{"ID":6537479,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11585","arxiv_id":"2607.11585","title":"Machine Learning-Based Reconstruction for Resistive Silicon Sensors","abstract":"Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented. This structure provides a 100\\% fill factor and enables good spatial resolution with a relaxed readout pitch. The same signal-sharing mechanism that makes interpolation possible complicates the readout: charge spreads across multiple pads, the useful information can approach the electronic-noise threshold, and matrix-inversion approaches can become computationally challenging and sensitive to off-diagonal noise. In this work, we study machine-learning-based reconstruction and compression for resistive silicon sensors. We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information beyond what is available from binary readouts or reduced-amplitude summaries. We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction and have been tested for FPGA deployment using \\hls. We also study routes towards bandwidth reduction with waveform rasterisation and window-selection methods, and extend the approach beyond the first model to topology-agnostic transformer-based architectures that use pad coordinates as part of the input. These models are designed to support arbitrary pad counts and geometries, mitigate edge distortions, preserve approximately $10~μ\\mathrm{m}$ position resolution for $500~μ\\mathrm{m}\\times500~μ\\mathrm{m}$ pitched sensors, and guide future resistive-silicon sensor designs","short_abstract":"Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented. This structure prov...","url_abs":"https://arxiv.org/abs/2607.11585","url_pdf":"https://arxiv.org/pdf/2607.11585v1","authors":"[\"Alexander Aoki\",\"Gaetano Barone\",\"Leena Diehl\",\"Gabriele Giacomini\",\"Vagelis Gkougkousis\",\"Hanshal Goyal\",\"Rohan Kher\",\"Daniel Li\",\"Anna Macchiolo\",\"Yevhenii Padnuik\",\"Daria Senina\",\"Samantha Sunnarborg\",\"Jessica Tang\",\"Alessandro Tricoli\",\"Lixing Wang\",\"Don C. Wong\"]","published":"2026-07-13T14:03:26Z","proceeding":"hep-ex","tasks":"[\"hep-ex\",\"cs.LG\",\"nucl-ex\"]","methods":"[\"Transformer\"]","has_code":false}