healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python

By A. Zonca, L. Singer, D. Lenz, M. Reinecke, C. Rosset, E. Hivon, K. G'orski. Journal of Open Source Software, 2019

693 citations recorded by the source corpus.

1 San Diego Supercomputer Center, University of California, San Diego, USA 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA 3 Max-Planck-Institute for Astrophysics, Garching, Germany 4 Institut d’Astrophysique de Paris, CNRS/Sorbonne Universite, Paris, France 5 Laboratoire Astroparticules et Cosmologie, CNRS/Université Paris Diderot, Paris, France 6 Goddard Space Flight Center, NASA, Greenbelt, Maryland, USA

Subfields: Artificial Intelligence, Astronomy and Astrophysics, Oceanography

View Paper: https://www.semanticscholar.org/paper/6936df3961517736de529ec0c2a3695fe03d1d50

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Citations in the corpus

The SRG/eROSITA all-sky survey (2024, positive): “To project the flux limits to a map, we subdivide the sky into HEALPix 13 pixels (Zonca et al. 2019; Górski et al. 2005), adopting a HEALPix resolution order of 6.”
The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and Its Implications for Structure Growth (2023, methodology): “Some of the results in this paper have been derived using the healpy Zonca et al. (2019) and HEALPix Górski et al. (2005) packages.”
DeepSphere: a graph-based spherical CNN (2020, background): “, healpy (Zonca et al., 2019), matplotlib (Hunter, 2007), SciPy (Virtanen et al., 2020), NumPy (Walt et al., 2011), TensorFlow (Abadi et al., 2015).”
Data Release 1 of the Dark Energy Spectroscopic Instrument (2025, positive): “Meanwhile, HEALPix coadds combine all the available exposures of targets observed on different tiles into a given HEALPix pixel on the sky ( nested scheme, using NSide=64 ; K. M. Górski et al. 2005; A. Zonca et al. 2019 ) ; HEALPix coadds can be found under spectro/redux/iron/healpix/ .”
The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters (2023, methodology): “Some of the results in this paper have been derived using the healpy (Zonca et al. 2019) and HEALPix (Górski et al. 2005) packages.”
Target Selection and Validation of DESI Luminous Red Galaxies (2022, neutral): “Software: Astropy (Astropy Collaboration et al. 2013, 2018), HEALPix/healpy (Górski et al. 2005; Zonca et al. 2019), Matplotlib (Hunter 2007), Numpy (Harris et al. 2020), scikitlearn (Pedregosa et al. 2011), Scipy (Virtanen et al. 2020).”
Measurements of the E -mode polarization and temperature- E -mode correlation of the CMB from SPT-3G 2018 data (2021, methodology): “In H18 this calculation was further verified for the input range 0 l 500 with the use of curved-sky HEALPix4 [48,49] simulations.”
The Atacama Cosmology Telescope: a measurement of the Cosmic Microwave Background power spectra at 98 and 150 GHz (2020, neutral): “They include CosmoMC [82, 100], CAMB [101], healpy [102], HEALPix [103], the SLATEC29 Fortran subroutine DRC3JJ.”
Constraints on the Cosmic Expansion History from GWTC–3 (2021, methodology): “Statistical galaxy catalog method We also use the gwcosmo code (Gray et al. 2020) in the pixelated sky scheme, i.e. using the HEALPix pixelization algorithm (Górski et al. 2005; Zonca et al. 2019), to infer H0 using information from galaxy surveys.”
CMB lensing from Planck PR4 maps (2022, methodology): “Some of the results in this paper have been derived using the healpy package [50].”
The Atacama Cosmology Telescope: DR4 maps and cosmological parameters (2020, positive): “They include CosmoMC [55, 82], CAMB [57], healpy [83], HEALPix [84], the SLATEC33 Fortran subroutine DRC3JJ.”