Polarization and variability of compact sources measured in Planck time-ordered data

Creators: Rocha, G.; Keskitalo, R.; Partridge, B.; Marscher, A. P.; O'Dea, C. P.; Pearson, T. J.; Górski, K. M.

Abstract

This paper introduces a new Planck Catalog of Polarized and Variable Compact Sources (PCCS-PV) comprising 153 sources, the majority of which are extragalactic. The data include both the total flux density and linear polarization measured by Planck with frequency coverage from 30 to 353 GHz, and temporal spacing ranging from days to years. We classify most sources as beamed, extragalactic radio sources; the catalog also includes several radio galaxies, Seyfert galaxies, and Galactic and Magellanic Cloud sources, including H II regions and planetary nebulae. An advanced extraction method applied directly to the multifrequency Planck time-ordered data, rather than the mission sky maps, was developed to allow an assessment of the variability of polarized sources. Our analysis of the time-ordered data from the Planck mission, tod2flux, allowed us to catalog the time-varying emission and polarization properties for these sources at the full range of polarized frequencies employed by Planck, 30-353 GHz. PCCS-PV provides the time-and frequency-dependent, polarized flux densities for all 153 sources. To illustrate some potential applications of the PCCS-PV, we conducted preliminary comparisons of our measurements of selected sources with published data from other astronomical instruments. In summary, we find general agreement between the Planck and the Institut de Radioastronomie Millimétrique (IRAM) polarization measurements as well as with the Metsähovi 37 GHz values at closely similar epochs. These combined measurements also show the value of PCCS-PV results and the PCCS2 catalog for filling in missing spectral (or temporal) coverage and helping to define the spectral energy distributions of extragalactic sources. In turn, these results provide useful clues as to the physical properties of the sources.

Additional Information

© ESO 2023. The authors would like to acknowledge Charles Lawrence for insightful comments that helped improve this paper and Mainak Singha for his help with Figs. 15 and 16 and Mike Peel, Clive Dickinson and Ricardo Tanausú Génova Santos for pointing out diculties in interpreting a previous version of Table 2 and an associated error in LFI bandpass handling. This research was conducted under the auspices of a NASA Astrophysics and Data Analysis Program (ADAP) award NNH18ZDA001N-ADAP. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This study made use of Very Long Baseline Array (VLBA) data from the VLBA-BU-BLAZAR project, funded by NASA through the Fermi Guest Investigator Program. The VLBA is an instrument of the National Radio Astronomy Observatory. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated by Associated Universities, Inc. The work at Boston University was supported in part by NASA through Fermi Guest Investigator Program grants 80NSSC17K0649 and 80NSSC20K1567. C. O'Dea received support from the Natural Sciences and Engineering Research Council (NSERC) of Canada. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 This research has made use of the SIMBAD database (Wenger et al. 2000), operated at CDS, Strasbourg, France. Softwares used in this work: Astropy (Astropy Collaboration 2013, 2018), HEALPix (Górski et al. 2005), Matplotlib (Hunter 2007; Caswell et al. 2021), Numpy (Harris et al. 2020), TOAST10, and Scipy (Virtanen et al. 2020).

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