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[ascl:2103.014]
QuickCBC: Rapid and reliable inference for binary mergers

QuickCBC is a robust end-to-end low-latency Bayesian parameter estimation algorithm for binary mergers. It reads in calibrated strain data, performs robust on-source spectral estimation, executes a rapid search for compact binary coalescence (CBC) signals, uses wavelet de-noising to subtract any glitches from the search residuals, produces low-latency sky maps and initial parameter estimates, followed by full Bayesian parameter estimation.

[submitted]
PyIMRPhenomD

This module implements IMRPhenomD in a pure Python code, compiled with the Numba just-in-time compiler. The structure of the code is closely related to the C code; the module provides nearly identical function interfaces in IMRPhenomD.py. The module implements the analytic first and second derivatives necessary to compute t(f) and t'(f), rather than computing them numerically, as is done in the C code. Using the analytic derivatives increases the code complexity but is wall-time faster and produces more numerically accurate results. The improvement in numerical accuracy is particularly significant for t'(f). In testing, PyIMRPhenomD is considerably faster than the C implementation. For large frequency grids, the Python version's speed-up is typically approximately a factor of 5 compared to the C version.

[submitted]
WDMWaveletTransforms

This module implements the fast forward and inverse WDM wavelet transforms in python from both the time and frequency domains. The frequency domain transforms are inherently faster and more accurate. The wavelet domain->frequency domain and frequency domain->wavelet domain transforms are nearly exact numerical inverses of each other for a variety of inputs tested, including gaussian random noise.