Deep learning in color: towards automated quark/gluon jet discrimination (1612.01551v3)

Abstract: Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

• The paper introduces a novel approach using color-encoded jet images and deep CNNs to distinguish quark and gluon jets with high accuracy.
• The CNNs, trained on both Pythia and Herwig event data, capture complex jet substructures, surpassing conventional physics observables.
• The study demonstrates the method's robustness and potential for real-time applications, reducing the need for human-designed features in particle physics.

An Essay on "Deep Learning in Color: Towards Automated Quark/Gluon Jet Discrimination"

The paper titled "Deep Learning in Color: Towards Automated Quark/Gluon Jet Discrimination" investigates the application of deep convolutional neural networks (CNNs) in distinguishing between quark-initiated and gluon-initiated jets produced in high-energy particle collisions, such as those probed at the Large Hadron Collider (LHC). The novelty of the approach lies in advancing the "jet image" paradigm by incorporating color information, thereby mimicking techniques employed in computer vision for enhanced image recognition.

Overview and Methodology

Quark and gluon jet discrimination is a challenging task due to the subtle differences in their radiation patterns. Traditional approaches rely on physicist-designed observables, which may not capture the full complexity of the problem. In contrast, this paper proposes treating jets as images where pixel intensities correspond to transverse momentum deposits. Furthermore, the paper introduces "color" to these images by assigning three distinct channels corresponding to the transverse momenta of charged particles, neutral particles, and the charged particle multiplicity.

The authors use CNNs to process the jet images, leveraging their ability to learn spatial hierarchies automatically. This endows the networks with the capability to distinguish underlying physical features without requiring explicit human-designed inputs. Event data are simulated for multiple transverse momentum ranges using both Pythia and Herwig event generators. The independent use of these generators addresses the diversity in simulated quark and gluon jets while testing the model's robustness across varying inputs.

Results and Insights

The deep CNNs developed in the paper match or surpass the discrimination power of traditional variables like girth and charged particle multiplicity individually and in combination. The ROC and SIC analysis indicates that the CNNs demonstrate superior performance, especially as they exploit color information in higher-energy jets, reflecting more complex internal structures.

Notably, while trained on Pythia-generated events, the CNNs show comparable efficacy when applied to Herwig-generated events. This insensitivity to the choice of simulation suggests that the CNNs capture fundamental characteristics of jet physics rather than artifacts specific to a particular dataset, a crucial factor for future applications to real collider data.

The research also probes into the role of network architecture, input preprocessing, and data augmentation in CNN efficacy, emphasizing that certain steps—like centering and normalization—significantly enhance performance. Additionally, the attempt to use merge layers reveals that CNNs inherently learn combinations of basic observables, such as quark/gluon multiplicity and jet shape, further validating their utility.

Implications and Future Directions

The implications of this work for particle physics are significant, suggesting that machine learning techniques like CNNs can reliably tackle complex classification tasks with limited reliance on human-engineered features. This holds tremendous promise for automating data analysis in high-energy physics, ultimately allowing physicists to focus on more insightful interpretations rather than preliminary data sorting.

Moreover, as simulations of particle events and associated uncertainties improve, deep learning models can become even more integral to experimental research efforts. The resilience of CNNs to variations across different simulators implies their potential utility in dynamically adjusting to novel data distributions, thereby fostering more accurate and efficient data-driven discoveries.

Future research could further explore real-time applications of deep learning at collider facilities, including deployment in low-latency environments for triggering systems. Another promising avenue is extending the model architecture to include attention mechanisms or integrating additional physical inputs, such as energy flow metrics, to enrich model predictive capabilities. Lastly, enhancing interpretability methods for these networks would aid in bridging the gap between machine learning outputs and theoretical insights, facilitating their integration into standard physics practice.