The new Herschel/PACS Point Source Catalogue^★

¹ Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Hungarian Research Network (HUN-REN), H-1121 Budapest, Konkoly Thege Miklós út 15–17., Hungary
² CSFK, MTA Centre of Excellence, Budapest, Konkoly Thege Miklós út 15–17., 1121, Hungary
e-mail: marton.gabor@csfk.org
³ Natural History Museum Vienna, Burgring 7, 1010 Vienna, Austria
⁴ Department of Astrophysics, University of Vienna, Türkenschanzstrasse 17, 1180 Vienna, Austria
⁵ Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland
⁶ California Institute of Technology, IPAC, Pasadena, CA, USA
⁷ ESAC/ESA, Camino Bajo del Castillo s/n, Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid, Spain

Received: 19 March 2024
Accepted: 3 June 2024

Abstract

Context. Herschel operated as an observatory, and therefore it did not cover the whole sky, but still observed ~8% of it. The first version of an overall Herschel/PACS Point Source Catalogue (PSC) was released in 2017. The data are still unique and are very important for research using far-infrared information, especially because no new far-infrared mission is foreseen for at least the next decade. In the framework of the NEMESIS project, we revisited all the photometric observations obtained by the PACS instrument on-board the Herschel space observatory, using more advanced techniques than before, including machine learning techniques.

Aims. Our aim was to build the most complete and most accurate Herschel/PACS catalogue to date. Our primary goal was to increase the number of real sources, and decrease the number of spurious sources identified on a strongly variable background, which is due to the thermal emission of the interstellar dust, mostly located in star-forming regions. Our goal was to build a blind catalogue, meaning that source extraction is conducted without relying on prior detections at various wavelengths, allowing us to detect sources never catalogued before.

Methods. The methods for data analysis have evolved continuously since the first release of a uniform Herschel/PACS catalogue. We define a hybrid strategy that includes classical and machine learning source identification and characterisation methods that optimise faint-source detection, providing catalogues at much higher completeness levels than before. Quality assessment also involves machine learning techniques. Our source extraction methodology facilitates a systematic and impartial comparison of sensitivity levels across various Herschel fields, a task that was typically beyond the scope of individual programmes.

Results. We created a high-reliability and a rejected source catalogue for each PACS passband: 70, 100, and 160 μm. With the high-reliability catalogue, we managed to significantly increase the completeness in all bands, especially at 70 μm. At the same time, while the number of high-reliability detections decreased, the number of sources matching with existing catalogues increased, suggesting that the purity is also higher than before. The photometric accuracy of our pipeline is ~1% based on comparison with the standard star models.