High Energy Physics Experiments Research Articles

Study on Detector Geometry Transformation and Visualization in Unity

In High Energy Physics (HEP) experiments, it is useful for physics analysis and outreach if the event display software can provide sophisticated visualization effects. Unity is professional software that provides 3D modeling and animation production. GDML format files are commonly used for detector descriptions in HEP experiments. In this work, we present a method for automating the import of GDML files to the Unity engine modeling through geometric transformation. With the conversion of the file format, the complicated geometric description of the HEP detector in a GDML file can be imported directly into the Unity engine using an FBX file. This format conversion was applied to BESIII experiments to achieve an automated geometry conversion to build geometry models in the Unity engine.

A 128-channel time-to-digital converter based on the 24-phase shift-clock sampling method for RPC

The Resistive Plate Chamber (RPC) are widely used in high energy physics (HEP) experiments. In recent years, new designs and application scenarios of RPCs abound, and an advanced time-to-digital converter (TDC) system is in demand. The TDC implemented in the field-programmable gate array (FPGA) is widely used in HEP experiments with changeable conditions as a flexible time measurement method. This article presents a multi-phase shift-clock sampling FPGA-based TDC with 128 channels. The TDC module is implemented in a Xilinx Kintex-7 FPGA. The sampling clock period (2.22 ns) is subdivided into 24 phases to achieve finer time resolution. The TDC performance measurements show that the root-mean-square (RMS) time resolution of the TDC module in the time interval measurement is 45.3 ∼ 57.7 ps, and the integral nonlinearity (INL) is smaller than 0.2368 least significant bit (LSB). In addition, some target designs are made to adapt to the application scenarios. This article also shows measurement results acquired from an RPC by this TDC. This TDC was used to measure the RPC efficiency plateau and the TOT of the RPC readout signal.

The JSUB task submission and management software

In near future, new high energy physics experiments with challenging data volume are coming into operations or are planned in IHEP, China. A DIRAC-based distributed computing system is being enabled to process the huge amount of data to be obtained in these experiments. To encourage better utilization of computing resources, it’s essential to give users handy tools that saves them from troubles of mastering various technical details of distributed computing and let them have comfortable control over their tasks.In this conference, we present the design and development of a extensible task submission and management system called JSUB, which provides users from different experiments with consistent solutions of managing massive jobs. JSUB covers functionalities including user interface, task creation and submission, run-time workflow control, task monitoring and management. Major features of this software include friendliness with extensions into different user scenarios, flexible task setting, and parametric job submission.Currently, the JSUB software has been developed and put into use for JUNO and CEPC experiments. The software is also highly extensible to other HEP experiments. The designs and techniques could be interesting to other experiments which also use DIRAC as workload management system. The software has only been previously reported inside JUNO collaboration, and this is the first time to be presented to a wider audience.

Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations

The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. Accurate LArTPC detector response simulations are computationally demanding, and can become a bottleneck in the analysis workflow. Compute devices such as General-Purpose Graphics Processing Units (GPGPUs) have the potential to substantially accelerate simulations compared to traditional CPU-only processing. The software development for these compute accelerators often carries the cost of specialized code refactorization and porting to match the target hardware architecture. With the rapid evolution and increased diversity of the computer architecture landscape, it is highly desirable to have a portable solution that also maintains reasonable performance. We report our ongoing effort in evaluating Kokkos as a basis for this portable programming model using LArTPC simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC simulations, data analysis, reconstruction and visualization.

Complete design of maximally-automated self-driven control mechanism for a large scale electronics system and its application to the ATLAS Phase-II TGC system

Establishing a reliable and efficient method to control electronics system consisting of many circuit boards is critical in the system design. Among unique requirements for the control in high energy physics experiments, we propose a maximally-automated and self-driven scheme for a system that exploits FPGAs, flash memory devices, and high-speed optical links. We have demonstrated our ideas in the ATLAS Phase-II Thin Gap Chamber system as the prototype for this new automated scheme. The method is widely applicable and knowledge and experience can be shared with other experiments using an FPGA-based large-scale electronics system.

The Open Data Detector Tracking System

Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments.

Event Building Algorithm in a Distributed Stream Processing Data Acquisition Platform: D-Matrix

Event building is an important task for data acquisition (DAQ) systems in high-energy physics experiments. D-Matrix is designed as a generic distributed stream processing DAQ platform, containing hardware description language (HDL)-based modules and software modules. In the D-Matrix system, the data transfer is abstracted into the form of a stream, and the event building is implemented through a cascade of various reusable stream processing modules. These stream processing modules will determine their work modes automatically based on a set of attributes, which describe the abstracted data stream characteristics, such as spatiotemporal organization and granularity. This article introduces the event building architecture, the physics data structure abstraction, and the algorithmic implementation of specific event building modules in the D-Matrix system. An application of the D-Matrix event building architecture in the CSR external-target experiment is presented in the end.

Radiation damage effects of protons and X-rays on silicon photomultipliers

Silicon photomultipliers (SiPMs) are highly-sensitive photodetectors emerging as the technology of choice for many applications. In high-energy physics experiments and as the detectors for space instruments they are often exposed to a large amount of irradiation. In recent years, there has been an increasing interest in assessing the performance deterioration of such detectors after ionizing and non-ionizing radiation, such as protons, neutrons and X or gamma rays. It is interesting to compare the radiation damage effects on several types of SiPMs, to assess the main deterioration mechanisms on SiPM performances. In this work we report on the irradiation by protons and X-rays of several types of small-area ( ∼ 1 mm 2 ) SiPMs, produced in FBK with different technologies. We show and compare the most interesting results of the online measurements, taken just after each irradiation step, for both irradiations. We performed and monitored the current variation during a room temperature annealing. Finally we characterized all the main functional performance of the irradiation SiPMs, highlighting the most interesting variations.

Software Training in High Energy Physics

Among the upgrades in current high energy physics (HEP) experiments and the new facilities coming online, solving software challenges has become integral to the success of the collaborations. The demand for human resources skilled in both HEP and software domains is increasing. With a highly distributed environment in human resources, the sustainability of the HEP ecosystem requires a continuous effort in the equipment of physicists with the required abilities in software development. In this paper, the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP) are presented. Experiment-agnostic, open, and accessible modules for training have been developed, focusing on common software material with ranges from core software skills needed by everyone to advanced training required to produce high-quality sustainable software. A basic software curriculum was built, and an introductory software training event has been prepared to serve HEP entrants. This program serves individuals with transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP.

Fully Geant4 compatible package for the simulation of Dark Matter in fixed target experiments

A package for the simulation of Dark Matter (DM) particles is presented. It is intended for fixed target experiments. We chose the Geant4 toolkit framework as a standard in order to be compatible with particle tracking programs used in many High Energy Physics (HEP) experiments. One part of the package is a collection of codes that return the cross sections of DM particles’ production in such processes as electron and muon bremsstrahlung off nuclei, annihilation in flight and Primakoff processes. In most cases they are calculated at runtime. In the case of bremsstrahlung processes the total cross sections are either calculated numerically at exact tree level (ETL) or calculated at runtime using approximations validated by comparing with results of the ETL calculations. Other parts contain codes for the simulation of the above mentioned processes and DM particle classes.

Evaluation of Selected Rare-Earth Scintillators for Gamma-Ray Sensing Applications

In the present work, selected dense rare-earth (RE) based scintillators such as gadolinium tantalate (GdTaO4), gadolinium tantalo-niobates Gd(Ta0.8Nb0.2)O4, lutetium based LuF3:Ce, LuAP:Ce, Lu2O3:Yb and Yb2O3 have been investigated for their gamma-ray sensing efficacy. The gamma-ray sensing properties of these RE scintillators have been confronted with modern lead tungstate (PWO) and lead fluoride (PbF2) scintillators being employed recently in high energy physics (HEP) experiments. The attenuation parameters namely linear attenuation coefficient (μ), half value layer (HVL) are compared for these rare-earth scintillators over wide energy range 1keV - 100GeV using Photon Shielding and Dosimetry (PSD) software toolkit. We also attempted to estimate build-up factors (BF’s) of these scintillation materials computed using the online platform Py-MLBUF. Our calculations depicted that scintillators containing high-Z rare earth elements exhibited better gamma-ray detection capabilities when compared with standard lead based PWO and PbF2 scintillators.

Meta-learning for multiple detector geometry modeling

The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. A detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process. With the upcoming high luminosity LHC upgrade, with more complex events and a much increased trigger rate, the amount of required simulated events will increase. Several research directions investigated the use of Machine Learning based models to accelerate particular detector response simulation. This results in a specifically tuned simulation and generally these models require a large amount of data for training. Meta learning has emerged recently as fast learning algorithm using small training datasets. In this paper, we propose a meta-learning model that “learns to learn” to generate electromagnetic showers using a first-order gradient based algorithm. This model is trained on multiple detector geometries and can rapidly adapt to a new geometry using few training samples.

Electron pulse-dilation diagnostic instruments.

During the past decade, a number of diagnostic instruments have been developed that utilize electron pulse-dilation to achieve temporal resolution in the 5-30ps range. These development efforts were motivated by the need for advanced diagnostics for high-energy density physics experiments around the world. The new instruments include single- and multi-frame gated imagers and non-imaging detectors that record continuous data streams. Electron pulse-dilation provides high-speed detection capability by converting incoming signals into a free electron cloud and manipulating the electron signal with electric and magnetic fields. Here, we discuss design details and applications of these instruments along with issues and challenges associated with employing the electron pulse-dilation technique. Additionally, methods to characterize instrument performance and improve tolerance to gamma and neutron background radiation are discussed.

GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response

High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration.In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.

NA61/SHINE online noise filtering using machine learning methods

The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.

Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector

Detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, running detailed models requires a significant amount of computing resources. It is desired to have approaches that are less resource-intensive. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as a basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works faster than the detailed simulation in an order of magnitude without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.

Configuration Information System for online processing and data monitoring in the NICA experiments

The collection, storage and processing of experimental data are an integral part of modern high-energy physics experiments. The Configuration Information System (CIS) based on the Configuration Database (ConfigDb) is an essential element of a complex of information systems developed for the NICA experiments. The CIS is used to store and provide data on the configuration of the experiment hardware and software systems when collecting data from the detectors in online mode. The ConfigDb stores both a set of various configuration parameters and descriptions of a sequence of online software tasks to be started and run during experiment sessions. The system allows loading configuration information, activating hardware setups and launching all necessary software applications with the required parameters on the specified distributed nodes. The CIS architecture mainly contains the ConfigDb and Configuration Manager, which uses the Dynamic Deployment System API for managing tasks and providing intercommunication. The manager loads configuration data from the database, starts software tasks in a described sequence and supports task process management. The client-server architecture of the CIS is presented. The client has been implemented as a Web service to manage configuration parameters by users.

Development and test of innovative Low-Gain Avalanche Diodes for particle tracking in 4 dimensions

The MIUR PRIN 4DInSiDe collaboration aims at developing the next generation of 4D (i.e., position and time) silicon detectors based on Low-Gain Avalanche Diodes (LGAD) that guarantee to operate efficiently in the future high-energy physics experiments. To this purpose, different areas of research have been identified, involving the development, design, fabrication and test of radiation-hard devices. This research has been enabled thanks to ad-hoc advanced TCAD modelling of LGAD devices, accounting for both technological issues as well as physical aspects, e.g. different avalanche generation models and combined surface and bulk radiation damage effects modelling. In this contribution, it is reviewed the progress and the relevant detector developments obtained during the research activities in the framework of the 4DInSiDe project. • TCAD modelling for the design of radiation-hard LGAD sensors for 4D tracking. • Gain layer compensation, (p ＋ - and n ＋ -doping) to preserve the gain at high fluences. • New design approach to resistive read-out sensors: DC-coupled RSD. • DC-RSD employs a direct coupling of the resistive layer to the read-out pads. • DC-coupled low resistivity strips between read-out pads to improve the resolution.

A neural-network-defined Gaussian mixture model for particle identification applied to the LHCb fixed-target programme

Particle identification at high-energy physics experiments typically relies on classifiers combining different experimental observables. In this document, an innovative approach employing machine learning techniques to describe their dependence from the relevant features is presented. The proposed method is applied to the fixed-target programme at the LHCb experiment, where the sample size of the particle identification calibration channels affects the experimental performance. It is demonstrated to perform better than a model based on the LHCb detailed simulation and to be fast and suitable to a large variety of use cases.

Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

Calorimeters play an important role in high-energy physics experiments. Their design includes electronic instrumentation, signal processing chain, computing infrastructure, and also a good understanding of their response to particle showers produced by the interaction of incoming particles. This is usually supported by full simulation frameworks developed for specific experiments so that their access is restricted to the collaboration members only. Such restrictions limit the general-purpose developments that aim to propose innovative approaches to signal processing, which may include machine learning and advanced stochastic signal processing models. This work presents the Lorenzetti Showers, a general-purpose framework that mainly targets supporting novel signal reconstruction and triggering strategies using segmented calorimeter information. This framework fully incorporates developments down to the signal processing chain level (signal shaping, energy estimation, and noise mitigation techniques) to allow advanced signal processing approaches in modern calorimetry and triggering systems. The developed framework is flexible enough to be extended in different directions. For instance, it can become a tool for the phenomenology community to go beyond the usual detector design and physics process generation approaches. Program summaryProgram Title: Lorenzetti ShowersCPC Library link to program files:https://doi.org/10.17632/sy64367452.1Developer's repository link:https://github.com/lorenzetti-hep/lorenzettiLicensing provisions: GPLv3Programming language: Python, C++.Nature of problem: In experimental high-energy physics, simulation is essential for experiment preparation, design and interpretations of ongoing acquisitions. Especially for calorimeters, an accurate simulation that can describe detector geometry, behavior to different physics processes and signal generation close to the readout electronics and data acquisition levels is required to properly develop signal processing and computational methods. Such detectors may face very challenging demands arising from the new designs, such as pileup mitigation and noise reduction tasks under unprecedented levels. In this sense, simulation requirements continuously increase in complexity and performance, because new physics searches require large datasets and accurate modeling to experimental effects.Solution method: The Lorenzetti Showers is an integrated software framework that provides complete calorimeter information close enough to the electronic readout chain. Thus, the proposed framework allows users to access cell readout values, configurable sensor pulse-shapes, crosstalk modeling, and different energy estimation methods. It aims at supporting designs that target low or high pileup operation conditions in an easy-to-use modular structure. The developed framework is based on Pythia 8 (particle generation) and Geant4 (interactions with the calorimeter technique under analysis). An efficient data recording structure was used to allow full access to the Lorenzetti Showers outputs. In summary, the Lorenzetti Showers tool provides to the scientific community a user-friendly, flexible, user-oriented, and low-level calorimeter simulation framework.Additional comments including restrictions and unusual features: The framework current version provides the implementation of a generic segmented calorimeter (electromagnetic and hadronic sections), which may be modified by the user, if desired. It allows the generation of particles interactions using Pythia 8 (native) or any generator compatible with the HepMC format (which may be integrated using an external input file) and propagation through a user-configurable calorimeter using Geant4.