Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning
A bstract We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applicat...
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| Veröffentlicht in: | The journal of high energy physics 2021-05, Vol.2021 (5), p.1-45, Article 13 |
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| container_title | The journal of high energy physics |
| container_volume | 2021 |
| creator | Anderson, Lara B. Gerdes, Mathis Gray, James Krippendorf, Sven Raghuram, Nikhil Ruehle, Fabian |
| description | A
bstract
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in ℙ
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| doi_str_mv | 10.1007/JHEP05(2021)013 |
| format | Article |
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bstract
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in ℙ
4
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bstract
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in ℙ
4
.</description><subject>Classical and Quantum Gravitation</subject><subject>Couplings</subject><subject>Differential and Algebraic Geometry</subject><subject>Elementary Particles</subject><subject>Field theory</subject><subject>High energy physics</subject><subject>Hyperspaces</subject><subject>Machine learning</subject><subject>Numerical methods</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Physics, Particles & Fields</subject><subject>Quantum Field Theories</subject><subject>Quantum Field Theory</subject><subject>Quantum Physics</subject><subject>Regular Article - Theoretical Physics</subject><subject>Relativity Theory</subject><subject>Science & Technology</subject><subject>String Theory</subject><subject>Strings</subject><subject>Superstring Vacua</subject><subject>Superstrings and Heterotic Strings</subject><issn>1029-8479</issn><issn>1029-8479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkEFrFTEURgexYG1dux1w0yJjbzLJTGYpQ2srLRW0C1chk9w885iXPJMMxX9v2pHqRugql3DOl5uvqt4S-EAA-rPPl-dfgJ9QoOQUSPuiOiRAh0awfnj5z_yqep3SFoBwMsBhdXMTzDK7xuAevUGf61HNanLNd7XUypv6691Je9qkHBedl4j1DnN0OtU2hl29U_qH81jPqKJ3fnNcHVg1J3zz5zyq7i7Ov42XzfXtp6vx43WjGW9z0_IWzGDVQGlLjOmNEmwCpimKyQLtNDOTNp1lhvOedAPTnJuJGMuJmQaL7VF1teaaoLZyH91OxV8yKCcfL0LcSBWz0zNKoygCF71VoFhHJtF1tqdEaaGRMEtK1rs1ax_DzwVTltuwRF_Wl5RTIgjvB1aos5XSMaQU0T69SkA-9C_X_uVD_7L0X4z3q3GPU7BJO_QanywoCm8FA1om4IUWz6dHl1V2wY9h8bmosKqp4H6D8e8H_rfbb2Rcpzc</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Anderson, Lara B.</creator><creator>Gerdes, Mathis</creator><creator>Gray, James</creator><creator>Krippendorf, Sven</creator><creator>Raghuram, Nikhil</creator><creator>Ruehle, Fabian</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature</general><general>Springer Nature B.V</general><general>SpringerOpen</general><scope>C6C</scope><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</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>DOA</scope></search><sort><creationdate>20210501</creationdate><title>Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning</title><author>Anderson, Lara B. ; Gerdes, Mathis ; Gray, James ; Krippendorf, Sven ; Raghuram, Nikhil ; Ruehle, Fabian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-3530d9fa92231dd7da84b04c2e8bf026c4dbcd6f4d5571694c55db1df51db9fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Classical and Quantum Gravitation</topic><topic>Couplings</topic><topic>Differential and Algebraic Geometry</topic><topic>Elementary Particles</topic><topic>Field theory</topic><topic>High energy physics</topic><topic>Hyperspaces</topic><topic>Machine learning</topic><topic>Numerical methods</topic><topic>Physical Sciences</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>Physics, Particles & Fields</topic><topic>Quantum Field Theories</topic><topic>Quantum Field Theory</topic><topic>Quantum Physics</topic><topic>Regular Article - Theoretical Physics</topic><topic>Relativity Theory</topic><topic>Science & Technology</topic><topic>String Theory</topic><topic>Strings</topic><topic>Superstring Vacua</topic><topic>Superstrings and Heterotic Strings</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anderson, Lara B.</creatorcontrib><creatorcontrib>Gerdes, Mathis</creatorcontrib><creatorcontrib>Gray, James</creatorcontrib><creatorcontrib>Krippendorf, Sven</creatorcontrib><creatorcontrib>Raghuram, Nikhil</creatorcontrib><creatorcontrib>Ruehle, Fabian</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</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>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>Anderson, Lara B.</au><au>Gerdes, Mathis</au><au>Gray, James</au><au>Krippendorf, Sven</au><au>Raghuram, Nikhil</au><au>Ruehle, Fabian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning</atitle><jtitle>The journal of high energy physics</jtitle><stitle>J. High Energ. Phys</stitle><stitle>J HIGH ENERGY PHYS</stitle><date>2021-05-01</date><risdate>2021</risdate><volume>2021</volume><issue>5</issue><spage>1</spage><epage>45</epage><pages>1-45</pages><artnum>13</artnum><issn>1029-8479</issn><eissn>1029-8479</eissn><abstract>A
bstract
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in ℙ
4
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| subjects | Classical and Quantum Gravitation Couplings Differential and Algebraic Geometry Elementary Particles Field theory High energy physics Hyperspaces Machine learning Numerical methods Physical Sciences Physics Physics and Astronomy Physics, Particles & Fields Quantum Field Theories Quantum Field Theory Quantum Physics Regular Article - Theoretical Physics Relativity Theory Science & Technology String Theory Strings Superstring Vacua Superstrings and Heterotic Strings |
| title | Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning |
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