SINAPSE introduces lightweight explainable neutron-gamma discrimination

According to the arXiv submission 2605.13627 (submitted 13 May 2026), Thomas Carreau and 13 coauthors introduce SINAPSE, described as a lightweight deep learning framework for accurate and explainable neutron-gamma discrimination in organic scintillators. The paper reports a dual-branch architecture that combines a 1-dimensional convolutional autoencoder for waveform denoising with a downstream classifier. The authors report applying random augmentations to high-SNR waveforms to emulate low-charge, low-SNR conditions, and claim SINAPSE delivers superior denoising vs conventional digital signal processing techniques and yields well-calibrated output probabilities (per the manuscript). The paper also reports using SHAP (SHapley Additive exPlanations) to attribute model decisions to pulse-shape features consistent with established pulse-shape discrimination (PSD) principles.

Editorial analysis - technical context: Researchers working on detector-level signal classification commonly use denoising autoencoders and joint denoise-classify architectures to improve signal fidelity before label-driven inference. Industry-pattern observations: applying random augmentation on high-quality waveforms to simulate low-charge regimes is a pragmatic approach to extend supervised models into data-poor SNR regimes without relying solely on unreliable PSD labels. Using SHAP for feature attribution follows a growing practice of combining model-level performance metrics with post hoc explainability to validate that learned features map to known physics.

Editorial analysis - context and significance: For experimental instrumentation and applied ML teams, the paper demonstrates an integration of denoising, calibration of output probabilities, and explainability in a single lightweight pipeline. Industry-pattern observations: similar pipelines can reduce dependence on handcrafted graphical cuts in low-charge regimes, but they require careful benchmarking against conventional DSP across SNR ranges and attention to calibration under domain shift.

Observers should look for an open-source release of the code and trained weights, quantitative benchmark tables comparing SINAPSE to standard PSD metrics across SNR bins, latency and memory footprints for on-detector or FPGA/edge deployment, and validations on independent experimental datasets. The arXiv submission provides the design and initial claims; replication and public benchmarks will determine practical adoption.