Contrastive learning for robust representations of neutrino data
In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2566881
- Journal Information:
- Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 9 Vol. 111; ISSN PRVDAQ; ISSN 2470-0010
- Publisher:
- American Physical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Old neutron stars as a new probe of relic neutrinos and sterile neutrino dark matter
Quasielastic lepton-nucleus scattering and the correlated Fermi gas model