Conveners
Uncertainties, Calibration & Theory
- Matthias Schroeder (Hamburg University (DE))
We propose a new method based on machine learning to play the devil's advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis.
We explore this idea with two alternative approaches, one relies on a...
Machine learning based jet tagging techniques have greatly enhanced the sensitivity of measurements and searches involving boosted final states at the LHC. However, differences between the Monte-Carlo simulations used for training and data lead to systematic uncertainties on tagger performance. This talk presents the performance of boosted top and W boson taggers when applied on data sets...
Deep neural network based classifiers allow for efficient estimation of likelihood ratios in high dimensional spaces. Classifier-based cuts are thus being used to process experimental data, for example in top tagging. To efficiently investigate new theory, it is essential to estimate the behavior of these cuts efficiently. We suggest circumventing the full simulation of the experimental setup...
Applications of Machine Learning to physics beyond the Standard Model are becoming increasingly invaluable for theorists. As a leading proposal for a theory of quantum gravity, string theory gives rise to a plethora of 4-dimensional EFTs upon compactification, the so-called string landscape. For decades, a prohibiting factor in analysing these EFTs has been the computational cost of standard...
Neural networks are a powerful tool for an ever-growing list of tasks. However, their enormous complexity often complicates developing theories describing how these networks learn. In our recent work, inspired by the development of statistical mechanics, we have studied the use of collective variables to explain how neural networks learn, specifically, the von Neumann entropy and Trace of the...
This talk will be about our work on using machine learning to understand Calabi-Yau metrics. These extra-dimensional metrics determine aspects of the low-energy EFTs arising from string theory which have been unavailable for several decades prior to works using machine learning methods.
