Alexandre Adam

Probabilistic Interpolation of Sagittarius A*’s Multiwavelength Light Curves Using Diffusion Models

Gabriel Sasseville

Julie Hlavacek-Larrondo

Daryl Haggard

Hadrien Paugnat

Gunther Witzel

Abstract Understanding the variability of Sagittarius A* (Sgr A*) requires coordinated, multiwavelength observations that span the electroma… (voir plus)gnetic spectrum. In this work, we focus on data from four key observatories: Chandra in the X-ray (2–8 keV), GRAVITY on the Very Large Telescope in the near-infrared (2.2 μ m), Spitzer in the infrared (4.5 μ m), and the Atacama Large Millimeter/submillimeter Array in the submillimeter (340 GHz). These multiband observations are essential for probing the physics of accretion and emission near the black hole’s event horizon, yet they suffer from irregular sampling, band-dependent noise, and substantial data gaps. These limitations complicate efforts to robustly identify flares and measure cross-band time lags, key diagnostics of the physical processes driving variability. To address this challenge, we introduce a diffusion-based generative model, for interpolating sparse, multivariate astrophysical time series. This represents the first application of score-based diffusion models to astronomical time series. We also present the first transformer-based model for light-curve reconstruction that includes calibrated uncertainty estimates. The models are trained on simulated light curves constructed to match the statistical and observational characteristics of real Sgr A* data. These simulations capture correlated multiband variability, realistic observation cadences, and wavelength-specific noise. We compare our models against a multi-output Gaussian process. The diffusion model achieves superior accuracy and competitive calibration across both simulated and real datasets, demonstrating the promise of diffusion models for high-fidelity, uncertainty-aware reconstruction of multiwavelength variability in Sgr A*.

2026-02-02

Astrophysical Journal (publié)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

2025-12-31

Zenodo (publié)

Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis

Ronan Legin

Gabriel Missael Barco

Adam Coogan

Nikolay Malkin

2025-11-23

ArXiv (prépublication)

Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images

Ronan Legin

Nikolay Malkin

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)

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies

Eirini Angeloudi

Marc Huertas-Company

Jesús Falcón-Barroso

Alina Boecker

Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constrainin… (voir plus)g its evolution. Spatially resolving these components provides crucial insights into a galaxy's mass assembly history. We aim to predict the spatial distribution of ex-situ stellar mass fraction in MaNGA galaxies, and to identify distinct assembly histories based on the radial gradients of these predictions in the central regions. We employ a diffusion model trained on mock MaNGA analogs (MaNGIA), derived from the TNG50 cosmological simulation. The model learns to predict the posterior distribution of resolved ex-situ stellar mass fraction maps, conditioned on stellar mass density, velocity, and velocity dispersion gradient maps. After validating the model on an unseen test set from MaNGIA, we apply it to MaNGA galaxies to infer the spatially-resolved distribution of their ex-situ stellar mass fractions - i.e. the fraction of stellar mass in each spaxel originating from mergers. We identify four broad categories of ex-situ mass distributions: flat gradient, in-situ dominated; flat gradient, ex-situ dominated; positive gradient; and negative gradient. The vast majority of MaNGA galaxies fall in the first category - flat gradients with low ex-situ fractions - confirming that in-situ star formation is the main assembly driver for low- to intermediate-mass galaxies. At high stellar masses, the ex-situ maps are more diverse, highlighting the key role of mergers in building the most massive systems. Ex-situ mass distributions correlate with morphology, star-formation activity, stellar kinematics, and environment, indicating that accretion history is a primary factor shaping massive galaxies. Finally, by tracing their assembly histories in TNG50, we link each class to distinct merger scenarios, ranging from secular evolution to merger-dominated growth.

2025-11-20

Astronomy & Astrophysics (publié)

caskade: building Pythonic scientific simulators

Adam Coogan

2025-09-14

Journal of Open Source Software (publié)

Probabilistic Interpolation of Sagittarius A*’s Multi-Wavelength Light Curves Using Machine Learning

G. Sasseville

Julie Hlavacek-Larrondo

Daryl Haggard

Hadrien Paugnat

G. Witzel

Abstract Understanding the variability of Sagittarius A* (Sgr A*) requires coordinated, multi-wavelength observations. However, these observ… (voir plus)ations suffer from irregular sampling, noise, and large data gaps, limiting our ability to identify flares and infer cross-wavelength time lags. We address this challenge using machine learning models designed for sparse, multivariate time series. In particular, we introduce a diffusion-based generative model, representing the first application of score-based diffusion models to astronomical time series. We also use a transformer-based model, and benchmark both against a multi-output Gaussian Process. The models are trained on simulated light curves that replicate the cadence, noise, and cross-band correlations of real Sgr A* data, including observations from ALMA, Spitzer, GRAVITY, and Chandra. Our results show that the diffusion model outperforms other methods in accuracy and uncertainty calibration, on both synthetic and real data. This work highlights the potential of generative models for reconstructing astrophysical variability from incomplete observations.

2025-07-31

Proceedings of the International Astronomical Union (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

Astronomical Journal (publié)

Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL

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.

2025-03-05

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

openreview.net

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

Gabriel Missael Barco

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.

2025-02-05

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.

2025-01-04

ArXiv (prépublication)