Probing the Flavor Dependence of Higgs Couplings with Charm Tagging
Final technical report for Early Career Award.
Gray, Heather↗
Engineering topics
Gray, Heather
Publications and source records attributed to Gray, Heather.
The scientific potential and technological challenges of the High-Luminosity Large Hadron Collider program
Here, we present an overview of the High-Luminosity (HL-LHC) program at the Large Hadron Collider (LHC), its scientific potential and technological challenges for both the accelerator and detectors. The HL-LHC program is expected to start circa 2027 and aims to increase the integrated luminosity delivered by the LHC by an order of magnitude at the collision energy of 14 TeV. This requires upgrades to the injector system, accelerator complex and luminosity levelling. The two experiments, ATLAS and CMS, require substantial upgrades to most of their systems in order to cope with the increased interaction rate, and much higher radiation levels than at the current LHC. We present selected examples based on novel ideas and technologies for applications at a hadron collider. Both experiments will replace their tracking systems. We describe the ATLAS pixel detector upgrade featuring novel tilted modules, and the CMS Outer Tracker upgrade with a new module design enabling use of tracks in the level-1 trigger system. CMS will also install state-of-the-art highly segmented calorimeter endcaps. Finally, we describe new picosecond precision timing detectors of both experiments. In addition, we discuss how the upgrades will enhance the physics performance of the experiments, and solve the computing challenges posed by the expected large data sets. The physics program of the HL-LHC is focused on precision measurements probing the limits of the Standard Model (SM) of particle physics and discovering new physics. We present a selection of studies that have been carried out to motivate the HL-LHC program. A central topic of exploration will be the characterization of the Higgs boson. The large HL-LHC data samples will extend the sensitivity of searches for new particles or new interactions whose existence has been hypothesized in order to explain shortcomings of the SM. Finally, we comment on the nature of large scientific collaborations.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Porting HEP Parameterized Calorimeter Simulation Code to GPUs
The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
The Tracking Machine Learning challenge : Throughput phase
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. created at the Large Hadron Collider (LHC): given O($10^5$) points, the participants had to connect them into O($10^4$) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived.
Amrouche, Sabrina↗