{
  "abstract": "We propose a data-driven procedure, based on convolutional variational autoencoders, to identify the presence of signal pulses in long time-series. The dataset consists of synthetic waveforms, each composed of non-gaussian noise and a log-normal shaped signal of variable intensity, with a length of 10,000 samples. The model heavily compresses the input waveforms, allowing a direct study of such a reduced representation. After training for 150 epochs on 7,500 waveforms, a region in the latent space where the network encodes time-series presenting only background noise emerges, allowing in turn to tag as candidates for containing a signal those falling outside. When applied on a test dataset of freshly generated waveforms, 100% of the events with a large pulses are correctly labelled, and this fraction only decreases for signal amplitudes comparable with accidental noise pulses. This approach was designed to fully exploit the measurements in dual-phase Liquid Argon Time Projection Chambers, as the one of the Recoil Directionality experiment, built in the context of the Darkside project.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Anastasi, G.A.; Albergo, S.F.; De Napoli, M.; Pino, N.; Puglia, S.M.; Tricomi, A.R. Exploiting the Latent Space of Deep AutoEncoders for the Identification of Signal Pulses in Noisy Time-Series.…",
  "arxiv": "2607.12632",
  "author_total": 6,
  "authors": [
    {
      "name": "Gioacchino Alex Anastasi"
    },
    {
      "name": "Sebastiano Francesco Albergo"
    },
    {
      "name": "Marzio De Napoli"
    },
    {
      "name": "Noemi Pino"
    },
    {
      "name": "Sebastiana Maria Puglia"
    },
    {
      "name": "Alessia Rita Tricomi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d57hyf2101fw7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.12632",
  "fulltext_url": null,
  "id": "d57hyf2101fw7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.12632",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.ins-det"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.12632",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d57hyf2101fw7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2607.12632",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.12632"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "Exploiting the latent space of deep AutoEncoders for the identification of signal pulses in noisy time-series",
  "title_text": "Exploiting the latent space of deep AutoEncoders for the identification of signal pulses in noisy time-series",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/d57hyf2101fw7",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3390/particles9030085",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}