18th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders

Session

Novel methods from information theory and ML

16 Jul 2026, 10:50

68. QCD Theory meets Information Theory

Benoit Assi (Fermilab)

16/07/2026, 10:50

Talk

We begin by reviewing how NLL accuracy is achieved in modern parton showers, highlighting the recent Sherpa implementation, and then introduce our new information-theoretic matching framework to achieve beyond NLL accuracy. By minimizing a Kullback–Leibler functional under constraints set by precision QCD input observables (including theory uncertainties), we embed high-order predictions into...

19. When Do ML Uncertainties Fail? - A PDF fit case study

Nina Elmer (University of Cambridge)

16/07/2026, 11:10

Talk

With the vast amount of data already produced by the LHC and the upcoming HL-LHC runs, reliable and precise uncertainty quantification for numerical tools, in particular neural networks, is essential. We present a systematic study of uncertainty estimation using various machine learning approaches, including neural networks, Gaussian processes, and neural tangent kernels.
As an underlying...

54. Using Agentic AI Without Losing the Physics: Lessons from LEP Data Analysis

Anthony Badea (University of Chicago (US)), Hannah Bossi (Massachusetts Inst. of Technology (US))

16/07/2026, 11:30

Talk

Agentic AI systems are rapidly moving beyond information retrieval and code completion toward the execution of complex, multi-step technical tasks. For experimental particle physics, this creates both an opportunity and a challenge: AI agents can accelerate analysis implementation, validation, and documentation, but physics measurements require controlled reasoning, reproducibility, and expert...

31. Going HyPER (and beyond): geometric machine learning methods for collider data analysis

Ethan Lewis Simpson (The University of Manchester (GB))

16/07/2026, 11:50

Talk

Modern analysis of collider data relies heavily on machine learning. This talk will discuss geometric machine learning methods, focusing on the HyPER tool: applying hypergraph representation learning to reconstruct short-lived particles in collider data. The method is extended through generative ML techniques to include production processes featuring neutrinos, making the HyPER tool a...

27. New event-wide strategies for increasing di-Higgs efficiency with Machine Learning at trigger level

Ana Rita Ferreira Carvalho (Instituto Superior Técnico and Laboratory of Instrumentation and Experimental Particle Physics (PT))

16/07/2026, 12:10

Talk

The measurement of the trilinear Higgs boson self coupling, accessible through Higgs pair production, is one of the most important goals of the High-Luminosity LHC (HL-LHC), since it allows to probe the shape of the Higgs potential, a crucial prediction of the electroweak symmetry breaking mechanism. However, the low pair production cross-section is a challenge, namely for the online event...