Regina Sarmiento

most citedERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

14 citations · 23 across the 3 of their papers we have counts for

collaborators

3 papers

astro-ph.IM2023★ 2 cited

A brief review of contrastive learning applied to astrophysics

Marc Huertas-Company, Regina Sarmiento, Johan Knapen

Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning…

astro-ph.GA2023★ 14 cited

ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

Eirini Angeloudi, Jesús Falcón-Barroso, Marc Huertas-Company +4

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between o…

astro-ph.GA2023★ 7 cited

Galaxy Morphology from $z \sim 6$ through the eyes of JWST

M. Huertas-Company, K. G. Iyer, E. Angeloudi +26

We analyze the Near Infrared ( $\sim 0.8 - 1 μ$ m) rest-frame morphologies of galaxies with $lo g M_{*} / M_{⊙} > 9$ in the redshift range $0 < z < 6$ , compare with previous HST-based results an…

A brief review of contrastive learning applied to astrophysics

ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

Galaxy Morphology from z∼6 through the eyes of JWST

Galaxy Morphology from $z \sim 6$ through the eyes of JWST