Determination of jet calibration and energy resolution in proton–proton collisions at $$\sqrt{s} = 8~\hbox {TeV}$$ using the ATLAS detector

Abstract

The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton–proton data produced at a centre-of-mass energy of 8 TeV with an integrated luminosity of $$20 \, \hbox {fb}^{-1}$$ 20 fb - 1 . Jets are reconstructed from clusters of energy depositions in the ATLAS calorimeters using the anti-$$k_t$$ k t algorithm. A jet calibration scheme is applied in multiple steps, each addressing specific effects including mitigation of contributions from additional proton–proton collisions, loss of energy in dead material, calorimeter non-compensation, angular biases and other global jet effects. The final calibration step uses several in situ techniques and corrects for residual effects not captured by the initial calibration. These analyses measure both the jet energy scale and resolution by exploiting the transverse momentum balance in $$\gamma $$ γ + jet, Z + jet, dijet, and multijet events. A statistical combination of these measurements is performed. In the central detector region, the derived calibration has a precision better than 1% for jets with transverse momentum $$150 \, \hbox {GeV} < p_{{\mathrm {T}}}<$$ 150 GeV < p T < 1500 GeV, and the relative energy resolution is $$(8.4\pm 0.6)\%$$ ( 8.4 ± 0.6 ) % for $$p_{{\mathrm {T}}}= 100 \, \hbox {GeV}$$ p T = 100 GeV and $$(23\pm 2)\%$$ ( 23 ± 2 ) % for $$p_{{\mathrm {T}}}= 20 \, \hbox {GeV}$$ p T = 20 GeV . The calibration scheme for jets with radius parameter $$R=1.0$$ R = 1.0 , for which jets receive a dedicated calibration of the jet mass, is also discussed.

Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics

“…Using this ML framework, we were able to take advantage of the full jet particle information in the CMS Open Simulation to improve the measured jet resolution by approximately 15%. Studies by the ATLAS collaboration have used sequential calibration on a handful of observables to improve their resolution by 5-10% [57], but the Gaussian Ansatz now allows for simultaneous calibrations of any number of input features. We look forward to further developments in ML-based calibration and correlations methods in HEP and beyond.…”

Optimizing observables with machine learning for better unfolding

“…These effects are suppressed using dedicated energy calibrations that depend on many features to improve their resolution [6][7][8][9][10]. However, most observables lack dedicated calibrations; for example, ATLAS [11] and CMS [12] do not have a dedicated calibration for the 𝑛-subjettiness [13,14].…”

Configuration and performance of the ATLAS b-jet triggers in Run 2

“…An improved jet energy calibration scheme, the global sequential jet calibration (GSC) [50,51] was introduced for 2017 data-taking in order to improve the jet energy resolution in the HLT. The GSC uses information about the longitudinal shower shapes of jets, and characteristics of associated tracks, to correct the energy scale of jets.…”