Enhancing Significance in Particle Physics Searches Using a Decorrelated Event Classifier Transformer Neural Network

Core Concepts

A transformer-based neural network with specialized training techniques can enhance the expected significance and reduce the correlation between the network's output and the reconstructed mass for particle physics resonance searches.

Abstract

The content describes the development of a new neural network architecture called the "event classifier transformer" for enhancing the significance of particle physics resonance searches. Key highlights:

The event classifier transformer is a transformer-based neural network that classifies signal and background events to bin the analysis region and improve the expected significance.

Novel training techniques are proposed to improve the performance of the event classifier transformer and other neural networks:

Specialized "extreme loss" function to enhance significance
Distance Correlation (DisCo) regularization to decorrelate the network output from the reconstructed mass
"Data scope training" to further improve significance and decorrelation

In the context of a simplified H→Z(ℓ⁺ℓ⁻)γ search, the event classifier transformer trained with the specialized techniques shows higher significance and lower mass correlation compared to boosted decision trees and feed-forward networks.

The results demonstrate the potential of the event classifier transformer and the targeted training techniques for improving the sensitivity of particle physics resonance searches.

Stats

The Higgs boson (pp→H) is generated with MADGRAPH5_aMC@NLO using the Higgs Effective Field Theory (HEFT) model and decayed to H→Z(ℓ⁺ℓ⁻)γ using PYTHIA8.
The background pp→Z(ℓ⁺ℓ⁻)γ is generated with MADGRAPH5_aMC@NLO at leading order.
The data set consists of 45 million events each for signal and background, for a total of 90 million events.
The signal events are scaled to the standard model cross section of 7.52 × 10⁻³ pb with a luminosity of 138 fb⁻¹.
The background is scaled to the standard model cross section of 55.5 pb with the same luminosity as the signal.

Quotes

"To increase the penalty when ŷ and y are different, while keeping the property that the minimum is achieved at ŷ = y, an alternative loss inspired from BCE called extreme loss is proposed."
"DisCo measures the dependence between ŷ and mass, where the value of DisCo is zero if and only if ŷ and mass are independent."
"The significance is calculated as followings, 1. Divide the data set into bins based on the neural network's output. The bins are constructed to have an equal number of signal events. 2. Calculate the significance of each bin. 3. Combine the significances of the bins."

Key Insights Distilled From

Training towards significance with the decorrelated event classifier transformer neural network

by Jaebak Kim at arxiv.org 04-26-2024

https://arxiv.org/pdf/2401.00428.pdf

Deeper Inquiries

How could the event classifier transformer architecture be further extended or modified to handle more complex particle physics tasks beyond resonance searches

To extend the event classifier transformer architecture for more complex particle physics tasks beyond resonance searches, several modifications and enhancements can be considered:

Incorporating Sequential Information: Particle physics data often involves sequential dependencies, such as particle tracks or decay chains. Modifying the transformer architecture to incorporate sequential information, similar to how it is done in natural language processing tasks, can capture these dependencies effectively.

Hierarchical Transformers: Introducing hierarchical transformer structures can help in capturing multi-scale features in particle physics data. This can be particularly useful in tasks where particles interact at different energy scales or in different regions of a detector.

Attention Mechanisms for Interaction: Developing attention mechanisms that focus on the interactions between particles or events can enhance the model's ability to understand complex particle interactions and dynamics.

Graph Neural Networks Integration: Integrating graph neural network components into the transformer architecture can enable the model to directly operate on graph representations of particle interactions, offering a more flexible and powerful framework for analyzing complex physics phenomena.

Uncertainty Estimation: Incorporating uncertainty estimation techniques, such as Bayesian neural networks or dropout layers, can provide valuable insights into the model's confidence in its predictions, which is crucial in particle physics experiments with inherent uncertainties.

What other specialized loss functions or training techniques could be explored to improve the performance of neural networks in particle physics applications

In addition to the specialized loss functions and training techniques already explored in the context of particle physics applications, the following approaches could be further investigated to enhance the performance of neural networks:

Adversarial Training: Incorporating adversarial training techniques can improve the robustness of neural networks against adversarial attacks and enhance their generalization capabilities in classifying signal and background events accurately.

Semi-Supervised Learning: Leveraging unlabeled data in conjunction with labeled data through semi-supervised learning methods can potentially boost the model's performance by utilizing the underlying structure of the data distribution more effectively.

Meta-Learning: Exploring meta-learning techniques can enable neural networks to adapt quickly to new particle physics tasks or datasets, leading to improved generalization and faster convergence during training.

Regularization Strategies: Experimenting with novel regularization strategies, such as mixup, label smoothing, or cutout, can help prevent overfitting and improve the model's ability to generalize to unseen data.

Ensemble Methods: Implementing ensemble methods, such as bagging or boosting, can combine multiple neural network models to enhance predictive performance and increase the model's robustness.

What are the potential implications of the decorrelation techniques developed in this work for other areas of machine learning beyond particle physics, such as fairness and interpretability

The decorrelation techniques developed in this work for particle physics applications can have significant implications for other areas of machine learning, such as fairness and interpretability:

Fairness in Machine Learning: By reducing the correlation between the model's output and specific features, such as demographic attributes, these techniques can help mitigate biases and promote fairness in machine learning models, ensuring equitable predictions across different groups.

Interpretability Enhancement: Decorrelation techniques can improve the interpretability of machine learning models by disentangling the influence of different input features on the model's predictions. This can aid in understanding the decision-making process of complex models and increase trust in their outputs.

Robustness to Adversarial Attacks: Decorrelated models are often more robust to adversarial attacks, as they are less likely to rely on spurious correlations that can be exploited by adversaries to manipulate the model's predictions.

Generalization Improvement: By reducing the correlation between features and the model's output, these techniques can enhance the model's generalization capabilities, leading to better performance on unseen data and improved overall model reliability.