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Sergio Alves Garre

Publications and source records attributed to Sergio Alves Garre.

3 recordsLinked to original sources

3C403: a candidate neutrino-emitting radio galaxy

3C403 is a well-known FRII radio galaxy with jets extending up to kiloparsec scales. We report its identification as the second most significant candidate among more than 150 sources examined using the 15-year neutrino dataset from the ANTARES Collaboration, making it one of the most promising radio-galaxy candidates for high-energy neutrino emission. Motivated by previous associations between blazars and neutrino events, we investigated the jet properties of 3C403 and their possible role in neutrino production. Multi-scale radio observations, from parsec to kiloparsec scales, reveal a stable, two-sided jet lying close to the plane of the sky, with no evidence of strong Doppler boosting, while X-ray data indicate a dominant, heavily absorbed accretion-related component. We also examined the recently proposed correlation between neutrino and hard X-ray fluxes - originally identified in blazars and Seyfert galaxies - and find that 3C403 occupies an intermediate location in the $L_ν$--$L_{\rm hX}$ plane between jet-dominated and corona-dominated systems. However, the current upper limit on its neutrino flux prevents a firm assessment of whether it follows the proposed relation. With radiatively efficient accretion ($λ_{\rm Edd}\sim10^{-2}$), strong hard X-ray emission, and a powerful but misaligned jet, 3C403 provides a physically motivated laboratory for exploring the interplay between coronal activity and jet environments in multimessenger scenarios of neutrino production in active galaxies.

astro-ph.HE↗

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.

astro-ph.IM↗

gSeaGen: the KM3NeT GENIE-based code for neutrino telescopes

The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between track-type and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project.

astro-ph.IM↗