Tootfinder

Cross-correlations between the CLAMATO Lyman-alpha forest and galaxies within the COSMOS field
Benjamin Zhang, Khee-Gan Lee, Andrei Cuceu, Andreu Font-Ribera, Rieko Momose
https://arxiv.org/abs/2509.07453

Cross-correlations between the CLAMATO Lyman-alpha forest and galaxies within the COSMOS field
We compute the 3D cross-correlation between the absorption of the $z\sim 2.3$ Lyman-alpha forest measured by the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey, and 1642 foreground galaxies with spectroscopic redshifts from several different surveys, including 3D-HST, CLAMATO, zCOSMOS-Deep, MOSDEF, and VUDS. For each survey, we compare the measured cross-correlation with models incorporating the galaxy linear bias as well as observed redshift dispersion and systematic r…

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
Nima Chartab, Andrew B. Newman, Gwen C. Rudie, Guillermo Blanc, Daniel D. Kelson, Mahdi Qezlou, Simeon Bird, Brian C. Lemaux, Olga Cucciati
https://arxiv.org/abs/2510.07445

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
We investigate the environmental dependence of galaxy properties at $z\sim2.5$ using the Ly$α$ Tomography IMACS Survey (LATIS), which provides high-resolution three-dimensional maps of intergalactic medium (IGM) overdensity via Ly$α$ forest tomography. Our analysis focuses on a UV-selected spectroscopic sample of 2185 galaxies from LATIS and a complementary set of 1157 galaxies from heterogeneous spectroscopic surveys in the COSMOS field. We compare these datasets to forward-modeled mock cata…

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Walter Silima, Fangxia An, Mattia Vaccari, Eslam A. Hussein, S. Randriamampandry
https://arxiv.org/abs/2510.00969

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragal…