TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.6.4.067
TI  - Optimal mass variables for semivisible jets
PY  - 2023/10/17
UR  - https://www.scipost.org/SciPostPhysCore.6.4.067
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 6
IS  - 4
SP  - 067
A1  - Pedro, Kevin
AU  - Shyamsundar, Prasanth
AB  - Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.
ER  -

@Article{10.21468/SciPostPhysCore.6.4.067,
	title={{Optimal mass variables for semivisible jets}},
	author={Kevin Pedro and Prasanth Shyamsundar},
	journal={SciPost Phys. Core},
	volume={6},
	pages={067},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.6.4.067},
	url={https://scipost.org/10.21468/SciPostPhysCore.6.4.067},
}