arXiv subjects

Vince Angelo A. Chavez

Publications and source records attributed to Vince Angelo A. Chavez.

4 recordsLinked to original sources

arXiv2026

Revisiting the $Λ(1405)$ pole structure with convolutional neural networks

We revisit the long-standing ambiguity surrounding the complex pole structure of the $Λ(1405)$ resonance by reframing it as a classification problem for convolutional neural networks (CNNs). By training our models to recognize subtle geometric variations on nearly degenerate lineshapes using targeted differential feature on empirical CLAS data, we establish a data-driven consensus on large inference samples. Our results show that the analytic structure characterized by two poles on the $[bt]$ sheet and additional pole on the $[bb]$ sheet globally dominates. The inference-guided pole parameter extraction reveals that the $Λ(1405)$ is a two-state system, characterized by a molecular state sitting below the $\bar{K}N$ threshold and a non-molecular state lying above the $Σπ$ threshold.

hep-ph↗

arXiv2025

Line shape analysis of $Λ(1405)$ in $γp \rightarrow K^+Σ^-π^+$ reaction using convolutional neural network

Interpreting peaks or dips that appear in an invariant mass distribution is a recurring challenge in hadron physics. These enhancements can be ambiguous, especially near a two-hadron threshold since kinematical and dynamical effects play an important role in their nature. One such enhancement is an exotic baryon $Λ(1405)$ which was first observed in 1973. Despite the few available experimental data, the statistics of the measurements of $Λ(1405)$ have improved for line shape analysis. The present consensus is that it is a structure of two poles both on the second Riemann sheet. However, there are still investigations of other pole structures corresponding to $Λ(1405)$. Lately, the use of a deep neural network in analyzing these line shapes has been proven to be effective, especially in distinguishing pole structures. Thus, in this study, we develop a convolutional neural network, a type of DNN, to determine the general pole structure that corresponds to $Λ(1405)$ found in the $Σ^-π^+$ invariant mass distribution measured by CLAS in their experiment involving the $γp \rightarrow K^+Σπ$ reaction. The CNN is trained using a two-channel uniformized $S$-matrix allowing us to control the position and the corresponding Riemann sheet of the poles. Our preliminary results show that the trained CNN can accurately distinguish pole structures in the $Σ^-π^+$ invariant mass distribution and agrees with the present consensus of a two-pole structure. This supports the preceding works on the $Λ(1405)$ and requires a thorough analysis of $Σ^+π^-$ and $Σ^0π^0$ invariant mass spectra.

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arXiv2024

A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements

Enhancements in the invariant mass distribution or scattering cross-section are usually associated with resonances. However, the nature of exotic signals found near hadron-hadron thresholds remain a puzzle today due to the presence of experimental uncertainties. In fact, a purely kinematical triangle diagram is also capable of producing similar structures, but do not correspond to any unstable quantum state. In this paper, we report for the first time, that a deep neural network can be trained to distinguish triangle singularity from pole-based enhancements with a reasonably high accuracy of discrimination between the two seemingly identical line shapes. We also identify the type of triangle enhancement that can be misidentified as a dynamic pole structure. We apply our method to confirm that the $P_ψ^N(4312)^+$ state is not due to a triangle singularity, but is more consistent with a pole-based interpretation, as determined solely through pure line-shape analysis. Lastly, we explain how our method can be used as a model-selection framework useful in studying other exotic hadron candidates.

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arXiv2024

Pole structure of $P_ψ^N(4312)^+$ via machine learning and uniformized S-matrix

We probed the pole structure of the $P_ψ^{N}(4312)^{+}$ using a trained deep neural network. The training dataset was generated using uniformized independent S-matrix poles to ensure that the obtained interpretation is as model-independent as possible. To prevent possible ambiguity in the interpretation of the pole structure, we included the contribution from the off-diagonal element of the S-matrix. Five out of the six neural networks we trained favor $P_ψ^{N}(4312)^{+}$ as possibly having a three-pole structure, with one pole on each of the unphysical sheets - a first in its report. The two poles can be associated to a pole-shadow pair which is a characteristic of a true resonance. On the other hand, the last pole is most likely associated with the coupled-channel effect. The combined effect of these poles produced a peak below the $Σ^{+}_C\bar{D}^0$ which mimic the line shape of a hadronic molecule.

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