@article{TEXTUAL,
      recid = {12111},
      author = {Badea, Anthony and Berlingen, Javier Montejo},
      title = {Data-driven and model-agnostic approach to solving  combinatorial assignment problems in searches for new  physics},
      journal = {Physical Review D},
      address = {2024-01-09},
      number = {TEXTUAL},
      abstract = {We present a novel approach to solving combinatorial  assignment problems in particle physics. The correct  assignment of decay products to parent particles is  achieved in a model-agnostic fashion by introducing a  neural network architecture, passwd-abc, which combines a  custom layer based on attention mechanisms and dual  autoencoders. We demonstrate how the network, trained  purely on background events in an unsupervised setting, is  capable of reconstructing correctly hypothetical new  particles regardless of their mass, decay multiplicity, and  substructure, and produces simultaneously an anomaly score  that can be used to efficiently suppress the background.  This model allows the extension of the suite of searches  for localized excesses to include nonresonant particle pair  production where the reconstruction of the two resonant  masses is thwarted by combinatorics.  },
      url = {http://knowledge.uchicago.edu/record/12111},
}