Enhancing Photon Identification with Neural Network Methods
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910012610183168 |
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| author | Frid, Yuval Barak, Liron |
| author_facet | Frid, Yuval Barak, Liron |
| contents | We investigate photon--pion discrimination in regimes where electromagnetic showers overlap at the scale of calorimeter granularity. Using full detector simulations with fine-grained calorimeter segmentation of approximately $0.025\times0.025$ in $(η,ϕ)$, we benchmark three approaches: boosted decision trees (BDTs) on shower-shape variables, dense neural networks (DNNs) on the same features, and a ResNet-based convolutional neural network operating directly on calorimeter cell energies. The ResNet significantly outperformed both baseline methods, achieving further gains when augmented with soft scoring and an auxiliary $ΔR$ regression head. Our results demonstrate that residual convolutional architectures, combined with physics-informed loss functions, can substantially improve photon identification in high-luminosity collider environments in which overlapping electromagnetic showers challenge traditional methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_10533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Photon Identification with Neural Network Methods Frid, Yuval Barak, Liron High Energy Physics - Experiment We investigate photon--pion discrimination in regimes where electromagnetic showers overlap at the scale of calorimeter granularity. Using full detector simulations with fine-grained calorimeter segmentation of approximately $0.025\times0.025$ in $(η,ϕ)$, we benchmark three approaches: boosted decision trees (BDTs) on shower-shape variables, dense neural networks (DNNs) on the same features, and a ResNet-based convolutional neural network operating directly on calorimeter cell energies. The ResNet significantly outperformed both baseline methods, achieving further gains when augmented with soft scoring and an auxiliary $ΔR$ regression head. Our results demonstrate that residual convolutional architectures, combined with physics-informed loss functions, can substantially improve photon identification in high-luminosity collider environments in which overlapping electromagnetic showers challenge traditional methods. |
| title | Enhancing Photon Identification with Neural Network Methods |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2511.10533 |