Loading…
Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks
A bstract The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem in high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, wel...
Saved in:
| Published in: | The journal of high energy physics 2019-04, Vol.2019 (4), p.1-43, Article 57 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03 |
| container_end_page | 43 |
| container_issue | 4 |
| container_start_page | 1 |
| container_title | The journal of high energy physics |
| container_volume | 2019 |
| creator | Karpie, Joseph Orginos, Kostas Rothkopf, Alexander Zafeiropoulos, Savvas |
| description | A
bstract
The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem in high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, well established in the study of inverse problems, to reconstruct the full
x
-dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time simulations in conjunction with a matching procedure. Using realistic mock data tests, we find that the ill-posed incomplete Fourier transform underlying the reconstruction requires careful regularization, for which both the Bayesian approach as well as neural networks are efficient and flexible choices. |
| doi_str_mv | 10.1007/JHEP04(2019)057 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_d535279b6d084732bd3107472dd3d003</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d535279b6d084732bd3107472dd3d003</doaj_id><sourcerecordid>2203602111</sourcerecordid><originalsourceid>FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03</originalsourceid><addsrcrecordid>eNp1UU1vEzEUXCGQKIUzVwsucAh9z_Z619xoVdqgSiAEZ8tfmzpN7GB7hfrvcdgKuHB6z6OZkedN171EeIcAw9mn68svwN9QQPkW-uFRd4JA5Wrkg3z8z_60e1bKFgB7lHDS3X31NsVS82xriBty0LmmSFxoUDBzDe0xzdEel0KmnPZknabJkxr2njhd9fsFPdf3vgQdyd7X2-QKqYlEP2e9a6P-TPmuPO-eTHpX_IuHedp9_3j57eJ6dfP5an3x4WZle4S6MhK9pEYYISY5CgkouB3dSO3IJ0_5KBhwii1oLzj3QCkyZyxyYdxgLLDTbr34uqS36pDDXud7lXRQv4GUN6qlDHbnletZTwdphIN2HEaNYwgDH6hzzAGw5vVq8UqlBlVsqN7etotFb6vCnnLsj6TXC-mQ04_Zl6q2ac6xZVSUAhNAEbGxzhaWzamU7Kc_X0NQxwrVUqE6VqhahU0Bi6I0Ztz4_Nf3f5JfEDWcaQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2203602111</pqid></control><display><type>article</type><title>Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks</title><source>Springer Nature - SpringerLink Journals - Fully Open Access </source><source>Publicly Available Content (ProQuest)</source><creator>Karpie, Joseph ; Orginos, Kostas ; Rothkopf, Alexander ; Zafeiropoulos, Savvas</creator><creatorcontrib>Karpie, Joseph ; Orginos, Kostas ; Rothkopf, Alexander ; Zafeiropoulos, Savvas ; Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States) ; Heidelberg Univ. (Germany) ; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC) ; College of William and Mary, Williamsburg, VA (United States)</creatorcontrib><description>A
bstract
The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem in high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, well established in the study of inverse problems, to reconstruct the full
x
-dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time simulations in conjunction with a matching procedure. Using realistic mock data tests, we find that the ill-posed incomplete Fourier transform underlying the reconstruction requires careful regularization, for which both the Bayesian approach as well as neural networks are efficient and flexible choices.</description><identifier>ISSN: 1029-8479</identifier><identifier>EISSN: 1029-8479</identifier><identifier>DOI: 10.1007/JHEP04(2019)057</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Bayesian analysis ; Classical and Quantum Gravitation ; Computer simulation ; Dependence ; Distribution functions ; Elementary Particles ; First principles ; Fourier transforms ; Hadrons ; High energy physics ; Ill posed problems ; Inverse problems ; Lattice QCD ; Lattice Quantum Field Theory ; Neural networks ; Nuclear physics ; NUCLEAR PHYSICS AND RADIATION PHYSICS ; Numerical methods ; Physics ; Physics and Astronomy ; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS ; Quantum chromodynamics ; Quantum Field Theories ; Quantum Field Theory ; Quantum Physics ; Regular Article - Theoretical Physics ; Regularization ; Relativity Theory ; String Theory</subject><ispartof>The journal of high energy physics, 2019-04, Vol.2019 (4), p.1-43, Article 57</ispartof><rights>The Author(s) 2019</rights><rights>Journal of High Energy Physics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03</citedby><cites>FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2203602111/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2203602111?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,777,781,882,25734,27905,27906,36993,44571,74875</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/1524153$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Karpie, Joseph</creatorcontrib><creatorcontrib>Orginos, Kostas</creatorcontrib><creatorcontrib>Rothkopf, Alexander</creatorcontrib><creatorcontrib>Zafeiropoulos, Savvas</creatorcontrib><creatorcontrib>Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)</creatorcontrib><creatorcontrib>Heidelberg Univ. (Germany)</creatorcontrib><creatorcontrib>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)</creatorcontrib><creatorcontrib>College of William and Mary, Williamsburg, VA (United States)</creatorcontrib><title>Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks</title><title>The journal of high energy physics</title><addtitle>J. High Energ. Phys</addtitle><description>A
bstract
The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem in high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, well established in the study of inverse problems, to reconstruct the full
x
-dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time simulations in conjunction with a matching procedure. Using realistic mock data tests, we find that the ill-posed incomplete Fourier transform underlying the reconstruction requires careful regularization, for which both the Bayesian approach as well as neural networks are efficient and flexible choices.</description><subject>Bayesian analysis</subject><subject>Classical and Quantum Gravitation</subject><subject>Computer simulation</subject><subject>Dependence</subject><subject>Distribution functions</subject><subject>Elementary Particles</subject><subject>First principles</subject><subject>Fourier transforms</subject><subject>Hadrons</subject><subject>High energy physics</subject><subject>Ill posed problems</subject><subject>Inverse problems</subject><subject>Lattice QCD</subject><subject>Lattice Quantum Field Theory</subject><subject>Neural networks</subject><subject>Nuclear physics</subject><subject>NUCLEAR PHYSICS AND RADIATION PHYSICS</subject><subject>Numerical methods</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>PHYSICS OF ELEMENTARY PARTICLES AND FIELDS</subject><subject>Quantum chromodynamics</subject><subject>Quantum Field Theories</subject><subject>Quantum Field Theory</subject><subject>Quantum Physics</subject><subject>Regular Article - Theoretical Physics</subject><subject>Regularization</subject><subject>Relativity Theory</subject><subject>String Theory</subject><issn>1029-8479</issn><issn>1029-8479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1UU1vEzEUXCGQKIUzVwsucAh9z_Z619xoVdqgSiAEZ8tfmzpN7GB7hfrvcdgKuHB6z6OZkedN171EeIcAw9mn68svwN9QQPkW-uFRd4JA5Wrkg3z8z_60e1bKFgB7lHDS3X31NsVS82xriBty0LmmSFxoUDBzDe0xzdEel0KmnPZknabJkxr2njhd9fsFPdf3vgQdyd7X2-QKqYlEP2e9a6P-TPmuPO-eTHpX_IuHedp9_3j57eJ6dfP5an3x4WZle4S6MhK9pEYYISY5CgkouB3dSO3IJ0_5KBhwii1oLzj3QCkyZyxyYdxgLLDTbr34uqS36pDDXud7lXRQv4GUN6qlDHbnletZTwdphIN2HEaNYwgDH6hzzAGw5vVq8UqlBlVsqN7etotFb6vCnnLsj6TXC-mQ04_Zl6q2ac6xZVSUAhNAEbGxzhaWzamU7Kc_X0NQxwrVUqE6VqhahU0Bi6I0Ztz4_Nf3f5JfEDWcaQ</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Karpie, Joseph</creator><creator>Orginos, Kostas</creator><creator>Rothkopf, Alexander</creator><creator>Zafeiropoulos, Savvas</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>Springer Berlin</general><general>SpringerOpen</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>DOA</scope></search><sort><creationdate>20190401</creationdate><title>Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks</title><author>Karpie, Joseph ; Orginos, Kostas ; Rothkopf, Alexander ; Zafeiropoulos, Savvas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Bayesian analysis</topic><topic>Classical and Quantum Gravitation</topic><topic>Computer simulation</topic><topic>Dependence</topic><topic>Distribution functions</topic><topic>Elementary Particles</topic><topic>First principles</topic><topic>Fourier transforms</topic><topic>Hadrons</topic><topic>High energy physics</topic><topic>Ill posed problems</topic><topic>Inverse problems</topic><topic>Lattice QCD</topic><topic>Lattice Quantum Field Theory</topic><topic>Neural networks</topic><topic>Nuclear physics</topic><topic>NUCLEAR PHYSICS AND RADIATION PHYSICS</topic><topic>Numerical methods</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>PHYSICS OF ELEMENTARY PARTICLES AND FIELDS</topic><topic>Quantum chromodynamics</topic><topic>Quantum Field Theories</topic><topic>Quantum Field Theory</topic><topic>Quantum Physics</topic><topic>Regular Article - Theoretical Physics</topic><topic>Regularization</topic><topic>Relativity Theory</topic><topic>String Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karpie, Joseph</creatorcontrib><creatorcontrib>Orginos, Kostas</creatorcontrib><creatorcontrib>Rothkopf, Alexander</creatorcontrib><creatorcontrib>Zafeiropoulos, Savvas</creatorcontrib><creatorcontrib>Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)</creatorcontrib><creatorcontrib>Heidelberg Univ. (Germany)</creatorcontrib><creatorcontrib>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)</creatorcontrib><creatorcontrib>College of William and Mary, Williamsburg, VA (United States)</creatorcontrib><collection>SpringerOpen</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>The journal of high energy physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karpie, Joseph</au><au>Orginos, Kostas</au><au>Rothkopf, Alexander</au><au>Zafeiropoulos, Savvas</au><aucorp>Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)</aucorp><aucorp>Heidelberg Univ. (Germany)</aucorp><aucorp>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)</aucorp><aucorp>College of William and Mary, Williamsburg, VA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks</atitle><jtitle>The journal of high energy physics</jtitle><stitle>J. High Energ. Phys</stitle><date>2019-04-01</date><risdate>2019</risdate><volume>2019</volume><issue>4</issue><spage>1</spage><epage>43</epage><pages>1-43</pages><artnum>57</artnum><issn>1029-8479</issn><eissn>1029-8479</eissn><abstract>A
bstract
The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem in high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, well established in the study of inverse problems, to reconstruct the full
x
-dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time simulations in conjunction with a matching procedure. Using realistic mock data tests, we find that the ill-posed incomplete Fourier transform underlying the reconstruction requires careful regularization, for which both the Bayesian approach as well as neural networks are efficient and flexible choices.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/JHEP04(2019)057</doi><tpages>43</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1029-8479 |
| ispartof | The journal of high energy physics, 2019-04, Vol.2019 (4), p.1-43, Article 57 |
| issn | 1029-8479 1029-8479 |
| language | eng |
| recordid | cdi_doaj_primary_oai_doaj_org_article_d535279b6d084732bd3107472dd3d003 |
| source | Springer Nature - SpringerLink Journals - Fully Open Access ; Publicly Available Content (ProQuest) |
| subjects | Bayesian analysis Classical and Quantum Gravitation Computer simulation Dependence Distribution functions Elementary Particles First principles Fourier transforms Hadrons High energy physics Ill posed problems Inverse problems Lattice QCD Lattice Quantum Field Theory Neural networks Nuclear physics NUCLEAR PHYSICS AND RADIATION PHYSICS Numerical methods Physics Physics and Astronomy PHYSICS OF ELEMENTARY PARTICLES AND FIELDS Quantum chromodynamics Quantum Field Theories Quantum Field Theory Quantum Physics Regular Article - Theoretical Physics Regularization Relativity Theory String Theory |
| title | Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T20%3A52%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reconstructing%20parton%20distribution%20functions%20from%20Ioffe%20time%20data:%20from%20Bayesian%20methods%20to%20neural%20networks&rft.jtitle=The%20journal%20of%20high%20energy%20physics&rft.au=Karpie,%20Joseph&rft.aucorp=Thomas%20Jefferson%20National%20Accelerator%20Facility%20(TJNAF),%20Newport%20News,%20VA%20(United%20States)&rft.date=2019-04-01&rft.volume=2019&rft.issue=4&rft.spage=1&rft.epage=43&rft.pages=1-43&rft.artnum=57&rft.issn=1029-8479&rft.eissn=1029-8479&rft_id=info:doi/10.1007/JHEP04(2019)057&rft_dat=%3Cproquest_doaj_%3E2203602111%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c510t-b91e92b6b66f98690164c8d82c84fe2486304212015644e02213dbc146bd7bc03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2203602111&rft_id=info:pmid/&rfr_iscdi=true |