbeke

Laurie Amen

Dhananjhay Bansal

Martin Bergeron

Toby Brown

Greg Burley

Hum Chand

Isaac Cheng

Ryan Cloutier

N. Dickson

Oleg Djazovski

Ivana Damjanov

James Doherty

K. Finner

Macarena García Del Valle Espinosa

Jennifer Glover

A. I. Gómez de Castro

Or Graur

Tim Hardy

Michelle Kao

D A Leahy

Deborah Lokhorst

A. I. Malz

Allison Man

Madeline A. Marshall

Sean McGee

Ryan McKenzie

Kai Michaud

Surhud S. More

David Morris

Patrick W. Morris

T. Moutard

Wasi Naqvi

Matthew Nicholl

G. Noirot

M. S. Oey

C. Opitom

Samir Salim

Bryan R. Scott

Charles Shapiro

Daniel Stern

Ashwin Subramaniam

David Thilke

I. Wevers

Dmitri Vorobiev

L. Y. Aaron Yung

Frédéric Zamkotsian

S. Aigrain

A. Alavi

Martin Barstow

Peter Bartosik

H. Bluhm

J. Bovy

Peter Cameron

R. Carlberg

J. Christiansen

Yuyang Chen

P. Crowther

Kristen Dage

Aaron Dotter

Patrick Dufour

Jean Dupuis

B. Dryer

A. Duara

Gwendolyn M. Eadie

Marielle R. Eduardo

V. Estrada-Carpenter

Sébastien Fabbro

A. Faisst

N. M. Ford

M. Fraser

Boris T. Gaensicke

Shashkiran Ganesh

Poshak Gandhi

Melissa L. Graham

R. Hamel

Martin Hellmich

John J. Hennessy

Kaitlyn Hessel

J. Heyl

Catherine Heymans

Renée Hložek

Michael Hoenk

Andrew Holland

Eric Huff

Ian Hutchinson

I. Iwata

April D. Jewell

Doug Johnstone

Maia Jones

Todd J. Jones

D. Lang

J. Lapington

Justin Larivière

C. Lawlor-Forsyth

Denis Laurin

Charles Lee

Ting S. Li

S. Lim

B. Ludwig

Matt Kozun

V. M

Robert Mann

Alan McConnachie

Evan McDonough

S. Metchev

David R. Miller

Takashi Moriya

Cameron Morgan

Julio F. Navarro

Y. Nazé

Shouleh Nikzad

Vivek Oad

N. N.-Q. Ouellette

E. Pass

Will J. Percival

Joe Postma

Nayyer Raza

G. T. Richards

Harvey Richer

Carmelle Robert

Erik Rosolowsky

J. Ruan

Sarah Rugheimer

S. Safi-Harb

Kanak Saha

Vicky Scowcroft

F. Sestito

Himanshu Sharma

James Sikora

G. Sivakoff

T. S. Sivarani

Patrick Smith

Warren Soh

R. Sorba

S. Subramanian

Hossen Teimoorinia

H. Teplitz

Shaylin Thadani

Shavon Thadani

Aaron Tohuvavohu

K. Venn

Nicholas Vieira

Jeremy J. Webb

P. Wiegert

Ryan Wierckx

Yanqin Wu

J. Yeung

S. K. Yi

2025-05-13

Journal of Astronomical Telescopes Instruments and Systems (publié)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors

Connor Bottrell

Laurence Perreaul-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.

2025-04-07

The Astronomical Journal (publié)

Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Matthew Ho

2024-06-15

ICML.cc/2024/Workshop/AI4Science (poster)

openreview.net

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Charles Wilson

Gabriel Missael Barco

2023-12-31

J. Open Source Softw. (publié)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

Alexandre Adam

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.

2023-11-02

NeurIPS.cc/2023/Workshop/Deep_Inverse (poster)

openreview.net

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Matthew Ho

Pablo Lemos

Shirley Ho

Benjamin Wandelt

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of th… (voir plus)e 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.

2023-10-03

Monthly Notices of the Royal Astronomical Society: Letters (publié)

Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy

Justin S. Spilker

Kedar A. Phadke

Manuel Aravena

Melanie Archipley

Matthew B. Bayliss

Jack E. Birkin

Matthieu Béthermin

James Burgoyne

Jared Cathey

Scott C. Chapman

Håkon Dahle

Anthony H. Gonzalez

Gayathri Gururajan

Christopher C. Hayward

Yashar D. Hezaveh

Ryley Hill

Taylor A. Hutchison

Keunho J. Kim

Seonwoo Kim

David Law … (voir 19 de plus)

Matthew A. Malkan

Daniel P. Marrone

Eric J. Murphy

Desika Narayanan

Alex Navarre

Grace M. Olivier

Jeffrey A. Rich

Jane R. Rigby

Cassie Reuter

James E. Rhoads

Keren Sharon

J.D. T. Smith

Manuel Solimano

Nikolaus Sulzenauer

Joaquin D. Vieira

David Law

Axel Weiß

Katherine E. Whitaker

Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavele… (voir plus)ngths. Polycyclic aromatic hydrocarbons (PAHs) are large organic molecules that trace millimeter-size dust grains and regulate the cooling of the interstellar gas within galaxies. Observations of PAH features in very distant galaxies have been difficult due to the limited sensitivity and wavelength coverage of previous infrared telescopes. Here we present JWST observations that detect the 3.3um PAH feature in a galaxy observed less than 1.5 billion years after the Big Bang. The high equivalent width of the PAH feature indicates that star formation, rather than black hole accretion, dominates the infrared emission throughout the galaxy. The light from PAH molecules, large dust grains, and stars and hot dust are spatially distinct from one another, leading to order-of-magnitude variations in the PAH equivalent width and the ratio of PAH to total infrared luminosity across the galaxy. The spatial variations we observe suggest either a physical offset between the PAHs and large dust grains or wide variations in the local ultraviolet radiation field. Our observations demonstrate that differences in the emission from PAH molecules and large dust grains are a complex result of localized processes within early galaxies.

2023-06-04

Nature (publié)

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with non-Gaussian Noise

Alexandre Adam

Laurence Perreault Levasseur

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the Cosmic Microwave Background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.

2023-05-31

The Astrophysical Journal Letters (publié)