ATLAS

Abstract:

Abstract The energy response of the ATLAS calorimeter is measured for single charged pions with transverse momentum in the range $$10<p_\text {T}<300$$ 10 < p T < 300 GeV. The measurement is performed using 139 $$\text {fb}^{-1}$$ fb - 1 of LHC proton–proton collision data at $$\sqrt{s}=13$$ s = 13 TeV taken in Run 2 by the ATLAS detector. Charged pions originating from $$\tau $$ τ -lepton decays are used to provide a sample of high- $$p_{\text {T}}$$ p T isolated particles, where the composition is known, to test an energy regime that has not previously been probed by in situ single-particle measurements. The calorimeter response to single-pions is observed to be overestimated by $${\sim }2\%$$ ∼ 2 % across a large part of the $$p_{\text {T}}$$ p T spectrum in the central region and underestimated by $${\sim }4\%$$ ∼ 4 % in the endcaps in the ATLAS simulation. The uncertainties in the measurements are $${\lesssim }1\%$$ ≲ 1 % for $$15<p_\text {T}<185$$ 15 < p T < 185 GeV in the central region. To investigate the source of the discrepancies, the width of the distribution of the ratio of calorimeter energy to track momentum, the energies per layer and response in the hadronic calorimeter are also compared between data and simulation.

Abstract:

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider (LHC). This paper aims to explore the use of generative models for increasing the statistics of Monte Carlo simulations in the final stage of data analysis by generating synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several state-of-the-art generative machine learning algorithms are adapted to this task, best performance is demonstrated by the normalizing flow architectures, which are capable of fast generation of an arbitrary number of new events. As an example of analysis-specific Monte Carlo simulated data, a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background is used. The applicability of normalizing flows with different model parameters and numbers of initial events used in training is investigated. The resulting event distributions are compared with the original Monte Carlo distributions using statistical tests and a simplified statistical analysis to evaluate their similarity and quality of reproduction required in a physics analysis environment in a systematic way.Comment: 28 pages, 22 figure