SciPost Submission Page
MACK: Mismodeling Addressed with Contrastive Knowledge
by Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris
Submission summary
| Authors (as registered SciPost users): | Dylan Rankin |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2410.13947v1 (pdf) |
| Date submitted: | 2024-10-23 14:25 |
| Submitted by: | Rankin, Dylan |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing