Low Mechanical loss coatings for reduced thermal noise in gravitational wave detectors

ORAL

Abstract

The sensitivity of current gravitational wave interferometric detectors is limited by Brownian noise: thermal motion originating from the elastic energy dissipation in the dielectric coatings used in the interferometer mirrors. We have identified mixtures of titanium dioxide (TiO2) and germanium dioxide (GeO2) that allows an improvement of almost a factor of 2 on the level of Brownian noise with respect to the state-of-the-art materials, the largest reduction in almost two decades of research. We show that by using a mixture of 44% TiO2 and 56% GeO2 in the high refractive index layers of the interferometer mirrors, it would be possible to achieve a thermal noise level in line with the design requirements for future upgrades. These results are a crucial step forward to produce the mirrors needed to meet the thermal noise requirements for the planned upgrades of the Advanced LIGO (Laser Interferometer Gravitational-Wave Observatory) and Virgo detectors.

*This work is supported by the National Science Foundation (NSF) LIGO program through Grants No. 1710957 and No. 1708010. We also acknowledge the support of the LSC Center for Coatings Research, jointly funded by the NSF and the Gordon and Betty Moore Foundation (GBMF). We are also grateful for support through NSF Grants No. PHY- 1707866, No. PHY-1708175, No. PHY-2011571, and No. PHY-2011706, and GBMF Grant No. 6793. The work carried out at U. Montreal benefited from the support of the NSERC, the CFI, and the FRQNT through the RQMP

Presenters

  • Gabriele Vajente

    • California Institute of Technology
    • Caltech

Authors

  • Gabriele Vajente

    • California Institute of Technology
    • Caltech
  • CARMEN S MENONI

    • Colorado State University
  • Le Yang

    • Colorado State University
  • Aaron Davenport

    • Colorado State University
  • Mariana A Fazio

    • University of New Mexico
  • Alena Ananyeva

    • California Institute of Technology
  • Liyuan Zhang

    • California Institute of Technology
  • GariLynn Billingsley

    • Caltech
  • Kiran Prasai

    • Stanford Univ
  • Ashot Markosyan

    • Stanford University
  • Riccardo Bassiri

    • Stanford University
    • Stanford Univ
  • Martin M Fejer

    • Stanford University
  • Martin Chicoine

    • Université de Montréal
  • François Schiettekatte

    • Université de Montréal