Point Source Detection and Localization

1
Point Source Detection and Localization

• Using the UW HealPixel database
• Toby Burnett University of Washington

2
The UW pixelized photon data base

• Define 8 energy bands
• Associate each level with a HealPixel level.
• Fill structure with pixels in a sparse structure
  sorted by position.
• Make selecting subset according to outer pixel
  level easy for projection integrals
• Numerous low energy photons are effectively
  binned
• Rare high energy photons occupy single pixels
• Simplifies database indexing

3
Image generation define a density function

• High energy photons are more localized we
  express this by defining photons/area
• Easily determined from the data base and the
  Healpix code.

3C273 density vs. all photons above 100 Mev
4
See the DC2 sky as a clickable map

• See http//glast.phys.washington.edu/DC2/healpix/
  
• Also http//glast.phys.washington.edu/dc2/healpix/
  source_table.htm for a nice table

5
Point source analysis

• Select conical region
• Known source, like Vela
• Perugia wavelet analysis
• Extract 8 sets of HealPixel lists from the data
  set
• Analyze each level with maximum likelihood,
  signal fraction and TS
• Perform global optimization with respect to the
  direction
• Perhaps repeat step 2

6
Simple Point Source Maximum Likelihood

• Assumptions
• All events from the source in energy band/pixel
  level can be described by the same PSF
• measured with AllGamma weighted according to 1/E2
  
• Average over position in detector, detector polar
  angle, zenith angle, etc measure using AllGamma
  data set.
• Use the power-law function
• Everything else is uniform
• Ignore variations from exposure, galactic
  diffuse, nearby sources
• Implementation details
• Select pixels from the cone only within a given
  maximum uumax.
• Normalized probability function iswhere ? is
  the signal fraction and is normalized over
  the range.
• Define log likelihood as weighted sum over
  pixels.
• First and second derivatives with respect to ?
  are quite simple, allowing fast solution
• After the solution, calculate the TS

7
PSF fits
8
Example MRF320

• Choose a high-latitude moderate-strength source
  MRF320!

9
MRF320 spectral fit
Loading data from file F/glast/data/DC2/allsky.ro
ot, selecting event type 0 photons found 840469
pixels created 438524 Spectrum of source
MRF0320 at ra, dec309.03, -18.59 level events
sig fraction TS 6 713 0.37 /- 0.03
128.4 7 359 0.67 /- 0.041 193.5
8 193 0.79 /- 0.049 142.5 9 50
1 /- 0.074 48.36 10 15 1 /-
0.38 20.86 11 5 0.91 /- 0.29
5.503 12 0 13 0total
539.1
Only class A front for now ?
Coordinates from catalog radius 10?
Catalog 7586(different likelihood definition)
10
Localization

• Algorithm Newtons method, add gradient and
  curvature for all levels, iterate until small
  change. Determine error circle radius from
  curvature.
• Note that a simple weighted sum is not a good
  estimator, in fact disastrous if ? 2.
• Note differs by (0.018, -0.035) from catalog
  position, 4 sigma away.
• How about a strong source? Vela localization is
  0.003 deg.
• Example MRF320

Gradient delta ra dec
error 1.602e004 0.0316
309.03 -18.59 0.0106 3192
0.00607 309.046 -18.618
0.0104 677.7 0.00129 309.048
-18.6237 0.0105 150.5 0.000287
309.048 -18.6249 0.0105
11
Next Steps

• Systematic comparison with catalog sources, with
  localization
• Improve speed
• Try to find new sources, near detection threshold