Track Reconstruction Software Research Articles

Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework

Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. Few examples of such parameters include the cut values limiting the search space of the algorithm, the approximations accounting for complex phenomena or the parameters controlling algorithm performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The opensource track reconstruction software framework known as “A Common Tracking Framework (ACTS)” offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS come equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS.

Detray: a compile time polymorphic tracking geometry description

A detailed geometry description is essential to any high quality track reconstruction application. In current C++ based track reconstruction software libraries this is often achieved by an object oriented, polymorphic geometry description that implements different shapes and objects by extending a common base class. Such a design, however, has been shown to be problematic when attempting to adapt these applications to run on heterogeneous computing hardware, particularly on hardware accelerators. We present detray, a compile time polymorphic and yet accurate track reconstruction geometry description which is part of the ACTS parallelization R&D effort. detray is built as an index based geometry description with a shallow memory layout, that uses variadic template programming to allow custom shapes and intersection algorithms rather than inheritance from abstract base classes. It is designed to serve as a potential geometry and navigation backend for ACTS and as such implements the ACTS navigation model of boundary portals and purely surface based geometric entities. detray is designed to work with a dedicated memory management library and thus can be instantiated as a geometry model in host and device code.

A Common Tracking Software Project

The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

Corryvreckan: a modular 4D track reconstruction and analysis software for test beam data

Corryvreckan is a versatile, highly configurable software with a modular structure designed to reconstruct and analyse test beam and laboratory data.It caters to the needs of the test beam community by providing a flexible offline event building facility to combine detectors with different readout schemes, with or without trigger information, and includes the possibility to correlate data from multiple devices based on timestamps.Hit timing information, available with high precision from an increasing number of detectors, can be used in clustering and tracking to reduce combinatorics.Several algorithms, including an implementation of Millepede-II, are provided for offline alignment.A graphical user interface enables direct monitoring of the reconstruction progress and can be employed for quasi-online monitoring during data taking.This work introduces the Corryvreckan framework architecture and user interface, and provides a detailed overview of the event building algorithm.The reconstruction and analysis capabilities are demonstrated with data recorded at the DESY II Test Beam Facility using the EUDAQ2 data acquisition framework with an EUDET-type beam telescope, a Timepix3 timing reference, a fine-pitch planar silicon sensor with CLICpix2 readout and the AIDA Trigger Logic Unit.The individual steps of the reconstruction chain are presented in detail.

Improvements to ATLAS Inner Detector Track reconstruction for LHC Run-3

This document summarises the main changes to the ATLAS experiment’s Inner Detector Track reconstruction software chain in preparation of LHC Run 3 (2022-2024). The work was carried out to ensure that the expected high-activity collisions with on average 50 simultaneous proton-proton interactions per bunch crossing (pile-up) can be reconstructed promptly using the available computing resources. Performance figures in terms of CPU consumption for the key components of the reconstruction algorithm chain and their dependence on the pile-up are shown. For the design pile-up value of 60 the updated track reconstruction is a factor of 2 faster than the previous version.

The Acts project: track reconstruction software for HL-LHC and beyond

The reconstruction of trajectories of the charged particles in the tracking detectors of high energy physics (HEP) experiments is one of the most difficult and complex tasks of event reconstruction at particle colliders. As pattern recognition algorithms exhibit combinatorial scaling to high track multiplicities, they become the largest contributor to the CPU consumption within event reconstruction, particularly at current and future hadron colliders such as the LHC, HL-LHC and FCC-hh. Current algorithms provide an extremely high standard of physics and computing performance and have been tested on billions of simulated and recorded data events. However, most algorithms date back to more than 20 years ago and maintaining them has become increasingly challenging. In addition, they are challenging to adapt to modern programming paradigms and parallel architectures. Acts is based on the well-tested and highly functioning components of LHC track reconstruction algorithms, implemented with modern software concepts and inherently designed for parallel architectures. Multithreading becomes increasingly important to balance the memory usage per CPU core. However, a fully multithreaded event processing framework blurs the clear border between events, which has in the past often been used as a clearly defined validity boundary for event conditions. Acts is equipped with a full contextual conditions concept that allows to run concurrent track reconstruction even in case of multiple detector alignments, conditions or varying magnetic field being processed at the same time. It provides an experiment and, in particular, framework-independent software toolkit and light-weight, highly optimised event data model for track reconstruction. Particular care is given to thread safety and data locality. It is designed as a toolbox that allows to implement and extend widely known pattern recognition algorithms, and in addition suitable for algorithm templating and R&D. Acts has been used as the fast simulation engine for the Tracking Machine Learning (TrackML) Challenge, and will provide reference implementation of several submitted solution programs of the two phases of the challenge.

Belle II Track Reconstruction and Results from first Collisions

In April 2018, e+e- collisions of the SuperKEKB B-Factory have been recorded by the Belle II detector in Tsukuba (Japan) for the first time. The new accelerator and detector represent a major upgrade from the previous Belle experiment and will achieve a 40 times higher instantaneous luminosity. Special considerations and challenges arise for track reconstruction at Belle II due to multiple factors. This high luminosity configuration of the collider increases the beam-induced background by many factors compared to Belle and a new track reconstruction software has been developed from scratch to achieve an excellent physics performance in this busy environment. Even though on average only eleven signal tracks are present in one event, all of them need to be reconstructed down to a transverse momentum of 50 MeV and no fake tracks should be present in the event. Many analyses at Belle II rely on the advantage that the initial state in B-factories is well known and a clean event reconstruction is possible if no tracks are left after assigning all tracks to particle hypotheses. This contribution will introduce the concepts and algorithms of the Belle II tracking software. Special emphasis will be put on the mitigation techniques developed to perform track reconstruction in high-occupancy events. First results from the data-taking with the Belle II detector will be presented.

The effect of electrostatic and gravity force on offset wire inside tube

In a straw-tube detector, a wire that is offset with respect to the tube axis experiences a Coulomb force when high voltage is applied between the anode wire and the tube. This force results in a shifting of the wire and straw, in addition to the gravitational sag, and is a function of the tube and wire radius, initial offset, high voltage, tension and length. The presence of such effects is well known, but the precise magnitude of the shift for the anode wires under conditions of detector operation have not been previously documented with measurable confidence. In this work, we provide the first systematic measurements for the wire shift in straw-tube detectors due to gravity and the electrostatic force using an x-ray scanner developed for the Mu2e experiment. The data are compared to the solutions of the differential equations governing the system, and we find a good match between the two. The solutions can predict the final wire and straw positions from the initial positions measured without the high voltage, and the final wire and straw positions can then be used as an input to the track reconstruction software to improve the track position resolution.

Test-beam activities and results for the ATLAS ITk pixel detector

The Phase-II upgrade of the LHC aims at an increase of the instantaneous luminosity up to about 5×1034 cm−2 s−1. To cope with the resulting challenges the current Inner Detector will be replaced by an all-silicon Inner Tracker (ITk) system. The Pixel Detector will have to deal with occupancies of about 300 hits/FE/s as well as a fluence of around 2×1016 neq cm−2. Various sensor layouts are under development, aiming at providing a high performance, cost effective pixel instrumentation to cover an active area of about 10 m2. These range from thin planar silicon, 3D silicon, to active CMOS sensors. After extensive characterization of the sensors in the lab, their charge collection properties and hit efficiency are measured in common testbeam campaigns, which provide valuable feedback for improvements of the layout. Testbeam measurements of the final prototypes will be used for the decision of which sensor types will be installed in ITk. The setups used in the ITk Pixel testbeam campaigns will be presented, including the common track reconstruction and analysis software. Results from the latest measurements will be shown, highlighting some of the developments and challenges for the ITk Pixel sensors.

ACTS: from ATLAS software towards a common track reconstruction software

Reconstruction of charged particles’ trajectories is a crucial task for most particle physics experiments. The high instantaneous luminosity achieved at the LHC leads to a high number of proton-proton collisions per bunch crossing, which has put the track reconstruction software of the LHC experiments through a thorough test. Preserving track reconstruction performance under increasingly difficult experimental conditions, while keeping the usage of computational resources at a reasonable level, is an inherent problem for many HEP experiments. Exploiting concurrent algorithms and using multivariate techniques for track identification are the primary strategies to achieve that goal.Starting from current ATLAS software, the ACTS project aims to encapsulate track reconstruction software into a generic, framework- and experiment-independent software package. It provides a set of high-level algorithms and data structures for performing track reconstruction tasks as well as fast track simulation. The software is developed with special emphasis on thread-safety to support parallel execution of the code and data structures are optimised for vectorisation to speed up linear algebra operations. The implementation is agnostic to the details of the detection technologies and magnetic field configuration which makes it applicable to many different experiments.

STAR reconstruction improvements for tracking with the heavy flavor tracker

The reconstruction and identification of charmed hadron decays provides an important tool for the study of heavy quark behavior in the Quark Gluon Plasma. Such measurements require high resolution to topologically identify decay daughters at vertices displaced < 100 microns from the primary collision vertex, placing stringent demands on track reconstruction software. To enable these measurements at RHIC, the STAR experiment has designed and employed the Heavy Flavor Tracker (HFT). It is composed of silicon-based tracking detectors, providing four layers of high-precision position measurements which are used in combination with hits from the Time Projection Chamber (TPC) to reconstruct track candidates. The STAR integrated tracking software (Sti) has delivered a decade of world-class physics. It was designed to leverage the discrete azimuthal symmetry of the detector and its simple radial ordering of components, permitting a flat representation of the detector geometry in terms of concentric cylinders and planes, and an approximate track propagation code. These design choices reflected a careful balancing of competing priorities, trading precision for speed in track reconstruction. To simplify the task of integrating new detectors, tools were developed to automatically generate the Sti geometry model, tying both reconstruction and simulation to the single source AgML geometry model. The increased precision and complexity of the HFT detector required a careful reassessment of this single geometry path and implementation choices. In this paper we will discuss the test suite and regression tools developed to improve reconstruction with the HFT, our lessons learned in tracking with high precision detectors and the tradeoffs between precision, speed and ease of use which were required.

Development and performance of track reconstruction algorithms at the energy frontier with the ATLAS detector

ATLAS track reconstruction software is continuously evolving to match the demands from the increasing instantaneous luminosity of the LHC, as well as the increased center-of-mass energy. These conditions result in a higher abundance of events with dense track environments, such as the core of jets or boosted tau leptons undergoing three-prong decays. These environments are characterised by charged particle separations on the order of the ATLAS inner detector sensor dimensions and are created by the decay of boosted objects. Significant upgrades were made to the track reconstruction software to cope with the expected conditions during LHC Run 2. In particular, new algorithms targeting dense environments were developed. These changes lead to a substantial reduction of reconstruction time while at the same time improving physics performance. The employed methods are presented and physics performance studies are shown, including a measurement of the fraction of lost tracks in jets with high transverse momentum.

Optimisation of the ATLAS Track Reconstruction Software for Run-2

The reconstruction of particle trajectories in the tracking detectors of experiments at the Large Hadron Collider (LHC) is one of the most complex parts in analysing the data from beam-beam collisions. To maximise the integrated luminosity during Run-1 of the LHC data taking period, the number of simultaneous proton-proton interactions per beam crossing (pile- up) was steadily increased. The track reconstruction is the most time consuming reconstruction component and scales non-linearly in high luminosity environments. Flat budget projections (at best) for computing resources during the upcoming Run-2 of the LHC together with the demands of reconstructing higher pile-up collision data at rates more than double compared to Run-1 have put pressure on the track reconstruction software to meet the available computing resources. The ATLAS experiment has thus performed a two year long software campaign which led to a reduction of the reconstruction time for Run-2 conditions by a factor of four: a major part of the changes were improvements to the track reconstruction, which was reduced by more than a factor of five without any loss of output information for subsequent physics analysis. We present the methods used for analysing the software, the planning and deployment of updates and new methods implemented to optimise both algorithmic performance and event data.

Planning for Evolution in a Production Environment: Migration from a Legacy Geometry Code to an Abstract Geometry Modeling Language in STAR

Increasingly detailed descriptions of complex detector geometries are required for the simulation and analysis of today's high-energy and nuclear physics experiments. As new tools for the representation of geometry models become available during the course of an experiment, a fundamental challenge arises: how best to migrate from legacy geometry codes developed over many runs to the new technologies, such as the ROOT/TGeo [1] framework, without losing touch with years of development, tuning and validation. One approach, which has been discussed within the community for a number of years, is to represent the geometry model in a higher-level language independent of the concrete implementation of the geometry. The STAR experiment has used this approach to successfully migrate its legacy GEANT 3-era geometry to an geometry Modelling Language (AgML), which allows us to create both native GEANT 3 and ROOT/TGeo implementations. The language is supported by parsers and a C++ class library which enables the automated conversion of the original source code to AgML, supports export back to the original AgSTAR[5] representation, and creates the concrete ROOT/TGeo geometry implementation used by our track reconstruction software. In this paper we present our approach, design and experience and will demonstrate physical consistency between the original AgSTAR and new AgML geometry representations.

Development of an automated nuclear emulsion analyzing system

Abstract This paper describes the development of an automated nuclear emulsion analyzing system, which is constructed of the following three parts, the nuclear emulsion “OPERA film”, the latest automated scanning system “S-UTS” and the three-dimensional track reconstruction software “NETSCAN”. The system is able to operate at a speed of 72 cm 2 /h and overcame the bottleneck of long time-consuming to recognize tracks in the nuclear emulsion by human eyes. The system is essential for the OPERA experiment, muon radiography, gamma-ray telescope and many other fields with nuclear emulsion.

Dedicated front-end and readout electronics developments for real time 3D directional detection of dark matter with MIMAC

A complete dedicated electronics, from front-end to back-end, was developed to instrument a MIMAC prototype. A front end ASIC able to monitor 64 strips of pixels and to provide their individual "Time Over Threshold" information has been designed. An associated acquisition electronics and a real time track reconstruction software have been developed to monitor a 512 channel prototype. This auto-triggered electronic uses embedded processing to reduce the data transfer to its useful part only, i.e. decoded coordinates of hit tracks and corresponding energy measurements. The electronic designs, acquisition software and the results obtained are presented.

Data acquisition electronics and reconstruction software for real time 3D track reconstruction within the MIMAC project

Directional detection of non-baryonic Dark Matter requires 3D reconstruction of low energy nuclear recoils tracks. A gaseous micro-TPC matrix, filled with either 3He, CF4 or C4H10 has been developed within the MIMAC project.A dedicated acquisition electronics and a real time track reconstruction software have been developed to monitor a 512 channel prototype. This auto-triggered electronic uses embedded processing to reduce the data transfer to its useful part only, i.e. decoded coordinates of hit tracks and corresponding energy measurements. An acquisition software with on-line monitoring and 3D track reconstruction is also presented.

Prospectives of the hadron program in ATLAS

One of the first measurements that will be made at the LHC by ATLAS deals with the properties of inelastic collisions, namely the central charged particle density and transverse momentum distributions. Current predictions of these distributions have large uncertainties in the LHC energy range. We describe the ATLAS minimum bias triggers, designed to select all kind of inelastic interactions, and the performance of the track reconstruction software which was adapted to soft particle track reconstruction. The precision with which the minimum bias distributions can be measured with early data is presented and the uncertainties on the inelastic distributions due to trigger bias is discussed.

Data acquisition electronics and reconstruction software for directional detection of dark matter with MIMAC

Directional detection of galactic dark matter requires 3D reconstruction of low energy nuclear recoils tracks. A dedicated acquisition electronics with auto triggering feature and a real time track reconstruction software have been developed within the framework of the MIMAC project of detector. This auto-triggered acquisition electronic uses embedded processing to reduce data transfer to its useful part only, i.e. decoded coordinates of hit tracks and corresponding energy measurements. An acquisition software with online monitoring and 3D track reconstruction is also presented.

Track reconstruction algorithms for the CBM experiment at FAIR

The Compressed Baryonic Matter (CBM) experiment at the future FAIR accelerator complex at Darmstadt is being designed for a comprehensive measurement of hadron and lepton production in heavy-ion collisions from 8-45 AGeV beam energy, producing events with large track multiplicity and high hit density. The setup consists of several detectors including as tracking detectors the silicon tracking system (STS), the muon detector (MUCH) or alternatively a set of Transition Radiation Detectors (TRD). In this contribution, the status of the track reconstruction software including track finding, fitting and propagation is presented for the MUCH and TRD detectors. The track propagation algorithm takes into account an inhomogeneous magnetic field and includes accurate calculation of multiple scattering and energy losses in the detector material. Track parameters and covariance matrices are estimated using the Kalman filter method and a Kalman filter modification by assigning weights to hits and using simulated annealing. Three different track finder algorithms based on track following have been developed which either allow for track branches, just select nearest hits or use the mentioned weighting method. The track reconstruction efficiency for central Au+Au collisions at 25 AGeV beam energy using events from the UrQMD model is at the level of 93-95% for both detectors.