2026-10-10T20:42:20Z https://www.scidb.cn/oai

10.57760/sciencedb.j00186.01035 2026-05-18T19:11:27Z

2026-05-18 Dataset for Machine Learning-Based Event Classification and Vertex Reconstruction in MATE-TPC Nuclear Reaction Experiments doi:10.57760/sciencedb.j00186.01035 en This dataset is derived from nuclear reaction experiments conducted using the MATE-TPC detector system, along with subsequent data analysis procedures. The raw detector signals were processed through a reconstruction pipeline to identify particle tracks and extract physical observables, including reaction vertex positions and related energy information. Machine learning techniques were further employed to classify elastic scattering events and fusion reaction events, as well as to reconstruct the reaction vertex.The dataset mainly contains data used to generate the figures and analysis results presented in the associated publication. The primary variables included in the dataset consist of three-dimensional charged particle track information (spatial coordinates: x, y, z) and the deposited charge (q). The data correspond to experimental runs acquired under the conditions of an active target time projection chamber, without explicit time-dependent variation.The data are stored in commonly used formats (e.g., TXT files, ROOT files, or NumPy binary files). Each row represents the track coordinates of a particle, and each column corresponds to a specific physical quantity, such as spatial coordinates (x, y, z) and deposited charge (q). All variable definitions and units are consistent with those used in the main text of the paper.During data processing, conventional methods were first applied to label events and reconstruct the reaction vertex, while background events and non-physical events were removed. Subsequently, machine learning models were trained using simulated data and then applied to experimental data to evaluate their performance. Due to the applied selection criteria, missing data are minimal, and events that do not satisfy the selection conditions are not included in the final dataset.Uncertainties in the dataset mainly arise from detector resolution, the accuracy of conventional data analysis methods, and the domain gap between experimental data and simulated data. These factors may introduce systematic deviations in the results obtained from machine learning models.The dataset is stored in widely accessible formats and can be readily read and processed using common scientific computing tools, such as Python (NumPy, pandas) and ROOT. machine learning; event classification; vertex reconstruction Zhang Minghui Lu Fenhua Tu Wanqin EMBARGO https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank