Neutrino Physics and Machine Learning (NPML 2025)

Session

AI/ML for New Physics Search

31 Oct 2025, 09:00

Koshiba Hall (University of Tokyo)

1. Search for keV-Sterile Neutrinos with TRISTAN at KATRIN Using a Neural Network-Based Approach

Luca Fallböhmer

31/10/2025, 09:00

Short talk (15min. + 5 min. Q/A)

Following the completion of its neutrino mass measurement program at the end of 2025, the KATRIN experiment aims to probe keV-scale sterile neutrinos by analyzing the full tritium beta decay spectrum with a novel detector system, TRISTAN. Leveraging KATRIN’s high source activity, this search is sensitive to mixing amplitudes at the parts-per-million level. However, extracting a potential...

10. Real-time Anomaly Detection in Liquid Argon Time Projection Chamber

Seokju Chung

31/10/2025, 09:20

Short talk (15min. + 5 min. Q/A)

Real-time anomaly detection provides a model-independent way to search for unexpected phenomena, complementing traditional model-driven triggers. At the LHC, autoencoder-based anomaly triggers in CMS such as CICADA (on raw calorimeter data) and AXOL1TL (on reconstructed objects) have already demonstrated the power of these methods, with similar efforts underway in ATLAS through GELATO....

49. Unsupervised clustering for neutrino event exploration

Callum Wilkinson

31/10/2025, 09:40

Short talk (15min. + 5 min. Q/A)

Neutrino interactions are poorly understood and modeled, with some processes entirely missing from simulation. This sometimes leads to to surprising observations from rudimentary data analysis techniques such as hand-scanning. Here, the use of contrastive learning and unsupervised clustering is investigated as a more systematic way to explore event types in data. Although the aim is to...