J. Bendavid

Abstract We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group as an input to the LHCC review of HL-LHC computing, which has started in May 2020.

We consider the problem of setting a confidence interval on a parameter of interest from a high-statistics counting experiment in the presence of systematic uncertainties modeled as unconstrained nuisance parameters. We use the profile-likelihood test statistic in the asymptotic limit for confidence interval setting and focus on the case where the likelihood function is derived from a finite sample of Monte Carlo simulated events. We prove as a general result that statistical uncertainties in the Monte Carlo sample affect the coverage of the confidence interval always in the same direction, namely they lead to a systematic undercoverage of the interval. We argue that such spurious effects might not be fully accounted for by statistical methods that are usually adopted in HEP measurements to counteract the effects of finite-size MC samples, such as those based on the Barlow-Beeston likelihood.

New machine learning based algorithms have been developed and tested for Monte Carlo integration based on generative Boosted Decision Trees and Deep Neural Networks. Both of these algorithms exhibit substantial improvements compared to existing algorithms for non-factorizable integrands in terms of the achievable integration precision for a given number of target function evaluations. Large scale Monte Carlo generation of complex collider physics processes with improved efficiency can be achieved by implementing these algorithms into commonly used matrix element Monte Carlo generators once their robustness is demonstrated and performance validated for the relevant classes of matrix elements.

The performance of electromagnetic calorimeter modules made of proton-irradiated PbWO4 crystals has been studied in beam tests. The modules, similar to those used in the Endcaps of the CMS electromagnetic calorimeter (ECAL), were formed from 5×5 matrices of PbWO4 crystals, which had previously been exposed to 24 GeV protons up to integrated fluences between 2.1× 1013 and 1.3× 1014 cm−2. These correspond to the predicted charged-hadron fluences in the ECAL Endcaps at pseudorapidity η = 2.6 after about 500 fb−1 and 3000 fb−1 respectively, corresponding to the end of the LHC and High Luminosity LHC operation periods. The irradiated crystals have a lower light transmission for wavelengths corresponding to the scintillation light, and a correspondingly reduced light output. A comparison with four crystals irradiated in situ in CMS showed no significant rate dependence of hadron-induced damage. A degradation of the energy resolution and a non-linear response to electron showers are observed in damaged crystals. Direct measurements of the light output from the crystals show the amplitude decreasing and pulse becoming faster as the fluence increases. The latter is interpreted, through comparison with simulation, as a side-effect of the degradation in light transmission. The experimental results obtained can be used to estimate the long term performance of the CMS ECAL.

New machine learning based algorithms have been developed and tested for Monte Carlo integration based on generative Boosted Decision Trees and Deep Neural Networks. Both of these algorithms exhibit substantial improvements compared to existing algorithms for non-factorizable integrands in terms of the achievable integration precision for a given number of target function evaluations. Large scale Monte Carlo generation of complex collider physics processes with improved efficiency can be achieved by implementing these algorithms into commonly used matrix element Monte Carlo generators once their robustness is demonstrated and performance validated for the relevant classes of matrix elements.

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

The relative yields of Υ mesons produced in pp and Pb-Pb collisions at √s_(NN) = 5.02  TeV and reconstructed via the dimuon decay channel are measured using data collected by the CMS experiment. Double ratios are formed by comparing the yields of the excited states, Υ(2S) and Υ(3S), to the ground state, Υ(1S), in both Pb-Pb and pp collisions at the same center-of-mass energy. The double ratios, [Υ(nS)/Υ(1S)]_(Pb−Pb)/[Υ(nS)/Υ(1S)]_(pp), are measured to be 0.308±0.055(stat)±0.019(syst) for the Υ(2S) and less than 0.26 at 95% confidence level for the Υ(3S). No significant Υ(3S) signal is found in the Pb-Pb data. The double ratios are studied as a function of collision centrality, as well as Υ transverse momentum and rapidity. No significant dependencies are observed.