arXiv subjects

Sergio Hernández

Publications and source records attributed to Sergio Hernández.

3 recordsLinked to original sources

arXiv2025

Dark photon constraints using the UHE gamma-ray emission from galactic sources. A Phenomenological Study

Context: Dark photons (Dph) appear in theories beyond the Standard Model of particles (SM). Under certain conditions, it is possible to have a mixing between SM photons and Dphs that should be observed as anomalies in the spectrum of astrophysical sources. Aim: To either find evidence of, or set constraints on the existence of Dphs with masses in the range of $μ\text{eV}$ using observations of two galactic sources observed at TeV energies. Methods: Using the flux of the Crab Nebula and MGRO J1908+06 at TeV energies reported by HAWC and LHAASO observatories, and assuming a model where Dphs can mix with SM photons in the vacuum; we compute the Test Statistic (TS) to search for evidence of Dphs in the form of variations/attenuation in the observed spectrum. Results: We do not find statistically significant evidence of the existence of $μ\text{eV}$ Dphs. Then, we compute the 68\% C.L. and 95\% C.L. exclusion regions for Dphs with masses in the range from $10^{-8}$ to $10^{-5}~\text{eV}$ and mixing angles with values between 0.01 and 1.0.

astro-ph.HE↗

arXiv2019

Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo

Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging technique that tends to improve generalization in large scale models such as deep neural networks. Although, HMC provides convergence guarantees for most standard Bayesian models, it does not handle discrete parameters arising from Dropout regularization. In this paper, we present a robust methodology for improving predictive uncertainty in classification problems, based on Dropout and Hamiltonian Monte Carlo. Even though Dropout induces a non-smooth energy function with no such convergence guarantees, the resulting discretization of the Hamiltonian proves empirical success. The proposed method allows to effectively estimate the predictive accuracy and to provide better generalization for difficult test examples.

cs.LG↗

arXiv2017

General Algorithmic Search

In this paper we present a metaheuristic for global optimization called General Algorithmic Search (GAS). Specifically, GAS is a stochastic, single-objective method that evolves a swarm of agents in search of a global extremum. Numerical simulations with a sample of 31 test functions show that GAS outperforms Basin Hopping, Cuckoo Search, and Differential Evolution, especially in concurrent optimization, i.e., when several runs with different initial settings are executed and the first best wins. Python codes of all algorithms and complementary information are available online.

math.OC↗