Abstract
The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still ...a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.
We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.
Running scientific workflow applications on high-performance computing systems provides promising results in terms of accuracy and scalability. An example is the particle track reconstruction ...research in high-energy physics that consists of multiple machine-learning tasks. However, as the modern HPC system scales up, researchers spend more effort on coordinating the individual workflow tasks due to their increasing demands on computational power, large memory footprint, and data movement among various storage devices. These issues are further exacerbated when intermediate result data must be shared among different tasks and each is optimized to fulfill its own design goals, such as the shortest time or minimal memory footprint. In this paper, we investigate the data management challenges presented in scientific workflows. We observe that individual tasks, such as data generation, data curation, model training, and inference, often use data layouts only best for one's I/O performance but orthogonal to its successive tasks. We propose various solutions by employing alternative data structures and layouts in consideration of two tasks running consecutively in the workflow. Our experimental results show up to a 16.46x and 3.42x speedup for initialization time and I/O time respectively, compared to previous approaches.
Background.This paper describes the impact of the Eat Smart School Nutrition Program, the food service component of the Child and Adolescent Trial for Cardiovascular Health (CATCH), on the percentage ...of calories from total fat and saturated fat and the sodium content of school breakfasts.Methods.Fifty-nine of the 96 CATCH schools offered breakfast. We collected 5 consecutive days of school breakfast menu, recipe, and vendor product information at three periods to assess the nutrient content of the school menus as offered.Results.At baseline (Fall 1991), intervention school breakfasts provided 28% of calories from total fat and control schools 30%. Decreases occurred over time in both groups, but no significant differences were attributable to the intervention (adjusted mean difference = −0.4;P= 0.77). Saturated fat exceeded the Eat Smart goal at baseline in all schools and by follow-up (Spring 1994), the reduction in mean percentage of calories from saturated fat was greater in intervention than in control schools (adjusted mean difference = −1.6%;P= 0.052). Sodium goals were not achieved. Mean calorie levels were maintained at or above Eat Smart goals throughout the study in both groups. Differences over time in other dietary variables (percentage of calories from protein and carbohydrate and mean levels of protein, carbohydrate, calcium, iron, vitamin A value, vitamin C, total sugars, and dietary fiber) were not statistically significant between groups. No significant reductions in student participation in the School Breakfast Program (SBP) occurred.Conclusions.The Eat Smart food service intervention improved school breakfast composition, but not significantly more so than in control schools. Fat and saturated fat in school breakfasts were lowered while maintaining calories, other essential nutrient levels, and student participation in the SBP. Secular trends and also the possibility that control schools were affected by the Eat Smart intervention may account for these findings.
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential ...requires sophisticated automated reconstruction techniques. This article describes NuGraph2, a Graph Neural Network (GNN) for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multi-head attention message-passing mechanism to perform background filtering and semantic labelling on these graph nodes, identifying those associated with the primary physics interaction with 98.0\% efficiency and labelling them according to particle type with 94.9\% efficiency. The network operates directly on detector observables across multiple 2D representations, but utilizes a 3D-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s/event on a CPU, and 0.005 s/event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this article.
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve ...excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based ...on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to ...form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
Tau neutrinos are the least studied particle in the Standard Model. This whitepaper discusses the current and expected upcoming status of tau neutrino physics with attention to the broad experimental ...and theoretical landscape spanning long-baseline, beam-dump, collider, and astrophysical experiments. This whitepaper was prepared as a part of the NuTau2021 Workshop.
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these ...solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.
