A calibratable jet-free HH(4b) search framework at the LHC

Contributed talk at the Higgs Pairs Workshop 2025. Update (August 2025): This work has been released as a paper on arXiv: 2508.15048. Indico link https://indico.cern.ch/event/1399335/contributions/6384808 Talk abstract A calibratable experimental strategy is proposed to enhance the HH(4b) search sensitivity via full-particle classification. Inspired by the competitive performance from the boosted-topology HH analysis, which uses state-of-the-art jet neural networks to analyze o(100) particles within large-R jets, this approach aims to extend its strong signal-to-background discrimination power beyond the boosted regime to a broader phase space accessible through conventional HH(4b) triggers. The approach involves training a universal classifier to distinguish X → Y₁Y₂ → bb̅bb̅ signals from QCD and tt̅ multijet backgrounds across a wide range of X and Y₁,₂ mass values, and simultaneously estimating the Y₁,₂ masses via a multiclass classification technique. Results demonstrate that the background suppression capability matches that of identifying two boosted X → bb̅ jets, revealing a scaling law governing signal and background yields in both cases. The framework is complemented by a robust signal calibration and validation procedure: event-level classifier calibration is performed using an orthogonal dimuon-triggered phase space and an "event hemisphere mixing" technique to construct fake ZZ(4b) events; validation is then conducted using genuine ZZ(4b) data passing the analysis trigger. With combined Run 2 and 3 datasets, the proposed strategy can achieve the first observation of the ZZ(4b) process and deliver a search sensitivity for HH(4b) comparable to HL-LHC projection. This approach holds a great premise to accelerate the pace of HH search at the LHC and advance our understanding of the Higgs self-coupling.