Speaker
Description
We demonstrate that the successful techniques developed for boosted HH(4b) analyses can be effectively extended to the resolved regime through advanced deep learning engineering. By leveraging O(100M) training samples, employing efficient state-of-the-art architectures and training frameworks, and analyzing objects containing O(100) particles, we can replicate the capabilities of Xbb taggers from the boosted regime across a broader phase space, significantly enhancing κλ measurements. To achieve this, we present a comprehensive calibratable experimental strategy. Our approach involves training a universal classifier to distinguish X → Y₁Y₂ → bbbb signals from QCD and ttbar multijet backgrounds across a wide range of X and Y₁,₂ mass values, while simultaneously estimating the Y₁,₂ masses via a multiclass classification technique. This discriminant is first calibrated using "fake ZZ(4b) events" generated through an event hemisphere mixing technique from a distinct di-muon triggered phase space, then validated in a search for genuine ZZ(4b) events that is capable of reaching observation (>5σ) sensitivity with Run2+3 datasets. We demonstrate that the HH(4b) sensitivity of this method is comparable to the HL-LHC projection, holding great promise to accelerate the pace of HH searches at the LHC.