相关论文: Fermi LAT AGN classification using supervised mach…
While it is well known that galaxies are composites of many emission processes, quantifying the various contributions remains challenging. In this work, we use unsupervised machine learning based clustering algorithms to evaluate the…
The Fermi Large Area Telescope First Source Catalog (1FGL) provided spatial, spectral, and temporal properties for a large number of gamma-ray sources using a uniform analysis method. After correlating with the most-complete catalogs of…
A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…
We cross-correlate the largest available Mid-Infrared (WISE), X-ray (3XMM) and Radio (FIRST+NVSS) catalogues to define the MIXR sample of AGN and star-forming galaxies. We pre-classify the sources based on their positions on the WISE…
Determining the frequency and duration of changing--look (CL) active galactic nuclei (AGNs) phenomena, where the optical broad emission lines appear or disappear, is crucial to understand the evolution of the accretion flow around…
We investigate the 1.4 GHz radio properties of 92 nearby (z<0.05) ultra hard X-ray selected Active Galactic Nuclei (AGN) from the Swift Burst Alert Telescope (BAT) sample. Through the ultra hard X-ray selection we minimise the biases…
We consider simple semi-analytic models that relate the active galactic nuclei (AGN) evolution to the merging history of their host dark matter haloes and quantify their ability of matching the AGN luminosity function and its spatial…
The Fermi-LAT has detected more than 3000 sources in the GeV $\gamma$-ray regime. The majority are extra-galactic and these sources are dominated by blazars. However, $\sim28$ per cent of the sources in Fermi 3LAC are listed as blazar…
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelled data points from noisy labelled data and 0.1% or 1% clean…
New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic…
In the third catalog of active galactic nuclei detected by the $Fermi$ Large Area Telescope Clean (3LAC) sample, there are 402 blazars candidates of uncertain type (BCU). The proposed analysis will help to evaluate the potential optical…
We find evidence for large-scale clustering amongst Fermi-selected BL Lac objects but not amongst Fermi-selected FSRQs. Using two-point correlation functions we have investigated the clustering properties of different classes of objects…
Upcoming Fast Radio Burst (FRB) surveys will search $\sim$10\,$^3$ beams on sky with very high duty cycle, generating large numbers of single-pulse candidates. The abundance of false positives presents an intractable problem if candidates…
We describe an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human…
Identifying AGNs in dwarf galaxies is critical for understanding black hole formation but remains challenging due to their low luminosities, low metallicities, and star formation-driven emission that can obscure AGN signatures. Machine…
About one third of Fermi Large Area Telescope (LAT) sources are unassociated. We perform multi-class classification of Fermi-LAT sources using machine learning with the goal of probabilistic classification of the unassociated sources. A…
The first wave of observations with JWST has revealed a striking overabundance of luminous galaxies at early times ($z>10$) compared to models of galaxies calibrated to pre-JWST data. Early observations have also uncovered a large…
Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework…
We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an…
(Abridged) Many classes of active galactic nuclei (AGN) have been defined entirely throughout optical wavelengths while the X-ray spectra have been very useful to investigate their inner regions. However, optical and X-ray results show many…