To explore the high frequency radio spectra of galaxies in clusters, we used NRAO's Very Large Array at four frequencies, 4.9 − 43 GHz, to observe 139 galaxies in low redshift (z < 0.25), X-ray detected, clusters. The clusters were selected from the survey conducted by Ledlow & Owen, who provided redshifts and 1.4 GHz flux densities for all the radio sources. We find that more than half of the observed sources have steep microwave spectra as generally expected (α < −0.5, in the convention S ∝ ν α ). However, 60 − 70% of the unresolved or barely resolved sources have flat or inverted spectra. Most of these show an upward turn in flux at ν > 22 GHz, implying a higher flux than would be expected from an extrapolation of the lower frequency flux measurements. Our results quantify the need for careful source subtraction in increasingly sensitive measurements of the Sunyaev-Zel'dovich effect in clusters of galaxies (as currently being conducted by, for instance, the Atacama Cosmology Telescope and South Pole Telescope groups).

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In this paper we present the results of an optical and near infrared identification of 514 radio sources from the FIRST survey (Faint Images of the Radio Sky Survey at Twenty centimetres) with a flux-density limit of 1 mJy in the NOAO Deep-Wide Field Survey (NDWFS) Boötes field. Using optical (Bw, R, I) and K band data with approximate limits of Bw∼ 25.5 mag, R∼25.8 mag, I∼ 25.5 mag and K∼19.4 mag, optical counterparts have been identified for 378 of 514 FIRST radio sources. This corresponds to an identification rate of 34% in four bands (BwRIK), 60% in optical bands (BwRI) and 74% in I band. Photometric redshifts for these sources have been computed using the hyperz code. The inclusion of quasar template spectra in hyperz is investigated. We note that the photometric data are, in many cases, best matched to templates with very short star-formation timescales and the inferred ages of identified galaxies depend strongly on the assumptions about the star-formation timescale. The redshifts obtained are fairly consistent with those expected from the K-z relation for brighter radio sources but there is more scatter in the K-z diagram at z<1.

Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into four classes: Fanaroff-Riley Class I (FRI), Fanaroff-Riley Class II (FRII), Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work is based on Convolutional Neural Networks (CNNs). The proposed architecture comprises three parallel blocks of convolutional layers combined and processed for final classification by two feed-forward layers. Our model classified selected classes of radio galaxy sources on an independent testing subset with an average of 96% for precision, recall, and F1 score. The best selected augmentation techniques were rotations, horizontal or vertical flips, and increase of brightness. Shifts, zoom and decrease of brightness worsened the performance of the model. The current results show that model developed in this work is able to identify different morphological classes of radio galaxies with a high efficiency and performance.