A Convolutional Neural Network Neutrino Event Classifier (1604.01444v3)

Abstract: Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

• The paper presents a CNN model that automatically extracts features from calorimeter image data to improve neutrino event classification.
• It employs dual-view inputs and inception modules to process X and Y data in parallel, enhancing computational efficiency and reducing bias.
• Experimental results demonstrate a significant increase in νe-CC classification efficiency, indicating the method's potential for broader applications in high-energy physics.

A Convolutional Neural Network Neutrino Event Classifier

The paper presents a convolutional neural network (CNN)-based approach for neutrino event classification in the context of high-energy physics, particularly within the NOvA experiment. The classification of particle interactions represents a crucial challenge in experimental high-energy particle physics (HEP). Traditionally, this has relied on reconstructing high-level features from raw data which is prone to errors and limited by preconceived feature sets. CNNs, proven successful in computer vision, are applied here to automatically extract features directly from particle interaction data, offering potential improvements over existing methods.

Methodology and Contributions

The researchers developed a CNN, named Convolutional Visual Network (CVN), tailored for the NOvA detector, which aims to classify neutrino interactions into several types: νμ-CC, νe-CC, ντ-CC, and ν NC interactions. This neural network leverages the high-resolution data characteristic of sampling calorimeters, treating the detections as images, analogous to how CNNs handle computer vision tasks. It uses a deep learning architecture inspired by GoogLeNet, comprising inception modules that facilitate feature extraction at various layers without exponential growth in computational complexity.

Key improvements over traditional HEP algorithms include:

1. The utilization of CNNs that operate on low-level data avoids potential biases and errors associated with higher-level feature reconstruction.
2. Enhanced representational and computational efficiency by treating the data input as a dual-view image (X and Y) and processing them in parallel CNN branches.

Training was conducted using a comprehensive set of simulated neutrino events, employing techniques such as dropout and regularization to mitigate overfitting. Additionally, data augmentation was performed to improve generalization by introducing noise and small transformations.

Results and Implications

CVN showed significant improvements in classification efficiencies compared with previous methods used in NOvA. In the νe-CC classification, CVN achieved 49% signal-detection-optimized efficiency, a substantial enhancement over previous algorithms’ 35%. For νμ-CC, the performance was on par with existing techniques, offering robust classification without underperformance.

This research has profound implications:

1. CVN holds potential for broader adoption in different types of HEP detectors beyond the NOvA experiment, suggesting a paradigm shift towards machine learning for data processing in particle physics.
2. The method’s adaptability and efficiency imply it can successfully tackle complex physics problems, including those involving elusive signal types like neutrino oscillations.
3. Future work could explore semantic segmentation for particle identification and extend these advancements to other domains within HEP that traditionally rely on manual feature extraction and reconstruction.

Overall, the CNN-based approach displayed in this paper points towards effective use of deep learning in domains well beyond standard computer vision applications, evidencing the versatility and power of deep learning architectures in scientific research endeavors.