Related papers: Machine Learning applied to Multifrequency Data in…
In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to distinguish…
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…
Machine learning (ML) and deep learning (DL) techniques are increasingly used across astrophysics, enabled by the growing availability of data and improved acquisition methods. These approaches now support tasks from redshift estimation to…
The recently published fourth Fermi Large Area Telescope source catalog (4FGL) reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the Active Galactic Nuclei…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
Recent works have developed samples of blazars from among the Fermi-LAT unassociated sources via machine learning comparisons with known blazar samples. Continued analysis of these new blazars tests the predictions of the blazar sequence…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from…
New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting millions of sources. To maximise the scientific value of these surveys, radio source components must be properly associated into physical sources…
Classification will be an important first step for upcoming surveys that will detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST and MOONS. The application of traditional methods of model fitting and…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
Astrophysical sources are now observed by many different instruments at different wavelengths, from radio to high-energy gamma-rays, with an unprecedented quality. Putting all these data together to form a coherent view, however, is a very…
We have made an estimation of the synchrotron peak frequency ($\nu_{peak}^{s}$) for six very low synchrotron peaked (VLSP) blazars. These objects were selected as VLSP candidates (with the $\nu_{peak}^{s} \leq 10^{13}$ Hz) from the archival…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
We utilize machine learning methods to distinguish BL Lacertae objects (BL Lac) from Flat Spectrum Radio Quasars (FSRQ) within a sample of likely X-ray blazar counterparts to Fermi 3FGL unassociated gamma-ray sources. From our previous…
The time evolution of the electromagnetic emission from blazars, in particular high frequency peaked sources (HBLs), displays irregular activity not yet understood. In this work we report a methodology capable of characterizing the time…
Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
The popularity of Machine Learning (ML) has been increasing in the last decades in almost every area, being the commercial and scientific fields the most notorious ones. Concerning particle physics, ML has been proved as a useful resource…