Double-charming Higgs identification using machine-learning assisted jet shapes

TL;DR

This paper explores a machine-learning approach using jet shapes to identify boosted charm quark pairs from Higgs and scalar decays, improving background discrimination in high-energy physics experiments.

Contribution

It introduces a novel method employing jet shape observables with boosted decision trees to enhance charm pair identification in collider data.

Findings

Achieved limits on Higgs to charm pair branching ratio of 6.09%.

Set a 95% confidence level limit of 0.01% for Higgs to scalar plus Z decays.

Demonstrated the effectiveness of using double charm signatures for resonance identification.

Abstract

We study the possibility of identifying a boosted resonance that decays into a charm pair against different sources of background using QCD event shapes, which are promoted to jet shapes. Using a set of jet shapes as input to a boosted decision tree, we find that observables utilizing the simultaneous presence of two charm quarks can access complementary information compared to approaches relying on two independent charm tags. Focusing on Higgs associated production with subsequent $H\to c \bar{c}$ decay and on a CP-odd scalar $A$ with $m_A \leq 10$ GeV we obtain the limits $\mathcal{B}r(H\rightarrow c\bar{c})\leq 6.09\%$ and $\mathcal{B}r(H\rightarrow A(\rightarrow c\bar{c}) Z)\leq 0.01\%$ at $95\%$ C. L..