Search

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. The Large Area Telescope onboard the Fermi gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray-emitting sources, among which a significant number of objects have been identified as active galactic nuclei. The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialization techniques and self-supervised learning for classifying blazars into BL Lacertae (BL Lac, also BLL) objects and flat-spectrum radio quasars (FSRQs). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance along with increase in inference speed (at least seven times more than existing algorithms). As a result, the best-performing model is deployed on multiple platforms so that any user irrespective of their coding background can use the tool. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalogue, 820 can be classified as BL Lacs and 295 can be classified as FSRQs. [ABSTRACT FROM AUTHOR]

With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active galactic nuclei (AGN), which are a class of supermassive black hole systems known for their intense multi-wavelength emissions and violent variability. Determining the redshifts of AGNs is essential for understanding their distances, which, in turn, sheds light on our current understanding of the structure of the nearby universe. However, the task involves a number of challenges, such as the need for meticulous follow-up observations across multiple wavelengths and astronomical facilities. In this study, we employ a simple yet effective deep learning model with a single hidden layer having 64 neurons and a dropout of 0.25 in the hidden layer on a sample of AGNs with known redshifts from the latest AGN catalogue, 4LAC-DR3, obtained from Fermi-LAT. We utilized their spectral, spatial, and temporal properties to robustly predict the redshifts of AGNs as well quantify their associated uncertainties by modifying the model using two different variational inference methods. We achieve a correlation coefficient of 0.784 on the test set from the frequentist model and 0.777 and 0.778 from both the variants of variational inference, and, when used to make predictions on the samples with unknown redshifts, we achieve mean predictions of 0.421, 0.415, and 0.393, with standard deviations of 0.258, 0.246, and 0.207 from the models, respectively. [ABSTRACT FROM AUTHOR]

We present a first systematic time series study of a sample of blazars observed by the Transiting Exoplanet Survey Satellite (TESS). By cross matching the positions of the sources in the TESS observations with those from Roma-BZCAT, 29 blazars including both BL Lacerate objects and flat-spectrum radio quasars were identified. The observation lengths of the 79 light curves of the sources, across all sectors on which the targets of interest have been observed by TESS , range between 21.25 and 28.2 d. The light curves were analysed using various methods of time series analysis. The results show that the sources exhibit significant variability with fractional variability spanning between 1.41 per cent and 53.84 per cent. The blazar flux distributions were studied by applying normal and log-normal probability density function models. The results indicate that optical flux histogram of the sources are consistent with normal probability density function with most of them following bimodal distribution as opposed to unimodal distribution. This suggests that the days-time-scale optical variability is contributed either by two different emission zones or two distinct states of short-term activity in blazars. Power spectral density analysis was performed by using the power spectral response method and the true power spectra of unevenly sampled light curves were estimated. The power spectral slopes of the light curves ranged from 1.7 to 3.2. [ABSTRACT FROM AUTHOR]