Related papers: An adaptive-binning method for generating constant…
Gamma rays measured by the Fermi-LAT satellite tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves…
We present a new representation of light curves, which is quite different from the binning method. Instead of choosing uniform bins, the reciprocal of interval between two successive photons is adopted to represent the counting rate. A…
Variability studies of jetted AGN, in particular blazars, have been used to gain a better understanding of the particle acceleration mechanisms in jets. However, statistical methods used for the characterization of variability often rely on…
The sensitivity of the Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope allows detection of thousands of new gamma-ray sources and detailed characterization of the spectra and variability of bright sources.…
There are only a few sources that are definitely known to be gamma-ray binaries. Two of these are listed as associations in the Fermi LAT Bright Source List. We are developing novel techniques to extract high signal-to-noise light curves of…
Adaptive binarization methodologies threshold the intensity of the pixels with respect to adjacent pixels exploiting the integral images. In turn, the integral images are generally computed optimally using the summed-area-table algorithm…
We present the results of the Fermi-Large Area Telescope (LAT) light curve (LC) modelling of selected blazars: six flat spectrum radio quasars (FSRQs) and five BL Lacertae (BL Lacs). All objects have densely sampled and long-term LCs, over…
Blazars are known to exhibit variability across a broad range of timescales. This behavior can include periodicity in their $\gamma$-ray emission, whose clear detection remains an ongoing challenge, partly due to the inherent stochasticity…
Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network…
In this paper we make an attempt to combine the two kinds of data from the Swift-XRT instrument (windowed timing and photon counting modes) and the from BAT. A thorough desription of the applied procedure will be given. We apply various…
This paper presents light curves and the first systematic characterization of variability of the 106 objects in the Fermi Large Area Telescope (LAT) Bright AGN Sample (LBAS). Weekly light curves obtained during the first 11 months of survey…
The analysis of Fermi Large Area Telescope (LAT) gamma-ray data in a given Region Of Interest (RoI) usually consists of performing a binned log-likelihood fit in order to determine the sky model that, after convolution with the instrument…
The light curves of variable stars are commonly described using simple trigonometric models, that make use of the assumption that the model parameters are constant in time. This assumption, however, is often violated, and consequently, time…
The availability of a robust and efficient routine for calculating light curves of a finite source magnified due to bending its light by the gravitational field of an intervening binary lens is essential for determining the characteristics…
The Fast Fourier Transform (FFT) is a fundamental tool for signal analysis, widely used across various fields. However, traditional FFT methods encounter challenges in adjusting the frequency bin interval, which may impede accurate spectral…
In this study, we conducted a systematic analysis of long-term Fermi-LAT \gamma-ray data for a sample of blazars, including FSRQs, BL\,Lacs, and BCUs, to investigate their $\gamma$-ray variability. We focused on light curves binned in 3-,…
We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds on ideas from conformal inference to provide a general…
We study the gamma-ray variability of 13 blazars observed with the Fermi Large Area Telescope (LAT). These blazars have the most complete light curves collected during the first 4 years of the Fermi sky survey. We model them with the…
Latent Diffusion Models (LDMs) are generally trained at fixed resolutions, limiting their capability when scaling up to high-resolution images. While training-based approaches address this limitation by training on high-resolution datasets,…
We present a concurrent fitting of spectra and light curves of the whole population of detected gamma-ray pulsars. Using a synchro-curvature model we compare our theoretical output with the observational data published in the Third Fermi…