activity
20202026
most citedHInet: Generating neutral hydrogen from dark matter with neural networks

25 citations · 199 across the 40 of their papers we have counts for

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Showing astro-ph.GAShow all

13 papers · 1 filter

astro-ph.GA2026

Cool Embeddings: Predicting Galaxy Cluster Cooling Times in IllustrisTNG and TNG-Cluster with AstroCLIP

Rawan Karam, Carter Lee Rhea, Julie Hlavacek-Larrondo +4

Foundation models trained on optical astronomical data have not previously been applied to X-ray cluster thermodynamics. We show that embeddings from AstroCLIP, a contrastive visio…

astro-ph.GA2025

Neural Deprojection of Galaxy Stellar Mass Profiles

M. J. Yantovski-Barth, Hengyue Zhang, Nolan Smyth +4

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable mapping. Specifically, we train a neura…

astro-ph.GA2025

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

Ronan Legin, Connor Stone, Alexandre Adam +5

We present new state-of-the-art lens models for strong gravitational lensing systems from the Sloan Lens ACS (SLACS) survey, developed within a Bayesian framework that employs high…

astro-ph.GA2025★ 3 cited

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

Eirini Angeloudi, Marc Huertas-Company, Jesús Falcón-Barroso +3

Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constraining its evolution. Spatially resolving the…

astro-ph.GA2025

Massive Extremely High-Velocity Outflow in the Quasar J164653.72+243942.2

Paola Rodríguez Hidalgo, Hyunseop Choi, Patrick B. Hall +6

We present the analysis of one of the most extreme quasar outflows found to date in our survey of extremely high velocity outflows (EHVO). J164653.72+243942.2 (z ~ 3.04) shows vari…

astro-ph.GA2024★ 5 cited

Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

Zehao Jin, Mario Pasquato, Benjamin L. Davis +9

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) w…

astro-ph.GA2024

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

Carter Rhea, Julie Hlavacek-Larrondo, Alexandre Adam +4

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a m…

astro-ph.GA2024★ 25 cited

Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at

Manuela Bischetti, Hyunseop Choi, Fabrizio Fiore +18

Although the mass growth of supermassive black holes during the Epoch of Reionisation is expected to play a role in shaping the concurrent growth of their host-galaxies, observatio…

astro-ph.GA2023★ 1 cited

Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra

Carter Rhea, Julie Hlavacek-Larrondo, Ralph Kraft +3

In the realm of X-ray spectral analysis, the true nature of spectra has remained elusive, as observed spectra have long been the outcome of convolution between instrumental respons…

astro-ph.GA2023

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

Nicolas Payot, Mario Pasquato, Alessandro Alberto Trani +2

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.…

astro-ph.GA2023

The search for the lost attractor

Mario Pasquato, Syphax Haddad, Pierfrancesco Di Cintio +8

N-body systems characterized by inverse square attractive forces may display a self similar collapse known as the gravo-thermal catastrophe. In star clusters, collapse is halted by…

astro-ph.GA2021★ 11 cited

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios

Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet +5

In the first paper of this series (Rhea et al. 2020), we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spec…

astro-ph.GA2020★ 6 cited

A Novel Machine Learning Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters

Carter L. Rhea, Julie Hlavacek-Larrondo, Laurence Perreault-Levasseur +2

The hot intra-cluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium comprised of various emitting components. Although previous studies of nearby galaxy…