Gendron-Marsolais, Marie-Lou

DOE OSTI2021

An ACA 1 mm survey of HzRGs in the ELAIS-S1: survey description and first results

ABSTRACT Radio-emitting jets might be one of the main ingredients shaping the evolution of massive galaxies in the Universe since early cosmic times. However, identifying early radio active galactic nuclei (AGNs) and confirming this scenario have been hard to accomplish, with studies of samples of radio AGN hosts at z > 2 becoming routinely possible only recently. With the above in mind, we have carried out a survey with the Atacama Compact Array (ACA or Morita Array) at 1.3 mm (rms = 0.15 mJy) of 36 high-redshift radio AGN candidates found within 3.9 deg2 in the ELAIS-S1 field. The work presented here describes the survey and showcases a preliminary set of results. The selection of the sample was based on three criteria making use of infrared (IR) and radio fluxes only. The criterion providing the highest selection rate of high-redshift sources (86 per cent at z > 0.8) is one combining an IR colour cut and radio flux cut (S5.8μm/S3.6μm > 1.3 and $S_{\rm 1.4\, GHz}\gt 1\,$ mJy). Among the sample of 36 sources, 16 show a millimetre (mm) detection. In eight of these cases, the emission has a non-thermal origin. A zsp = 1.58 object, with a mm detection of non-thermal origin, shows a clear spatial offset between the jet-dominated mm continuum emission and that of the host’s molecular gas, as traced by serendipitously detected CO(5-4) emission. Among the objects with serendipitous line detections there is a source with a narrow jet-like region, as revealed by CS(6-5) emission stretching 20 kpc out of the host galaxy.

Messias, Hugo G.↗

DOE OSTI2020

A Novel Machine Learning Approach to Disentangle Multitemperature Regions in Galaxy Clusters

The hot intracluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium that comprises various emitting components. Although previous studies of nearby galaxy clusters, such as the Perseus, the Coma, or the Virgo cluster, have demonstrated the need for multiple thermal components when spectroscopically fitting the ICM’s X-ray emission, no systematic methodology for calculating the number of underlying components currently exists. In turn, underestimating or overestimating the number of components can cause systematic errors in the emission parameter estimations. In this paper, we present a novel approach to determining the number of components using an amalgam of machine learning techniques. Synthetic spectra containing a various number of underlying thermal components were created using well-established tools available from the Chandra X-ray Observatory. The dimensions of the training set was initially reduced using principal component analysis and then categorized based on the number of underlying components using a random forest classifier. Our trained and tested algorithm was subsequently applied to Chandra X-ray observations of the Perseus cluster. Our results demonstrate that machine learning techniques can efficiently and reliably estimate the number of underlying thermal components in the spectra of galaxy clusters, regardless of the thermal model (MEKAL versus APEC). We also confirm that the core of the Perseus cluster contains a mix of differing underlying thermal components. We emphasize that although this methodology was trained and applied on Chandra X-ray observations, it is readily portable to other current (e.g., XMM-Newton, eROSITA) and upcoming (e.g., Athena, Lynx, XRISM) X-ray telescopes. The code is publicly available at https://github.com/XtraAstronomy/Pumpkin.

79 ASTRONOMY AND ASTROPHYSICS↗