Calibration parameters and error estimation with Maximum Likelihood approach in LIGO

Hsiang-Yu Huang^1,3*, Evan Goetz^4,5,6, Afif Ismail², Miftahul Ma‘arif^1,3, Yuki Inoue^1,2,3, Jeff. S. Kissel⁷, Antonios Kontos⁸, Ethan Payne^9,10,11,12, Jameson G. Rolins⁹

¹Department of Physics, National Central University, Taoyuan, Taiwan
²Institute of Physics, Academia Sinica, Taipei, Taiwan
³Center for High Energy and High Field Physics, National Central University, Taoyuan, Taiwan
⁴Louisiana State University, Baton Rouge, LA 70803, USA
⁵Missouri University of Science and Technology, Rolla, MO 65409, USA
⁶University of British Columbia, Vancouver, BC V6T 1Z4, Canada
⁷LIGO Hanford Observatory, Richland, WA 99352, USA
⁸Bard College, 30 Campus Rd, Annandale-On-Hudson, NY 12504, USA
⁹LIGO Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
¹⁰OzGrav-ANU, Centre for Gravitational Astrophysics, College of Science, The Australian National University, ACT 2601, Australia
¹¹School of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia
¹²OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, Australia

* Presenter:Hsiang-Yu Huang, email:hyhuanggw170817@gate.sinica.edu.tw

Calibration in gravitational wave detector is to determine calibration parameters and error, and estimate the response of interferometer. LIGO is US gravitational wave detector experiment. It consists of two gravitational wave detectors, located in Washington and Louisiana state. The fourth Gravitational Wave observation(O4) will start in March, 2023. pyDARM, a Python-based calibration tool, is developed by LIGO. This tool is used to model LIGO interferometer system, called differential arm (DARM) loop. By this, we can process the calibration measurement data, calibration parameters estimation with MCMC and then produce the uncertainty estimation with Gaussian Process Regression (GPR). Currently, we develop a new feature, fast calibration parameters estimation with Maximum likelihood approach in pyDARM. This approach provide independent, consistent parameters estimation with MCMC we use at present. This new feature will be included in pyDARM version 0.2 release. In this presentation, I will summarize current status of pyDARM development and preliminary result with Maximum likelihood approach.

Keywords: Calibration, pyDARM, error estimation, Maximum Likelihood approach, LIGO