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تسجيل مستخدم جديدEnergy reconstruction in a liquid argon calorimeter cell using convolutional neural networks
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The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a difficult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.
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We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localiz
ation of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their
physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the texttt{LArSoft}xspace framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a standalone version on Intel architectures. This new version has been incorporated back into texttt{LArSoft}xspace so that it can be generally used. These methods will also be applied to other low-level reconstruction algorithms of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.
Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product $^{10}$C is an important background in the region of inter
est between 2-3 MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network that uses the temporal and spatial correlations in light emissions to identify $^{10}$C background events. With a typical kiloton-scale detector configuration like the KamLAND detector, we find that the algorithm is capable of identifying 61.6% of the $^{10}$C at 90% signal acceptance. A detector with perfect light collection could identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.
Precise calorimetric reconstruction of 5-50 MeV electrons in liquid argon time projection chambers (LArTPCs) will enable the study of astrophysical neutrinos in DUNE and could enhance the physics reach of oscillation analyses. Liquid argon scintillat
ion light has the potential to improve energy reconstruction for low-energy electrons over charge-based measurements alone. Here we demonstrate light-augmented calorimetry for low-energy electrons in a single-phase LArTPC using a sample of Michel electrons from decays of stopping cosmic muons in the LArIAT experiment at Fermilab. Michel electron energy spectra are reconstructed using both a traditional charge-based approach as well as a more holistic approach that incorporates both charge and light. A maximum-likelihood fitter, using LArIATs well-tuned simulation, is developed for combining these quantities to achieve optimal energy resolution. A sample of isolated electrons is simulated to better determine the energy resolution expected for astrophysical electron-neutrino charged-current interaction final states. In LArIAT, which has very low wire noise and an average light yield of 18 pe/MeV, an energy resolution of $sigma/E simeq 9.3%/sqrt{E} oplus 1.3%$ is achieved. Samples are then generated with varying wire noise levels and light yields to gauge the impact of light-augmented calorimetry in larger LArTPCs. At a charge-readout signal-to-noise of S/N $simeq$ 30, for example, the energy resolution for electrons below 40 MeV is improved by $approx$ 10%, $approx$ 20%, and $approx$ 40% over charge-only calorimetry for average light yields of 10 pe/MeV, 20 pe/MeV, and 100 pe/MeV, respectively.
Liquid Argon Time Projection Chambers (LArTPCs) are ideal detectors for precision neutrino physics. These detectors, when located deep underground, can also be used for measurements of proton decay, and astrophysical neutrinos. The technology must be
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