AGN Feedback in groups and clusters of galaxies

The lack of very cool gas at the cores of groups and clusters of galaxies, even where the cooling time is significantly shorter than the Hubble time, has been interpreted as evidence of sources that re-heat the intergalactic medium. Most studies of rich clusters adopt AGN feedback to be this source of heating. From ongoing GMRT projects involving clusters and groups, we demonstrate how low-frequency GMRT radio observations, together with Chandra/XMM-Newton X-ray data, present a unique insight into the nature of feedback, and of the energy transfer between the AGN and the IGM.Comment: 5 pages, 3 figures, To appear in ASP Conference Series, Vol. 407, The Low-Frequency Radio Universe, Eds: D. J. Saikia, D. A. Green, Y. Gupta and T. Venturi (Invited talk, conference held at NCRA-TIFR, Pune, INDIA, 8-12 December, 2008

Deep Chandra Observations of HCG 16 - I. Active Nuclei, Star formation and Galactic Winds

We present new, deep Chandra X-ray and Giant Metrewave Radio Telescope 610~MHz observations of the spiral-galaxy-rich compact group HCG 16, which we use to examine nuclear activity, star formation and the high luminosity X-ray binary populations in the major galaxies. We confirm the presence of obscured active nuclei in NGC 833 and NGC 835, and identify a previously unrecognized nuclear source in NGC 838. All three nuclei are variable on timescales of months to years, and for NGC 833 and NGC 835 this is most likely caused by changes in accretion rate. The deep Chandra observations allow us to detect for the first time an Fe-K$\alpha$ emission line in the spectrum of the Seyfert 2 nucleus of NGC 835. We find that NGC 838 and NGC 839 are both starburst-dominated systems, with only weak nuclear activity, in agreement with previous optical studies. We estimate the star formation rates in the two galaxies from their X-ray and radio emission, and compare these results with estimates from the infra-red and ultra-violet bands to confirm that star formation in both galaxies is probably declining after galaxy-wide starbursts were triggered ~400-500 Myr ago. We examine the physical properties of their galactic superwinds, and find that both have temperatures of ~0.8 keV. We also examine the X-ray and radio properties of NGC 848, the fifth largest galaxy in the group, and show that it is dominated by emission from its starburst.Comment: 18 pages, 11 figures, 11 tables, accepted for publication in ApJ; updated references and fixed typos identified at proof stag

Heating the hot atmospheres of galaxy groups and clusters with cavities: the relationship between jet power and low-frequency radio emission

We present scaling relations between jet power and radio power measured using the Giant Metrewave Radio Telescope (GMRT), Chandra and XMM-Newton, for a sample of 9 galaxy groups combined with the Birzan et al. sample of clusters. Cavity power is used as a proxy for mechanical jet power. Radio power is measured at 235 MHz and 1.4 GHz, and the integrated 10 MHz-10 GHz radio luminosity is estimated from the GMRT 610-235 MHz spectral index. The use of consistently analysed, high resolution low-frequency radio data from a single observatory makes the radio powers for the groups more reliable than those used by previous studies, and the combined sample covers 6-7 decades in radio power and 5 decades in cavity power. We find a relation of the form Pjet proportional to Lradio^~0.7 for integrated radio luminosity, with a total scatter of sigma_Lrad=0.63 and an intrinsic scatter of sigma_i,Lrad=0.59. A similar relation is found for 235 MHz power, but a slightly flatter relation with greater scatter is found for 1.4 GHz power, suggesting that low-frequency or broad band radio measurements are superior jet power indicators. We find our low-frequency relations to be in good agreement with previous observational results. Comparison with jet models shows reasonable agreement, which may be improved if radio sources have a significant low-energy electron population. We consider possible factors which could bias our results or render them more uncertain, and find that correcting for such factors in those groups we are able to study in detail leads to a flattening of the Pjet:Lradio relation.Comment: Accepted for publication in ApJ, 7 pages, 3 figure

Performance of a cognitive load inventory during simulated handoffs: Evidence for validity.

BackgroundAdvancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.ObjectiveTo develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.MethodsBased on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas' Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.ResultsConfirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson's r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = -0.96, p < 0.001), and handoff accuracy (r = -0.34, p < 0.001).ConclusionThese results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load