2021/05/31 by Patrick Komiske, W. P. McCormack, W. Patrick McCormack +1
Computer Science · Physics and Astronomy · #Artificial intelligence #Benchmark (surveying) #Boson #Computational Physics and Python Applications #Computer science #Curse of dimensionality #Higgs boson #Large Hadron Collider #Observable #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Phase space #Physics #Physics beyond the Standard Model #Quantum mechanics #Standard Model (mathematical formulation) #Theoretical physics #hep-ex #hep-ph #physics.data-an
paper · pdf · doi:10.1103/physrevd.104.076027
12 pages, 10 figures, 1 table
arxiv created 2021/06/07 · openalex publication_date 2021/10/27 · arxiv updated 2021/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Direct searches for new particles at colliders have traditionally been factorized into model proposals by theorists and model testing by experimentalists. With the recent advent of machine learning methods that allow for the simultaneous unfolding of all observables in a given phase space region, there is a new opportunity to blur these traditional boundaries by performing searches on unfolded data. This could facilitate a research program where data are explored in their natural high dimensionality with as little model bias as possible. We study how the information about physics beyond the Standard Model is preserved by full phase space unfolding using an important physics target at the Large Hadron Collider (LHC); exotic Higgs boson decays involving hadronic final states. We find that if the signal cross section is high enough, information about the new physics is visible in the unfolded data. We will show that in some cases, quantifiably all of the high-level information about the new physics is encoded in the unfolded data. Finally, we show that there are still many cases when the unfolding does not work fully or precisely, such as when the signal cross section is small. This study will serve as an important benchmark for enhancing unfolding methods for the LHC and beyond.