ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Keywords

A list of keywords associated with codes in the ASCL.

NASA (172), Kepler (31), Spitzer (13), TESS (13), Fermi (6), HITS (6), HST (5), ROSAT (4), Swift (4), CGRO (3), LISA (3), RXTE (3), ASCA (2), Chandra (2), COBE (2), Geotail (2), Heliophysics (2), Herschel (2), LRO (2), Magellan (2), MRO (2), NICER (2), Polar (2), Rosetta (2), Wind (2), WISE (2), WMAP (2), Apollo (1), Cassini (1), Dawn (1), GOES (1), Hinode (1), Hitomi (1), InSight (1), INTEGRAL (1), ISO (1), Juno (1), JWST (1), K2 (1), Lucy (1), Lunar Quest (1), MAVEN (1), MESSENGER (1), MGS (1), NEAR (1), New Horizons (1), NISAR (1), NuSTAR (1), OSIRIS-REx (1), Parker Solar Probe (1), Psyche (1), RHESSI (1), SDO (1), SOFIA (1), SOHO (1), STEREO (1), Suzaku (1), THEMIS (1), TRMM (1)

Codes associated with 'LISA'

[ascl:2307.014] Synthetic LISA: Simulator for LISA-like gravitational-wave observatories

Synthetic LISA simulates the LISA science process at the level of scientific and technical requirements. The code generates synthetic time series of the LISA fundamental noises, as filtered through all the TDI observables, and provides a streamlined module to compute the TDI responses to gravitational waves, according to a full model of TDI, including the motion of the LISA array, and the temporal and directional dependence of the armlengths.

[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.