The rms-flux relation In Black Hole Binaries
Credit: ESA
Lucy Heil (Leicester) With: Simon Vaughan & Phil Uttley (Leicester) (Southampton)
The rms-flux relation In Black Hole Binaries Credit: ESA Lucy Heil - - PowerPoint PPT Presentation
The rms-flux relation In Black Hole Binaries Credit: ESA Lucy Heil (Leicester) With: Simon Vaughan & Phil Uttley (Leicester) (Southampton) The RMS-flux relation V4641 First identified in Uttley & McHardy (2001) XRB Cyg X-1
Credit: ESA
Lucy Heil (Leicester) With: Simon Vaughan & Phil Uttley (Leicester) (Southampton)
The RMS-flux relation
First identified in Uttley & McHardy (2001) XRB Cyg X-1 and NS SAX J1808.4-3658 Seen in - Selected observations in a few XRBs, NSs, AGN and a ULX Optically in XRBs Cyg X-1 studied in detail by Gleissner et.al. (2004)
V4641
Puts shot to shot noise models?
Measuring the RMS-flux relation
RMS values are binned to at least 20 points per bin
Most RXTE archival data for XTE J1118+480 GS 1354-64 4U 1543-475 XTE J1550-564 XTE J1650-500 GRO J1655-40 GX 339-4 XTE J1859+226 H 1743-322
XTE J1550-564
641 Good Observations
k Cx
Apparently ubiquitous across all observations
possible to process
Caveats:
Limited to observations above 3% fractional rms
Excludes many soft state observations
Observations with QPOs don't always behave
themselves
Sudden changes in power spectral shape exclude
some observations
GX 339-4
10% RMSfrac
RMSfrac Green- Red
Mean Flux RMS
B
A A
GRO J1655-40 XTE J1650-500
Hardness Green -> Blue -> Pink
RMS-flux relation appears to be ubiquitous for noise For stationary observations only Over both short and long terms Large number of observations with negative intercepts Extra components in light curve Results are consistent with Cygnus X-1 (Gleissner et. al. 2004).
But extended to new states
Analysis is still ongoing...
Following criteria used: Kendall's tau > 0.5 Tau test > 2.0σ X2 test < 3.0σ RMSfrac > 3%
Data outside this range but with RMSfrac > 3% then investigated
Good data needs: Long observation > 1 ks High count-rate > 100 ct/s/PCU High RMSfrac > 3%
good observations
Suggest secondary component which does not
Doesn't produce true linear relation Better to fit the variance – mean relation Parabolic however, and much harder to
constrain