Correction: Application of geometric deep learning for tracking of hyperons in a straw tube detector

Abstract This study draws inspiration from straw folding patterns to develop an innovative straw energy-absorbing block and a novel straw bumper system for pedestrian protection. The study employs the THUMS 4.0 dummy leg model coupled with a detailed finite element model of vehicle front-end structures to simulate pedestrian leg impact scenarios. Key design parameters of the straw structure were meticulously optimized according to the specific morphological and dimensional requirements of automotive front crash beams and exterior panels. The implemented solution features connecting plates that enable precise alignment along the vehicle’s crash beam profile. This redesigned straw energy absorbing blocks (SEB) system replaces conventional foam energy absorbers at vehicle front ends, facilitating comprehensive investigation of pedestrian leg injury mechanisms and dynamic responses during collision events. A systematic parametric study evaluates the protective efficacy of straw tube configurations, examining critical variables including layer quantity with particular focus on 4.5-layer and 7.5-layer arrangements, cytosolic small diameters, and row alignment patterns. Three distinct bumper prototypes incorporating varied straw tube geometries were developed for comparative performance analysis. Experimental results demonstrate substantial injury mitigation, with SEB implementations achieving stress reduction of 58.1% in femur and 9.0% in tibia compared to baseline foam absorbers. Building upon these findings, the research culminated in the development of an enhanced Straw Front Bumper system demonstrating remarkable protective capabilities. Quantitative analysis revealed significant stress reduction metrics, achieving 66.6% decrease in femoral stress and 15.2% reduction in tibial stress when compared with traditional bumper designs. Through systematic evaluation of three distinct SEB row configurations, this study yields critical engineering insights for advancing vehicle safety systems. The results conclusively validate straw-derived energy-absorbing architectures as biomechanically optimized solutions, thereby introducing transformative design frameworks for next-generation pedestrian protection technologies in automotive engineering.

Abstract We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural networks (FCNs), and (ii) a geometric deep learning (GDL) model known as graph neural networks (GNNs). The models have been implemented to reconstruct signals in the non-Euclidean detector geometry of the future antiproton experiment PANDA. In particular, the GDL model shows promising results for cases where other, more conventional track-finders fall short: (i) tracks from low-momentum particles that frequently occur in hadron physics experiments and (ii) tracks from long-lived particles such as hyperons, hence originating far from the beam-target interaction point. Benchmark studies using Monte Carlo simulated data from PANDA yield an average technical reconstruction efficiency of 92.6% for high-multiplicity muon events, and 97.1% for the $$\Lambda$$ Λ daughter particles in the reaction $$\bar{p}p \rightarrow \bar{\Lambda }\Lambda \rightarrow \bar{p}\pi ^+ p\pi ^-$$ p ¯ p → Λ ¯ Λ → p ¯ π + p π - . Furthermore, the technical tracking efficiency is found to be larger than 70% even for particles with transverse momenta $$p_\text {T}$$ p T below 100 MeV/ c . For the long-lived $$\Lambda$$ Λ hyperons, the track reconstruction efficiency is fairly independent of the distance between the beam-target interaction point and the $$\Lambda$$ Λ decay vertex. This underlines the potential of machine-learning-based tracking, also for experiments at low- and intermediate-beam energies.

Abstract Dedicated simulation studies are being conducted to predict performance of the Straw Tracker in the SPD experiment. Garfield++ package allows us to model signals from the individual Tracker elements, straw tubes. LTSpice software models response of an analogue electronic circuit to a given input signal. Straw tube signals generated with Garfield++ are used as an input to the straw readout electronics models to study influence of different readout electronics parameters on the Straw Tracker performance. The obtained dependence of the straw signal time and charge resolution on different parameters, such as preamplifier peaking time and the discriminator threshold are presented.

Abstract Straw tube detectors are widely employed in cosmic ray and high-energy physics experiments for their high spatial and temporal resolution. The stability and precision of their operation strongly depend on the composition of the working gas mixture. This work presents the development and calibration of a dynamic gas supply system designed for use with straw detectors. The system integrates RRG-12 and RRG-15 mass flow controllers regulated via a KGS-3 control unit, enabling real-time mixing and flow control of Ar/CO₂ mixtures. Flow calibration using the volumetric water displacement method confirmed a linear correlation between controller settings and actual flow rates, with deviations below 6%. Additionally, spectral calibration was carried out using an Fe-55 radioactive source to fine-tune gas composition by comparing the ionization response with that of a reference gas mixture. The results demonstrate that the system provides accurate, stable, and reproducible gas delivery suitable for both laboratory use and large-scale experimental setups involving straw detectors. Future applications include muon track reconstruction using the developed gas system.

Development of a setup for precision measurement of the time parameters of thin-walled straw tubes for the COMET experiment

The charged particles are tracked in the high energy physics detectors to provide the information of their properties. One of the tracking detector is straw tube detector that has been used by many experiments. The motivation behind the current work is to study the primary ionization and particle identification using straw tube detectors. Additionally, we report the decay of 60CO for the study of gamma peaks since these are used in cobalt therapy, that is beneficial for cancer/tumor treatment. The various studies like primary ionization, spatial co-ordinate distributions in the different gas mixtures, transition radiation, drift velocities of electrons and diffusion coefficients using different xenon-based gas mixtures have been done. These studies have been done after the optimization of xenon-based gas mixtures for a deeper understanding. The gas mixture that shows maximum transition radiation among the xenon-based gas mixtures was found to be Xe : He : CH4 :: 30 : 55 : 15. The gas mixture that posses maximum primary ionization has been observed to be Xe:CO2 :: 70:30. Finally, the simulations have been carried out for the particle identification in the straw tube detectors with different particles i.e., muons, pions and kaons.

Abstract This study explores the modeling and prediction of the straw tube detector performance using Garfield++ and LTSpice. Straw Tube Trackers (STT) are important components in high-energy physics experiments because of their large detection area and low material cost, making them an attractive option. The aim is to accurately predict drift time and signal shape of straw response, which are crucial for optimizing readout electronics of future STTs. The research involves integrating Garfield++, a simulation toolkit, with LTSpice, an electronic circuit emulator, to simulate detector responses comprehensively. The study validates simulation predictions with experimental measurements from VMM3 and Tiger ASICs, focusing on refining model accuracy. Key challenges include measuring signal timing and charge simultaneously to enhance detector performance. This work advances the precision and reliability of particle detection systems, optimizing readout systems and improving straw tube detector performance. Ongoing efforts focus on developing new ASIC prototypes for future high-energy physics applications.

The former Outer Tracker of LHCb/CERN based on gas-filled straw tubes has been kindlydonated to GSI for further use in experiments at GSI and FAIR and predominantly in PANDA.This formidable gaseous tracking detector of LHCb employed in LHC Run 1 and Run 2performed continuously and above expectations until its replacement by aScintillating Fibre Tracker to address the challenges after upgrades to the LHC.Upon arrival at GSI, the whole straw tube detector is being stored and gradually prepared for various use cases.

The NA64 experiment is aimed at searching for mediator particles that carry out the interaction between standard matter and dark matter. The main goal of the experiment is to detect Light Dark Matter (LDM) particles with masses below 1 GeV. The missing energy/momentum technique is used in the experiment, e.g. in the muon run energy of muons in the final state is expected to be 80 GeV. The NA64 experiment has a fairly extensive detector system, including several types of detectors. Straw trackers are used in both muon and electron modes of the experiment. One of the essential components of the detection system along the beamline is the straw tube detector. Straw trackers allow one to reconstruct the tracks of primary and secondary particles and subsequently estimate their momentum based on the curvature of the track. This paper presents a straw tracker model built using the Geant4 software package. Secondary radiation particles were also included in straw tube responses. The main goal of this work is to construct and analyze a straw tracker model using the GEANT4 toolkit. The research methodology is based on the use of the Monte Carlo method, which underlies the work of GEANT4. The results of the study have practical significance, consisting in the use of models for optimizing the technical and design parameters of straw trackers.

The drift chambers of the HADES experiment at SIS-18 at GSI, Darmstadt/Germany, form the main tracking system of the spectrometer. Designed more than twenty years ago, the whole front-end electronics chain is being replaced with state-of-the-art electronics to cope with the increasing failure rate of the old electronics and with advanced requirements of the experiment, e.g. the trigger rate.The new analog signal processing is based on the PASTTREC ASIC, developed for the Straw Tube Tracker of the PANDA Experiment. The digitization of data happens in FPGA-based TDCs.The main challenges of the project are the strict spatial constraints given by the experiment setup to place the front-end boards and the noise sensitivity of the large area gas detectors. In addition, the power consumption needed to be kept low due to thermal constraints.

Many current physics experiments require precision reconstruction of low-energy particles. One example is the Mu2e experiment, which requires reconstructing an isolated 105 MeV electron with better than 500 KeV/c momentum resolution. Mu2e uses a low-mass straw tube tracker, and a CsI crystal calorimeter, to reconstruct tracks. In this paper, we present the design and performance of a track reconstruction algorithm optimized for Mu2e’s unusual requirements. The algorithm is based on the KinKal [1] kinematic Kalman filter track fit package. KinKal supports multiple track parameterizations, including one optimized for looping tracks, such as Mu2e signal tracks, and others optimized for straight or slightly-curved tracks, such as the high-momentum (>1 GeV/c) cosmic ray muons used to calibrate and align the Mu2e detectors. All KinKal track parameterizations include the track origin time, to correctly model correlations arising from measurements that couple time and space, such as the straw drift time or the calorimeter cluster time. KinKal employs magnetic field inhomogeneity and material effect correction algorithms with 10−4 fractional precision. The Mu2e fit uses Artificial Neural Net functions to discriminate background hits from signal hits, and to resolve the straw tube hit left-right ambiguity, while iterating the extended Kalman filter. The efficiency, accuracy, and precision of the Mu2e track reconstruction, as tested on detailed simulations of Mu2e data, are presented.

The aim of this work is to describe method of modeling straw signal using Garfield++ interface to LTspice. Straw Tube Trackers will be use in the SPD experiment. When designing such large scale and complex detector it is of extreme importance to run precise simulations. The physical task of this research is to reliably predict drift time and shape signal, which is important for further modeling of electronics for SPD Straw Trackers

We present the LOcal Track Finder (lotf) algorithm, a method that performs charged-particle trajectory reconstruction using the Straw Tube Tracker, one of the central trackers of the antiProton ANnihilation at DArmstadt (PANDA) detector. The algorithm builds upon the neighboring relations of the tubes to connect individual hits and form track candidates. In addition, it uses a local fitting procedure to handle regions where several tracks overlap and utilizes a system of virtual nodes to reconstruct the z-information of the particle trajectories. We generated 30,000 events to assess the performance of our approach and compared its global track assignment efficiency with respect to two other track reconstruction methods. lotf has (1) an average of 85% of found tracks, (2) the largest number of Fully Pure tracks, (3) the lowest amount of incorrect reconstructions, and (4) is significantly faster than the other two approaches. Further, we compared the z-reconstruction performance with one of the two alternative methods and show that lotf improves the median z-error by a factor of 8.7. Finally, we tested our method using 3750 data sets composed of 4 events each, demonstrating that our approach handles cases in which events are mixed. The raw (without parallelization) average reconstruction rate is about 68,000 hits/s, which makes the present algorithm promising for online data selection and processing.

PASTTREC is an 8-channel readout Application-Specific Integrated Circuit (ASIC) designed for the Straw Tracking System (STS) in the HADES experiment and for the Straw Tube Tracker (STT) and the Forward Tracker (FT) detectors in the PANDA experiment, both at the FAIR facility. Since more than 5000 ASICs were produced for both experiments, efficient qualification tests are required. For this purpose, the multi-chip test setup and dedicated verification procedures were developed. In this paper, the measurement results for the first batch of 140 Front-End Boards (FEBs), each hosting two ASICs, are presented. Since a significant number (280) of PASTTREC chips were tested, obtained statistical distributions of key parameters are also reported. The estimated yield (97%) and the process-related spread of the chip parameters are discussed.

The Mu2e experiment — Searching for charged lepton flavor violation

LEUKOENCEPHALOPATHY FROM HEROIN INHALATION: CHASING THE DRAGON

In this paper, an optimized boron-coated straw tube neutron multiplicity counter is used to simulate neutron multiplicity for six different shapes of weapons-grade plutonium samples. It was found that under the point model equation, the mass simulation results for samples of different shapes showed different degrees of negative deviations with increasing mass, and the deviations increased with increasing mass. On this basis, the reasons for the deviation are analyzed and a multilayer perceptron is introduced, and three counting rates and preset qualities of samples of different qualities under six shapes are trained. The results showed that the multi-layer perceptron could better fit the relationship between the counting rates of different shapes and the preset quality. The sparrow search algorithm was introduced to optimize the initial weights and thresholds of the multilayer perceptron, and the optimized model was used to predict another four samples of different shapes. The results show that the optimized multilayer perceptron can better fit the relationship between the counting rate and the mass of samples with different shapes. When the sample mass is less than 3 kg, the relative deviation of the three counting rates for samples with four shapes to the sample mass prediction is less than 2%, which meets the requirements of neutron multiplicity measurement.

This paper reports on the design and construction of a High Voltage Distribution System (HVDS) for a “Smart Switch” developed to provide high voltage (HV) to a series of stacked straw detector planes in the Mu2e Electron Tracking Detector (ETD). Each plane is composed of three 120 deg. Crescent-shaped panels, and each panel is composed of 96 straw tubes. The HVDS provides HV on-off, current monitoring, recording, and crowbar for over-current protection. The inter-communication system is based on TCP/IP. The HVDS supplies HV with long term stability, and accurately monitors the applied voltage and currents.

Track finding algorithm using the track road method for the PANDA Forward Tracker