Alexandre Adam

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 non-trivial noise properties of real astronomical images and the point spread function (PSF) 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 \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

2025-04-08

The Astronomical Journal (publié)

Solving Bayesian inverse problems with diffusion priors and off-policy RL

Luca Scimeca

Siddarth Venkatraman

Moksh J. Jain

Minsu Kim

Yoshua Bengio

Glen Berseth

Nikolay Malkin

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (voir plus)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.

2025-03-06

ICLR.cc/2025/Workshop/DeLTa (poster)

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Gabriel Missael Barco

Connor Stone

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (voir plus)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.

2025-02-06

The Astrophysical Journal (publié)

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

No'e Dia

M. J. Yantovski-Barth

Micah Bowles

Anna M. M. Scaife

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e 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.

2025-01-05

ArXiv (prépublication)

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

No'e Dia

M. J. Yantovski-Barth

Micah Bowles

Anna M. M. Scaife

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e 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.

2025-01-05

ArXiv (prépublication)

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Minsu Kim

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Improved off-policy training of diffusion samplers

Minsu Kim

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (voir plus)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at [this link](https://github.com/GFNOrg/gfn-diffusion) as a base for future work on diffusion models for amortized inference.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

Marine Prunier

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (voir plus)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-

2024-09-16

ArXiv (prépublication)

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

Antoine Bourdin

Ronan Legin

Matthew Ho

2024-06-16

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

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Minsu Kim

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Minsu Kim

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Minsu Kim

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)