A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
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| Format: | Preprint |
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2024
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| _version_ | 1866909405863215104 |
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| author | Co, Darwin Alexander O. Chavez, Vince Angelo A. Sombillo, Denny Lane B. |
| author_facet | Co, Darwin Alexander O. Chavez, Vince Angelo A. Sombillo, Denny Lane B. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_18265 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements Co, Darwin Alexander O. Chavez, Vince Angelo A. Sombillo, Denny Lane B. High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Theory 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. |
| title | A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2403.18265 |