A. Dávila-Rivera | Searcharxiv

arXiv2025

Radiative decays of the $Σ_c$, $Ξ'_c$ and $Ω_c$ charmed baryons

In this work, we study the radiative decays of the $Σ_c$, $Ξ'_c$ and $Ω_c$ charmed baryons, which belong to the flavor sextet ($\bf {6}_{\rm F}$), within the constituent quark model formalism. The electromagnetic transitions are calculated from the ground and $P$-wave states to ground states, as well as from the second shell states to both the ground and $P$-wave final states. These decays play a crucial role in confirming the existence of certain resonances when strong decays are not allowed. Moreover, electromagnetic decay widths are particularly useful for identifying resonances when states have the same mass and total decay width. A relevant case is the $Ω_c (3327)$ state, whose branching ratios between the strong decay channels are comparable; thus, the radiative decay widths may help assign this state. We also make the assignment of the recently discovered $Ξ'_c(2923)^{+}$ baryon, which is consistent with being the isospin partner of the $Ξ'_c(2923)^{0}$. This study presents, for the first time, the calculation of electromagnetic decays for $D_ρ$-wave states, $ρ-λ$ mixed states, and $ρ$-mode radially excited states in the charm sector. Throughout our calculations, we account for uncertainties arising from both experimental and model-dependent errors.

hep-ph↗

arXiv2024

Exotic and physics-informed support vector machines for high energy physics

In this article, we explore machine learning techniques using support vector machines with two novel approaches: exotic and physics-informed support vector machines. Exotic support vector machines employ unconventional techniques such as genetic algorithms and boosting. Physics-informed support vector machines integrate the physics dynamics of a given high-energy physics process in a straightforward manner. The goal is to efficiently distinguish signal and background events in high-energy physics collision data. To test our algorithms, we perform computational experiments with simulated Drell-Yan events in proton-proton collisions. Our results highlight the superiority of the physics-informed support vector machines, emphasizing their potential in high-energy physics and promoting the inclusion of physics information in machine learning algorithms for future research.

hep-ex↗