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))
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.
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...
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...
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...
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...
