Abstract
The first data release from Apertif survey contains 3074 radio continuum images, covering a thousand square degrees of the sky. The observations were performed between August 2019 and July 2020. The continuum images were produced at a central frequency 1355 MHz, with a bandwidth of ~150 MHz and angular resolution of up to 10″. In this work, we introduce and apply a new method to obtain a primary beam model based on a machine-learning approach, namely, Gaussian process regression. The primary beam models obtained with this method have been published, along with the data products for the first Apertif data release. We applied the method to the continuum images, carried out a mosaicking process on their basis, and extracted the source catalog. The catalog contains 249672 radio sources, many of which have been detected for the first time at these frequencies. We cross-matched the coordinates with the NVSS, LOFAR/DR1/value-added, and LOFAR/DR2 catalogs – resulting in 44523, 22825, and 152824 common sources, respectively. The first sample provides a unique opportunity for detecting long-term transient sources, which have significantly changed their flux density over the past 25 yr. A combination of the second and the third samples provides valuable information on the spectral properties of the sources in addition to redshift estimates.
Subject
Space and Planetary Science,Astronomy and Astrophysics
Cited by
10 articles.
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