Searching for codes credited to 'Williams, Thomas G.'
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[ascl:2105.020]
PAP: PHANGS-ALMA pipeline
Leroy, Adam K.;
Hughes, Annie;
Liu, Daizhong;
Pety, Jerome;
Rosolowsky, Erik;
Saito, Toshiki;
Schinnerer, Eva;
Schruba, Andreas;
Usero, Antonio;
Faesi, Christopher M.;
Herrera, Cinthya N.;
Chevance, Melanie;
Hygate, Alexander P. S.;
Kepley, Amanda A.;
Koch, Eric W.;
Querejeta, Miguel;
Sliwa, Kazimierz;
Will, David;
Wilson, Christine D.;
Anand, Gagandeep S. Barnes, Ashley;
Belfiore, Francesco;
Beslic, Ivana;
Bigiel, Frank;
Blanc, Guillermo A.;
Bolatto, Alberto D.;
Boquien, Mederic;
Cao, Yixian;
Chandar, Rupali;
Chastenet, Jeremy;
Chiang, I-Da;
Congiu, Enrico;
Dale, Daniel A.;
Deger, Sinan;
den Brok, Jakob S.;
Eibensteiner, Cosima;
Emsellem, Eric;
Garcıa-Rodrıguez, Axel;
Glover, Simon C. O.;
Grasha, Kathryn;
Groves, Brent;
Henshaw, Jonathan D.;
Jimenez Donaire, Maria J.;
Kim, Jenny J.;
Klessen, Ralf S.;
Kreckel, Kathryn;
Kruijssen, J. M. Diederik;
Larson, Kirsten L.;
Lee, Janice C.;
Mayker, Ness;
McElroy, Rebecca;
Meidt, Sharon E.;
Mok, Angus;
Pan, Hsi-An;
Puschnig, Johannes;
Razza, Alessandro;
Sanchez-Blazquez, Patricia;
Sandstrom, Karin M.;
Santoro, Francesco;
Sardone, Amy;
Scheuermann, Fabian;
Sun, Jiayi;
Thilker, David A.;
Turner, Jordan A.;
Ubeda, Leonardo;
Utomo, Dyas;
Watkins, Elizabeth J.;
Williams, Thomas G.
The PHANGS-ALMA pipeline process data from radio interferometer observations. It uses CASA (ascl:1107.013), AstroPy (ascl:1304.002), and other affiliated packages to process data from calibrated visibilities to science-ready spectral cubes and maps. The PHANGS-ALMA pipeline offers a flexible alternative to the scriptForImaging script distributed by ALMA. The pipeline runs in two separate software environments: CASA 5.6 or 5.7 (staging, imaging and post-processing) and Python 3.6 or later (derived products) with modern versions of several packages.
[ascl:2411.021]
McFine: Muli-component hyperfine fitting tool
McFine performs complex, multi-component hyperfine spectra fitting in astronomical data. It turns line intensities into gas conditions using a fully automated Bayesian method. Written in Python, the code uses Markov chain Monte Carlo (MCMC) to characterize model denegeracies. It handles local thermodynamic equilibrium (LTE) and radiative-transfer (RT) models and can fit individual spectra and data cubes; given a data cube, it can also use the neighboring information to attempt a better fit. McFine also fits the minimum number of distinct components to avoid overfitting.