Penalized splines for smooth representation of high-dimensional Monte Carlo datasets

Published: 1 September 2013| Version 1 | DOI: 10.17632/rp5hpmgrd8.1
Nathan Whitehorn, Jakob van Santen, Sven Lafebre


Abstract Detector response to a high-energy physics process is often estimated by Monte Carlo simulation. For purposes of data analysis, the results of this simulation are typically stored in large multi-dimensional histograms, which can quickly become both too large to easily store and manipulate and numerically problematic due to unfilled bins or interpolation artifacts. We describe here an application of the penalized spline technique (Marx and Eilers, 1996) [1] to efficiently compute B-spline rep... Title of program: Photospline Catalogue Id: AEPK_v1_0 Nature of problem An algorithm to smoothly represent histogram, including mathematical operations and convolutions. Using histograms of Monte Carlo simulation for likelihood fitting can be unstable due to binning artifacts from statistical fluctuations and hard bin-to-bin transitions. This package provides a toolkit for using penalized spline fits on extremely large multi-dimensional datasets to reduce or eliminate such issues. Versions of this program held in the CPC repository in Mendeley Data AEPK_v1_0; Photospline; 10.1016/j.cpc.2013.04.008 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)



Computational Physics, Computational Method