Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays

2026-09-15

AI Summary

本文针对重共振质量重构问题，采用自监督学习预训练变压器编码器，然后进行微调以提高在存在系统不确定性情况下的鲁棒性和准确性。

Abstract

Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches at collider experiments. Supervised learning approaches to this problem often struggle to generalize well due to the presence of various systematic uncertainties and distribution shifts. Exhausting all possible variations in the labeled data can be very compute-intensive, while a failure of the model to generalize can corrupt the reconstructed resonance widths that are critical in peak-hunting analyses. In this work, following the foundation model paradigm, we use a self-supervised approach to pre-train a transformer encoder with VICReg to learn an embedding invariant to various corruptions, then fine-tune it for mass regression on a heavy resonance with masses ranging from 2.5 to 6.5 TeV and a SUSY-like cascade decay into an eleven-body final state. We show that the pre-trained model reconstructs sharper resonance peaks and has a more stable performance under various realistic corruptions, compared to a supervised model of the same architecture trained on the same augmented data from scratch.

Problem

• self-supervised learning
• resonance mass regression
• systematic uncertainties
• distribution shifts
• cascade decays

Innovation

• self-supervised learning
• transformer encoder
• VICReg
• mass regression
• resonance peaks