arXiv subjects

J. García-Méndez

Publications and source records attributed to J. García-Méndez.

3 recordsLinked to original sources

arXiv2026

Search for dark matter annihilation in the Sun using the full ANTARES dataset

Weakly Interacting Massive Particles (WIMPs) are among the most popular candidates for particle dark matter. In certain scenarios, WIMPs could be gravitationally captured into massive celestial objects and then annihilate into Standard Model particles, possibly leading to the emission of neutrinos. The ANTARES neutrino telescope, located in the Mediterranean Sea, is sensitive to neutrinos with energies ranging from a few tens of GeV to the TeV scale, making it well suited to search for the annihilation of WIMPs with masses below a few TeV/c$^2$. One of the closest potential astrophysical sources of neutrinos from WIMP annihilations is the Sun. In this work, a search for high-energy neutrinos originating from the Sun, using the full dataset recorded by ANTARES from 2007 to 2022 was conducted. The analysis yielded no significant evidence for a neutrino excess over the expected background, and upper limits on the WIMP-proton interaction cross section have been obtained, for both spin-dependent and spin-independent processes. These limits are valid for dark matter masses ranging from 35 GeV/c$^2$ to 10 TeV/c$^2$ for the annihilation benchmark channels WIMP$+$WIMP $\longrightarrow$ $b\bar{b},τ^+τ^-, W^+W^-$, assuming a 100$\%$ branching ratio for each of these channels. These results improve on those previously obtained by the ANTARES Collaboration by a factor of two-three and are consistent with those reported by other experiments in direct and indirect searches for dark matter.

astro-ph.HE↗

arXiv2025

Deep Learning Framework for Enhanced Neutrino Reconstruction of Single-line Events in the ANTARES Telescope

We present the $N$-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino events ($\sim$ 100 GeV). $N$-Fit is a neural network model that relies on deep learning and combines several advanced techniques in machine learning --deep convolutional layers, mixture density output layers, and transfer learning. This framework divides the reconstruction process into two dedicated branches for each neutrino event topology --tracks and showers-- composed of sub-models for spatial estimation --direction and position-- and energy inference, which later on are combined for event classification. Regarding the direction of single-line events, the $N$-Fit algorithm significantly refines the estimation of the zenithal angle, and delivers reliable azimuthal angle predictions that were previously unattainable with traditional $χ^2$-fit methods. Improving on energy estimation of single-line events is a tall order; $N$-Fit benefits from transfer learning to efficiently integrate key characteristics, such as the estimation of the closest distance from the event to the detector. $N$-Fit also takes advantage from transfer learning in event topology classification by freezing convolutional layers of the pretrained branches. Tests on Monte Carlo simulations and data demonstrate a significant reduction in mean and median absolute errors across all reconstructed parameters. The improvements achieved by $N$-Fit highlight its potential for advancing multimessenger astrophysics and enhancing our ability to probe fundamental physics beyond the Standard Model using single-line events from ANTARES data.

physics.comp-ph↗

arXiv2021

Deep learning reconstruction in ANTARES

ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by secondary particles from neutrino interactions. The event reconstruction and background discrimination is challenging and machine-learning techniques are explored to improve the performance. In this contribution, two case studies using deep convolutional neural networks are presented. In the first one, this approach is used to improve the direction reconstruction of low-energy single-line events, for which the reconstruction of the azimuth angle of the incoming neutrino is particularly difficult. We observe a promising improvement in resolution over classical reconstruction techniques and expect to at least double our sensitivity in the low-energy range, important for dark matter searches. The second study employs deep learning to reconstruct the visible energy of neutrino interactions of all flavors and for the multi-line setup of the full detector.

astro-ph.IM↗