activity
20242026
collaborators

7 papers

hep-ph2026

: Solar Neutrinos for Direct Detection

Dorian W. P. Amaral, David Cerdeño, Andrew Cheek +2

We introduce Solar Neutrinos for Direct Detection (): an open-source Python package that enables the computation of the solar neutrino rate spectrum at direct detec…

hep-ph2026

Neutrino NSI in archaeological Pb

D. Alloni, G. Benato, P. Carniti +47

Dark matter direct detection experiments can observe solar neutrinos via coherent elastic neutrino-nucleus scattering, making it possible to test new physics in the neutrino sector…

hep-ph2026

New benchmarks for direct detection of freeze-in dark matter in vector portal models

David Cerdeño, Patrick Foldenauer, Rafael López Noé +1

We investigate the freeze-in of MeV-scale fermionic dark matter (DM) that couples to the Standard Model via a new vector mediator to assess the potential that future direct detecti…

hep-ph2026

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…

hep-ph2025

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…

hep-ph2025

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…