Measurement of the top quark pole mass using $\text{t}\bar{\text{t}}$+jet events in the dilepton final state at $\sqrt{s}=13$ TeV

TL;DR

This paper measures the top quark pole mass by analyzing $ ext{t}ar{ ext{t}}$+jet events at 13 TeV, employing machine learning for event reconstruction and unfolding to extract a precise mass value.

Contribution

It introduces a novel method combining differential cross section analysis with machine learning techniques to determine the top quark pole mass at the LHC.

Findings

Top quark pole mass measured as 172.94 ± 1.37 GeV.

Used 36.3 fb$^{-1}$ of CMS data at 13 TeV.

Employs advanced kinematic reconstruction and unfolding methods.

Abstract

In these proceedings, a measurement of the differential cross section of top quark-antiquark pair ($\text{t}\bar{\text{t}}$) production in association with one additional jet ($\text{t}\bar{\text{t}}$+jet) as a function of the inverse of the invariant mass of the $\text{t}\bar{\text{t}}$+jet system, $\rho=340\,\text{GeV}/m_{\text{t}\bar{\text{t}}+\text{jet}}$, is presented. The normalized $\text{t}\bar{\text{t}}$+jet cross section is used to extract values for the top quark pole mass $m_{\text{t}}^{\text{pole}}$ by a comparison to theoretical predictions at next-to-leading order accuracy. The used data set corresponds to an integrated luminosity of 36.3fb$^{-1}$ of proton--proton collisions as collected by the CMS experiment at $\sqrt{s}=$ 13 TeV. Events with two opposite-sign leptons are analyzed. Machine learning techniques are employed to improve the kinematic reconstruction of the main observable and the event classification. The unfolding to the parton level is performed using a profiled likelihood fit. Given the ABMP16 parton distribution functions as a reference set, a value of $m_{\text{t}}^{\text{pole}}=172.94\pm1.37\,\text{GeV}$ is extracted using the normalized $\text{t}\bar{\text{t}}$+jet cross section.