Abstract
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are particularly useful for establishing robustness and gaining physical insight. We introduce a procedure to automate the construction of a large class of observables that are chosen to completely specify $M$-body phase space. The procedure is validated on the task of distinguishing $H\rightarrow b\bar{b}$ from $g\rightarrow b\bar{b}$, where $M=3$ and previous brute-force approaches to construct an optimal product observable for the $M$-body phase space have established the baseline performance. We then use the new method to design tailored observables for the boosted $Z'$ search, where $M=4$ and brute-force methods are intractable. The new classifiers outperform standard $2$-prong tagging observables, illustrating the power of the new optimization method for improving searches and measurement at the LHC and beyond.
Highlights
Effective identification of hadronic decays of boosted heavy particles like the top quark or W, Z and Higgs (H) bosons is essential for analyses at the Large Hadron Collider (LHC)
We first demonstrate that this new procedure produces an observable for ungroomed H → bbdiscrimination with the same performance as the β3 observable proposed in Ref. [8]
The final values for the parameters fa; ...; eg obtained through the optimization are presented in Table I, along with those obtained in the previous study
Summary
Effective identification of hadronic decays of boosted heavy particles like the top quark or W, Z and Higgs (H) bosons is essential for analyses at the Large Hadron Collider (LHC). Modern machine-learning (ML) methods have emerged as useful tools for automating the creation of optimal observables for classification. In addition to improving classification performance, ML techniques may be able to make jet tagging more independent from simulation and robust to differences between simulation and data as well as between sideband and signal regions [36,37,38,39,40,41,42,43] These and related techniques have been proposed as more model-agnostic approaches to new particle searches [44,45,46,47,48].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
