![Bites of FM4S: [1] Physics-inspired representations](/event/1473554/logo-3452117835.png){kind=logo}
Speaker
Description
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 and generate expressive representations that are insensitive to simulation specifics. 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.
| Theme of discussion | Physics-inspired representations |
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