Corpus ID: 219260369

PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics

  title={PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics},
  author={Corey Adams and Kazuhiro Terao and Taritree Wongjirad},
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… Expand
3 Citations
Point Proposal Network for Reconstructing 3D Particle Positions with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
A simple algorithm is demonstrated to cluster 3D voxels into individual track-like particle trajectories with a clustering efficiency, purity, and Adjusted Rand Index of 96 %, 93 %, and 91 % respectively. Expand
A Living Review of Machine Learning for Particle Physics
This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments. Expand
Convolutional Neural Networks for Shower Energy Prediction in Liquid Argon Time Projection Chambers
Abstract: When electrons with energies ofO (100) MeV pass through a liquid argon time projection chamber (LArTPC), they deposit energy in the form of electromagnetic showers. Methods to reconstructExpand


Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
This work presents the first machine learning-based approach to the reconstruction of Michel electrons, a standard candle for energy calibration in LArTPC due to their very well-understood energy spectrum, and shows the strong promise of scalable data reconstruction technique using deep neural networks for large scale L ArTPC detectors. Expand
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
This work demonstrates the applicability of GNNs to these two diverse particle reconstruction problems, which have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Expand
Aggregated Residual Transformations for Deep Neural Networks
On the ImageNet-1K dataset, it is empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy and is more effective than going deeper or wider when the authors increase the capacity. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
Deep Residual Learning for Image Recognition
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. Expand
Densely Connected Convolutional Networks
The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. Expand
GEMS: Underwater spectrometer for long-term radioactivity measurements
Abstract GEMS (Gamma Energy Marine Spectrometer) is a prototype of an autonomous radioactivity sensor for underwater measurements, developed in the framework for a development of a submarineExpand
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual RecognitionExpand
The Pascal Visual Object Classes (VOC) Challenge
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse. Expand
ICARUS), Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers
  • Detectors and Associated Equipment 527,
  • 2004