**➥ Tip!** Refine or expand your search. Authors are sometimes listed as 'Smith, J. K.' instead of 'Smith, John' so it is useful to search for last names only. Note this is currently a simple phrase search.

[ascl:2108.022]
COSMIC: Compact Object Synthesis and Monte Carlo Investigation Code

Breivik, Katelyn; Coughlin, Scott; Zevin, Michael; Rodriguez, Carl L.; Kremer, Kyle; Ye, Claire S.; Andrews, Jeff J.; Kurkowski, Michael; Digman, Matthew C.; Larson, Shane L.; Rasio, Frederic A.

COSMIC (Compact Object Synthesis and Monte Carlo Investigation Code) generates synthetic populations with an adaptive size based on how the shape of binary parameter distributions change as the number of simulated binaries increases. It implements stellar evolution using SSE (ascl:1303.015) and binary interactions using BSE (ascl:1303.014). COSMIC can also be used to simulate a single binary at a time, a list of multiple binaries, a grid of binaries, or a fixed population size as well as restart binaries at a mid point in their evolution. The code is included in CMC-COSMIC (ascl:2108.023).

[ascl:2307.023]
PyIMRPhenomD: Stellar origin black hole binaries population estimator

PyIMRPhenomD estimates the population of stellar origin black hole binaries for LISA observations using a Bayesian parameter estimation algorithm. The code reimplements IMRPhenomD (ascl:2307.019) in a pure Python code, compiled with the Numba just-in-time compiler. The module implements the analytic first and second derivatives necessary to compute t(f) and t'(f) rather than computing them numerically. Using the analytic derivatives increases the code complexity but produces faster and more numerically accurate results; the improvement in numerical accuracy is particularly significant for t'(f).

[ascl:2307.037]
WDMWaveletTransforms: Fast forward and inverse WDM wavelet transforms

WDMWaveletTransforms 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. WDMWaveletTransforms has both command line and Python interfaces.