Speaker
Description
Unfolding is an inverse data transformation process that aims to correct high-level physics observable quantities obtained from a set of data for possible distortions introduced by the instrument. Unfolding is a crucial step to enable experimental outcomes to be directly comparable to theoretical predictions. Traditional unfolding methods used in experimental High Energy Physics consist in statistical inference of a particle-level distribution from a corresponding detector-level distribution. While capable of high accuracy and precision, these techniques have many shortcomings including limitation in scalability, flexibility, and dependence on simulations. Here we discuss a novel AI-based approach to multidimensional object-wise unfolding, first introduced by [Pazos, Beauchemin et al] using conditional Denoising Diffusion Probabilistic Models (cDDPM). While addressing many of the challenges posed by traditional unfolding methods, this new approach offers the opportunity for interesting philosophical insights. For example, this approach blurs the distinction between low-level pattern identification and high-level information about physics processes. Another epistemologically interesting aspect of this algorithm is that it requires inductive bias to better approximate a universal unfolding tool, making theory-ladenness a desideratum. Finally, the AI-based algorithm does not seem more or less opaque than the traditional statistical-based approach, highlighting the importance of the critical assessment of the performance of any algorithm, and indicating a continuity between machine-learning methods and previous statistical methods for the analysis of observational data.