HackAnalysis 2: A powerful and hackable recasting tool

TL;DR

HackAnalysis 2 is a versatile, hackable recasting tool that offers fast development, new physics routines, neural network integration, and improved analysis capabilities for high-energy physics research.

Contribution

This version introduces compressed event storage, neural network interface via ONNX, systematic uncertainty computation, and enhanced analysis features, making it highly adaptable for physics recasting.

Findings

Includes several new electroweakino analyses.

Enables fast experimental limit computations.

Provides improved integration with analysis tools.

Abstract

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.