2010/04/12 by Nikolai Gagunashvili, Gagunashvili, Nikolai · 1 citation
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #62-07 (Primary) #Applications (stat.AP) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design #Statistical Mechanics and Entropy #Statistics and Probability (physics.data-an) #astro-ph.IM #hep-ex #msc:62-07 #physics.data-an #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1004.2006
19 pages, 7 figures
openalex publication_date 2010/04/12 · arxiv created 2011/05/25 · arxiv updated 2011/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A procedure for unfolding the true distribution from experimental data is presented. Machine learning methods are applied for simultaneous identification of an apparatus function and solving of an inverse problem. A priori information about the true distribution from theory or previous experiments is used for Monte-Carlo simulation of the training sample. The training sample can be used to calculate a transformation from the true distribution to the measured one. This transformation provides a robust solution for an unfolding problem with minimal biases and statistical errors for the set of distributions used to create the training sample. The dimensionality of the solved problem can be arbitrary. A numerical example is presented to illustrate and validate the procedure.