Track Finder Research Articles

Firmware implementation of a displaced muon reconstruction algorithm for the Phase-2 Upgrade of the CMS muon system

Displaced muons serve as a crucial indicator of novel physics extending beyond the standard model. Several theoretical frameworks posit the existence of muons with extended lifetimes, which can materialize through collisions at the LHC. This study presents the firmware implementation of an algorithm to reconstruct displaced muons using the Overlap Muon Track Finder (OMTF) of the CMS Level-1 Trigger System (L1T) targeting the High Luminosity Large Hadron Collider (HL-LHC). The firmware response is evaluated at the simulation level, comparing the results with those of the emulator, and then the algorithm is implemented on an FPGA.

Reconstructing charged-particle trajectories in the PANDA Straw Tube Tracker using the LOcal Track Finder (LOTF) algorithm

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.

A 40 MHz Level-1 trigger scouting system for the CMS Phase-2 upgrade

The CMS Phase-2 upgrade for the HL-LHC aims at preserving and expanding the current physics capability of the experiment under extreme pileup conditions. A new tracking system incorporates a track finder processor, providing tracks to the Level-1 (L1) trigger. A new high-granularity calorimeter provides fine-grained energy deposition information in the endcap region. New front-end and back-end electronics feed the L1 trigger with high-resolution information from the barrel calorimeter and the muon systems. The upgraded L1 will be based primarily on the Xilinx Ultrascale Plus series of FPGAs, capable of sophisticated feature searches with resolution often similar to the offline reconstruction. The L1 Data Scouting system (L1DS) will capture L1 intermediate data produced by the trigger processors at the beam-crossing rate of 40 MHz, and carry out online analyses based on these limited-resolution data. The L1DS will provide fast and virtually unlimited statistics for detector diagnostics, alternative luminosity measurements, and, in some cases, calibrations. It also has the potential to enable the study of otherwise inaccessible signatures, either too common to fit in the L1 trigger accept budget or with requirements that are orthogonal to “mainstream” physics. The requirements and architecture of the L1DS system are presented, as well as some of the potential physics opportunities under study. The first results from the assembly and commissioning of a demonstrator currently being installed for LHC Run-3 are also presented. The demonstrator collects data from the Global Muon Trigger, the Layer-2 Calorimeter Trigger, the Barrel Muon Track Finder, and the Global Trigger systems of the current CMS L1. This demonstrator, as a data acquisition (DAQ) system operating at the LHC bunch-crossing rate, faces many of the challenges of the Phase-2 system, albeit with scaled-down connectivity, reduced data throughput and physics capabilities, providing a testing ground for new techniques of online data reduction and processing.

The Apollo ATCA design for the CMS track finder and the pixel readout at the HL-LHC

The challenging conditions of the High-Luminosity LHC require tailored hardware designs for the trigger and data acquisition systems. The Apollo platform features a “Service Module” with a powerful system-on-module computer that provides standard ATCA communications and application-specific “Command Modules” with large FPGAs and high speed optical fiber links. The CMS version of Apollo will be used for the track finder and the pixel readout. It features up to two large FPGAs and more than 100 optical links with speeds up to 25 Gb/s. We study carefully the design and performance of the board by using customized firmware to test power consumption, heat dissipation, and optical link integrity. This paper presents the results of these performance tests, design updates, and future plans.

Track Finding for the PANDA Detector Based on Hough Transformations

The PANDA experiment at FAIR (Facility for Antiproton and Ion Research) in Darmstadt is currently under construction. In order to reduce the amount of data collected during operation, it is essential to find as many true tracks as possible and to be able to distinguish them from false tracks. Part of the preparation for the experiment is the development of a fast online track finder. This work presents an online track finding algorithm based on Hough transformations, which is comparable in quality and performance to the currently best offline track finder in PANDA. In contrast to most track finders the algorithm can handle the challenge of extended hits delivered by PANDA’s central Straw Tube Tracker and thus benefit from its precise spatial resolution. Furthermore, optimization methods are presented that improved the ghost ratio as well as the speed of the algorithm by 70 %. Due to further development potential in terms of track finding for secondary particles and speed optimization on GPUs, this algorithm promises to exceed the quality and speed of other track finders developed for PANDA.

A 3D track finder for the Belle II CDC L1 trigger

Machine learning methods are integrated into the pipelined first level (L1) track trigger of the upgraded flavor physics experiment Belle II at KEK in Tsukuba, Japan. The novel triggering techniques cope with the severe background from events outside the small collision region provided by the new SuperKEKB asymmetric-energy electron-positron collider. Using the precise drift-time information of the central drift chamber which provides axial and stereo wire layers, a neural network L1 trigger estimates the 3D track parameters of tracks, based on input from the axial wire planes provided by a 2D track finder. An extension of this 2D Hough track finder to a 3D finder is proposed, where the single hit representations in the Hough plane are trained using Monte Carlo. This 3D finder improves the track finding efficiency by including the stereo sense wires as input. The estimated polar track angle allows a specialization of the subsequent neural networks to sectors in the polar angle.

40 MHz Level-1 Trigger Scouting for CMS

The CMS experiment will be upgraded for operation at the HighLuminosity LHC to maintain and extend its physics performance under extreme pileup conditions. Upgrades will include an entirely new tracking system, supplemented by a track finder processor providing tracks at Level-1, as well as a high-granularity calorimeter in the endcap region. New front-end and back-end electronics will also provide the Level-1 trigger with high-resolution information from the barrel calorimeter and the muon systems. The upgraded Level-1 processors, based on powerful FPGAs, will be able to carry out sophisticated feature searches with resolutions often similar to the offline ones, while keeping pileup effects under control. In this paper, we discuss the feasibility of a system capturing Level-1 intermediate data at the beam-crossing rate of 40 MHz and carrying out online analyzes based on these limited-resolution data. This 40 MHz scouting system would provide fast and virtually unlimited statistics for detector diagnostics, alternative luminosity measurements and, in some cases, calibrations. It has the potential to enable the study of otherwise inaccessible signatures, either too common to fit in the Level-1 accept budget, or with requirements which are orthogonal to “mainstream” physics, such as long-lived particles. We discuss the requirements and possible architecture of a 40 MHz scouting system, as well as some of the physics potential, and results from a demonstrator operated at the end of Run-2 using the Global Muon Trigger data from CMS. Plans for further demonstrators envisaged for Run-3 are also discussed.

Track Reconstruction with PANDA at FAIR

The future PANDA experiment at FAIR aims to investigate a wide range of physics processes in antiproton-proton collisions at rates of up to 20 MHz, while employing a purely software-based event filter. To educate the trigger decisions, a full event reconstruction has to be carried out in real time. This challenge is amplified when considering tracks from particles with long lifetimes and displaced decay vertices, which add to the complexity of the reconstruction algorithms. Here, we present modifications to a cellular automatonbased track finder taking detector time-stamps into account in addition to spatial information, as well as several pattern recognition methods for longitudinal track reconstrucion with PANDA’s Straw Tube Tracker.

The Impact of Applying Wildcards to Disabled Modules for Ftk Pattern Banks on Efficiency and Data Flow

Online selection is an essential step to collect the most relevant collisions from the very large number of collisions inside the ATLAS detector at the Large Hadron Collider (LHC). The Fast TracKer (FTK) is a hardware based track finder, built to greatly improve the ATLAS trigger system capabilities for identifying interesting physics processes through track-based signatures. The FTK is reconstructing after each Level-1 trigger all tracks with pT > 1 GeV, such that the high-level trigger system gains access to track information at an early stage. FTK track reconstruction starts with a pattern recognition step. Patterns are found with hits in seven out of eight possible detector layers. Disabled detector modules, as often encountered during LHC operation, lead to efficiency losses. To recover efficiency, WildCards (WC) algorithms are implemented in the FTK system. The WC algorithm recovers inefficiency but also causes high combinatorial background and thus increased data volumes in the FTK system, possibly exceeding hardware limitations. To overcome this, a refined algorithm to select patterns is developed and investigated in this article.

PENCARIAN RUTE TERPENDEK MENGGUNAKAN ALGORITMA DIJKSTRA PADA SISTEM INFORMASI GEOGRAFIS PEMETAAN STASIUN PENGISIAN BAHAN BAKAR UMUM

The capital city of West Sumatera, Padang, had so many event to increase its tourism. Every year, the domestic tourist coming and passing by to Padang, increasing the consumption of fuel in Padang. However, the information about location of gas station in Padang is not easy-to access. The focus of this project is to provide the information about location of gas station in Padang. This project aims to help people to find track between gas station. The systems are designed with a web-based map layers represented by MapServer. The data (maps) was made with ArcGIS and MapInfo, digitized into shapefile form (vector model), the track finder using Dijkstra’s Algorithm. As well as general contained in the database storage system using the application postresql and PostGIS, implemented into web pages via PHP. This outcomes of this project is a geographic information system that provides general information of gas station in Padang. Visitors can find the location based on the name of the gas station. Then the system provide the track between gas station and track from the point that visitor decide. General information will be displayed in tabular form and location information is displayed in the form of maps showing the location of the point. Keywords: Geograpic Information System, Algorithm, Dijkstra, MapServer, OpenLayer, PostgreSQL/PostGis.

The algorithm of the CMS Level-1 Overlap Muon Track Finder trigger

The CMS Level-1 trigger, based on custom electronics built around FPGA devices, was upgraded in 2016 to achieve the required performance with almost two times higher LHC luminosity than originally designed. The upgraded Level-1 muon trigger merges data from the three muon detectors in the CMS (DT, CSC, and RPC) in the track reconstruction stage — contrary to the legacy trigger which comprised three separate systems each based on one muon detector. This approach allows better use of the detector redundancy and improved measurement of the muon transverse momentum . However, it is particularly challenging in the barrel-endcap transition region, where up to 18 muon chamber layers are present, the detector geometry is complex, and the magnetic field bending the muon tracks is heterogeneous. For this region the Overlap Muon Track Finder (OMTF) uses a novel algorithm based on a naive Bayes classifier. The algorithm identifies the muon tracks and measures their momentum by calculating the probabilities of matching the detector hits to defined transverse momentum hypotheses. The algorithm was tailored to the needs of muon measurements based on the detector data (e.g. to cope with missing hits or multi-muon events) and implementation in FPGA technology. The algorithm details, performance, and optimization methods are discussed in this report. • A novel algorithm was developed for the upgrade of the CMS L1 muon trigger. • It is based on a naive Bayes classifier - a classic machine learning algorithm. • The algorithm performance is significantly better than the legacy muon trigger.

Level-1 track finding with an all-FPGA system at CMS for the HL-LHC

The CMS experiment at the 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 T ) and vertex position. This tracking detector will be upgraded to coincide with the installation of the High-Luminosity LHC (HL-LHC), which will provide up to about 1 0 35 cm − 2 s − 1 to CMS, or up to 200 collisions per 25 ns bunch crossing. This new tracker must maintain the nominal physics performance in this more challenging environment. Novel tracking modules that utilize closely spaced silicon sensors to discriminate on track p T have been developed to allow the readout of only hits compatible with p T > 2-3 GeV tracks to off-detector trigger electronics. This would enable the use of tracking information to produce the Level-1 trigger of the experiment, a requirement to keep the Level-1 triggering rate below the 750 kHz target, while maintaining physics sensitivity. A concept for an all-FPGA based track finder using a fully time-multiplexed architecture is presented. Hardware demonstrators have been used to prove the feasibility and capability of such a system using two different reconstruction algorithms. The proposed scaling of the demonstrators to the final system is showed as well as the new technologies used in the first prototypes.

Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS

The first implementation of a Machine Learning Algorithm inside a Level-1 trigger system at the LHC is presented. The Endcap Muon Track Finder (EMTF) at CMS uses Boosted Decision Trees (BDTs) to infer the momentum of muons in the forward region of the detector, based on 25 different variables. Combinations of these variables representing 230 distinct patterns are evaluated offline using regression BDTs. The predictions for the 230 input variable combinations are stored in a 1.2 GB look-up table in the EMTF hardware. The BDTs take advantage of complex correlations between variables, the inhomogeneous magnetic field, and non-linear effects – like inelastic scattering – to distinguish high momentum signal muons from the overwhelming low-momentum background. The new momentum algorithm reduced the background rate by a factor of three with respect to the previous analytic algorithm, with further improvements foreseen in the coming year.

Time-based Reconstruction of Free-streaming Data in CBM

Traditional latency-limited trigger architectures typical for conventional experiments are inapplicable for the CBM experiment. Instead, CBM will ship and collect time-stamped data into a readout buffer in a form of a time-slice of a certain length and deliver it to a large computer farm, where online event reconstruction and selection will be performed. Grouping measurements into physical collisions must be performed in software and requires reconstruction not only in space, but also in time, the so-called 4-dimensional track reconstruction and event building. The tracks, reconstructed with 4D Cellular Automaton track finder, are combined into event-corresponding clusters according to the estimated time in the target position and the errors, obtained with the Kalman Filter method. The reconstructed events are given as inputs to the KF Particle Finder package for short-lived particle reconstruction. The results of time-based reconstruction of simulated collisions in CBM are presented and discussed in details.

An FPGA based track finder for the L1 trigger of the CMS experiment at the High Luminosity LHC

A new tracking detector is under development for use by the CMS experiment at the High-Luminosity LHC (HL-LHC). A crucial requirement of this upgrade is to provide the ability to reconstruct all charged particle tracks with transverse momentum above 2–3 GeV within 4 μs so they can be used in the Level-1 trigger decision. A concept for an FPGA-based track finder using a fully time-multiplexed architecture is presented, where track candidates are reconstructed using a projective binning algorithm based on the Hough Transform, followed by a combinatorial Kalman Filter. A hardware demonstrator using MP7 processing boards has been assembled to prove the entire system functionality, from the output of the tracker readout boards to the reconstruction of tracks with fitted helix parameters. It successfully operates on one eighth of the tracker solid angle acceptance at a time, processing events taken at 40 MHz, each with up to an average of 200 superimposed proton-proton interactions, whilst satisfying the latency requirement. The demonstrated track-reconstruction system, the chosen architecture, the achievements to date and future options for such a system will be discussed.

The CMS Barrel Muon trigger upgrade

The increase of luminosity expected by LHC during Phase1 will impose tighter constraints for rate reduction in order to maintain high efficiency in the CMS Level1 trigger system. The TwinMux system is the early layer of the muon barrel region that concentrates the information from different subdetectors: Drift Tubes, Resistive Plate Chambers and Outer Hadron Calorimeter. It arranges the slow optical trigger links from the detector chambers into faster links (10 Gbps) that are sent in multiple copies to the track finders. Results from collision runs, that confirm the satisfactory operation of the trigger system up to the output of the barrel track finder, will be shown.

Software and firmware co-development using high-level synthesis

Accelerating trigger applications on FPGAs (using VHDL/Verilog) at the CMS experiment at CERN's Large Hadron Collider warrants consistency between each trigger firmware and its corresponding C++ model. This tedious and time consuming process of convergence is exacerbated during each upgrade study. High-level synthesis, with its promise of increased productivity and C++ design entry bridges this gap exceptionally well. This paper explores the “single source code” approach using Vivado-HLS tool for redeveloping the upgraded CMS Endcap Muon Level-1 Track finder (EMTF). Guidelines for tight latency control, optimal resource usage and compatibility with CMS software framework are outlined in this paper.

Implementation of the data acquisition system for the Overlap Muon Track Finder in the CMS experiment

The Overlap Muon Track Finder (OMTF) is the new system developed during the upgrade of the CMS experiment which includes the upgrade of its Level-1 trigger. It uses the novelty approach to finding muon candidates based on data received from three types of detectors: RPC, DT, and CSC . The upgrade of the trigger system requires also upgrade of the associated Data Acquisition (DAQ) system. The OMTF DAQ transmits the data from the connected detectors that were the basis for the Level-1 trigger decision. To increase its diagnostic potential, it may also transmit the data from a few bunch crossings (BXes) preceding or following the BX, in which the L1 trigger was generated. The paper describes the technical concepts and solutions used in the OMTF DAQ system. The system is still under development. However, it successfully passed the first tests.

Parallel 4-Dimensional Cellular Automaton Track Finder for the CBM Experiment

The CBM experiment (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of rare probes at interaction rates up to 10 MHz with data flow of up to 1 TB/s. It requires a novel read-out and data-acquisition concept with self-triggered electronics and free-streaming data. In this case resolving different collisions is not a trivial task and event building must be performed in software online. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES).The FLES reconstruction and selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. The Cellular Automaton (CA) track finder algorithm was adapted towards time-slice-based reconstruction and included into the CBMROOT framework. In this article, we describe the modification done to the algorithm, as well as the performance of the developed time-based approach.

FPGA Online Tracking Algorithm for the PANDA Straw Tube Tracker

A novel FPGA based online tracking algorithm for helix track reconstruction in a solenoidal field, developed for the PANDA spectrometer, is described. Employing the Straw Tube Tracker detector with 4636 straw tubes, the algorithm includes a complex track finder, and a track fitter. Implemented in VHDL, the algorithm is tested on a Xilinx Virtex-4 FX60 FPGA chip with different types of events, at different event rates. A processing time of 7 $\mu$s per event for an average of 6 charged tracks is obtained. The momentum resolution is about 3\% (4\%) for $p_t$ ($p_z$) at 1 GeV/c. Comparing to the algorithm running on a CPU chip (single core Intel Xeon E5520 at 2.26 GHz), an improvement of 3 orders of magnitude in processing time is obtained. The algorithm can handle severe overlapping of events which are typical for interaction rates above 10 MHz.