arXiv subjects

Dmitry V. Malyshev

Publications and source records attributed to Dmitry V. Malyshev.

8 recordsLinked to original sources

arXiv2026

Identifying potentially missed extended sources in the Fermi-LAT 4FGL Catalog using clustering analysis

Many Fermi Large Area Telescope (LAT) sources remain unassociated. Some may be extended sources represented by multiple catalog entries, as found for HESS J1813-178. We search for clusters of unassociated Fermi-LAT sources and test whether single extended-source models describe them better than multiple catalog sources. We applied DBSCAN to 4FGL sources with a linking scale of $0.3^\circ$ over 5 GeV-1 TeV. Each cluster contains at least one unassociated source and up to one extended or point-like associated source from selected categories, including pulsars, pulsar wind nebulae and supernova remnants. Using Fermipy, we compared extended- and multiple-source models and characterized each candidate spectrally and morphologically, focusing on the Galactic plane. We identified 48 clusters containing 124 sources, each with at least one unassociated source. For all 8 clusters passing our quality selection, an extended-source model is statistically preferred. At linking scales of $0.4^\circ$ and $0.5^\circ$, all 8 clusters retain their core sources. Cross-matches with the Second Fermi Galactic Extended Sources Catalog (2FGES) and the HESS Galactic Plane Survey (HGPS) showed that 5 clusters overlap known extended sources. The other 3 clusters, including one cluster with a morphology dependent on the interstellar emission model, have no counterpart in either catalog and are new extended-source candidates. Spatial clustering combined with likelihood-based model comparison can uncover extended sources missed in the Fermi-LAT catalog and complements existing searches.

astro-ph.HE↗

arXiv2025

Diffuse continuum emission and large extended sources at MeV energies

Future gamma-ray survey instruments, such as newASTROGAM and AMEGO-X, will significantly improve previous and current all-sky surveys at MeV energies. In this paper we discuss the continuum emission from the Milky Way, two prominent large extended sources, the Fermi bubbles and Loop I, and the extragalactic gamma-ray background. We highlight the importance of measurements in the MeV to GeV energy range for understanding CR production and propagation in the Galaxy, for the determination of the nature of the Fermi bubbles and Loop I, and for exploring the origin of the extragalactic gamma-ray background.

astro-ph.HE↗

arXiv2025

Supermassive black holes and their surroundings: MeV signatures

The gravitational potential of supermassive black holes is so powerful that it triggers some of the most intense phenomena in the Universe. Accretion onto these objects and relativistic jet emission from their vicinity are observable across a wide range of frequencies and throughout cosmic history. However, despite this wealth of data, many aspects of their underlying mechanisms remain elusive. Investigating this phenomena across all frequencies is crucial, yet some energy windows are still poorly explored. One such window is the MeV energy range: many key signatures related to the emission from the SMBH environment - both in quiescent and active phases - are expected to lie between one and several hundreds MeV. In this work, we explore some of the open questions regarding the behavior and emission processes in the surroundings of SMBHs, and how these questions might be approached. From the elusive nature of Fermi bubbles around our Galactic Centre, to the origin of high-energy neutrinos in the nuclei and jets of Active Galactic Nuclei, to the nature and emission mechanisms of the most powerful blazars, the MeV window stands out as a crucial key to understanding SMBH physics.

astro-ph.HE↗

arXiv2025

Towards resolving the Galactic center GeV excess with millisecond-pulsar-like sources using machine learning

Excess of gamma rays around the Galactic center (GC) observed in the Fermi Large Area Telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The spherical morphology and the spectral energy distribution with a peak around a few GeV are consistent with emission from annihilation of dark matter particles. Other possible explanations include a distribution of millisecond pulsars (MSPs). One of the caveats of the MSP hypothesis is the relatively small number of associated MSPs near the GC. In this paper, we perform a multiclass classification of Fermi-LAT sources using machine learning and determine the contribution from unassociated MSP-like sources near the GC. The spectral energy distribution, spatial morphology, and the source count distribution are consistent with expectations for a population of MSPs that can explain the gamma-ray excess. Possible caveats of the contribution from the unassociated MSP-like sources are discussed.

astro-ph.HE↗

arXiv2024

Classification of Fermi-LAT unassociated sources with machine learning in the presence of dataset shifts

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 particular attention is paid to the fact that the distributions of associated and unassociated sources are different as functions of source parameters. In this work, we address this problem in the framework of dataset shifts in machine learning.

astro-ph.HE↗

arXiv2024

Galactic center GeV excess and classification of Fermi-LAT sources with machine learning

Excess of gamma rays with a spherical morphology around the Galactic center (GC) observed in the Fermi large area telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The excess has been interpreted by annihilating dark matter as well as emission from a population of unresolved millisecond pulsars (MSPs). We use a multi-class classification of Fermi-LAT sources with machine learning to study the distribution of MSP-like sources among unassociated Fermi-LAT sources near the GC. We find that the source count distribution of MSP-like sources is comparable with the MSP explanation of the GC excess.

astro-ph.HE↗

arXiv2024

Effect of covariate shift on multi-class classification of Fermi-LAT sources

Probabilistic classification of unassociated Fermi-LAT sources using machine learning methods has an implicit assumption that the distributions of associated and unassociated sources are the same as a function of source parameters, which is not the case for the Fermi-LAT catalogs. The problem of different distributions of training and testing (or target) datasets as a function of input features (covariates) is known as the covariate shift. In this paper, we, for the first time, quantitatively estimate the effect of the covariate shift on the multi-class classification of Fermi-LAT sources. We introduce sample weights proportional to the ratio of unassociated to associated source probability density functions so that associated sources in areas, which are densely populated with unassociated sources, have more weight than the sources in areas with few unassociated sources. We find that the covariate shift has relatively little effect on the predicted probabilities, i.e., the training can be performed either with weighted or with unweighted samples, which is generally expected for the covariate shift problems. The main effect of the covariate shift is on the estimated performance of the classification. Depending on the class, the covariate shift can lead up to 10 - 20% reduction in precision and recall compared to the estimates, where the covariate shift is not taken into account.

astro-ph.HE↗

arXiv2023

Multi-class classification of Fermi-LAT sources with hierarchical class definition

In the paper we develop multi-class classification of Fermi-LAT gamma-ray sources using machine learning with hierarchical determination of classes. One of the main challenges in the multi-class classification of the Fermi-LAT sources is that the size of some of the classes is relatively small, for example with less than 10 associated sources belonging to a class. In the paper we propose a hierarchical structure for the determination of the classes. This enables us to have control over the size of classes and to compare the performance of the classification for different numbers of classes. In particular, the class probabilities in the two-class case can be computed either directly by the two-class classification or by summing probabilities of children classes in multi-class classification. We find that the classifications with few large classes have comparable performance with classifications with many smaller classes. Thus, on the one hand, the few-class classification can be recovered by summing probabilities of classification with more classes while, on the other hand, the classification with many classes gives a more detailed information about the physical nature of the sources. As a result of this work, we construct three probabilistic catalogs. Two catalogs are based on Gaussian mixture model for the determination of the groups of classes: one with the requirement for the minimal number of sources in a group to be larger than 100, which results in six final groups of physical classes, while the other one has the requirement that the minimal number of sources in a group is larger than 15, which results in nine groups of physical classes. The third catalog is based on a random forest determination of the groups of classes with the requirement that the minimal number of sources in a group is larger than 100, which results in six groups of classes. (abridged)

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