AMI-CL J0300+2613: A Galactic anomalous-microwave-emission ring masquerading as a galaxy cluster

The Arcminute Microkelvin Imager (AMI) carried out a blind survey for galaxy clusters via their Sunyaev-Zel'dovich effect decrements between 2008 and 2011. The first detection, known as AMI-CL J0300+2613, has been reobserved with AMI equipped with a new digital correlator with high dynamic range. The combination of the new AMI data and more recent high-resolution sub-mm and infra-red maps now shows the feature in fact to be a ring of positive dust-correlated Galactic emission, which is likely to be anomalous microwave emission (AME). If so, this is the first completely blind detection of AME at arcminute scales

Identification of neural networks that contribute to motion sickness through principal components analysis of fos labeling induced by galvanic vestibular stimulation

Motion sickness is a complex condition that includes both overt signs (e.g., vomiting) and more covert symptoms (e.g., anxiety and foreboding). The neural pathways that mediate these signs and symptoms are yet to identified. This study mapped the distribution of c-fos protein (Fos)-like immunoreactivity elicited during a galvanic vestibular stimulation paradigm that is known to induce motion sickness in felines. A principal components analysis was used to identify networks of neurons activated during this stimulus paradigm from functional correlations between Fos labeling in different nuclei. This analysis identified five principal components (neural networks) that accounted for greater than 95% of the variance in Fos labeling. Two of the components were correlated with the severity of motion sickness symptoms, and likely participated in generating the overt signs of the condition. One of these networks included neurons in locus coeruleus, medial, inferior and lateral vestibular nuclei, lateral nucleus tractus solitarius, medial parabrachial nucleus and periaqueductal gray. The second included neurons in the superior vestibular nucleus, precerebellar nuclei, periaqueductal gray, and parabrachial nuclei, with weaker associations of raphe nuclei. Three additional components (networks) were also identified that were not correlated with the severity of motion sickness symptoms. These networks likely mediated the covert aspects of motion sickness, such as affective components. The identification of five statistically independent component networks associated with the development of motion sickness provides an opportunity to consider, in network activation dimensions, the complex progression of signs and symptoms that are precipitated in provocative environments. Similar methodology can be used to parse the neural networks that mediate other complex responses to environmental stimuli. © 2014 Balaban et al

Sunyaev-Zel'dovich profile fitting with joint AMI-Planck analysis

We develop a Bayesian method of analysing Sunyaev-Zel'dovich measurements of galaxy clusters obtained from the Arcminute Microkelvin Imager (AMI) radio interferometer system and from the Planck satellite, using a joint likelihood function for the data from both instruments. Our method is applicable to any combination of Planck data with interferometric data from one or more arrays. We apply the analysis to simulated clusters and find that when the cluster pressure profile is known a-priori, the joint dataset provides precise and accurate constraints on the cluster parameters, removing the need for external information to reduce the parameter degeneracy. When the pressure profile deviates from that assumed for the fit, the constraints become biased. Allowing the pressure profile shape parameters to vary in the analysis allows an unbiased recovery of the integrated cluster signal and produces constraints on some shape parameters, depending on the angular size of the cluster. When applied to real data from Planck-detected cluster PSZ2 G063.80+11.42, our method resolves the discrepancy between the AMI and Planck $Y$-estimates and usefully constrains the gas pressure profile shape parameters at intermediate and large radii

A 15.5 GHz detection of the galaxy cluster minihalo in RXJ1720.1+2638

RXJ1720.1+2638 is a cool-core, 'relaxed-appearing' cluster with a minihalo previously detected up to 8.4 GHz, confined by X-ray-detected cold fronts. We present observations of the minihalo at 13 - 18 GHz with the Arcminute Microkelvin Imager telescope, simultaneously modelling the Sunyaev-Zel'dovich signal of the cluster in conjunction with Planck and Chandra data in order to disentangle the non-thermal emission of the minihalo. We show that the previously-reported steepening of the minihalo emission at 8.4 GHz is not supported by the AMI data and that the spectrum is consistent with a single power-law up to 18 GHz. We also show the presence of a larger-scale component of the minihalo extending beyond the cold fronts. Both of these observations could be explained by the 'hadronic' or 'secondary' mechanism for the production of relativistic electrons, rather than the currently-favoured 're-acceleration' mechanism and/or multiple episodes of jet activity from the active galactic nucleus in the brightest cluster galaxy