Speaker
Description
Cluster counting is a highly promising particle identification technique for drift chambers in particle physics experiments. In this paper, we trained neural network models, including a Long Short-Term Memory (LSTM) model for the peak-finding algorithm and a Convolutional Neural Network (CNN) model for the clusterization algorithm, using various hyperparameters such as loss functions, activation functions, numbers of neurons, batch sizes, and different numbers of epochs. These models were trained utilizing high performance computing (HPC) resources provided by the ReCas computing center. The best LSTM peak-finding model was selected based on the highest area under the curve (AUC) value, while the best CNN clusterization model was chosen based on the lowest mean square error (MSE) value among all configurations. The training was conducted on momentum ranges from 200 MeV to 20 GeV and 180 GeV.
The trained models (LSTM and CNN) were subsequently tested on samples with momenta of 2GeV, 4 GeV, 6 GeV, 8 GeV, 10 GeV and 180 GeV. The simulation parameters included 90% Helium and 10% Isobutane, a cell size of 0.8 cm, a sampling rate of 2 GHz, a time window of 400 ns, 10000 events, and a 45-degree angle between the muon particle track and the z-axis (sense wire) of the drift tube chamber. The testing aimed to evaluate the performance of the LSTM model for peak finding and the CNN model for clusterization.
Significance
The peak finding is essential for the cluster counting technique. The new developed algorithm based on machine learning overcomes the traditional algorithm such as derivatives for the peak finding.
| Experiment context, if any | The study is applied for the IDEA Drift Chamber, FCC. |
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