most citedLeveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies

6 citations · 12 across the 4 of their papers we have counts for

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astro-ph.GA2026

X-ray Characterization of Galaxy Groups and Hickson Groups in COSMOS-Web to z~3.8 I. X-ray Luminosity Function Evolution

Ghassem Gozaliasl, Günther Hasinger, Gavin Leroy +35

Galaxy groups are the dominant halo environment in the Universe and a sensitive laboratory for feedback and gas-cycling physics, yet a uniform, intermediate-redshift X-ray characte…

astro-ph.GA2025★ 1 cited

Physical properties of galaxies and the UV Luminosity Function from to in COSMOS-Web

Maximilien Franco, Caitlin M. Casey, Hollis B. Akins +37

We present measurements of the rest-frame ultraviolet luminosity function (UVLF) in three redshift bins over -14 from the JWST COSMOS-Web survey. Our samples, selected us…

astro-ph.GA2025★ 6 cited

Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies

Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi +4

Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy di…

astro-ph.GA2025★ 5 cited

COSMOS Web: Morphological quenching and size-mass evolution of brightest group galaxies from z = 3.7

Ghassem Gozaliasl, Lilan Yang, Jeyhan Kartaltepe +47

We present a comprehensive study of the structural evolution of Brightest Group Galaxies (BGGs) from redshift to using the \textit{James Webb Space Telesc…

astro-ph.GA2025

COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps

Fatemeh Abedini, Ghassem Gozaliasl, Akram Hasani Zonoozi +38

The COSMOS-Web survey, with its unparalleled combination of multiband data, notably, near-infrared imaging from JWST's NIRCam (F115W, F150W, F277W, and F444W), provides a transform…