Radio/Gamma-ray time lags:

Large samples and statistics

Walter Max-MoerbeckOn behalf of the OVRO 40m blazar monitoring team

AGN Monitoring WorkshopMPIfR, Bonn, Germany

March 14, 2011

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!

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

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

Radio lags Radio precedes

• Example cross-correlations. 3-month Fermi detections, using 11-months of Fermi data and 2 years of radio monitoring

β_radio = 2.5, β_gamma =

2.0

• Significance evaluated using simulated data with a power-law PSD ~ 1/f^β

Radio/gamma-ray time lags and their significance

Radio lags Radio precedes

• Example cross-correlations. 3-month Fermi detections, using 11-months of Fermi data and 2 years of radio monitoring

β_radio = 2.5, β_gamma =

2.0

• Significance evaluated using simulated data with a power-law PSD ~ 1/f^β

Radio/gamma-ray time lags and their significance

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

βradio = 2.5, βgamma-ray= 2.0 βradio = 2.0, βgamma-ray= 1.5

βradio = 0.0, βgamma-ray= 0.0

Significance versus PSD power-law exponent

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

Statistical test for the cross-correlation:

Measuring the PSD

J0017-0512

J0238+1636

Example light curves Goodness of fit –radio data

Some PSDs are hard to constrain, we need longer time series

A large fraction have well constrained PSDs slopes

β

β

n>

/Nn

>/N

PSD measurements first results

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

Gamma-ray detected

Gamma-ray non detected

Cross-correlation the next step

Include all sources on 1LAC (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

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