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Radio/Gamma-ray time lags: Large samples and statistics

Radio/Gamma-ray time lags: Large samples and statistics. Walter Max-Moerbeck On behalf of the OVRO 40m blazar monitoring team AGN Monitoring Workshop MPIfR, Bonn, Germany March 14, 2011. Correlated radio/gamma-ray variability.

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Radio/Gamma-ray time lags: Large samples and statistics

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  1. Radio/Gamma-ray time lags:Large samples and statistics Walter Max-Moerbeck On behalf of the OVRO 40m blazar monitoring team AGN Monitoring Workshop MPIfR, Bonn, Germany March 14, 2011

  2. Correlated radio/gamma-ray variability • The hypothesis of correlated variability in radio and gamma-ray is popular • It would indicate a common spatial origin for radio and gamma-ray emission • But it needs to be proven!

  3. Correlated radio/gamma-ray variability • Our approach: • Large sample of objects • Preselected as gamma-ray candidates • Observed independently of gamma-ray state • High cadence, observed twice per week • Statistical tests for correlations

  4. A first look at the radio/gamma-ray cross-correlation • Data • Radio data published in Richards et al 2011 (ApJ submitted) • 2 year light curves for CGRaBS sources + a few calibrators • Gamma-ray data published in blazar variability paper, Abdo et al. 2010 ApJ, 722, 520 • 106 sources • 11-month light curves, weekly sampling • 52/106 are in the CGRaBS sample

  5. Radio/gamma-ray time lags and their significance β_radio = 2.5, β_gamma = 2.0 • Significance evaluated using simulated data with a power-law PSD ~ 1/f^β Radio lags Radio precedes • Example cross-correlations. 3-month Fermi detections, using 11-months of Fermi data and 2 years of radio monitoring

  6. Radio/gamma-ray time lags and their significance β_radio = 2.5, β_gamma = 2.0 • Significance evaluated using simulated data with a power-law PSD ~ 1/f^β Radio lags Radio precedes • Example cross-correlations. 3-month Fermi detections, using 11-months of Fermi data and 2 years of radio monitoring

  7. Statistical test for the cross-correlation:Measuring the PSD • The significance level depends on the model used for the light curves • It is commonly assumed that it is red-noise with a simple power-law PSD • Uneven sampling complicates the model fitting • We use the method of Uttley et al 2002 MNRAS 332, 231 • With some modifications • Basic idea is to simulate data with a given PSD and process it as the data. The mean PSDs and deviations are used for model fitting

  8. Significance versus PSD power-law exponent βradio= 2.5,βgamma-ray= 2.0 βradio= 2.0, βgamma-ray= 1.5 βradio= 0.0, βgamma-ray=0.0

  9. Significance for longer time series 1 year of gamma-ray and 2 years of radio – dotted lines 5 years of gamma-ray and 6 years of radio – solid lines

  10. Statistical test for the cross-correlation:Measuring the PSD Example light curves Goodness of fit –radio data J0017-0512 A large fraction have well constrained PSDs slopes n>/N β J0238+1636 Some PSDs are hard to constrain, we need longer time series n>/N β

  11. PSD measurements first results Gamma-ray detected Gamma-ray non detected • The distribution of PSD power-law indices is different for gamma-ray detected/non-detected sources • This is consistent with gamma-ray quiet objects looking like white noise, without flares • A peak near beta~2.0 can be used when measuring significance

  12. Cross-correlation the next step • Include all sources on1LAC (Fermi first year catalog) with 2 years of data in gamma-ray and at least 2 years in radio, more for CGRaBS • Main problem is to extract all the gamma-ray light curves and deal with upper limits, sparse or adaptive sampling • ~400 sources in our program • 221 CGRaBS

  13. Summary • Paper in preparation using published Fermi and OVRO data • PSD is characterized for all radio sources • Cross-correlation significance will incorporate this new constraints on the variability behavior of blazars • Will submit before Fermi Symposium • Next step is to extend this to a larger set of gamma-ray sources and longer light curves at both bands

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