Resolving Histogram Binning Dilemmas with Binless and Binfull Algorithms

TL;DR

This paper introduces two 'debinning' algorithms that eliminate binning bias in histograms by reconstructing the underlying distribution from data, enhancing analysis accuracy in sciences like high energy physics.

Contribution

It presents novel binless and binfull algorithms to remove histogram binning bias, allowing more accurate probability distribution estimation from data.

Findings

Algorithms successfully reconstruct distributions without binning bias

Comparison highlights strengths and weaknesses of each method

Discussion on future applications and improvements

Abstract

The histogram is an analysis tool in widespread use within many sciences, with high energy physics as a prime example. However, there exists an inherent bias in the choice of binning for the histogram, with different choices potentially leading to different interpretations. This paper aims to eliminate this bias using two "debinning" algorithms. Both algorithms generate an observed cumulative distribution function from the data, and use it to construct a representation of the underlying probability distribution function. The strengths and weaknesses of these two algorithms are compared and contrasted. The applicability and future prospects of these algorithms is also discussed.