High Energy Astrophysical Phenomena (astro-ph.HE)

Abstract: The atmospheric lepton fluxes play a crucial role in many particle and astroparticle physics experiments, e.g. in establishing the neutrino signal and the muon background for neutrino oscillation measurements, or the atmospheric background for astrophysical neutrino searches. The Matrix Cascade Equations (MCEq) code is a numerical tool used to model the atmospheric lepton fluxes by solving a system of coupled differential equations for particle production, interaction, and decay at extremely low computational costs. Previously, the MCEq framework only accommodated longitudinal development of air showers, an approximation that works well for neutrino and muon fluxes at high energies (O(10 GeV) and above). However, for accurate calculations of atmospheric lepton angular distributions at lower energies, the lateral component of hadronic cascades becomes significant, necessitating three-dimensional calculation schemes. We introduce "2D MCEq", an efficient numerical approach for combined longitudinal and angular evolution of air showers that retains the low computational complexity. The accuracy of the "2D MCEq" is affirmed by its benchmark comparison with the standard Monte Carlo code CORSIKA. This study paves the way for efficient three-dimensional calculations of atmospheric neutrino fluxes.

Abstract: I present the effervescent zone model to account for the compact dense circumstellar material (CSM) around the progenitor of the core collapse supernova (CCSN) SN 2023ixf. The effervescent zone is composed of bound dense clumps that are lifted by stellar pulsation and envelope convection to distances of tens AUs, and then fall back. The dense clumps provide most of the compact CSM mass and exist alongside the regular (escaping) wind. I crudely estimate that for a compact CSM within ~30 AU that contains ~0.01 Mo, the density of each clump is >3000 times the density of the regular wind at the same radius and that the total volume filling factor of the clumps is several percent. The clumps might cover only a small fraction of the CCSN photosphere in the first days post-explosion, accounting for the lack of strong narrow absorption lines. The long-lived effervescent zone is compatible with no evidence for outbursts in the years prior to SN 2023ixf explosion and the large-amplitude pulsations of its progenitor, and it is an alternative to the CSM scenario of several-years-long high mass loss rate wind.

Abstract: Machine learning based approaches are emerging as very powerful tools for many applications including source classification in astrophysics research due to the availability of huge high quality data from different surveys in observational astronomy. The Large Area Telescope on board \emph{Fermi} satellite (\emph{Fermi}-LAT) has discovered more than 6500 high energy gamma-ray sources in the sky from its survey over a decade. A significant fraction of sources observed by the \emph{Fermi}-LAT either remains unassociated or has been identified as \emph{Blazar Candidates of Uncertain type} (BCUs). We explore the potential of eXtreme Gradient Boosting (XGBoost)- a supervised machine learning algorithm to identify the blazar subclasses among a sample of 112 BCUs of the 4FGL catalog whose X-ray counterparts are available within 95$\%$ uncertainty regions of the \emph{Fermi}-LAT observations. We have used information from the multi-wavelength observations in IR, optical, UV, X-ray and $\gamma$-ray wavebands along with the redshift measurements reported in the literature for classification. Among the 112 uncertain type blazars, 62 are classified as BL Lacertae objects (BL Lacs) and 6 have been classified as Flat Spectrum Radio Quasars (FSRQs). This indicates a significant improvement with respect to the multi-perceptron neural network based classification reported in the literature. Our study suggests that the gamma-ray spectral index, and IR color indices are the most important features for identifying the blazar subclasses using the \emph{XGBoost} classifier. We also explore the importance of redshift in the classification BCU candidates.

Abstract: We describe a model of the short gamma-ray burst (SGRB) population under a `quasi-universal jet' scenario in which jets can differ in their on-axis peak prompt emission luminosity $L_c$, but share a universal angular luminosity profile $\ell(\theta_v)=L(\theta_v)/L_c$ as a function of the viewing angle $\theta_v$. The model is fitted, through a Bayesian hierarchical approach inspired by gravitational wave (GW) population analyses, to 3 observed SGRB samples simultaneously: the Fermi/GBM sample of SGRBs with spectral information in the catalogue (367 events); a flux-complete sample of 16 Swift/BAT SGRBs also detected by GBM, with a measured redshift; and a sample of SGRBs with a binary neutron star (BNS) merger counterpart, which only includes GRB~170817A at present. The results favour a narrow jet core with half-opening angle $\theta_c=2.1_{-1.4}^{+2.4}$ deg (90\% credible intervals from our fiducial `full sample' analysis) whose on-axis peak luminosity is distributed as $p(L_c) \propto L_c^{-A}$ with $A=3.2_{-0.4}^{+0.7}$ above a minimum luminosity $L_c^\star = 5_{-2}^{+11}\times 10^{51}$ erg s$^{-1}$. For $\theta_v>\theta_c$, the luminosity scales as a power law $\ell\propto \theta_v^{-\alpha_L}$ with $\alpha_L=4.7_{-1.4}^{+1.2}$, with no evidence for a break. While the model implies an intrinsic `Yonetoku' correlation between $L$ and the peak photon energy $E_p$, its slope is somewhat shallower $E_p\propto L^{0.4\pm 0.2}$ than the apparent one, and the normalization is offset towards larger $E_p$, due to selection effects. The implied local rate density of SGRBs is between about 100 up to several thousands of events per Gpc$^{3}$ yr, in line with the BNS merger rate density inferred from GW observations. Based on the model, we predict 0.2 to 1.3 joint GW+SGRB detections per year by the Advanced GW detector network and Fermi/GBM during the O4 observing run.

Abstract: Fast radio bursts (FRBs) are extremely energetic, millisecond-duration radio flashes that reach Earth from extragalactic distances. Broadly speaking, FRBs can be classified as repeating or (apparently) non-repeating. It is still unclear, however, whether the two types share a common physical origin, differing only in their activity rate. Here we report on an unprecedented observing campaign that targeted one hyperactive repeating source, FRB 20201124A, for more than $2000~\mathrm{hr}$ using four $25-32\mathrm{-m}$ class radio telescopes. In total, we detect $46$ high-energy bursts, many more than one would expect given previous observations of lower-energy bursts using larger radio telescopes. We find a high-energy burst distribution that resembles that of the non-repeating FRB population, suggesting that apparently non-repeating FRB sources may simply be the rarest bursts from repeating sources. We also discuss how FRB 20201124A contributes strongly to the all-sky FRB rate and how similar sources would be observable even at very high redshift.

Abstract: The potential association between Tidal Disruption Events (TDEs) and high-energy astrophysical neutrinos implies the acceleration of cosmic rays. These accelerated particles will initiate electromagnetic (EM) cascades spanning from keV to GeV energies by the processes related to neutrino production. We model the EM cascade and neutrino emissions by numerically solving the time-dependent transport equations and discuss the implications for AT2019dsg and AT2019fdr in the X-ray and $\gamma$-ray bands. We show that the $\gamma$-ray constraints from \emph{Fermi} can constrain the size of the radiation zone and the maximum energy of injected protons, and that the corresponding expected neutrino event numbers in follow-up searches are limited to be less than about 0.1. Depending on the efficiency of $p\gamma$ interactions, the X-ray and $\gamma$-ray signals can be expected closer to the peak of the optical-ultraviolet (OUV) luminosity, or to the time of the neutrino production.