The Landscape of Unfolding with Machine Learning

Abstract: Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.

• The paper introduces and evaluates diverse machine learning methods, such as OmniFold and generative models, to enhance data unfolding in high-energy physics.
• ML-based methods allow for unbinned and high-dimensional data processing, demonstrating improved accuracy over traditional techniques on benchmark datasets.
• Integrating these machine learning techniques into experiments can streamline data analysis, manage computational resources, and enable broader scientific engagement with unfolded distributions.

Overview of "Modern Machine Learning Tools for Unfolding"

The paper "Modern Machine Learning Tools for Unfolding" explores the application of ML technologies in the context of data unfolding within high-energy physics experiments. The traditional approach for analyzing data from particle physics experiments at facilities such as the Large Hadron Collider (LHC) involves using forward simulations to predict detector-level event signatures from theoretical models. Unfolding, in this context, refers to the process of inferring true particle-level distributions from the observed data. The innovative contribution of this paper lies in leveraging a diverse array of ML techniques to improve the unfolding process, circumventing the limitations posed by traditional binning and low-dimensional statistics.

Key Scientific Contributions

1. Diverse ML-based Approaches: The paper introduces and evaluates multiple machine learning methodologies for unfolding, including enhanced versions of known techniques and newly proposed methods like OmniFold, and generatively focused approaches such as conditional invertible neural networks (cINN), Bayesian networks, and Generative Adversarial Networks (GANs).
2. Unbinned and High-Dimensional Capabilities: One significant advantage of these ML-based approaches is the ability to process unbinned data, maintaining the complete resolution of data without pre-binning. This capability is particularly advantageous when dealing with high-dimensional data spaces, facilitating more detailed and accurate unfolding.
3. Method Comparison on Benchmark Datasets: The authors benchmark the performance of these methods using two standard datasets, ensuring a rigorous comparison. They demonstrate that ML-based unfolding techniques can precisely reproduce particle-level spectra across complex observables, outperforming traditional methods in scenarios involving intricate correlations and higher dimensions.
4. Conceptual Diversity for Problem-Specific Solutions: A noteworthy implication of the study is the conceptual diversity offered by different ML methodologies. Each approach comes with its respective strengths, making the toolkit adaptable for various types of experimental conditions and analyses required to probe the Standard Model and search for new physics phenomena.

Practical and Theoretical Implications

• Experimental Integration: The integration of ML-based unfolding in LHC experiments could democratize data access, enabling a broader scientific community to engage in data analysis by providing unfolded distributions rather than raw detector outputs.
• Future Experimental Design and Global Analyses: Improved unfolding methodologies can streamline data processing in high-luminosity scenarios and facilitate global analyses, such as combinations across multiple experiments using frameworks like Standard Model Effective Theory (SMEFT).
• Computational Resource Management: By reducing the computational burden associated with the need for detailed simulations for every new hypothesis, ML techniques could permit more efficient data analysis workflows, thereby optimizing the usage of computational resources.
• Conceptual Extension: The paper also suggests potential future extensions of these ML techniques to parton-level unfolding and broader applications in experimental physics requiring detailed statistical correction processes.

Conclusion and Future Directions

This work provides foundational insights into the utilization of advanced machine learning algorithms for unfolding in high-energy physics and sets the stage for their broader application in upcoming experiments needing refined data interpretation capabilities. Future work will need to address the model dependencies inherent in any unfolding process and continue refining these ML strategies to ensure robustness and general applicability in dynamic experimental environments.