25 citations · 75 across the 15 of their papers we have counts for
15 papers
Revisiting the pole structure with convolutional neural networks
Julius B. Pagayon, Vince Angelo A. Chavez, Denny Lane B. Sombillo
We revisit the long-standing ambiguity surrounding the complex pole structure of the resonance by reframing it as a classification problem for convolutional neural networ…
Deep learning topological inference-guided pole parameter extraction
Julius B. Pagayon, Klarence Tomas R. Cervantes, Denny Lane B. Sombillo
We perform a data-driven study of the doubly charmed tetraquark candidate . An ensemble of deep neural network classifiers, trained on synthetic amplitudes with controlle…
Learning Pole Structures of Hadronic States using Predictive Uncertainty Estimation
Felix Frohnert, Denny Lane B. Sombillo, Evert van Nieuwenburg +1
Matching theoretical predictions to experimental data remains a central challenge in hadron spectroscopy. In particular, the identification of new hadronic states is difficult, as…
Effects of closely spaced thresholds on line shapes with near-threshold enhancement
Exan John D. F. Carpio, Denny Lane B. Sombillo
Hidden-charm pentaquarks were first experimentally detected by LHCb in 2019, one of which is the exotic state. The nature of this state remains uncertain w…
Feature extraction in partial wave analysis using -matrix approach
Adam B. Mapa, Denny Lane B. Sombillo
Structures in the invariant mass distribution are often linked to unstable intermediate states or resonances. In experiments, many signals are detected which have broad, overlappin…
Line shape analysis of in reaction using convolutional neural network
Vince Angelo A. Chavez, Denny Lane B. Sombillo
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-hadr…
Analysis of hidden-charm pentaquarks as triangle singularities via deep learning
Darwin Alexander O. Co, Denny Lane B. Sombillo
Identifying the nature of near-threshold enhancements is hindered by the limited resolution of experimental data leading to multiple conflicting interpretations. A prominent exampl…
Pole structure of via machine learning and uniformized S-matrix
Leonarc Michelle Santos, Vince Angelo A. Chavez, Denny Lane B. Sombillo
We probed the pole structure of the using a trained deep neural network. The training dataset was generated using uniformized independent S-matrix poles to ensu…
A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
Darwin Alexander O. Co, Vince Angelo A. Chavez, Denny Lane B. Sombillo
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 t…
Interpretation of near-threshold peaks using the method of independent S-matrix poles
Leonarc Michelle Santos, Denny Lane B. Sombillo
We propose a model-independent analysis of near-threshold enhancements using independent S-matrix poles. In this formulation, we constructed a Jost function with controllable zeros…
Formulation of causality-preserving quantum time of arrival theory
Denny Lane B. Sombillo, Neris I. Sombillo
We revisit the quantum correction to the classical time of arrival to address the unphysical instantaneous arrival in the limit of zero initial momentum. In this study, we show tha…
Classifying near-threshold enhancement using deep neural network
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
One of the main issues in hadron spectroscopy is to identify the origin of threshold or near-threshold enhancement. Prior to our study, there is no straightforward way of distingui…
Unveiling the pole structure of S-matrix using deep learning
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
Particle scattering is a powerful tool to unveil the nature of various subatomic phenomena. The key quantity is the scattering amplitude whose analytic structure carries the inform…
Model independent analysis of coupled-channel scattering: a deep learning approach
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
We develop a robust method to extract the pole configuration of a given partial-wave amplitude. In our approach, a deep neural network is constructed where the statistical errors o…
Classifying Pole of Amplitude Using Deep Neural Network
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
Most of exotic resonances observed in the past decade appear as peak structure near some threshold. These near-threshold phenomena can be interpreted as genuine resonant states or…