ML4Jets2024

Session

Plenary talks

Nov 4, 2024, 9:40 AM

LPNHE, Paris, France

Conveners

Plenary talks: ML by ML

• There are no conveners in this block

Plenary talks: Experimental overview

• There are no conveners in this block

Plenary talks: MadNIS

• Gregor Kasieczka (Hamburg University (DE))

Plenary talks: ML by ML

• Anja Butter (Centre National de la Recherche Scientifique (FR))

Plenary talks: Realtime Analysis

• There are no conveners in this block

Plenary talks: Astro

• Mark Dayvon Goodsell (Centre National de la Recherche Scientifique (FR))

Plenary talks: Theory Overview

• Mark Dayvon Goodsell (Centre National de la Recherche Scientifique (FR))

119. Symbolic machine learning in physics

Francois Charton (Meta AI Paris)

11/4/24, 9:40 AM

Transformers excel at symbolic data manipulation, but most of their applications in physics deal with numerical calculations. I present a number of applications of symbolic AI in mathematics, and one in theoretical physics: learning scattering amplitudes.

120. Experimental highlights: Edge AI for particle physics

Jennifer Ngadiuba (FNAL)

11/4/24, 10:50 AM

The ever-growing data volumes produced by HEP experiments, particularly at the CERN Large Hadron Collider (LHC) and upcoming facilities, demand innovative approaches to data processing and analysis. Traditional data acquisition and processing methods are no longer adequate for handling the scale, speed, and complexity of this data. In response, the field has seen a transformative shift toward...

121. Experimental highlights: The State of ML in LHC Science

Daniel Thomas Murnane (Niels Bohr Institute, University of Copenhagen)

11/4/24, 11:30 AM

Informed by the many fields in which machine learning (ML) has made impacts, the coming years promise to see exciting improvements in the discovery and measurement power of LHC experiments. But stepping back from the many exploratory studies ongoing, there are already dozens of concrete and rigorous public LHC results leveraging advanced ML. This review will examine common themes of those...

122. MadNIS - A journey towards the first ML event generator

Ramon Winterhalder (Università degli Studi di Milano)

11/4/24, 1:50 PM

High-precision simulations based on first principles are a cornerstone of LHC physics research. In view of the HL-LHC era, there is an ever-increasing demand for both accuracy and speed in simulations. In this talk, I will first explain the basic principles of LHC event generation and highlight current methodologies and their bottlenecks. Afterwards, I will delve into the MadNIS journey and...

126. Time Series Anomaly Detection: Overview and New Trends

Paul Boniol (INRIA, ENS)

11/4/24, 4:00 PM

Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this talk, we take a holistic view of anomaly detection in time series, discussing the challenges and research opportunities in this field. In addition, we will focus on the challenges related...

125. Realtime reconstruction: Machine learning in reconstruction at LHC

Simon Akar (University of Cincinnati (US))

11/6/24, 4:00 PM

The Large Hadron Collider (LHC) at CERN pushes the boundaries of particle physics, generating data at unprecedented rates and requiring advanced computational techniques to process information in real time. While experimental environments between LHC experiments can differ, common challenges can be identified in the area of real-time reconstruction including the use of specialized trigger...

123. Implicit and Explicit Simulation-Based Inference for Cosmology

François LANUSSE

11/8/24, 9:00 AM

Physical models in the form of simulations offer an avenue to model the data in all of its complexity, but until very recently using such models to estimate physical fields and parameters remained an open problem.

In this talk, I will discuss two possible points of view on simulators, depending on whether they are “black-box” or “open-box” models, and the different methodologies and...

124. Theory Overview

Sven-Ludwig Krippendorf (LMU MUNICH)

11/8/24, 10:50 AM

In this talk I will give a biased review on the work at the intersection of machine learning and theoretical physics. This includes how we can use transformers to obtain symbolic expressions without having information about the target expression. In turn, I present a benchmark human physicists have failed in solving, namely that of compact Calabi-Yau metrics and give a short status report on...