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Cosmic Static and X-ray Vision: Studying GPS/CSS sources at radio and X-ray wavelengths Joe Callingham The University of Sydney / CASS Multi-Wavelength.

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Presentation on theme: "Cosmic Static and X-ray Vision: Studying GPS/CSS sources at radio and X-ray wavelengths Joe Callingham The University of Sydney / CASS Multi-Wavelength."— Presentation transcript:

1 Cosmic Static and X-ray Vision: Studying GPS/CSS sources at radio and X-ray wavelengths Joe Callingham The University of Sydney / CASS Multi-Wavelength Emission Conference, Sydney – February 2015 NRAO/AUI

2 What are CSS/GPS sources? ›Originally empirical classification: -Powerful AGN with concave spectrum -GPS turnover ~ 1 GHz, CSS turnover ~ 150 MHz (?) -Small physical sizes. GPS < 1 kpc, CSS ~ 1 – 10 kpc -Hosts vary - quasars, radio galaxies and Seyferts , Owsianik et al 1998

3 Radio detected galaxy GPS CSO Core- jet CSS CSO Core- jet Acronym Soup Spectral classification Morphology classification

4 Core- jet Acronym Spaghetti Morphology classification Kunert-Bajraszewska et al. 2010

5 Why Study CSS/GPS Sources? ›Unique view of early stages of AGN activity. Probe of environment to tens pc scale. ›How many sources go from birth to A team sources (Cyg A, Her A etc)? ›Are they confined to small spatial scales due to ‘youth’ or ‘frustrated’? ›Cause of the turnover in spectrum? NASA, ESA, S. Baum and C. O'Dea (RIT), R. Perley and W. Cotton (NRAO/AUI/NSF), and the Hubble Heritage Team Despite three decades since the discovery of GPS sources as a significant population, the nature of the absorption mechanism is still unresolved and restricting progress with these sources. Stawarz et al. (2008)

6 The spectral revolution has begun! ›~10% of MWA radio population? -turnover solely due to synchrotron self-absorption or free-free absorption? -are CSS/GPS properties only because of youth? -or are some “frustrated” sources confined by dense gas? ›Three absorption models: -Homogenous free-free -Inhomogeneous free-free -SSA

7 Models of GPS Radio Spectra Homogeneous free-free model Tingay & De Kool (2003)

8 Models of GPS Radio Spectra Inhomogeneous free-free model (Bicknell et al. 1997, Begelman 1999)

9 Models of GPS Radio Spectra Synchrotron self-absorption (SSA) model (Kellermann 1966) Prediction of 2.5 slope – never seen

10 New Extreme GPS Source PKS B ›Low frequency data has a gradient of ~2.4 – steepest known. Spectral width ~0.6 decade of freq. – smallest known. ›Test bed for models of GPS/CSS spectra. ›~120 mas scale, ~1000 pc ›MWA and CABB data vital in disentangling competing models. Flux Density (Jy) Frequency (GHz)

11 Flux Density (Jy) Frequency (GHz) χ

12 Flux Density (Jy) Frequency (GHz) χ Spectral break

13 Flux Density (Jy) χ Frequency (GHz)

14 Spectral Time Evolution Adapted from Lindfors et al. (2006) Frequency Flux Density

15 Going from one source… ›Spectral modelling of PKS shows power of MWA and CABB in constraining spectrum. ›Ruling out SSA. ›Old and dying GPS sources? Will we see more? ›Implications break moved below the spectrum for USS source Klamer et al. 2006

16 MWA and GLEAM Survey ›GLEAM is the MWA all sky survey ›Two minute snapshot observations ›Five declination bands (+18 to -72 degrees). ›Continuous coverage between 80 and 230 MHz. ›Confusion limited and ~half a million sources detected down to ~10 mJy. Natasha Hurley-Walker

17 New GPS Sources Flux Density (mJy) Frequency (MHz) χ

18 New GPS Sources Flux Density (mJy) Frequency (MHz) χ

19 New GPS Sources Flux Density (mJy) Frequency (MHz) χ

20 Finding high-z galaxies Nick Seymour

21 H I Absorption ›Provides a unique insight into the idea of jets being ‘frustrated’. ›Variability in H I profile? Thoughts of clouds moving through lobes/jets. Allison et al. Araya et al. (2010)

22 Who are you calling confined? ›Frustrated sources since as linear size increased N HI decreased? Pihlstrom et al. 2003

23 X-ray Vision ›Insights into youth vs frustration ›Origin of X-ray emission is still debated as resolution limiting progress. From: -Shocks from expanding radio lobes? -Accretion disk? -IC scattering? -Synchrotron from jets? Cen A; ESO; NASA/CXC/CfA

24 X-ray Vision ›Vink et al. (2006) found average N H ∼ cm −2 – similar to column densities observed toward other types of radio galaxies ›Results argue against the frustration scenario since much higher X-ray absorption column densities expected if the expansion of the radio jets is confined. ›Small, biased sample. Only X-ray detected sources! Those with greatest HI densities may not be detected. Vink et al. 2006

25 X-ray Column Density ›Where is the absorption occurring? Co-spatial not disc since 1:1. ›Correlation between column density of HI from radio and H from X-ray. ›Column densities much larger than thought with ~10 23 cm −2. Ostorero et al. 2010

26 Conclusions ›Spectral modelling of PKS shows power of MWA and CABB in constraining spectrum. ›Will MWA reveal a new population of dying sources? ›X-ray and radio observations help constrain environmental properties. ›Ruling out SSA. ›Nail down what the absorption mechanism - vital for evolutionary models.

27

28 Death to the Power Law Days of the power law are numbered!

29 Why Bayes? Aesthetics: ›Philosophy – accepting a theory rather than rejecting a hypothesis. ›Chi-squared evaluates the significance of the mismatch between theory and experiment, not whether the hypothesis is true. ›Rigorous theoretical framework

30 Why Bayes? Positives - Practical: ›Full PDFs for each model parameter ›Prior knowledge can be used to get a more accurate result and place physical constraints. ›Can deal with non-Gaussian uncertainties (e.g. calibration errors) ›Marginalise over nuisance parameters (e.g. noise floor.) ›Objective model selection more robust than reduced chi-squared. ›Less likely to get stuck in a local minimum due to implementation. ›Hyperparameters

31 Why Bayes? Negatives: ›Less ‘natural’ to think about – integrals, baggage of another statistical language etc. ›More computationally expensive ›In simple cases, often converges to the same parameter values as less computationally expensive methods do. ›More difficult and time consuming to code. ›Can be influenced by prior knowledge.

32 Application on PKS Inhomogeneous free-free model (Bicknell et al. 1997)

33 PKS


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