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Searching for codes credited to 'Bernardi, Gianni'

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[ascl:1606.004] HIBAYES: Global 21-cm Bayesian Monte-Carlo Model Fitting

HIBAYES implements fully-Bayesian extraction of the sky-averaged (global) 21-cm signal from the Cosmic Dawn and Epoch of Reionization in the presence of foreground emission. User-defined likelihood and prior functions are called by the sampler PyMultiNest (ascl:1606.005) in order to jointly explore the full (signal plus foreground) posterior probability distribution and evaluate the Bayesian evidence for a given model. Implemented models, for simulation and fitting, include gaussians (HI signal) and polynomials (foregrounds). Some simple plotting and analysis tools are supplied. The code can be extended to other models (physical or empirical), to incorporate data from other experiments, or to use alternative Monte-Carlo sampling engines as required.

[ascl:2312.030] matvis: Fast matrix-based visibility simulator
Kittiwisit, Piyanat; Murray, Steven G.; Garsden, Hugh; Bull, Philip; Cain, Christopher; Parsons, Aaron R.; Sipple, Jackson; Abdurashidova, Zara; Adams, Tyrone; Aguirre, James E.; Alexander, Paul; Ali, Zaki S.; Baartman, Rushelle; Balfour, Yanga; Beardsley, Adam P.; Berkhout, Lindsay M.; Bernardi, Gianni; Billings, Tashalee S.; Bowman, Judd D.; Bradley, Richard F.; Burba, Jacob; Carey, Steven; Carilli, Chris L.; Chen, Kai-Feng; Cheng, Carina; Choudhuri, Samir; DeBoer, David R.; de Lera Acedo, Eloy; Dexter, Matt; Dillon, Joshua S.; Dynes, Scott; Eksteen, Nico; Ely, John; Ewall-Wice, Aaron; Fagnoni, Nicolas; Fritz, Randall; Furlanetto, Steven R.; Gale-Sides, Kingsley; Gehlot, Bharat Kumar; Ghosh, Abhik; Glendenning, Brian; Gorce, Adelie; Gorthi, Deepthi; Greig, Bradley; Grobbelaar, Jasper; Halday, Ziyaad; Hazelton, Bryna J.; Hewitt, Jacqueline N.; Hickish, Jack; Huang, Tian; Jacobs, Daniel C.; Josaitis, Alec; Julius, Austin; Kariseb, MacCalvin; Kern, Nicholas S.; Kerrigan, Joshua; Kim, Honggeun; Kohn, Saul A.; Kolopanis, Matthew; Lanman, Adam; La Plante, Paul; Liu, Adrian; Loots, Anita; Ma, Yin-Zhe; MacMahon, David H. E.; Malan, Lourence; Malgas, Cresshim; Malgas, Keith; Marero, Bradley; Martinot, Zachary E.; Mesinger, Andrei; Molewa, Mathakane; Morales, Miguel F.; Mosiane, Tshegofalang; Neben, Abraham R.; Nikolic, Bojan; Devi Nunhokee, Chuneeta; Nuwegeld, Hans; Pascua, Robert; Patra, Nipanjana; Pieterse, Samantha; Qin, Yuxiang; Rath, Eleanor; Razavi-Ghods, Nima; Riley, Daniel; Robnett, James; Rosie, Kathryn; Santos, Mario G.; Sims, Peter; Singh, Saurabh; Storer, Dara; Swarts, Hilton; Tan, Jianrong; Thyagarajan, Nithyanandan; van Wyngaarden, Pieter; Williams, Peter K. G.; Xu, Zhilei; Zheng, Haoxuan

matvis simulates radio interferometric visibilities at the necessary scale with both CPU and GPU implementations. It is matrix-based and applicable to wide field-of-view instruments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA), as it does not make any approximations of the visibility integral (such as the flat-sky approximation). The only approximation made is that the sky is a collection of point sources, which is valid for sky models that intrinsically consist of point-sources, but is an approximation for diffuse sky models. The matvix matrix-based algorithm is fast and scales well to large numbers of antennas. The code supports both CPU and GPU implementations as drop-in replacements for each other and also supports both dense and sparse sky models.