Detection prospects for multi-GeV neutrinos from collisionally heated GRBs

Neutrinos with energies ranging from GeV to sub-TeV are expected to be produced in γ-ray Bursts (GRBs) as a result of the dissipation of the jet kinetic energy through nuclear collisions occurring around or below the photosphere, where the jet is still optically thick to high-energy radiation. So far, neutrino emission from the inelastic collisional model in GRBs has been poorly investigated from the experimental point of view. In the present work, we discuss prospects for identifying neutrinos produced in such collisionally heated GRBs with the large-volume neutrino telescopes KM3NeT and IceCube, including their low-energy extensions, KM3NeT/ORCA and DeepCore, respectively. We evaluated the detection sensitivity for neutrinos from both individual and stacked GRBs, exploring bulk Lorentz factor values ranging from 100 to 600. As a result of our analysis, individual searches appear feasible only for extreme sources, characterized by γ-ray fluence values at the level of Fγ ≥ 10−2 erg cm−2. In turn, it is possible to detect a significant flux of neutrinos from a stacking sample of ∼ 900 long GRBs (which could be detected by current γ-ray satellites in about five years) already with DeepCore and KM3NeT/ORCA. The detection sensitivity increases with the inclusion of data from the high-energy telescopes, IceCube and KM3NeT/ARCA, respectively

Detection prospects for low-energy neutrinos from collisionally heated GRBs with current and future neutrino telescopes

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Neutrino predictions from choked Gamma-Ray Bursts and comparison with the observed cosmic diffuse neutrino flux

The strong constraints from the Fermi-LAT data on the isotropic gamma-ray background suggest that the neutrinos observed by IceCube might possibly come from sources that are hidden to gamma-ray observations. A possibility emerged in recent years is that neutrinos may come from jets of collapsing massive stars which fail to break out of the stellar envelope, and for this reason they are known as choked jets, or choked Gamma-Ray Bursts (GRBs). We here show our predictions of neutrino flux and spectrum expected from these sources, focusing on Type II SNe, through detailed calculations of pγ interactions and accounting for all the neutrino production channels and scattering angles. We provide predictions of expected event rates for ANTARES, IceCube, and the next generation neutrino telescope KM3NeT.We also compute the contribution of the choked GRB population to the diffuse astrophysical neutrino flux, thus providing constraints on the local rate of this source population as to reproduce the observed neutrino flux

TeV emission from Gamma Ray Bursts, checking the hadronic model

Gamma-Ray Bursts (GRBs) are the most luminous explosions in the Universe. Their luminous prompt emission makes them detectable from cosmological distances. Most GRBs have been detected below a few MeV, however at least a hundred GRBs have been detected at high (0.1 GeV) energies and observed up to tens of GeV with the Fermi Large Area Telescope (LAT). Some GRBs have been observed at (0.1–1) TeV by ground-based imaging atmospheric Cherenkov telescopes. To date, the high energy emission mechanism is not understood. In this paper we review the possible leptonic and hadronic mechanisms capable of producing the ∼ TeV emission detected in GRBs. In paricular we concentrate on the hadronic origin of this radiation component and discuss in detail the numerical simulation elaborated to reproduce the observed sub-TeV observations of GRB190114C

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Temporal studies on GRB light curves for neutrino flux predictions in multi-collision zone models

For the analysis here presented, the most luminous GRB detected by Konus-WIND until now was selected, namely GRB 110918A. Through the NeuCosmA code, simulating multi-zone collisions during the GRB prompt emission, a synthetic light curve representative of the observed one has been obtained, in order to study how characteristic parameters of the inner engine activity influence the light curve. This is achieved by varying the input parameters values in the simulation. In agreement with other previous studies, the inner engine is found to be more variable than the observed light curve. Furthermore, the Empirical Mode Decomposition method is applied and its stability is analyzed. Finally, the first multi-collision neutrino flux estimate for a real GRB is presented. Assuming the flux evaluated through the simulation reproducing the GRB gamma-ray light curve and taking into account the data taking conditions and the detector response during the GRB episode, the number of expected signal in the ANTARES neutrino telescope from GRB 110918A was calculated to be µs=6.1×10-4, which is found to be a factor 20 lower than previous estimations with one-zone models

Neutrino predictions from choked Gamma-Ray Bursts and comparison with the observed cosmic diffuse neutrino flux

The strong constraints from the Fermi-LAT data on the isotropic gamma-ray background suggest that the neutrinos observed by IceCube might possibly come from sources that are hidden to gamma-ray observations. A possibility emerged in recent years is that neutrinos may come from jets of collapsing massive stars which fail to break out of the stellar envelope, and for this reason they are known as choked jets, or choked Gamma-Ray Bursts (GRBs). We here show our predictions of neutrino flux and spectrum expected from these sources, focusing on Type II SNe, through detailed calculations of pγ interactions and accounting for all the neutrino production channels and scattering angles. We provide predictions of expected event rates for ANTARES, IceCube, and the next generation neutrino telescope KM3NeT.We also compute the contribution of the choked GRB population to the diffuse astrophysical neutrino flux, thus providing constraints on the local rate of this source population as to reproduce the observed neutrino flux