Report of the 1990 HEPAP (High Energy Physics Advisory Panel) subpanel on SSC (Superconducting Super Collider) physics

This article describes the beginnings of the Superconducting Super Collider (SSC). The narrative starts in the early 1980s with the discussion of the process that led to the recommendation by the US high energy physics community to initiate work on a multi-TeV hadron collider. The article then describes the formation in 1984 of the Central Design Group (CDG) charged with directing and coordinating the SSC R&D and subsequent activities which led in early 1987 to the SSC endorsement by President Reagan. The last part of the article deals with the site selection process, steps leading to the initial Congressional appropriation of the SSC construction funds and the creation of the management structure for the SSC Laboratory.

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Report of the Subpanel on High Energy Physics Manpower of the High Energy Physics Advisory Panel

10.2172/6918025 ◽

1978 ◽

Author(s):

Keyword(s):

High Energy Physics ◽

High Energy ◽

Advisory Panel ◽

Energy Physics

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Calorimetry with deep learning: particle simulation and reconstruction for collider physics

The European Physical Journal C ◽

10.1140/epjc/s10052-020-8251-9 ◽

2020 ◽

Vol 80 (7) ◽

Cited By ~ 5

Author(s):

Dawit Belayneh ◽

Federico Carminati ◽

Amir Farbin ◽

Benjamin Hooberman ◽

Gulrukh Khattak ◽

...

Keyword(s):

Deep Learning ◽

High Energy Physics ◽

High Energy ◽

Particle Identification ◽

Training Data ◽

Particle Simulation ◽

Collider Physics ◽

Cell Level ◽

Reconstruction Model ◽

Energy Physics

Abstract Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

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