Eikos: a Bayesian unfolding method for differential cross-section measurements

A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Di Sipio, Riccardo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Di Sipio, Riccardo
description A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.
doi_str_mv 10.48550/arxiv.1808.01930
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1808_01930</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1808_01930</sourcerecordid><originalsourceid>FETCH-LOGICAL-a670-7f031e700a888514606c516623631188eaa768db5385f26d783cc6d68b96cef23</originalsourceid><addsrcrecordid>eNotz7tOAzEUBFA3FCjwAVT4B3ax1_H1DR1E4SFF0KRf3fiRWOzayN4g8veEQDXFjEY6jN1I0c5Ra3FH5Tt-tRIFtkIulLhkb6v4kes9J_5IR18jJX5IIQ8uph0f_bTPjodcuIsh-OLTFGngtuRam-rtFHM6rageih9PZb1iF4GG6q__c8Y2T6vN8qVZvz-_Lh_WDYERjQlCSW-EIETUcg4CrJYAnQIlJaInMoBuqxXq0IEzqKwFB7hdgPWhUzN2-3d7BvWfJY5Ujv0vrD_D1A-HEEho</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Eikos: a Bayesian unfolding method for differential cross-section measurements</title><source>arXiv.org</source><creator>Di Sipio, Riccardo</creator><creatorcontrib>Di Sipio, Riccardo</creatorcontrib><description>A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.</description><identifier>DOI: 10.48550/arxiv.1808.01930</identifier><language>eng</language><subject>Physics - Data Analysis, Statistics and Probability ; Physics - High Energy Physics - Experiment</subject><creationdate>2018-08</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1808.01930$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1808.01930$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Di Sipio, Riccardo</creatorcontrib><title>Eikos: a Bayesian unfolding method for differential cross-section measurements</title><description>A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.</description><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - High Energy Physics - Experiment</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7tOAzEUBFA3FCjwAVT4B3ax1_H1DR1E4SFF0KRf3fiRWOzayN4g8veEQDXFjEY6jN1I0c5Ra3FH5Tt-tRIFtkIulLhkb6v4kes9J_5IR18jJX5IIQ8uph0f_bTPjodcuIsh-OLTFGngtuRam-rtFHM6rageih9PZb1iF4GG6q__c8Y2T6vN8qVZvz-_Lh_WDYERjQlCSW-EIETUcg4CrJYAnQIlJaInMoBuqxXq0IEzqKwFB7hdgPWhUzN2-3d7BvWfJY5Ujv0vrD_D1A-HEEho</recordid><startdate>20180806</startdate><enddate>20180806</enddate><creator>Di Sipio, Riccardo</creator><scope>GOX</scope></search><sort><creationdate>20180806</creationdate><title>Eikos: a Bayesian unfolding method for differential cross-section measurements</title><author>Di Sipio, Riccardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-7f031e700a888514606c516623631188eaa768db5385f26d783cc6d68b96cef23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - High Energy Physics - Experiment</topic><toplevel>online_resources</toplevel><creatorcontrib>Di Sipio, Riccardo</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Di Sipio, Riccardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Eikos: a Bayesian unfolding method for differential cross-section measurements</atitle><date>2018-08-06</date><risdate>2018</risdate><abstract>A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.</abstract><doi>10.48550/arxiv.1808.01930</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.1808.01930
ispartof
issn
language eng
recordid cdi_arxiv_primary_1808_01930
source arXiv.org
subjects Physics - Data Analysis, Statistics and Probability
Physics - High Energy Physics - Experiment
title Eikos: a Bayesian unfolding method for differential cross-section measurements
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T14%3A21%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Eikos:%20a%20Bayesian%20unfolding%20method%20for%20differential%20cross-section%20measurements&rft.au=Di%20Sipio,%20Riccardo&rft.date=2018-08-06&rft_id=info:doi/10.48550/arxiv.1808.01930&rft_dat=%3Carxiv_GOX%3E1808_01930%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true