Pith Number

pith:C5EJD7DV

pith:2026:C5EJD7DVU2MBGNYBOJYRBIYEVJ

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SINAPSE: A lightweight deep learning framework for accurate and explainable neutron-$\gamma$ discrimination

Adrien Matta, Audrey Chatillon, Beno\^it Mauss, Charl\`ene Surault, Cyril Lenain, David Etasse, David Regnier, Jason Surbrook, Julien Taieb, Matthew Devlin, Owen Syrett, Patrick Copp, Pierre Morfouace, Thomas Carreau

A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.

arxiv:2605.13627 v1 · 2026-05-13 · physics.ins-det · physics.data-an

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

SINAPSE achieves superior denoising performance compared to conventional digital signal processing techniques, and outputs well-calibrated probabilities, consistent with traditional graphical cuts.

C2weakest assumption

That random augmentations applied to high-SNR waveforms faithfully reproduce the noise statistics and pulse-shape distortions present in real low-charge experimental data.

C3one line summary

SINAPSE uses a dual-branch neural network with a 1D convolutional autoencoder for denoising and a classifier for neutron-gamma discrimination, trained via random augmentations on high-SNR data and validated with SHAP explanations.

References

34 extracted · 34 resolved · 1 Pith anchors

[1] R. C. Haight, H. Y. Lee, T. N. Taddeucci, J. M. O’Donnell, B. A. Perdue, N. Fotiades, M. Devlin, J. L. Ullmann, A. Laptev, T. Bredeweg,et al., Two detector arrays for fast neutrons at lansce, Journal

[2] T. Mart´ ınez, D. Cano-Ott, J. Castilla, A. Garcia, J. Marin, G. Martinez, E. Mendoza, C. Santos, F. Tera, D. Villamarin,et al., Monster: a tof spectrometer forβ- delayed neutron spectroscopy, Nuclear 2014

[3] P. Talou and R. Vogt,Nuclear fission: theories, experi- ments and applications(Springer Nature, 2023) 2023

[4] Brooks, Development of organic scintillators, Nuclear Instruments and Methods162, 477 (1979) 1979

[5] X. Fabian, G. Baulieu, L. Ducroux, O. St´ ezowski, A. Boujrad, E. Cl´ ement, S. Coudert, G. de France, N. Er- duran, S. Ert¨ urk,et al., Artificial neural networks for neutron/γdiscrimination in the n 2021

Receipt and verification

First computed	2026-05-18T02:44:17.789261Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39

Aliases

arxiv: 2605.13627 · arxiv_version: 2605.13627v1 · doi: 10.48550/arxiv.2605.13627 · pith_short_12: C5EJD7DVU2MB · pith_short_16: C5EJD7DVU2MBGNYB · pith_short_8: C5EJD7DV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ce1226a6738c413844610aace7ef0e742f5efc49344e7967c5c46f11f8e741c3",
    "cross_cats_sorted": [
      "physics.data-an"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.ins-det",
    "submitted_at": "2026-05-13T14:53:47Z",
    "title_canon_sha256": "51b78325251b5c79e036753c805087a9e3163e7bd0ccc33e19f44bdb265fe9ac"
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  "source": {
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    "kind": "arxiv",
    "version": 1
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}