Improving the performance of weak supervision searches using transfer and meta-learning
Journal
Journal of High Energy Physics
Journal Volume
2024
Journal Issue
2
Start Page
138
ISSN
1029-8479
Date Issued
2024-02-20
Author(s)
Abstract
Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we seek to create neural networks that can learn from less experimental signal by using transfer and meta-learning. The general idea is to first train a neural network on simulations, thereby learning concepts that can be reused or becoming a more efficient learner. The neural network would then be trained on experimental data and should require less signal because of its previous training. We find that transfer and meta-learning can substantially improve the performance of weak supervision searches.
Subjects
New Gauge Interactions; Specific BSM Phenomenology
SDGs
Publisher
Springer Science and Business Media LLC
Type
journal article