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Register a new userTracking Algorithms for TPCs using Consensus-Based Robust Estimators
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A tracking algorithm based on consensus-robust estimators was implemented for the analysis of experiments with time-projection chambers. In this work, few algorithms beyond RANSAC were successfully tested using experimental data taken with the AT-TPC, ACTAR and TexAT detectors. The present tracking algorithm has a better inlier-outlier detection than the simple sequential RANSAC routine. Modifications in the random sampling and clustering were included to improve the tracking efficiency. Very good results were obtained in all the test cases, in particular for fitting short tracks in the detection limit.
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Xenon time projection chambers (TPCs) have become a well-established detection technology for neutrinoless double beta decay searches in $^{136}$Xe. I discuss the motivations for this choice. I describe the status and prospects of both liquid and gaseous xenon TPC projects for double beta decay.
A novel light detection scheme has been tested for use in medium-pressure gas TPCs, in view of rare events searches in low energy particle physics. It has the advantage of minimal interference with the ionization collection system, used for track imaging. It provides an absolute time reference, which allows an absolute determination of the Z coordinate of events, along the direction of the drift field. This makes possible a fiducial cut along the Z-axis, allowing to reduce the background from the ends of the drift volume.
The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardware-based trigger system. There are many challenges involved in achieving this: a large input data rate of about 20--40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles (stubs); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 $mu,$s. This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.
Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.
The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 94% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in $ u_{mu} CC$ interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.
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