arXiv subjects

Marta Babicz

Publications and source records attributed to Marta Babicz.

4 recordsLinked to original sources

arXiv2026

Transformer-Based Pulse Shape Discrimination in HPGe Detectors with Masked Autoencoder Pre-training

Pulse-shape discrimination (PSD) in high-purity germanium (HPGe) detectors is central to rare-event searches such as neutrinoless double-beta decay (0vBB), yet conventional approaches compress each waveform into a small set of summary parameters, potentially discarding information in the full time series that is relevant for classification. We benchmark transformer-based models that operate directly on digitised waveforms using the Majorana Demonstrator AI/ML data release. Models are trained to reproduce the collaboration-provided accept/reject labels for four standard PSD cuts and to regress calibrated energy. We compare supervised training from scratch, masked autoencoder (MAE) self-supervised pre-training followed by fine-tuning, and a feature-based gradient-boosted decision tree (GBDT) baseline. Transformers outperform GBDT across all PSD targets, with the largest gains on the most challenging labels and on the combined PSD-pass definition. MAE pre-training improves sample efficiency, reducing labelled-data requirements by factors of 2-4 in low-label regimes. For energy regression, both transformer variants show a small common underestimation on the test split, while fine-tuning modestly narrows the residual distribution. These results motivate follow-up studies of robustness across detectors and operating conditions and of performance near QBB.

hep-ex↗

arXiv2025

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

cs.LG↗

arXiv2022

Adversarial methods to reduce simulation bias in neutrino interaction event filtering at Liquid Argon Time Projection Chambers

For current and future neutrino oscillation experiments using large Liquid Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D Submanifold Sparse Convolutional Network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the Short Baseline Neutrino (SBN) program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying Domain Adversarial Neural Networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adverserial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.

hep-ex↗

arXiv2019

Future Opportunities in Accelerator-based Neutrino Physics

This document summarizes the conclusions of the Neutrino Town Meeting held at CERN in October 2018 to review the neutrino field at large with the aim of defining a strategy for accelerator-based neutrino physics in Europe. The importance of the field across its many complementary components is stressed. Recommendations are presented regarding the accelerator based neutrino physics, pertinent to the European Strategy for Particle Physics. We address in particular i) the role of CERN and its neutrino platform, ii) the importance of ancillary neutrino cross-section experiments, and iii) the capability of fixed target experiments as well as present and future high energy colliders to search for the possible manifestations of neutrino mass generation mechanisms.

hep-ex↗