AI@LHC 2026: what's next?

Session

Lightening talks

27 May 2026, 16:00

w202 (Peking University)

20. Potential of di-Higgs observation via a calibratable jet-free HH->4b framework

Tianyi Yang (Peking University (CN))

27/05/2026, 16:00

We present a calibratable, jet-free framework that enhances the search significance of the flagship LHC channel HH→4b by more than a factor of five compared to existing approaches. The method employs a mass-decorrelated discriminant to identify h1h2→4b with variable h1,2 masses and a simultaneous estimator of (mh1,mh2), both derived from multiclass classification on all-particle inputs. The HH...

17. Learning Detector-Level Probability Distributions with Sliced Wasserstein Distance

Chucheng Pan (Wuhan University (CN))

27/05/2026, 16:15

Oral talks

High-energy physics is intrinsically a field that relies on the accurate modeling and comparison of high-dimensional probability distributions, arising from complex detector responses and multi-body final states. The Sliced Wasserstein Distance (SWD) provides a powerful and computationally efficient loss function tailored for high-dimensional probability distributions. By projecting high...

21. “Hadron-in-jet” AI tagging to detect rare decays

Linrui Chen (Wuhan University)

27/05/2026, 16:30

We investigate a novel class of boosted-object signatures at the LHC, where a high-pT fat jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays.

1. Quantum autoencoder for anomalous couplings

shuai zhang (LNNU)

Oral talks

Given the success of the Standard Model (SM), any signals of physics beyond the Standard Model (NP) are expected to be small. Consequently, future searches for NP and precision tests of the SM will rely on high-luminosity collider experiments. The large volumes of data produced, together with the increased complexity of rare processes involving multiple final-state particles, pose significant...