8 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…