{
  "abstract": "The High-Granularity Dual-Readout Calorimeter (HG-DREAM) designed for FCC-ee aims to achieve unprecedented energy resolution through fine three-dimensional shower imaging, simultaneous measurement of Cherenkov and scintillation signals, and machine learning capabilities. A key enabling technology is longitudinal segmentation via timing measurements, where multiple energy deposits along optical fibers are distinguished by the arrival times of Cherenkov photons. We present quantized one-dimensional convolutional neural networks that predict pulse locations directly from 80-sample, 16 ns waveforms digitized at 200 ps, and evaluate their implementation in front-end electronics. Three architectures, a compact baseline and two variants using dilation and stride to reach the $\\sim$ 20-sample receptive field set by the SiPM pulse width, achieve ROC AUC above 0.95 with four to eight filters per layer. Fixed-point quantization to a $<$16,8$>$ baseline costs less than $10^{-3}$ in AUC relative to the floating-point reference. Applied to pairwise deposits, the smallest model separates two pulses with better than 95\\% probability at longitudinal separations of 20 cm and above. Synthesized at 400 MHz, this model uses 9663 LUT on a Xilinx XCVU13P FPGA and occupies $\\sim$ 18500 $μ$$m^2$ in TSMC 28 nm CMOS with reconfigurable weights. Assuming a triggered readout architecture for FCC-ee, the $\\sim$ 500 kHz Z-pole trigger rate will be the most stringent throughput requirement across all FCC-ee operating modes. The ASIC implementation of the model sustains 4.8 MHz, exceeding the 0.5 MHz requirement by an order of magnitude, while the reconfigurable-weight FPGA implementation fails to do so. Sustaining the 40 MHz FCC-ee Z-pole bunch-crossing frequency in the triggerless architecture is not feasible with the models developed and the hardware targeted in this work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22269",
  "author_total": 9,
  "authors": [
    {
      "name": "Spencer Allen"
    },
    {
      "name": "Matteo Cremonesi"
    },
    {
      "name": "Jordan Damgov"
    },
    {
      "name": "Erdem Yigit Ertorer"
    },
    {
      "name": "Janna Goodman"
    },
    {
      "name": "Giuseppe Di Guglielmo"
    },
    {
      "name": "James Hirschauer"
    },
    {
      "name": "Shuichi Kunori"
    },
    {
      "name": "Peter Meiring"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0wpcjmr0a8exr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22269",
  "fulltext_url": null,
  "id": "0wpcjmr0a8exr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22269",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "physics.ins-det"
    ],
    [
      "openalex",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7214011178",
  "pdf_url": "https://arxiv.org/pdf/2609.22269",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0wpcjmr0a8exr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.22269",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22269"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Real-Time SiPM Pulse Deconvolution for a High-granularity Dual-readout Calorimeter with Neutral Networks",
  "title_text": "Real-Time SiPM Pulse Deconvolution for a High-granularity Dual-readout Calorimeter with Neutral Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/0wpcjmr0a8exr",
  "venue": "arXiv",
  "versions": []
}