Conveners
Interpretability, Robustness, and Uncertainties: Intro
- Daniel Whiteson (University of California Irvine (US))
- Chase Owen Shimmin (Yale University (US))
Interpretability, Robustness, and Uncertainties: Uncertainties
- Daniel Whiteson (University of California Irvine (US))
- Chase Owen Shimmin (Yale University (US))
Interpretability, Robustness, and Uncertainties: Information Content
- Chase Owen Shimmin (Yale University (US))
- Daniel Whiteson (University of California Irvine (US))
Interpretability, Robustness, and Uncertainties: Constructing Observables
- Daniel Whiteson (University of California Irvine (US))
- Chase Owen Shimmin (Yale University (US))
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity comes at the expense of instability in the identification of the large set of free parameters. This worok presents a recipe to improve the prediction accuracy of such models in three steps: i) accounting for...
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this talk we will present an argument cautioning against the usage of this method to meet the simulation requirements of an experiment, namely that data generated by a GAN cannot...
We show how Bayesian neural networks can be used to estimate uncertainties associated with regression, classification, and now also generative networks. For generative INNs, the combination of the learned density and uncertainty maps also provide insights into how these networks learn. These results show that criticizing the use of neural networks in LHC physics as black boxes is a...
Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced...
Nearly five years ago we introduced tree-based recursive NN models for jet physics, which intuitively reflected the sequence of 1-to-2 splittings found in a parton shower. Subsequently, tree-based models like JUNIPR were developed as (probabilistic) generative models that could be used for classification and reweighing. One result that somewhat undermined the narrative of the connection...
A framework is presented to extract and understand decision-making information from a deep neural network classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks that have been provided XAUG variables and those...
Four-tops (and its backgrounds) is very hard to model at the LHC, it represents a unique window for detecting top-philic NP, and its current measurements have some tension with theory and predictions. We find that simple, clean and powerful Bayesian Inference can be applied on the data to infer signal and background true distributions. We propose that these results could be used in a novel...
Machine learning (ML) is pushing through boundaries in computational physics.
Jet physics, with it's large and detailed dataset, is particularly well suited.
In this talk I will discuss the application of an unusual ML technique, Spectral Clustering, to jet formation.
Spectral clustering differers from much of ML as it has no "black-box" elements.
Instead, it is based on a simple,...
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses, and suitable kinematic variables for many well-motivated event topologies have been developed in collider physics. Here we propose a deep-learning-based algorithm to design good event variables that are sensitive to a wide range of the unknown model parameter values. We demonstrate...
