Norraphat Srimanobhas

In this contribution we report the status of the CMS Geant4 simulation and the prospects for Run-3 and Phase-2. Firstly, we report about our experience during the start of Run-3 with Geant4 10.7.2, the common software package DD4hep for geometry description, and VecGeom runtime geometry library. In addition, FTFP_BERT_EMM Physics List and CMS configuration for tracking in magnetic field have been utilized. For the first time, for the Grid mass production of Monte-Carlo, this combination of components is used. Further simulation improvements are under development targeting Run-3 such as the switch to the new Geant4 11.1 in production, that provides several features important for the optimization of simulation, for example the new transportation process with built-in multiple scattering, neutron general process, custom tracking manager, G4HepEm sub-library, and others. We will present evaluation of various options, validation results, and the final choice of simulation configuration for 2023 production and beyond. The performance of the CMS full simulation for Run-2 and Run-3 will also be discussed. CMS development plan for the Phase-2 Geant4 based simulation is very ambitious, and it includes a new geometry description, physics, and simulation configurations. The progress on new detector descriptions and full simulation will be presented as well as the R&D in progress to reduce compute capacity needs.

Supersymmetry proposes that for each Standard Model fermion there exists a boson superpartner and each Standard Model boson has a fermion superpartner. Once new particles are discovered, e.g. at the Large Hadron Collider, a crucial question will be if these will be the supersymmetric partners of the Standard Model particles with the correct spin, i.e. spin-0 for the partners of the fermions. Hence, the spin of these new particles has to be investigated. In this paper, we present a study on the angular correlations of the products from sparticle decay chains via the neutralino, at √s = 14 TeV in pp collisions. Three mSUGRA benchmark points are used as examples to compare the different characteristics of the decay chain of interest. The effects of the spin can be seen through the invariant mass distributions of outgoing leptons and quark. An event selection for the study is proposed and is applied to the inclusive SUSY event samples simulated using fast detector simulation.

Physics analysis at the Compact Muon Solenoid requires both the production of simulated events and processing of the data collected by the experiment. Since the end of the LHC Run-I in 2012, CMS has produced over 20 billion simulated events, from 75 thousand processing requests organised in one hundred different campaigns. These campaigns emulate different configurations of collision events, the detector, and LHC running conditions. In the same time span, sixteen data processing campaigns have taken place to reconstruct different portions of the Run-I and Run-II data with ever improving algorithms and calibrations. The scale and complexity of the events simulation and processing, and the requirement that multiple campaigns must proceed in parallel, demand that a comprehensive, frequently updated and easily accessible monitoring be made available. The monitoring must serve both the analysts, who want to know which and when datasets will become available, and the central production teams in charge of submitting, prioritizing, and running the requests across the distributed computing infrastructure. The Production Monitoring Platform (pMp) web-based service, has been developed in 2015 to address those needs. It aggregates information from multiple services used to define, organize, and run the processing requests. Information is updated hourly using a dedicated elastic database and the monitoring provides multiple configurable views to assess the status of single datasets as well as entire production campaigns. This contribution will describe the pMp development, the evolution of its functionalities, and one and half year of operational experience.

In Particle Data Analysis laboratory activity, aimed at undergraduate and high school students, the student is tasked with classifying collision events which contain two muons decaying from J/ψ meson. The activity provides 2000 collision events from the CMS detector, selected by CMS outreach community. However, classifying 2000 collision events by hand can be a tedious task for any human, so a smaller subset of collision events are usually used in the activity to save time. We built a machine learning classifier which mimic the student's classification based on a subset of collision events handed to the student, using some information from data in corresponding collision event. The information used in this system is parts of muon trajectory, extracted from files suited for CMS event viewer on the internet, as well as the four-momentum of both muons, available from the same source. With this system, students can input a subset of graded events into the system, and the system will be able to illustrate the results if the student worked on all 2000 collision events using his/her logic. Users can download the code from our repository and follow easy instructions to replicate this activity.

Abstract One burden of high energy physics data analysis is uncertainty within the measurement, both systematically and statistically. Even with sophisticated neural network techniques that are used to assist in high energy physics measurements, the resulting measurement may suffer from both types of uncertainties. Fortunately, most types of systematic uncertainties are based on knowledge from information such as theoretical assumptions, for which the range and behaviour are known. It has been proposed to mitigate such systematic uncertainties by using a new type of neural network called adversarial neural network (ANN) that would make the discriminator less sensitive to these uncertainties, but this has not yet been demonstrated in a real-life LHC analysis. This work investigates ANNs using as a benchmark the search for the production of four top quarks, an extremely rare physics process at the LHC and one of the important processes that can prove or disprove the Standard Model. The search for four top quarks in some cases is sensitive to large systematic uncertainties. The expected cross section upper limit for four top quark production is calculated using traditional neural networks and adversarial neural networks based on simulated proton-proton collisions within the Compact Muon Solenoid detector within Large Hadron Collider, and are compared to existing results. The improvement and further considerations to the search for rare processes at the LHC will be discussed.

Abstract In this paper, we study the benefit of applying loop transformations to a part of module in the CMS software. Particularly, we focus at the effect of loop transformations in term of performance improvement from the optimization process of compilers. Loop optimizations have been considered at low-level phase, such as loop unrolling using compiler directive. For high-level code transformations such as index set splitting and loop reordering, we adopt the polyhedral model to simplify the transformations. In this study, our loop optimization has been evaluated quantitatively. We study the impact on loops execution speed up and its instruction executed. Our observation shows that high-level loop optimizations can reduce both execution time and the number of instruction. This behavior suggested that simple loop transformations can trigger other optimizations. Moreover, we not only improve the overall performance, but also reduce the number of instruction. The results show that loop optimizations yield the speed up between 1.5 and 1.7.