We present the initial results of an ongoing survey with the Karl G. Jansky Very Large Array targeting the CO(J = 1-0) transition in a sample of 30 submillimeter-selected, dusty star-forming galaxies (SFGs) at z = 2-5 with existing mid-J CO detections from the Atacama Large Millimeter/submillimeter Array and NOrthern Extended Millimeter Array, of

Next-generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine-learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than

The Fermi Large Area Telescope (LAT) lightcurve repository (LCR) is a publicly available, continually updated library of gamma-ray lightcurves of variable Fermi-LAT sources generated over multiple timescales. The Fermi-LAT LCR aims to provide publication-quality lightcurves binned on timescales of 3, 7, and 30 days for 1525 sources deemed variable

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a

We present $850\, \mu {\rm m}$ imaging of the XMM-LSS field observed for 170 h as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of $9\, {\rm deg}^2$, reaching a moderate depth of $1\sigma \simeq 4\, {\rm mJy\, beam^{-1}}$. This is the largest contiguous area of extragalactic sky