LoTSS Deep fields DR1

This page provides public access to the first data release of the LoTSS Deep Fields. These are deep LOFAR pointings in three well-studied extragalactic fields: Elais-N1 (16:11:00 +55:00:00), Lockman Hole (10:47:00 +58:05:00), and Boötes (14:32:00 +34:30:00). Each pointing covers around 20 square degrees, reaching rms depths in the central regions of about 19, 25 and 36 μJy/bm, respectively at central frequencies at or near 144 MHz (check papers for details). The data release also includes multi-wavelength data over the central sky areas, together with source-associated and cross-matched catalogues over the central ~10 square degree region of each field with the highest quality multi-wavelength data. The radio datasets for Bootes and Lockman are described by Tasse et al. (2021) and Elais-N1 is described by Sabater et al. (2021). The multi-wavelength data, source association and cross-matching are described by Kondapally et al. (2021), with corresponding photometric redshifts described by Duncan et al. (2021).

From this page you may follow links to:

If you make any scientific use of the publicly available data linked here, we kindly request that you include the following acknowledgement:

LOFAR data products were provided by the LOFAR Surveys Key Science project (LSKSP; https://lofar-surveys.org/) and were derived from observations with the International LOFAR Telescope (ILT). LOFAR (van Haarlem et al. 2013) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the ILT foundation under a joint scientific policy. The efforts of the LSKSP have benefited from funding from the European Research Council, NOVA, NWO, CNRS-INSU, the SURF Co-operative, the UK Science and Technology Funding Council and the Jülich Supercomputing Centre.
and please in addition cite whichever of the survey description papers is relevant to the data you use.