An Exploration of Learnt Representations of W Jets

Autor:	Collins, Jack H.
Rok vydání:	2021
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	I present a Variational Autoencoder (VAE) trained on collider physics data (specifically boosted $W$ jets), with reconstruction error given by an approximation to the Earth Movers Distance (EMD) between input and output jets. This VAE learns a concrete representation of the data manifold, with semantically meaningful and interpretable latent space directions which are hierarchically organized in terms of their relation to physical EMD scales in the underlying physical generative process. The variation of the latent space structure with a resolution hyperparameter provides insight into scale dependent structure of the dataset and its information complexity. I introduce two measures of the dimensionality of the learnt representation that are calculated from this scaling. Comment: Published version, to appear in ICLR workshop Deep Generative Models for Highly Structured Data. Additional appendices
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.10919 Zobrazit plný text záznamu View this record from Arxiv