Transformers Meet Physics: PLuM's Leap in Jet Tagging

Machine learning and physics have often seemed like distant cousins. But when they come together, the results can be transformative. Enter PLuM, a new multimodal architecture that marries the hierarchical structure of Lund planes with the raw power of transformer-based taggers.

Why Transformers and Lund Planes?

The Lund plane offers a physics-grounded, hierarchical look at Quantum Chromodynamics (QCD) radiation in jets. On the other hand, transformers have taken the machine learning world by storm with their ability to learn directly from raw data. So, why not combine the two? That's the idea behind PLuM, which projects particle data and Lund plane splittings into a shared latent space, using a unified transformer to process both. But the question is, can transformers truly grasp the intricate structure of QCD, or does explicit physics representation still hold unique value?

Practical Gains in Jet Tagging

Here's where it gets practical. PLuM shows systematic improvements for top-quark and Higgs-to-bottom-antibottom (H→bb̅) tagging. Yet, for H→cc̅ or H→4q topologies, the gains are less clear. This suggests that while b-jet formations benefit from explicit hierarchical info, other topologies might already be effectively captured at the constituent level. In production, this could mean pinpointing specific cases where enhanced physics representation truly adds value.

Take high-stakes LHC analyses, like the Lorentz-boosted di-Higgs searches in a four b-quark final state (HH(4b)). At a 25% di-Higgs efficiency working point, PLuM achieves a remarkable 25% higher background rejection compared to baseline models. This isn't just an incremental improvement. it's a significant leap forward.

The Future of QCD Analysis

Why should you care? Because this approach could redefine how we tackle QCD analysis. If structured representations of QCD radiation can retain discriminating value even in the transformer era, it opens the door for hybrid models that use both raw data and physical insights. The demo is impressive, but the deployment story is messier. As this research progresses, it may reshape particle physics and machine learning.

Yet, we must ask: will this sophisticated blend of physics and machine learning become the new norm? Or will it remain a niche approach, reserved for those specific scenarios where the physics representation shines? The real test is always the edge cases.