sPHENIX is a high energy nuclear physics experiment under construction at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory (BNL). The primary physics goals of sPHENIX are to study the quark-gluon-plasma, as well as the partonic structure of protons and nuclei, by measuring jets, their substructure, and heavy flavor hadrons in $p$$+$$p$, $p$+Au, and Au+Au collisions. sPHENIX will collect approximately 300 PB of data over three run periods, to be analyzed using available computing resources at BNL; thus, performing track reconstruction in a timely manner is a challenge due to the high occupancy of heavy ion collision events. The sPHENIX experiment has recently implemented the A Common Tracking Software (ACTS) track reconstruction toolkit with the goal of reconstructing tracks with high efficiency and within a computational budget of 5 seconds per minimum bias event. This paper reports the performance status of ACTS as the default track fitting tool within sPHENIX, including discussion of the first implementation of a time projection chamber geometry within ACTS.
sPHENIX is a new experiment that is being constructed at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The primary physics goals of sPHENIX are to measure jets, their substructure, and the upsilon resonances in $p$$+$$p$, $p$+Au and Au+Au collisions. To realize these goals, a tracking system composed of a time projection chamber and several silicon detectors will be used to identify tracks that correspond to jets and upsilon decays. However, the sPHENIX experiment will collect approximately 200 PB of data utilizing a finite-sized computing center; thus, performing trac…
Beam test results of the Intermediate Silicon Tracker for sPHENIX
National Central University（中央大学）；Nishina Center for Accelerator-Based Science, RIKEN（理化学研究所理仁加速器科学中心）；RIKEN BNL Research Center（理化学研究所布鲁克海文研究中心）；Department of Mathematical and Physical Sciences, Nara Women’s University（奈良女子大学数理科学系）；Physics Department, Brookhaven National Laboratory（布鲁克海文国家实验室物理系）；Advanced Science Research Center, Japan Atomic Energy Agency（日本原子能研究机构先进科学研究中心）；Department of Physics, Rikkyo University（立教大学物理学系）
The sPHENIX detector to be installed at RHIC in 2022 is designed to precisely measure jets, jet correlations, and dilepton pairs in heavy-ion collisions. With these measurements in mind, sPHENIX will employ a compact TPC covering 20cm < r < 78 cm and |η| < 1.1 as the central tracker. Utilizing an optimized Ar-CF4 gas mixture, zigzag readout pads, a 1.4 T solenoid, and a modified SAMPA chip for streaming readout, the TPC will provide a position resolution sufficient for measuring target observables in a high event rate environment. The design of the TPC will be discussed, as well as test beam d…
sPHENIX is a new collaboration and future detector project at Brookhaven National Laboratory's Relativistic Heavy Ion Collider (RHIC). It seeks to answer fundamental questions on the nature of the quark gluon plasma (QGP), including its temperature dependence and coupling strength, by using a suite of precision jet and upsilon measurements that probe different length scales of the QGP. This will be achieved with large acceptance, $|η| < 1$ and $0$-$2π$ in $ϕ$, electromagentic and hadronic calorimeters and precision tracking enabled by a $1.5$ T superconducting magnet. With the increased lumino…
Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Central China Normal University（华中师范大学）；University of North Texas（北得克萨斯大学）；Georgia Institute of Technology（佐治亚理工学院）；Fermilab（费米实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Michigan（密歇根大学）；New Jersey Institute of Technology（新泽西理工学院）
The reconstruction of charged particle trajectories is one of the most complex and CPU consuming parts of event processing in high energy experiments. At future hadron colliders such as the High-Luminosity Large Hadron Collider (HL-LHC) or the Future Circular Collider (FCC), the significantly increased number of simultaneous collisions will result in a much more challenging tracking environment. Concurrent algorithms exploiting modern computing architectures with many cores and accelerators are necessary to maintain and improve the tracking performance. Based on the tracking experience at LHC,…
Particle track reconstruction, in which the trajectories of charged particles are determined, is a critical and time consuming component of the full event reconstruction chain. The underlying software is complex and consists of a number of mathematically intense algorithms, each dealing with a particular tracking sub-process. These algorithms have many input parameters that need to be supplied in advance. However, it is difficult to determine the configuration of these parameters that produces the best performance. Currently, the input parameter values are decided on the basis of prior experie…
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.
In high energy physics (HEP) experiments, the reconstruction of charged particle trajectories is one of the most fundamental yet computationally expensive parts of event processing. At future hadron colliders such as the High-Luminosity Large Hadron Collider (HL-LHC), there can be up to ten thousand particles per event. This increases the track reconstruction time by a factor about 5 compared to the current tracking environment. Efficient and fast tracking software is necessary to maintain and improve the tracking performance. This can benefit from both fast tracking algorithms and modern comp…
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the pre…
Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through hardware architectures other than x86 CPUs, with GPUs being a common alternative. GPUs can provide excellent computational performance at a good price point for tasks that can be suitably parallelized. Charged particle (track) reconstruction is a computationally expensive component of HEP data reconstruction, and thus needs to use available resources in an efficient way. In this paper, an implementation of Kalman filt…
The Liquid Argon Time Projection Chamber offers an innovative technology for a new class of massive detectors for rare-event detection. It is a precise tracking device that allows three-dimensional spatial reconstruction with mm-scale precision of the morphology of ionizing tracks with the imaging quality of a "bubble chamber", provides $dE/dx$ information with high sampling rate, and acts as high-resolution calorimeter for contained events. First proposed in 1977 and after a long maturing process, its holds today the potentialities of opening new physics opportunities by providing excellent t…
