PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
This provides a public dataset for researchers in high-energy physics to accelerate development in particle detector analysis, though it is incremental as it introduces a new dataset rather than a novel method.
The authors tackled the lack of public datasets for particle imaging in high-energy physics by creating PILArNet, a 2D and 3D open dataset with 300,000 simulated samples for key analysis tasks, which facilitates benchmarking and reduces barriers to entry.
Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare different solutions. Furthermore, large datasets have been used to train high-performing feature finders which are then used in new approaches to problems beyond that initially defined. In order to encourage the rapid development in the analysis of data collected using liquid argon time projection chambers, a class of particle detectors used in high energy physics experiments, we have produced the PILArNet, first 2D and 3D open dataset to be used for a couple of key analysis tasks. The initial dataset presented in this paper contains 300,000 samples simulated and recorded in three different volume sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with auxiliary information about simulated particles in the volume, and is made available for public research use. In this paper we describe the dataset, tasks, and the method used to procure the sample.