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Bibliographic Details
Main Author: Goodsell, Mark D.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2406.10042
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author Goodsell, Mark D.
author_facet Goodsell, Mark D.
contents This is the manual for the version 2 of HackAnalysis, a powerful, lightweight, versatile and, most importantly, hackable, recasting tool. New features in this version include: compressed event format storage for ultra-fast development; integration of new physics and mathematics routines via RestFrames and Eigen; automatic computation of systematic uncertainties; an interface to ONNX for neural networks; easy implementation of new models via QNUMBERS blocks; a python package for interfacing to pyhf, spey and in-built statistics routines for fast computation of experimental limits; improved integration with BSMArt for fast scanning, and a new batch running/convergence check. Several new (electroweakino) analyses are included with this release.
format Preprint
id arxiv_https___arxiv_org_abs_2406_10042
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle HackAnalysis 2: A powerful and hackable recasting tool
Goodsell, Mark D.
High Energy Physics - Phenomenology
This is the manual for the version 2 of HackAnalysis, a powerful, lightweight, versatile and, most importantly, hackable, recasting tool. New features in this version include: compressed event format storage for ultra-fast development; integration of new physics and mathematics routines via RestFrames and Eigen; automatic computation of systematic uncertainties; an interface to ONNX for neural networks; easy implementation of new models via QNUMBERS blocks; a python package for interfacing to pyhf, spey and in-built statistics routines for fast computation of experimental limits; improved integration with BSMArt for fast scanning, and a new batch running/convergence check. Several new (electroweakino) analyses are included with this release.
title HackAnalysis 2: A powerful and hackable recasting tool
topic High Energy Physics - Phenomenology
url https://arxiv.org/abs/2406.10042