Speaker
Description
Supernova neutrinos provide a unique probe of neutrino physics under extreme conditions and core-collapse dynamics while simultaneously enabling multimessenger observations of supernovae. The DarkSide-20k experiment, a dual-phase liquid argon time projection chamber, is sensitive to supernova neutrinos through both coherent elastic neutrino-nucleus scattering (CE$\nu$NS) and the charged-current interaction $^{40}\mathrm{Ar}(\nu_e,e)^{40}\mathrm{K}$. Real-time detection of supernova bursts in DarkSide-20k presents significant challenges due to stringent latency constraints, evolving detector conditions, and the requirement for low false-positive rates.
In response, we explore a machine learning (ML) approach based on autoencoder-driven anomaly detection to identify deviations from background in streaming detector data. Compared to traditional statistical burst-search techniques, such methods have the potential to improve sensitivity to low-statistics and distant galactic supernova signals, increase robustness against changing detector conditions, and reduce reliance on specific astrophysical models.
We present a real-time analysis pipeline for supernova neutrino detection, including Geant4-based detector simulations, model training, preprocessing of streaming data, real-time model application, and performance evaluation with respect to sensitivity and false positive control. Initial studies using a simplified neural network model indicate the potential of ML-driven techniques to enhance DarkSide-20k’s capabilities as a real-time supernova neutrino observatory and to contribute to global alert networks such as the SuperNova Early Warning System (SNEWS2.0).
| Contribution types | Standard talk (20min + 5min Q/A) |
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