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

We present Spitzer 10-34 $\mu{\rm m}$ spectroscopic observations of the diffuse gas in the inner region of the star-forming region IC 348 of the Perseus Molecular Cloud. We find evidence for the strongest mid-IR bands of common molecules as H 2, OH, H 2O, CO 2, and NH 3 and of several carbonaceous molecules which may play an important role in the

Stellar spectral classification has been highly useful in the study of stars. While there is a currently accepted spectral classification system for carbon stars, the subset of hydrogen-deficient carbon (HdC) stars has not been well described by such a system, due predominantly to their rarity and their variability. Here we present the first system

Wide-field time-domain photometric sky surveys are now finding hundreds of eclipsing white dwarf plus M dwarf binaries, a population encompassing a wealth of information and potential insight into white dwarf and close binary astrophysics. Precise follow-up observations are essential in order to fully constrain these systems and capitalize on the

Recent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here, we investigate three such samplers to motivate and validate the algorithm and settings