The highest frequency detection of a radio relic : 16 GHz AMI observations of the 'Sausage' cluster

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society: Letters. © 2014 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.We observed the cluster CIZA J2242.8+5301 with the Arcminute Microkelvin Imager at 16 GHz and present the first high radio-frequency detection of diffuse, non-thermal cluster emission. This cluster hosts a variety of bright, extended, steep-spectrum synchrotron-emitting radio sources, associated with the intracluster medium, called radio relics. Most notably, the northern, Mpc-wide, narrow relic provides strong evidence for diffusive shock acceleration in clusters. We detect a puzzling, flat-spectrum, diffuse extension of the southern relic, which is not visible in the lower radio-frequency maps. The northern radio relic is unequivocally detected and measures an integrated flux of 1.2 ± 0.3 mJy. While the low-frequency (<2 GHz) spectrum of the northern relic is well represented by a power law, it clearly steepens towards 16 GHz. This result is inconsistent with diffusive shock acceleration predictions of ageing plasma behind a uniform shock front. The steepening could be caused by an inhomogeneous medium with temperature/density gradients or by lower acceleration efficiencies of high energy electrons. Further modelling is necessary to explain the observed spectrum.Peer reviewe

AMI observations of unmatched Planck ERCSC LFI sources at 15.75 GHz

The Planck Early Release Compact Source Catalogue includes 26 sources with no obvious matches in other radio catalogues (of primarily extragalactic sources). Here we present observations made with the Arcminute Microkelvin Imager Small Array (AMI SA) at 15.75 GHz of the eight of the unmatched sources at declination > +10 degrees. Of the eight, four are detected and are associated with known objects. The other four are not detected with the AMI SA, and are thought to be spurious.Comment: 6 pages, 5 figures, 4 table

Radio continuum observations of Class I protostellar disks in Taurus: constraining the greybody tail at centimetre wavelengths

We present deep 1.8 cm (16 GHz) radio continuum imaging of seven young stellar objects in the Taurus molecular cloud. These objects have previously been extensively studied in the sub-mm to NIR range and their SEDs modelled to provide reliable physical and geometrical parametres.We use this new data to constrain the properties of the long-wavelength tail of the greybody spectrum, which is expected to be dominated by emission from large dust grains in the protostellar disk. We find spectra consistent with the opacity indices expected for such a population, with an average opacity index of beta = 0.26+/-0.22 indicating grain growth within the disks. We use spectra fitted jointly to radio and sub-mm data to separate the contributions from thermal dust and radio emission at 1.8 cm and derive disk masses directly from the cm-wave dust contribution. We find that disk masses derived from these flux densities under assumptions consistent with the literature are systematically higher than those calculated from sub-mm data, and meet the criteria for giant planet formation in a number of cases.Comment: submitted MNRA

Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps

We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of hydrodynamic re-simulations of 324 large galaxy clusters. Mock, idealised Compton-{\it y} and X-ray maps were constructed for the sample, capturing them out to a radius of $2R_{200}$. The idealised nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase $\Delta${\it M/M} $\geq$ 0.75, and the other half serve as a control, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55 and $F_{1}$ score of 0.53. Using a multichannel model relative to a single channel model, we obtain a 3\% improvement in BA score, and a 6\% improvement in $F_{1}$ score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilise regions out to larger distances for mergers with respect to non-mergers, greater than $\sim1.2 R_{200}$ and $\sim0.7 R_{200}$ for SZ and X-ray respectively.Comment: 15 pages, 17 figures, published in MNRA

AMI-LA radio continuum observations of Spitzer c2d small clouds and cores: Perseus region

We present deep radio continuum observations of the cores identified as deeply embedded young stellar objects in the Perseus molecular cloud by the Spitzer c2d programme at a wavelength of 1.8 cm with the Arcminute Microkelvin Imager Large Array (AMI-LA). We detect 72% of Class 0 objects from this sample and 31% of Class I objects. No starless cores are detected. We use the flux densities measured from these data to improve constraints on the correlations between radio luminosity and bolometric luminosity, infrared luminosity and, where measured, outflow force. We discuss the differing behaviour of these objects as a function of protostellar class and investigate the differences in radio emission as a function of core mass. Two of four possible very low luminosity objects (VeLLOs) are detected at 1.8 cm.Comment: 18 pages, 9 figures, accepted MNRA

Sunyaev–Zel’dovich observations with AMI of the hottest galaxy clusters detected in the XMM–Newton Cluster Survey

We have obtained deep Sunyaev–Zel’dovich (SZ) observations towards 15 of the hottest XMM Cluster Survey (XCS) clusters that can be observed with the Arcminute Microkelvin Imager (AMI). We use a Bayesian analysis to quantify the significance of our SZ detections. We detect the SZ effect at high significance towards three of the clusters and at lower significance for a further two clusters. Towards the remaining 10 clusters, no clear SZ signal was measured. We derive cluster parameters using the XCS mass estimates as a prior in our Bayesian analysis. For all AMI-detected clusters, we calculate large-scale mass and temperature estimates while for all undetected clusters we determine upper limits on these parameters. We find that the large-scale mean temperatures derived from our AMI SZ measurements (and the upper limits from null detections) are substantially lower than the XCS-based core-temperature estimates. For clusters detected in the SZ, the mean temperature is, on average, a factor of 1.4 lower than temperatures from the XCS. Our upper limits on the cluster temperature of undetected systems are lower than the mean XCS derived temperature

The radio source count at 93.2 GHz from observations of 9C sources using AMI and CARMA

We present results from follow-up observations of a sample of 80 radio sources, originally detected as part of the 15.2-GHz Ninth Cambridge (9C) survey. The observations were carried out, close to simultaneously, at two frequencies: 15.7 GHz, using the Arcminute Microkelvin Imager (AMI) Large Array, and 93.2 GHz, using the Combined Array for Research in Millimeter-wave Astronomy (CARMA). There is currently little direct information on the 90-GHz-band source count for S ≲ 1 Jy. However, we have used the measured 15.7-to-93.2-GHz spectral-index distribution and 9C source count to predict the differential source count at 93.2 GHz as 26 ± 4(S/Jy)^(−2.15) Jy^(−1) sr^(−1); our projection is estimated to be most accurate for 10 ≲ S ≲ 100 mJy. Our estimated differential count is more than twice the 90-GHz prediction made by Waldram et al.; we believe that this discrepancy is because the measured 43-GHz flux densities used in making their prediction were too low. Similarly, our prediction is significantly higher than that of Sadler et al. at 95 GHz. Since our spectral-index distribution is similar to the 20-to-95-GHz distribution measured by Sadler et al. and used in making their prediction, we believe that the difference is almost entirely attributable to the dissimilarity in the lower frequency counts used in making the estimates