Line shape analysis of $Λ(1405)$ in $γp \rightarrow K^+Σ^-π^+$ reaction using convolutional neural network
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909638967951360 |
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| author | Chavez, Vince Angelo A. Sombillo, Denny Lane B. |
| author_facet | Chavez, Vince Angelo A. Sombillo, Denny Lane B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Line shape analysis of $Λ(1405)$ in $γp \rightarrow K^+Σ^-π^+$ reaction using convolutional neural network Chavez, Vince Angelo A. Sombillo, Denny Lane B. High Energy Physics - Phenomenology High Energy Physics - Experiment 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. |
| title | Line shape analysis of $Λ(1405)$ in $γp \rightarrow K^+Σ^-π^+$ reaction using convolutional neural network |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2506.04622 |