Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Since the best solution is a trade-off between different kinds of limitations, a quick turn over is necessary to evaluate physics performance for different techniques in different configurations. Two typical problems which slow down evaluation of physics performance for particular approaches to calorimeter detector technologies and configurations are:
- Emulating particular detector properties including raw detector response together with a signal processing chain to adequately simulate a calorimeter response for different signal and background conditions. This includes combining detector properties obtained from the general Geant simulation with properties obtained from different kinds of bench and beam tests of detector and electronics prototypes.
- Building an adequate reconstruction algorithm for physics reconstruction of the detector response which is reasonably tuned to extract the most of the performance provided by the given detector configuration.
Being approached from the first principles, both problems require significant development efforts. Fortunately, both problems may be addressed by using modern machine learning approaches, that allow a combination of available details of the detector techniques into corresponding higher level physics performance in a semi-automated way. In this paper, we discuss the use of advanced machine learning techniques to speed up and improve the precision of the detector development and optimisation cycle, with an emphasis on the experience and practical results obtained by applying this approach to epitomising the electromagnetic calorimeter design as a part of the upgrade project for the LHCb detector at LHC.
National Research University Higher School of Economics（国家研究型高等经济大学）；The Yandex School of Data Analysis（Yandex数据分析学院）
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using ma…
We present a new approach to identification of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identification of photons and neutral pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum $π^0$ decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected …
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture -- the Bounded Information Bottleneck Autoencoder -- for modelling electromagnetic showers in t…
Accurate power measurements of electronic components operating at high frequencies are vital in determining where power losses occur in a system such as a power converter. Such power measurements must be carried out with equipment that can accurately measure real power at high frequency. We present the design of a high speed calorimeter to address this requirement, capable of reaching a steady state in less than 10 minutes. The system uses Peltier thermoelectric coolers to remove heat generated in a load resistance, and was calibrated against known real power measurements using an artificial n…
We have been developing a serializer application-specific integrated circuit (ASIC) based on a commercial 0.25-μm silicon-on-sapphire (SOS) CMOS technology for the ATLAS liquid argon calorimeter front-end electronics upgrade. The first prototype, a 5 Gbps 16:1 serializer has been designed, fabricated, and tested in lab environment and in a 200 MeV proton beam. The test results indicate that the first prototype meets the design goals. The second prototype, a double-lane, 8 Gbps per lane serializer is under development. The post-layout simulation indicates that 8 Gbps is achievable. In this pape…
The GlueX forward calorimeter is an array of 2800 lead glass modules that was constructed to detect photons produced in the decays of hadrons. A background to this process originates from hadronic interactions in the calorimeter, which, in some instances, can be difficult to distinguish from low energy photon interactions. Machine learning techniques were applied to the classification of particle interactions in the GlueX forward calorimeter. The algorithms were trained on data using decays of the $ω$ meson, which contain both true photons and charged particles that interact with the calorimet…
The ATLAS experiment at CERN measures energy of proton-proton (p-p) collisions with a repetition frequency of 40 MHz at the Large Hadron Collider (LHC). The readout electronics of liquid-argon (LAr) calorimeters are being prepared for high luminosity-LHC (HL-LHC) operation as part of the phase-II upgrade, anticipating a pileup of up to 200 simultaneous p-p interactions. The increase of the number of p-p interactions implies that calorimeter signals of up to 25 consecutive collisions overlap, making energy reconstruction more challenging. In order to achieve the goal of the HL-HLC, field-progra…
Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
Facultad de Ciencias, Universidad Nacional Autónoma de México（墨西哥国立自治大学理学院）；Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México（墨西哥国立自治大学核科学研究所）
Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
High Energy Nuclear and Particle Physics Division, Saha Institute of Nuclear Physics - a CI of Homi Bhabha National Institute（萨哈核物理研究所高能核与粒子物理系（霍米·巴巴国立学院下属机构））；Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole polytechnique, Institut Polytechnique de Paris（勒普朗斯-林盖特实验室，法国国家科学研究中心/法国国家粒子物理研究所，巴黎综合理工学院，巴黎理工学院）；Department of Physics and Astronomy, Stony Brook University - SUNY（纽约州立大学石溪分校物理与天文学系）；University of Virginia（弗吉尼亚大学）；Department of High Energy Physics, Tata Institute of Fundamental Research（塔塔基础研究所高能物理系）
To cope with the enhanced luminosity at the Large Hadron Collider (LHC) in 2021, the ATLAS collaboration is planning a major detector upgrade. As a part of this, the Level 1 trigger based on calorimeter data will be upgraded to exploit the fine granularity readout using a new system of Feature EXtractors (FEX), which each reconstruct different physics objects for the trigger selection. The jet FEX (jFEX) system is conceived to provide jet identification (including large area jets) and measurements of global variables within a latency budget of less then 400ns. It consists of 6 modules. A singl…
The upgrade of the DA$Φ$NE machine layout requires a modification of the size and position of the inner focusing quadrupoles of KLOE-2 thus asking for the realization of two new calorimeters covering the quadrupoles area. To improve the reconstruction of $K_L\to 2π^0$ events with photons hitting the quadrupoles a calorimeter with high efficiency to low energy photons (20-300 MeV), time resolution of less than 1 ns and space resolution of few cm, is needed. To match these requirements, we are designing a tile calorimeter, QCALT, where each single tile is readout by mean of SiPM for a total gran…
I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal possible information. The shape of the energy depositions for muons and pions in the crystals around an extrapolate…
Machine Learning Approaches for Improved Scalability of Metallic Magnetic Calorimeters
GSI Helmholtzzentrum für Schwerionenforschung（GSI亥姆霍兹重离子研究中心）；Helmholtz-Institute Jena（耶拿亥姆霍兹研究所）；Institute for Optics- and Quantumelectronics, Friedrich-Schiller-University Jena（耶拿弗里德里希·席勒大学光学与量子电子学研究所）；Hasso-Plattner-Institute（哈索·普拉特纳研究所）
The technique of discrimination of the $e^+e^-\to e^+e^-$ and $e^+e^-\to π^+π^-$ events in energy range $0.5 < \sqrt{s} < 1$ GeV by energy deposition in the calorimeter of SND detector was developed by applying machine learning method. Identification efficiency for $e^+e^-\to e^+e^-$ and $e^+e^-\to π^+π^-$ events in the range from 99.3 to 99.8 % has been achived.
The ATLAS hadronic Tile Calorimeter will undergo major upgrades to the on- and off-detector electronics in preparation for the High Luminosity program of the Large Hadron Collider (HL-LHC) in 2026, so that the system can cope with the HL-LHC increased radiation levels and out-of-time pileup. The on-detector electronics of the upgraded system will continuously digitize and transmit all photo-multiplier signals to the off-detector systems at a 40 MHz rate. The off-detector electronics will store the data in pipeline buffers, produce digital hadronic tower sums for the ATLAS Level-0 trigger syste…
Since May 1999 the BABAR detector has been taking data at the PEP-II asymmetric electron-positron collider at the Stanford Linear Accelerator Center, California. This experiment requires a very large data sample and the PEP-II accelerator uses intense beams to deliver the high collision rates needed. This poses a severe challenge to the BaBar trigger system, which must reject the large rate of background signals resulting from the high beam currents whilst accepting the collisions of interest with very high efficiency. One of the systems that performs this task is the Level 1 Calorimeter Trigg…
Prototypes of electromagnetic and hadronic imaging calorimeters developed and operated by the CALICE collaboration provide an unprecedented wealth of highly granular data of hadronic showers for a variety of active sensor elements and different absorber materials. In this presentation, we discuss detailed measurements of the spatial and the time structure of hadronic showers to characterise the different stages of hadronic cascades in the calorimeters, which are then confronted with GEANT4-based simulations using different hadronic physics models. These studies also extend to the two different…
Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry
Stanford University（斯坦福大学）；Istituto Nazionale di Fisica Nucleare, Sezione di Milano-Bicocca（意大利国家核物理研究所米兰-比科卡分部）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Università degli Studi di Milano-Bicocca（米兰比科卡大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）；Istituto Nazionale di Fisica Nucleare Sezione di Perugia（意大利国家核物理研究所佩鲁贾分部）
A universal vision transformer for fast calorimeter simulations
Centre for Cosmology, Particle Physics（宇宙学、粒子物理与现象学研究中心）；Marietta Blau Institute for Particle Physics (MBI Vienna), Austrian Academy of Sciences (ÖAW), Austria（玛丽埃塔·布劳粒子物理研究所（MBI维也纳），奥地利科学院（ÖAW），奥地利）
The Tile Calorimeter (TileCal) is one detector of the ATLAS experiment at the Large Hadron Collider (LHC). TileCal is a sampling calorimeter made of steel plates and plastic scintillators which are readout using approximately 10,000 PhotoMultipliers Tubes (PMTs). In 2024, the LHC will undergo a series of upgrades towards a High Luminosity LHC (HL-LHC) to deliver up to 7.5 times the current nominal instantaneous luminosity. The ATLAS Tile Phase II Upgrade will accommodate detector and Data AcQuisition (DAQ) system to the HL-LHC requirements. The detector electronics will be redesigned using a n…
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and ba…
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
School of Physics and Astronomy, University of Edinburgh（爱丁堡大学物理与天文学学院）；Department of Physics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校物理系）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；State Key Laboratory of Dark Matter Physics, Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学暗物质物理国家重点实验室、李政道研究所及物理与天文学学院）；Key Laboratory for Particle Astrophysics and Cosmology (MOE) & Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University（教育部粒子天体物理与宇宙学重点实验室及上海粒子物理与宇宙学重点实验室，上海交通大学）
The LHC will undergo a major upgrade starting in 2025 towards the High Luminosity LHC (HL-LHC) to increase the instantaneous luminosity by a factor of 5 to 7 compared to the nominal value. The Phase-II Upgrade (2025-2027) will require the trigger and readout electronics of the ATLAS experiment to operate with the stringent conditions imposed by the HL-LHC. During this upgrade, both on- and off-detector readout electronics of TileCal will be completely replaced with a new data acquisition which will provide full-granularity information to the ATLAS trigger system. The Compact Processing Modules…
NICPB（国家化学物理研究所）；University of California San Diego（加州大学圣迭戈分校）；European Center for Nuclear Research (CERN)（欧洲核子研究中心）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；California Institute of Technology（加州理工学院）
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs partic…
GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
William & Mary, Department of Data Science（威廉玛丽学院数据科学系）；William & Mary, Department of Physics（威廉玛丽学院物理系）；University of Glasgow, School of Physics and Astronomy（格拉斯哥大学物理与天文学学院）；Thomas Jefferson National Accelerator Facility（泰勒·杰弗里斯国家加速器设施）
Usage of scanning coordinate-measuring machines for inspection of screw threads has become a common practice nowadays. Compared to touch trigger probing, scanning capabilities allow to speed up measuring process while still maintaining high accuracy. However, in some cases accuracy drasticaly depends on the scanning speed. In this paper a compensation method is proposed allowing to reduce the influence of some dynamic effects while scanning screw threads on coordinate-measuring machines.
