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PreprintarXiv licencearXiv

Universal enhancement of the pairing gap in a dilute Fermi superfluid

  • Silas R. Beane
  • Roland C. Farrell
  • Cameron P. Shotwell
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Abstract

At zero temperature, a dilute Fermi gas with an attractive contact interaction forms a superfluid with gapped fermionic excitations. We show that the gap at weak coupling can be organized as a trans-series expansion in the interaction strength and the asymptotic Gor'kov-Melik-Barkhudarov gap. Low-energy virtual quasiparticles generate corrections that are linear in the gap with a leading coefficient nearly 25× larger than the other known coefficients. This substantially increases the gap at intermediate densities, highlighting that systematic corrections are crucial for quantitative predictions.

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Cite

BibTeXRIS
BibTeX
@misc{beane2026universal,
  title = {{Universal enhancement of the pairing gap in a dilute Fermi superfluid}},
  author = {Silas R. Beane and Roland C. Farrell and Cameron P. Shotwell},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.20434},
  eprint = {2610.20434},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.20434},
}
RIS
TY  - UNPB
TI  - Universal enhancement of the pairing gap in a dilute Fermi superfluid
AU  - Silas R. Beane
AU  - Roland C. Farrell
AU  - Cameron P. Shotwell
PY  - 2026
DA  - 2026/10/14
PB  - arXiv
DO  - 10.48550/arxiv.2610.20434
UR  - https://arxiv.org/abs/2610.20434
AB  - At zero temperature, a dilute Fermi gas with an attractive contact interaction forms a superfluid with gapped fermionic excitations. We show that the gap at weak coupling can be organized as a trans-series expansion in the interaction strength and the asymptotic Gor'kov-Melik-Barkhudarov gap. Low-energy virtual quasiparticles generate corrections that are linear in the gap with a leading coefficient nearly 25× larger than the other known coefficients. This substantially increases the gap at intermediate densities, highlighting that systematic corrections are crucial for quantitative predictions.
ER  -