Bites of FM4S: [1] Physics-inspired representations

Contribution List

2. Introduction to Bites of FM4S

Gaia Grosso (IAIFI, MIT), Raghav Kansal (California Institute of Technology)

20/11/2024, 14:00

3. Boosting the LHC resonance search program with Sophon

Congqiao Li (Peking University (CN))

20/11/2024, 14:10

We introduce a novel experimental methodology, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), designed to enhance the LHC resonance search program in the Lorentz-boosted regime. Sophon leverages the principles of “large models for large-scale classification”, employing the advanced deep learning algorithm to train a classifier across an extensive (o(100)) set of...

4. Anomaly preserving neural embeddings for New Physics discovery at the LHC

Kyle Sidney Metzger (ETH Zurich (CH))

20/11/2024, 15:00

Discovering the occurrence of unexpected physical processes in collider data could unveil new fundamental laws governing our Universe. However, the extreme size, rate and complexity of the datasets generated at the Large Hadron Collider (LHC) pose unique challenges to detect them. Typically, this is addressed by transforming high-dimensional, low-level detector data into physically meaningful...

8. Rapid Parameter Estimation for Kilonovae Using Likelihood-Free Inference

Malina Desai

20/11/2024, 15:20

Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference with a pre-trained embedding network, optimized to efficiently process kilonova light curves. Our method is capable of retrieving two intrinsic parameters of the kilonova light curves with a comparable accuracy and...

5. You can observe a lot by just watching (new AI strategies for the LHC and beyond)

Philip Coleman Harris (Massachusetts Inst. of Technology (US))

20/11/2024, 15:50

With large amounts of data, a Higgs boson discovery, and world-leading constraints on an enormous amount of parameters and interactions, the Large Hadron Collider has been a phenomenal tool. We show new results built on contrastive learning and semi-supervised learning strategies where, through physics-motivated choices, we teach an AI to visualize many processes simultaneously, allowing it to...

1. MACK: Mismodeling Addressed with Contrastive Knowledge

Dylan Sheldon Rankin (University of Pennsylvania (US))

20/11/2024, 16:40

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...

6. Product Manifold Machine Learning for Physics

Nathaniel Sherlock Woodward (Massachusetts Inst. of Technology (US))

20/11/2024, 17:00

Particle jets are collimated flows of partons which evolve into tree-like structures through stochastic parton showering and hadronization. The hierarchical nature of particle jets aligns naturally with hyperbolic space, a non-Euclidean geometry that captures hierarchy intrinsically. To leverage the benefits of non-Euclidean geometries, we develop jet analysis in product manifold (PM) spaces,...

7. Physics-inspired representations for LIGO data

Malina Desai