#PRASANTH SHYAMSUNDAR SOFTWARE#
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR Matchev, Alexander Roman and Prasanth Shyamsundar 21 December 2020 Journal of High Energy Physics, Vol. The above copyright notice and this permission notice shall be included in allĬopies or substantial portions of the Software. To use, copy, modify, merge, publish, distribute, sublicense, and/or sellĬopies of the Software, and to permit persons to whom the Software isįurnished to do so, subject to the following conditions: In the Software without restriction, including without limitation the rights Of this software and associated documentation files (the "Software"), to deal
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#PRASANTH SHYAMSUNDAR LICENSE#
CopyrightĬopyright © 2019 Konstantin Matchev and Prasanth Shyamsundar ThickBrick is licensed under the MIT License (click to expand). The package documentation mentions the methods used where appropriate.
This list does not include the now-mainstream algorithms and ideas from mathematics, statistics, machine learning, etc, used in the package. Given the special circumstances and uncertainties with COVID-19 this year, SSI-2020 is a reduced program of just three online lectures each morning through the two weeks, without the usual afternoon programs in regular editions. Matchev, Prasanth Shyamsundar, "Optimal event selection and categorization in high energy physics, Part 1: Signal discovery", arXiv:1911.12299. The SLAC Summer Institute (SSI) is an annual two-week-long Summer School tradition since 1973. If you use the algorithms implemented in ThickBrick in your work, please consider citing the original papers that introduced them. Project website: References and citation guide The algorithms are intended to train event selectors and categorizers that maximize the sensitivity of physics analyses to the presence of a signal being searched for, or to the value of a parameter being measured. Prasanth Shyamsundar (Fermi National Accelerator Laboratory), 11:00 This work generalizes the quantum amplitude amplification (Grover’s) and amplitude estimation algorithms to work with non-Boolean oracles, leading to two new algorithms. ThickBrick is a Python 3 implementation of certain data selection and categorization algorithms originally conceived in the context of data analysis in high energy physics.
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