Related papers: Boost recall in QSO selection from highly imbalanc…

In this paper, we describe a procedure for modelling strong lensing galaxy clusters with parametric methods, and to rank models quantitatively using the Bayesian evidence. We use a publicly available Markov chain Monte-Carlo (MCMC) sampler…

We present a submillimetre survey of seven high-z galaxy clusters (0.64<z<1.0) using the Submillimetre Common-User Bolometer Array (SCUBA) at 850 and 450 um. The targets, of similar richness and redshift, are selected from the Red-sequence…

We apply a recently developed method for classifying broad absorption line quasars (BALQSOs) to the latest QSO catalogue constructed from Data Release 5 of the Sloan Digital Sky Survey. Our new hybrid classification scheme combines the…

Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H$\alpha$-excess point sources in the Southern Sky, combining photometric data from 12 S-PLUS filters with machine…

The Planck sub-mm surveys detected the brightest strongly gravitationally lensed dusty galaxies in the sky. The combination of their extreme gravitational flux boosting and image stretching offers the unique possibility of measuring in…

Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer screening. We developed a multi-modal artificial intelligence…

Identifications of quasars at intermediate redshifts (2.2<z<3.5) are inefficient in most previous quasar surveys as their optical colors are similar to those of stars. The near-IR K-band excess technique has been suggested to overcome this…

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To…

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly,…

We present a sample of $i_{775}$-dropout candidates identified in five Hubble Advanced Camera for Surveys fields centered on Sloan Digital Sky Survey QSOs at redshift $z\sim 6$. Our fields are as deep as the Great Observatory Origins Deep…

The 7 and 15 um observations of selected fields in the Galactic Plane obtained with ISOCAM during the ISOGAL program offer an unique possibility to search for previously unknown YSOs, undetected by IRAS because of lower sensitivity or…

We have compiled a catalog of 903 candidates for type 1 quasars at redshifts 3<z<5.5 selected among the X-ray sources of the serendipitous XMM-Newton survey presented in the 3XMM-DR4 catalog (the median X-ray flux is 5x10^{-15} erg/s/cm^2…

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The…

The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying…

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data…

Microquasars are ideal natural laboratories for understanding accretion/ejection processes, studying the physics of relativistic jets, and testing gravitational phenomena. Nevertheless, these objects are difficult to find in our Galaxy. The…

We develop a new statistical method to reanalyse angular correlations between background QSOs and foreground galaxies that are supposed to be a consequence of dark matter inhomogeneities acting as weak gravitational lenses. The method is…

Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training…

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…