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Progress on the spectroscopy of lattice gauge theories using spectral densities
Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024), Volume: 466, Start page: 137
Swansea University Authors:
Niccolo Forzano, Ed Bennett , Biagio Lucini
, Maurizio Piai
, Fabian Zierler
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DOI (Published version): 10.22323/1.466.0137
Abstract
Spectral densities encode non-perturbative information crucial in computing physical observables in strongly coupled field theories. Using lattice gauge theory data, we perform a systematic study to demonstrate the potential of recent technological advances in the reconstruction of spectral densitie...
Published in: | Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024) |
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ISSN: | 1824-8039 |
Published: |
Trieste, Italy
Sissa Medialab
2024
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Online Access: |
Check full text
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URI: | https://cronfa.swan.ac.uk/Record/cronfa68043 |
Abstract: |
Spectral densities encode non-perturbative information crucial in computing physical observables in strongly coupled field theories. Using lattice gauge theory data, we perform a systematic study to demonstrate the potential of recent technological advances in the reconstruction of spectral densities. We develop, maintain and make publicly available dedicated analysis code that can be used for broad classes of lattice theories. As a test case, we analyse the (4) gauge theory coupled to an admixture of fermions transforming in the fundamental and two-index antisymmetric representations. We measure the masses of mesons in energy-smeared spectral densities, after optimising the smearing parameters for available lattice ensembles. We present a summary of the mesons mass spectrum in all the twelve (flavored) channels available, including also several excited states. |
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College: |
Faculty of Science and Engineering |
Funders: |
This work has been supported in part by the ExaTEPP projects EP/X017168/1 and EP/X01696/1, the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1, the STFC Consolidated Grants No. ST/X508834/1, ST/P00055X/1, ST/T000813/1, ST/X000648/1, and ST/P000630/1, a STFC new applicant scheme grant, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1C1B3001379), the IBS under the project code, IBS-R018-D1, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460), the Taiwanese NSTC grant 1122112-M-A49-021-MY3, the Royal Society Wolfson Research Merit Award WM170010, and the Leverhulme Trust Research Fellowship No. RF-2020-4619, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 813942. A.L is funded in part by l’Agence Nationale de la Recherche (ANR), under grant ANR-22-CE31-0011.
We used the Swansea SUNBIRD cluster (part of the Supercomputing Wales project) and AccelerateAI A100 GPU system. Supercomputing Wales and AccelerateAI are part funded by the European Regional Development Fund (ERDF) via Welsh Government. We also use the DiRAC Data Intensive service (CSD3) at the University of Cambridge, the DiRAC Extreme Scaling service at The University of Edinburgh, and the DiRAC Data Intensive service at Leicester. The DiRAC Data Intensive service (CSD3) is managed by the University of Cambridge University Information Services on behalf of the STFC DiRAC HPC Facility. The DiRAC Data Intensive service at Leicester is operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility. The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This DiRAC equipment is funded by BEIS, UKRI and STFC capital funding and operation grants. DiRAC is part of the UKRI Digital Research Infrastructure. |
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