Valerio Bertacchi, Tadeas Bilka, Nils Braun, Giulia Casarosa, Luigi Corona, Sam Cunliffe, Filippo Dattola, Gaetano De Marino, Michael De Nuccio, Giacomo De Piet… 展开作者
Valerio Bertacchi, Tadeas Bilka, Nils Braun, Giulia Casarosa, Luigi Corona, Sam Cunliffe, Filippo Dattola, Gaetano De Marino, Michael De Nuccio, Giacomo De Pietro, Thanh Van Dong, Giulio Dujany, Patrick Ecker, Michael Eliachevitch, Tristan Fillinger, Oliver Frost, Rudolf Fruehwirth, Uwe Gebauer, Alexander Glazov, Nicolas Gosling, Aiqiang Guo, Thomas Hauth, Martin Heck, Mateusz Kaleta, Jakub Kandra, Claus Kleinwort, Thomas Kuhr, Simon Kurz, Peter Kvasnicka, Jakob Lettenbichler, Thomas Lueck, Alberto Martini, Felix Metzner, Dmitrii Neverov, Carsten Niebuhr, Eugenio Paoloni, Sourav Patra, Leo Piilonen, Cyrille Praz, Markus Tobias Prim, Christian Pulvermacher, Sebastian Racs, Navid Rad, Petar Rados, Martin Ritter, Giuliana Rizzo, Armine Rostomyan, Bianca Scavino, Tobias Schlueter, Benjamin Schwenker, Stefano Spataro, Bjoern Spruck, Henrikas Svidras, Francesco Tenchini, Yuma Uematsu, James Webb, Christian Wessel, Laura Zani
This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba, Japan. The algorithm is designed to balance the requirements of a high efficiency to find charged particles with a good track parameter resolution, a low rate of spurious tracks, and a reasonable demand on CPU resources. The software is implemented in a flexible, modular manner and employs a diverse selection of global and local track-finding algorithms to achieve an optimal performance.
The Belle II detector at the SuperKEKB accelerator has a level 1 trigger implemented in field-programmable gate arrays. Due to the high luminosity of the beam, a trigger that effectively rejects beam induced background is required. A three dimensional tracking algorithm for the level 1 trigger that uses the Belle II central drift chamber detector response is being developed to reduce the recorded beam background while having a high efficiency for physics of interest. In this paper, we describe the three dimensional track trigger that finds and fits track parameters which we developed.
We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without…
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…
The Belle II experiment will go into operation at the upgraded SuperKEKB collider in 2016. SuperKEKB is designed to deliver an instantaneous luminosity $\mathcal{L}=8\times10^{35}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$. The experiment will therefore have to cope with a much larger machine background than its predecessor Belle, in particular from events outside of the interaction region. We present the concept of a track trigger, based on a neural network approach, that is able to suppress a large fraction of this background by reconstructing the $z$ (longitudinal) position of the event vertex wit…
The Belle II experiment at the SuperKEKB collider in Japan is designed to indirectly probe new physics using approximately 50 times the data recorded by its predecessor. An accurate determination of the decay-point position of subatomic particles such as beauty and charm hadrons as well as a precise measurement of low-momentum charged particles will play a key role in this pursuit. These will be accomplished by an inner tracking device comprising two layers of pixelated silicon detector and four layers of silicon vertex detector based on double-sided microstrip sensors. We describe herein the …
DEPleted Field Effect Transistor (DEPFET) active pixel detectors combine a first amplification stage with a fully depleted sensor in one single device, resulting in a very good signal-to-noise ratio even for thin sensors. DEPFET pixels are produced in MOS technology with two metal and two poly-silicon layers and have been developed for the use in Xray imaging and tracking in particle physics experiments. The sensor concept will be presented and all aspects of operation will be detailed with the focus on its application at the upgraded detector Belle II under preparation for the high-luminosity…
Since the start of data taking in spring 2019 at the SuperKEKB collider (KEK, Japan) the Belle II Silicon Vertex Detector (SVD) has been operating reliably and with high efficiency, while providing high quality data: high signal-to-noise ratio, greater than 99% hit efficiency, and precise spatial resolution. These attributes, combined with stability over time, result in good tracking efficiency. Currently the occupancy, dominated by beam-background hits, is quite low (about 0.5% in the innermost layer), causing no problems to the SVD data reconstruction. In view of the operation at higher lumi…
The Belle II experiment, located at the SuperKEKB collider at the high-energy research facility KEK in Tsukuba, Japan, started operation in 2018. Compared to the predecessor experiment Belle, Belle II plans to increase the peak luminosity by a factor of about 40, by employing nano-beam technology in the interaction region. In particular the new, innermost sub-detector of Belle II - the Pixel Vertex Detector (PXD) - is in close proximity to the interaction point. This allows for the detection of particles, which do not leave a signal in the outer sub-detectors. Among these, Highly Ionizing Part…
The Belle II experiment at the SuperKEKB energy-asymmetric $e^+ e^-$ collider is a substantial upgrade of the B factory facility at the Japanese KEK laboratory. The design luminosity of the machine is $8\times 10^{35}$ cm$^{-2}$s$^{-1}$ and the Belle II experiment aims to record 50 ab$^{-1}$ of data, a factor of 50 more than its predecessor. From February to July 2018, the machine has completed a commissioning run, achieved a peak luminosity of $5.5\times 10^{33}$ cm$^{-2}$s$^{-1}$, and Belle II has recorded a data sample of about 0.5 fb$^{-1}$. Main operation of SuperKEKB has started in March…
GENFIT is an experiment-independent track-fitting toolkit that combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, PANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing…
The Silicon Vertex Detector (SVD), with its four double-sided silicon strip sensor layers, is one of the two vertex sub-detectors of Belle II operating at SuperKEKB collider (KEK, Japan). Since 2019 and the start of the data taking, the SVD has demonstrated a reliable and highly efficient operation, even running in an environment with harsh beam backgrounds that are induced by the world's highest instantaneous luminosity. In order to provide the best quality track reconstruction with an efficient pattern recognition and track fit, and to correctly propagate the uncertainty on the hit's positio…
In 2019 the Belle II experiment started data taking at the asymmetric SuperKEKB collider (KEK, Japan) operating at the Y(4S) resonance. Belle II will search for new physics beyond the Standard Model by collecting an integrated luminosity of 50~ab$^{-1}$. The silicon vertex detector (SVD), consisting of four layers of double-sided silicon strip sensors, is one of the two vertex sub-detectors. The SVD extrapolates the tracks to the inner pixel detector (PXD) with enough precision to correctly identify hits in the PXD belonging to the track. In addition the SVD has standalone tracking capability …
During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 $μ$s on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and s…
We report on the R\&D for a first prototype of a silicon tracker based on an alternative approach for fast track finding. The working principle is inspired from neurobiology, in particular by the processing of visual images by the brain as it happens in nature. It is based on extensive parallelisation of data distribution and pattern recognition. In this work we present the design of a practical device that consists of a telescope based on single-sided silicon detectors; we describe the data acquisition system and the implementation of the track finding algorithms using available digital logic…
With the High Luminosity LHC upgrades, incorporating tracking information into the CMS Level-1 trigger becomes necessary in order to maintain a manageable trigger rate and good trigger performance e.g. to retain thresholds for electroweak physics. The main challenges Level-1 track finding faces are the large data throughput from the detector at a collision rate of 40 MHz and a 4 microsecond latency budget to reconstruct charged particle tracks with sufficiently low transverse momentum to be used in the Level-1 trigger decision. Dedicated all-FPGA hardware systems with time-multiplexed architec…
The relative differences in coordinates of Cylindrical-Gas-Electron-Multiplier-Detector-based Inner Tracker (CGEM-IT) clusters are studied to search for track segments in CGEM-IT. With the full simulation of single muon track samples, clear patterns are found and parameterized for the correct cluster combinations. The cluster combinations satisfying the patterns are selected as track segment candidates in CGEM-IT with an efficiency higher than 99%. The parameters of the track segments are obtained by a helix fitting. Some chi-squared quantities, evaluating the differences in track parameters b…
We apply deep learning methods as a track finding algorithm to the PANDA Forward Tracking Stations (FTS). The problem is divided into three steps: The first step relies on an Artificial Neural Network (ANN) that is trained as a binary classifier to build track segments in three different parts of the FTS, namely FT1,FT2, FT3,FT4, and FT5,FT6. The ANN accepts hit pairs as an input and outputs a probability that they are on the same track or not. The second step builds 3D track segments from the 2D ones and is based on the geometry of the detector. The last step is to match the track segments fr…
The High-Luminosity LHC is expected to deliver proton-proton collisions every 25 ns with an estimated 140-200 pileup interactions per bunch crossing. Ultrafast track finding is vital for handling Level 1 trigger rates in such conditions. An FPGA-based track trigger system, capable of finding tracks with momenta above 2 GeV, is presented.
The LHC experiments are designed to detect large amount of physics events produced with a very high rate. Considering the future upgrades, the data acquisition rate will become even higher and new computing paradigms must be adopted for fast data-processing: General Purpose Graphics Processing Units (GPGPU) is a novel approach based on massive parallel computing. The intense computation power provided by Graphics Processing Units (GPU) is expected to reduce the computation time and to speed-up the low-latency applications used for fast decision taking. In particular, this approach could be hen…
The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is designed to study a wide range of high energy physics phenomena. It employs a large all-silicon tracker within a 3.8 T magnetic solenoid, which allows precise measurements of transverse momentum ($p_\mathrm{T}$) and vertex position. This tracking detector will be upgraded to coincide with the operation of the High-Luminosity LHC, which will provide luminosities of up to $7.5\,\times\,10^{35}\,\mathrm{cm^{-2}s^{-1}}$ to CMS, or 200 collisions per 25 ns bunch crossing. This new tracker must maintain the nominal phys…
The GPU-accelerated track finding method is investigated to track electrons from neutrinoless muon decay in the COMET Phase-I experiment. Inside the cylindrical drift chamber, one third of the signal electron trajectories are composed of multiple turns where the correct hit assignments to each turn partition are significant in the track finding. Scanning all possible track seeds of position and momentum can resolve the hit-to-turn assignment problem with a high robustness, but requires a huge computational cost: The initial track seeds $(θ,z,p_x,p_y,p_z)$ have broad uncertainties, so there exi…
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging pro…
The search for charged particle tracks in detectors with linear sensing elements, such as wire chambers, strip silicon detectors etc., starts with identification of straight track segments. The latter are deduced by constructing all possible combinations of activated detector elements. In the high multiplicity environment, with many activated detector elements, this causes large combinatorics and significantly reduces the performance of the track finding algorithms. In this report we trace this problem back to determination of the parameters of a straight line build from a number of skew lines…
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. To stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Ha…
We propose a novel fast track finding system capable of reconstructing four dimensional particle trajectories in real time using precise space and time information of the hits. Recent developments in silicon pixel detectors achieved 150 ps time resolution and intense R&D is in progress to improve the timing performance, aiming at 10 ps. The use of the precise space and time information allows the suppression of background hits not compatible with the time of passage of the particle and the determination of its time evolution. The fast track finding device that we are proposing is based on a ma…
We report on the design and test results of a prototype processor for the CMS Level-1 trigger that performs 3-D track reconstruction and measurement from data recorded by the cathode strip chambers of the endcap muon system. The tracking algorithms are written in C++ using a class library we developed that facilitates automatic conversion to Verilog. The code is synthesized into firmware for field-programmable gate-arrays from the Xilinx Virtex-II series. A second-generation prototype has been developed and is currently under test. It performs regional track-finding in a 60 degree azimuthal se…
A pattern matching based tracking algorithm, named MdcPatRec, is used for the reconstruction of charged tracks in the drift chamber of the BESIII detector. This paper addresses the shortage of segment finding in MdcPatRec algorithm. An extended segment construction scheme and the corresponding pattern dictionary are presented. Evaluation with Monte-Carlo and experimental data show that the new method can achieve higher efficiency for low transverse momentum tracks.
We describe the Hybrid seeding, a standalone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing.
Conformal tracking is an innovative and comprehensive pattern recognition technique using a cellular automaton-based track finding performed in a conformally-mapped space. It is particularly well-suited for light-weight silicon systems with high position resolution, such as the next generation of tracking detectors designed for future electron-positron colliders. The algorithm has been developed and validated with simulated data of the CLICdet tracker. It has demonstrated not only excellent performance in terms of tracking efficiency, fake rate and track parameters resolution but also robustne…
The expected performance of track reconstruction with LHC events using the CMS silicon tracker is presented. Track finding and fitting is accomplished with Kalman Filter techniques that achieve efficiencies above 99% on single muons with pT>1 GeV/c. Difficulties arise in the context of standard LHC events with a high density of charged particles, where the rate of fake combinatorial tracks is very large for low pT tracks, and nuclear interactions in the tracker material reduce the tracking efficiency for charged hadrons. Recent improvements with the CMS track reconstruction now allow to effici…
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical tracking methods, such as the Kalman filter, cannot process them fast enough. To solve this problem, we developed two neural network algorithms based on deep learning architectures for track recognition in pixel and strip-based particle detectors. These are TrackNETv3 for local (track by track) and RDGraphNet for global (all tracks in an event) tracking. These algo…
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. To stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Ha…
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be…
For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationall…
Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, …
Calorimeter-assisted track finding algorithm takes advantage of the finely segmented electromagnetic calorimeter proposed for the SiD detector concept by looking for "MIP stubs" produced by charged particles in the calorimeter, and using them as seeds for pattern recognition in the tracker. The algorithm allows for efficient reconstruction of tracks that cannot be found using seeds provided by the vertex detector, even if standalone pattern recognition in the outer tracker is difficult. The algorithm has been implemented as a package in the org.lcsim framework. Current status of the package an…
Kyushu University（九州大学）；The University of Tokyo（东京大学）；Osaka University（大阪大学）；Osaka City University（大阪市立大学）
Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC recons…
The software of track reconstruction of the vertex chamber of BESII-VCJULI was studied when it was transplanted from the HP-Unix platform to PC-Linux. The problems of distinct dictionary storage and precision treatment in these two different platforms were found and settled in the modified software. Then the obvious differences of the candidate track number in an event and some track parameters caused by them were reduced from 74% to 0.02% and from 5% to 0.5%, respectively. As a result, the quality of the track finding was greatly improved and the CPU time saved.
The upcoming PANDA experiment at FAIR will be among a new generation of particle physics experiments to employ a novel event filtering system realised purely in software, i.e. a software trigger. To educate its triggering decisions, online reconstruction algorithms need to offer outstanding performance in terms of efficiency and track quality. We present a method to reconstruct longitudinal track parameters in PANDA's Straw Tube Tracker, which is general enough to be easily added to other track finding algorithms that focus on transversal reconstruction. For the pattern recognition part of thi…
