arXiv subjects

Aizhan Akhmetzhanova

Publications and source records attributed to Aizhan Akhmetzhanova.

4 recordsLinked to original sources

arXiv2026

Feedback-Conditional 3D Reconstruction of Cosmic Baryons with Flow Matching

The complex relationship between galaxies and diffuse gas at large scales depends strongly on astrophysical feedback and cosmology. The dispersion measure (DM) of Fast Radio Bursts (FRBs) is a column of cosmic baryon density and is dominated by the intergalactic medium, which is not necessarily captured by traditional halo model prescriptions. We introduce a generative model to reconstruct 3D gas density fields from galaxy surveys, including redshift space distortions. The probabilistic model is trained on the CAMELS50 simulation suite (IllustrisTNG) and learns to predict the posterior distribution of gas fields conditioned on stellar mass-weighted galaxy density, cosmological parameters, the minimum stellar mass threshold for the galaxy sample, and the baryon suppression power spectrum as a measure of baryonic feedback. Our method uses the 3D gas reconstruction to estimate per-sightline FRB DMs and their posteriors. We validate the model on test boxes by showing agreement between predicted and true DM across individual lines of sight and the overall DM distribution. The model generalizes to a box size $\sim$\,4$^3$ times larger in volume than the training boxes and can cover the volumes spanned by cosmological FRB sightlines. The model's 3D gas density predictions vary with input power spectrum and feedback scenarios, offering an inference path to constrain feedback models from FRBs and other probes such as X-rays, the SZ effect, and weak lensing.

astro-ph.CO↗

arXiv2025

A Data-Driven Prism: Multi-View Source Separation with Diffusion Model Priors

A common challenge in the natural sciences is to disentangle distinct, unknown sources from observations. Examples of this source separation task include deblending galaxies in a crowded field, distinguishing the activity of individual neurons from overlapping signals, and separating seismic events from an ambient background. Traditional analyses often rely on simplified source models that fail to accurately reproduce the data. Recent advances have shown that diffusion models can directly learn complex prior distributions from noisy, incomplete data. In this work, we show that diffusion models can solve the source separation problem without explicit assumptions about the source. Our method relies only on multiple views, or the property that different sets of observations contain different linear transformations of the unknown sources. We show that our method succeeds even when no source is individually observed and the observations are noisy, incomplete, and vary in resolution. The learned diffusion models enable us to sample from the source priors, evaluate the probability of candidate sources, and draw from the joint posterior of the source distribution given an observation. We demonstrate the effectiveness of our method on a range of synthetic problems as well as real-world galaxy observations.

cs.LG↗

arXiv2025

Detecting Model Misspecification in Cosmology with Scale-Dependent Normalizing Flows

Current and upcoming cosmological surveys will produce unprecedented amounts of high-dimensional data, which require complex high-fidelity forward simulations to accurately model both physical processes and systematic effects which describe the data generation process. However, validating whether our theoretical models accurately describe the observed datasets remains a fundamental challenge. An additional complexity to this task comes from choosing appropriate representations of the data which retain all the relevant cosmological information, while reducing the dimensionality of the original dataset. In this work we present a novel framework combining scale-dependent neural summary statistics with normalizing flows to detect model misspecification in cosmological simulations through Bayesian evidence estimation. By conditioning our neural network models for data compression and evidence estimation on the smoothing scale, we systematically identify where theoretical models break down in a data-driven manner. We demonstrate a first application to our approach using matter and gas density fields from three CAMELS simulation suites with different subgrid physics implementations.

astro-ph.CO↗

arXiv2023

Data Compression and Inference in Cosmology with Self-Supervised Machine Learning

The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that leverages the paradigm of self-supervised machine learning in a novel manner to construct representative summaries of massive datasets using simulation-based augmentations. Deploying the method on hydrodynamical cosmological simulations, we show that it can deliver highly informative summaries, which can be used for a variety of downstream tasks, including precise and accurate parameter inference. We demonstrate how this paradigm can be used to construct summary representations that are insensitive to prescribed systematic effects, such as the influence of baryonic physics. Our results indicate that self-supervised machine learning techniques offer a promising new approach for compression of cosmological data as well its analysis.

astro-ph.CO↗