5 papers
Stochastic galactic supernova flux of semi-relativistic particles
David Alonso-González, David Cerdeño, Marina Cermeño +1
New exotic particles with MeV masses, such as axion-like particles or light dark matter, can be emitted from core-collapse supernovae (SNe) with semi-relativistic velocities. Due t…
Probing a diffuse flux of axion-like particles from galactic supernovae with neutrino water Cherenkov detectors
David Alonso-González, David Cerdeño, Marina Cermeño +1
In this article, we claim that axion-like particles (ALPs) with MeV masses can be produced with semi-relativistic velocities in core-collapse supernovae (SNe), generating a diffuse…
Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection
David Cerdeno, Martin de los Rios, Andres D. Perez
We carry out a Bayesian analysis of dark matter (DM) direct detection data to determine particle model parameters using the Truncated Marginal Neural Ratio Estimation (TMNRE) machi…
Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri
The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liq…
Disentangling axion-like particle couplings to nucleons via a delayed signal in Super-Kamiokande from a future supernova
David Alonso-González, David Cerdeño, Marina Cermeño +1
In this work, we show that, if axion-like particles (ALPs) from core-collapse supernovae (SNe) couple to protons, they would produce very characteristic signatures in neutrino wate…