parameter_estimation

Repository to learn signal parameter estimation with PyMC. The final goal is to estimate the mass quotient parameter for a nonspinning binary black hole signal. The estimation should be done with a custom likelihood function which can be found at Canizares, Priscilla, et al. "Accelerated gravitational wave parameter estimation with reduced order modeling.", (Eq. 1).

To build the likelihood function we use Scikit-reducedmodel, a package that performs subdomain partition to achieve higher precision.

Step 1: Random signal parameter estimation with predefined likelihood

Learn pyMC package: perform parameter estimation on a simple signal

Step 2: Gravitational wave parameter estimation with predefined likelihood

we use pyMC with a gravitational signal to estimate mass quotient q

Step 3: Gravitational wave parameter estimation with custom likelihood

Step 4: Gravitational wave parameter estimation with ROQs likelihood

References

[1] Canizares, Priscilla, et al. "Accelerated gravitational wave parameter estimation with reduced order modeling." Physical review letters 114.7 (2015): 071104.

[2] Canizares, Priscilla, et al. "Gravitational wave parameter estimation with compressed likelihood evaluations." Physical Review D—Particles, Fields, Gravitation, and Cosmology 87.12 (2013): 124005.

[3] Tiglio, Manuel, and Aarón Villanueva. "Reduced order and surrogate models for gravitational waves." Living Reviews in Relativity 25.1 (2022): 2.

[4] Cerino, Franco, J. Andrés Diaz-Pace, and Manuel Tiglio. "An automated parameter domain decomposition approach for gravitational wave surrogates using hp-greedy refinement." Classical and Quantum Gravity 40.20 (2023): 205003.