Download Source detection at Saclay

Jean Ballet, CEA Saclay
GSFC, 31 May 2006
All-sky source search
Aim: Look for a fast method to find sources over the whole sky
 MR_FILTER
 Optimal filter
 Combining energy bands
 Iteration
All-sky source search. MR_FILTER
 MR_FILTER is an all-purpose wavelet algorithm developed at Saclay by J.L.
Starck.
 Works on cartesian images.
 Special mode for Poisson statistics
 Output is a smoothed image, keeping only significant positive structures
 2nd step to locate excesses (SExtractor)
 Accepts background model in input
 Currently used as place holder in catalog generation
All-sky source search.
Optimal filter
 Generalization of the idea of convolving an image with the (known) PSF, to
account for a structured background
 Filter is PSF/(1+BKG) in Fourier space
 Works on cartesian images (same filter over entire image)
 Accounts for Poisson statistics
 Sky partitioned into 11 pieces (2 CAR in Galactic plane up to +/- 10°, 4 CAR
or AIT at mid-latitudes up to 45°, 2 ARC around the poles above 45°).
Avoids too much geometrical distortion, and adapts the filter to local
conditions
Optimal filter 2
• Variance is computed at each point, knowing the background shape
(use number of photons from the image under the filter when larger).
But not Gaussian distribution.
• Signal / dispersion at each point is converted into an effective gaussian
significance depending on the local density of counts and the filter
shape (same principle as is used in wavelet methods for each
coefficient). Entire image can be converted assuming background is
locally flat (then same conversion everywhere)
Putting energy bands
together
 Simplest solution is to run
algorithm over each energy
band separately and merge
source lists.
Merging source lists
 Estimate position error. In the checkout 3 simulation, it was found that the
formula 1.8 HWHM / significance + pixelsize / 2.5 provides a reasonable
estimate of the (1 σ) position error, where HWHM is the half width at half
maximum of the PSF in the band
 Start from high energy (best PSF), identify with lower energy sources using
tolerance 3 times larger, keep only unambiguous associations. Combine
positions and errors using as weight 1/sigma^2, where sigma is the position
error itself.
All-sky source search. Iteration
 Methods based on filtering have trouble finding weak sources in the wings of
bright ones (because they generate ripples around sources).
 The solution is to iterate. After the bright sources have been detected, they
can be modelled and entered into the diffuse emission.
 Start with merged source lists
 Feed to likelihood pipeline, to weed out non significant sources
 Run the band likelihood pipeline, to get accurate flux in each band even for
sources which do not have a power law spectrum
 Run gtmodelmap on the large images used by the source detection methods,
readjusting the diffuse emission to account for the bright sources
 All those steps take quite long
All-sky source search. Comparison
Energy band from 100 to
300 MeV, toward North
Galactic pole
gtmodelmap on the basis
of the result of the band
likelihood pipeline (sources
only)
Map with coordinate grid
is with MR_FILTER source
list
Map without coordinates
is with optimal filter source
list
Differences in source
position, and a few faint
sources
Iteration: MR_FILTER
Energy band from 3 to 100 GeV, toward (l,b) = (90,0) in the Galactic plane.
Size of the image is about 60 x 20°
Same color coding before and after iteration
Many excesses not confirmed by likelihood, bright sources leave secondaries
Iteration: optimal filter
Energy band from 3 to 100 GeV, toward the Galactic center.
Size of the image is about 40 x 20°
Same color coding before and after iteration (0 to 5 sigma)
Very bright sources removed already, fainter sources go away nicely
Putting energy bands
together 2
A better solution is to add
log(likelihood) values (before
applying threshold).
Galactic center region, 40 x 30°
4 bands
Several scales are visible
Transform significance maps into probability, add log(probability), correct the
distribution for the number of bands, transform back to significance
The broad PSF at low energy adds a complication. A true source can induce
false detections in its vicinity (via its low energy wings). Therefore do it only after
all 1st pass sources have been removed
Jean Ballet, CEA Saclay
GSFC, 31 May 2006
Source detection studies
Investigated 2 imaging based methods
1.
MR_FILTER improved. Works well (in combination with SExtractor), but
does not provide source significance.
2.
Optimal filter works all right. Finds fewer sources (hopefully fewer spurious
sources), but returns source significance.
3.
Energy band combination is promising
4.
Is iteration worth the cost in computing time ?