Abstract
The need for large scale and high fidelity simulated samples for the ATLAS experiment motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms at interpolation as well as image generation, Variational Auto-Encoders and Generative Adversarial Networks are investigated for modeling the response of the electromagnetic calorimeter for photons in a central calorimeter region over a range of energies. The synthesized showers are compared to showers from a full detector simulation using Geant4. This study demonstrates the potential of using such algorithms for fast calorimeter simulation for the ATLAS experiment in the future.
Highlights
In this work we describe some main features of FORM, such as the preprocessor and $-variables with emphasizing on benefit of metaprogramming, and introduce a new feature: a topology generator
The theoretical particle physics community has needed complicated and cumbersome computations based on perturbative quantum field theory in order to precisely predict or explain observable quantities that have been measured in experiments
FORM is advantageous for handling gigantic mathematical expressions that do not fit inside the physical memory of computers; its dedicated sorting algorithms, which utilize sequential access to hard disk drives, enable users to manipulate huge expressions on disks efficiently
Summary
Author(s): Ueda, Takahiro; Kaneko, Toshiaki; Ruijl, Ben; Vermaseren, Jozef A.M. Publication Date: 2020-07-07 Permanent Link: https://doi.org/10.3929/ethz-b-000428712 Originally published in: Journal of Physics: Conference Series 1525(1), http://doi.org/10.1088/1742-6596/1525/1/012013 Rights / License: Creative Commons Attribution 3.0 Unported This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.
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