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In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program. [ABSTRACT FROM AUTHOR]

The Circular Electron Positron Collider (CEPC) is a future experiment aimed at studying the properties of the Higgs boson with high precision. This requires excellent track reconstruction and particle identification (PID) performance, which is achieved in the 4th conceptual detector design of the CEPC experiments by combining a silicon tracker and a drift chamber. The drift chamber not only improves track reconstruction but also provides excellent PID with the cluster counting method. To evaluate the performance of this design accurately, a detailed simulation is necessary. In this paper, we present a refined drift chamber simulation by combining Geant4 and Garfield++. However, traditional waveform simulation using Garfield++ is extremely time-consuming, which motivates us to develop a fast waveform simulation method using a neural network. We validate the method using real data from the BESIII experiment. The results demonstrate the effectiveness of our approach and provide valuable insights for future experiments. [ABSTRACT FROM AUTHOR]

The alignment of the Belle II tracking system, composed of a pixel and strip vertex detectors and central drift chamber, is described by approximately sixty thousand parameters; from local alignment of sensors and wires to relative global alignment of the sub-detectors. In the next data reprocessing, scheduled since Spring 2021, we aim to determine all parameters in a simultaneous fit by Millepede II, where recent developments allow to achieve a direct solution of the full problem in about one hour and make it practically feasible for regular detector alignment. The tracking detectors and the alignment technique are described and the alignment strategy is discussed in the context of studies on simulations and experience obtained from recorded data. Preliminary results and further refinements based on studies of real Belle II data are presented. [ABSTRACT FROM AUTHOR]

The tracking efficiency and the quality for the drift chamber of the BESIII experiment is essential to the physics analysis. The tracking efficiency of the drift chamber of BESIII is high for the high momentum tracks but still have room to improve for the low momentum tracks, especially for the tracks with multiple turn. A novel way to use a convolutional network called U-Net network is represented to solve the identification of the first turn's hits for the multiple-turn tracks. [ABSTRACT FROM AUTHOR]

The Belle II experiment has started to take data in 2018, studying e+e- collisions at the KEK facility in Tsukuba (Japan), in a center of mass energy range of the Bottomonium states. The tracking system includes a combination of hit measurements coming from the vertex detector, made of pixel detectors and double-sided silicon strip detectors, and acentral drift chamber, inside a solenoid of 1.5 T magnetic field. Once the pattern recognition routines have identified the track candidates, hit measurements are fitted taking into account the different information coming from different detectors, the energy loss in the materials and the inhomogeneity of the magnetic field. Track fitting is performed by the generic track-fitting software GENFIT, which includes a Kalman filter improved by a deterministic annealing filter, in order to reject outlier hits coming from not correctly associated hits by the pattern recognition. Several mass hypotheses are used in the fit, in order to achieve the best track parameter estimation for each particle kind. This article presents the design of the track fitting in the Belle II software, showing results in terms of track parameter estimation as well as computing performances. [ABSTRACT FROM AUTHOR]

The reconstruction of multi-turn curling tracks is a big challenge for the drift chamber of the COMET [1] experiment. A method of deterministic annealing filter that implements a global competition between hits from different turns is introduced. This method assigns the detector measurements to the candidate track based on the weighted mean of fitting quality on different turns. We present here the study of multi-turn track fitting based on the simulated tracks. We found that the algorithm can successfully distinguish the hits from different turns. [ABSTRACT FROM AUTHOR]

The current status of the analysis of the experimental data measured by the drift chambers in the BM@N experiment is presented. Apart from the previously published analysis based on the deuteron beam data, this study shows preliminary results for the carbon data at energy 4:5 GeV per nucleon. Some practical issues, such as evaluation and possible removal of the combinatorial background, as well as reliability of the employed track reconstruction algorithm are discussed as well. [ABSTRACT FROM AUTHOR]