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Register a new userBeam induced space-charge effects in Time Projection Chambers in low-energy nuclear physics experiments
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Added by
Jaspreet Singh Randhawa
Publication date
2019
fields
Physics
and research's language is
English
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Tracking capabilities in Time Projection Chambers (TPCs) are strongly dictated by the homogeneity of the drift field. Ion back-flow in various gas detectors, mainly induced by the secondary ionization processes during amplification, has long been known as a source of drift field distortion. Here, we report on beam-induced space-charge effects from the primary ionization process in the drift region in low-energy nuclear physics experiment with Active Target Time Projection Chamber (AT-TPC). A qualitative explanation of the observed effects is provided using detailed electron transport simulations. As ion mobility is a crucial factor in the space-charge effects, the need for a careful optimization of gas properties is highlighted. The impact of track distortion on tracking algorithm performance is also discussed.
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Time projection chambers (TPCs) are widely used in nuclear and particle physics. They are particularly useful when measuring reaction products from heavy ion collisions. Most nuclear experiments at low energy are performed in a fixed target configuration, in which the unreacted beam will pass through the detection volume. As the beam intensity increases, the buildup of positive ions created from the ionization of the detector gas by the beam creates the main source of space charge, distorting the nominal electric field of the TPC. This has a profound effect on the accuracy of the measured momenta of the emitted particles. In this paper we will discuss the magnitude of the effects and construct an observable more appropriate for fixed target experiments to study the effects. We also will present an algorithm for correcting the space charge and some of the implications it has on the momentum determination.
We report the demonstration of a low-power pixelated readout system designed for three-dimensional ionization charge detection and digital readout of liquid argon time projection chambers (LArTPCs). Unambiguous 3D charge readout was achieved using a custom-designed system-on-a-chip ASIC (LArPix) to uniquely instrument each pad in a pixelated array of charge-collection pads. The LArPix ASIC, manufactured in 180 nm bulk CMOS, provides 32 channels of charge-sensitive amplification with self-triggered digitization and multiplexed readout at temperatures from 80 K to 300 K. Using an 832-channel LArPix-based readout system with 3 mm spacing between pads, we demonstrated low-noise ($<$500 e$^-$ RMS equivalent noise charge) and very low-power ($<$100 $mu$W/channel) ionization signal detection and readout. The readout was used to successfully measure the three-dimensional ionization distributions of cosmic rays passing through a LArTPC, free from the ambiguities of existing projective techniques. The system design relies on standard printed circuit board manufacturing techniques, enabling scalable and low-cost production of large-area readout systems using common commercial facilities. This demonstration overcomes a critical technical obstacle for operation of LArTPCs in high-occupancy environments, such as the near detector site of the Deep Underground Neutrino Experiment (DUNE).
We present a comprehensive analysis of electronic recoil vs. nuclear recoil discrimination in liquid/gas xenon time projection chambers, using calibration data from the 2013 and 2014-16 runs of the Large Underground Xenon (LUX) experiment. We observe strong charge-to-light discrimination enhancement with increased event energy. For events with S1 = 120 detected photons, i.e. equivalent to a nuclear recoil energy of $sim$100 keV, we observe an electronic recoil background acceptance of $<10^{-5}$ at a nuclear recoil signal acceptance of 50%. We also observe modest electric field dependence of the discrimination power, which peaks at a field of around 300 V/cm over the range of fields explored in this study (50-500 V/cm). In the WIMP search region of S1 = 1-80 phd, the minimum electronic recoil leakage we observe is ${(7.3pm0.6)times10^{-4}}$, which is obtained for a drift field of 240-290 V/cm. Pulse shape discrimination is utilized to improve our results, and we find that, at low energies and low fields, there is an additional reduction in background leakage by a factor of up to 3. We develop an empirical model for recombination fluctuations which, when used alongside the Noble Element Scintillation Technique (NEST) simulation package, correctly reproduces the skewness of the electronic recoil data. We use this updated simulation to study the width of the electronic recoil band, finding that its dominant contribution comes from electron-ion recombination fluctuations, followed in magnitude of contribution by fluctuations in the S1 signal, fluctuations in the S2 signal, and fluctuations in the total number of quanta produced for a given energy deposition.
The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original points. We define an appropriate distance measure on point triplets and then apply a single-link hierarchical clustering on the triplets. Compared to parametric approaches like RANSAC or the Hough transform, the new algorithm has the advantage of potentially finding trajectories even of shapes that are not known beforehand. This feature is particularly important in low-energy nuclear physics experiments with Active Targets operating inside a magnetic field. The algorithm has been validated using data from experiments performed with the Active Target Time Projection Chamber developed at the National Superconducting Cyclotron Laboratory (NSCL).The results demonstrate the capability of the algorithm to identify and isolate particle tracks that describe non-analytical trajectories. For curved tracks, the vertex detection recall was 86% and the precision 94%. For straight tracks, the vertex detection recall was 96% and the precision 98%. In the case of a test set containing only straight linear tracks, the algorithm performed better than an iterative Hough transform.
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response. We extend the scope of ML application to experimental data by incorporating realistic detector response of the S$pi$RIT Time Projection Chamber into the heavy-ion simulation events generated from the UrQMD model to resemble experimental data. At 3 fm, the predicted impact parameter is 2.8 fm if simulation events with perfect detector is used for training and testing; 2.4 fm if detector response is included in the training and testing, and 5.8 fm if ML algorithms trained with perfect detector is applied to testing data that has included detector response. The last result is not acceptable illustrating the importance of including the detector response in developing the ML training algorithm. We also test the model dependence by applying the algorithms trained on UrQMD model to simulated events from four different transport models as well as using different input parameters on UrQMD model. Using data from Sn+Sn collisions at E/A=270 MeV, the ML determined impact parameters agree well with the experimentally determined impact parameter using multiplicities, except in the very central and very peripheral regions. ML selects central collision events better and allows impact parameters determination beyond the sharp cutoff limit imposed by experimental methods.
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