7 June 06 1 UW Point Source Detection and Localization: Compare with DC2 truth Toby Burnett University of Washington.

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7 June 06 1 UW Point Source Detection and Localization: Compare with DC2 truth Toby Burnett University of Washington

7 June 06 2 See my LATDOC: projected resolution varies with , , and the efficiency  as Note that for the Gaussian case, , it becomes  /  N, where N is the total number of events (This ignores the effect of background, which degrades the resolution) Expected position Resolution

7 June 06 3 The UW Point source analysis A crucial element is the maximum likelihood analysis. –Note that while it is binned , the bins are small compared with the PSF resolution. Designed to be fast. –Eight energy bands, each binned independently A candidate must correspond to a good fit, 2 criteria: –TS, or resolution in the rate above given threshold –Localization: error circle limit Apply to the flux>1e-8 subset that Seth thinks should be detectable: –683 sources –But 8 pairs are within 0.2 deg, 6 of those actually compound

7 June 06 4 Source detectability Analyze each of the 683-8=675 sources, use true starting point. –Declare “findable” if: Projected error < 0.2 deg TS > 5 –592 pass Compare predicted position with actual: –561 have difference < 0.5 deg. (590 for 1 deg) How does distribution compare? Following plots for the found source candidates

7 June 06 5 Projected error distribution all events (526); flux>1e-8 (374); flux>1e-7 (209); flux>1e-6 (24)

7 June 06 6 Confidence Levels Compare measured with predicted deviations: statistical prediction is chi-squared distribution with 2 degrees of freedom, an exponential in the chi squared. Expect flat distribution. Note low-probability tail is higher in the plane.

7 June 06 7 Status Good: –UW source finding works well –Localization predicted error, based on optimal maximum likelihood fit, is consistent with data Needs work: –Efficiency can be improved: examine detectable sources that were rejected –Error and TS can be improved by Bayesian prior on background –Large number of spurious candidates need to be filtered –Must determine confidence level for existence