Spectral functions from two-point correlations serve as multiplicity-independent ML inputs and improve expected gluino mass reach by 150-250 GeV in a fully hadronic ttbar vs gluino benchmark.
Altakach313/ml_spectralf_fullyhadronic: Companion code and models for machine learning fully hadronic events with spectral functions v1.0.0,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
hep-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Machine learning fully hadronic events with spectral functions
Spectral functions from two-point correlations serve as multiplicity-independent ML inputs and improve expected gluino mass reach by 150-250 GeV in a fully hadronic ttbar vs gluino benchmark.