Gamma-ray Large Area Space Telescope

1. Gamma-ray Large Area Space Telescope Challenges in Analyzing Data from the GLAST Large Area Telescope James Chiang GLAST SSC/ UMBC

2. e– e+ GLAST Large Area Telescope (LAT) • Within its first few weeks, the LAT will double the number of celestial gamma rays ever detected • 5-year design life, goal of 10 years Spectrum Astro 1.8 m Tracker ACD 3000 kg Calorimeter

3. Scanning the Gamma-Ray Sky with the LAT Will also observe GRBs, Galactic diffuse emission, Dark Matter searches

4. Scanning the Gamma-Ray Sky with the LAT Will also observe GRBs, Galactic diffuse emission, Dark Matter searches

5. Sources must be fit simultaneously. Broad and energy-dependent PSFs: 68 < 3.5º for 100 MeV (on axis) and < 0.1º for 10 GeV Emission from nearby point sources overlap. Intrinsic source spectrum affects the degree of source confusion. “Source region” must be significantly larger than the “region-of-interest” (ROI). Anticenter region: Analyzing LAT Data

6. Analyzing LAT Data • Each event effectively has its own response function: • Large FOV,  2.4 sr • Strong variation of response as a function of photon incident angle, Aeff cos  • Scanning mode of operation: 95 min orbit  continuous aspect changes of 4º/min.

7. Galactic Diffuse Emission • Emission results from cosmic ray interactions with interstellar gas. • Models rely on HI & CO observations for the gas distribution • These observations reveal structures on angular scales similar to the PSF:

8. Pushing the confusing limit: If it is composed of unresolved blazars, we expect the LAT to find 103-104 new sources outside of the Galactic plane. Implications for blazar luminosity function (Chiang & Mukherjee 1998; Salomon & Stecker 1996; Willis 1996) Extragalactic Diffuse

9. Nuts and bolts of the Statistical Model • Use a standard factoring of the total response, R: A = effective area, D = energy dispersion, P = psf, E = photon energy, p = photon direction, L(t) represents the time variation of the instrument orientation and internal degrees of freedom, primes indicate measured quantities. • The Source Model: This accounts for point sources, Galactic diffuse emission, extragalactic diffuse, and other diffuse and possibly time varying sources (e.g., LMC, Moon, SNRs, etc.).

10. Convolving with the Instrument Response • The region-of-interest (ROI) is the extraction region for the data in measured energy, direction, and arrival time. • Folding the source model through the instrument response yields the event distribution function, M, (i.e., the expected counts given the model) in the space of measured quantities: The “source region”, SR, is the part of the sky defined to contain all sources that contribute significantly to the ROI. • For standard analyses, we will treat “steady” sources, so that

11. The Unbinned Likelihood • The objective function we would like to maximize is • The sum is taken over all events, indexed by j, lying within the ROI. Compare to binned Poisson likelihood: • The predicted number of observed events is the integral of M over the ROI:

12. Performance • An example fit, the 17 strongest 3EG sources in the Galactic anticenter region (34 free parameters): • black points: 1 day simulation time, 1.7k events, 98 cpu secs on a 2.8 GHz Pentium 4 machine. • blue: 1 week, 11k events, 745 cpu secs. • Similar results are found when Galactic and extragalactic diffuse components are included (for a factor ~ 4 more events). • Execution time ~O(Nevents)  binned analysis?

13. Source Detection and Localization • Following EGRET analyses, we rely on “test-statistic” maps for detailed source detection and localization: • A point source is moved from each map location to the next and the maximum log-likelihood is evaluated. • The peak of the resulting Ts map is taken as the best fit location, and the 50, 68, 95, & 99% C.L. contours correspond to Ts = 1.4, 2.3, 6.0, & 9.1 according to Wilks’ Theorem (Mattox et al. 1996). • Accurate source positions rely on the other sources being accurately modeled. As with EGRET, an iterative “clean” algorithm will likely be required.

14. Source model: 3C 279, 3C 273, Galactic and extragalactic diffuse. Normalization of Galactic diffuse component must be correct in order to obtain accurate source locations. Example Ts Map Calculation

15. Source Detection Methods • Test statistic maps are useful for positional error contours, but for finding candidate sources, faster methods will be needed. • “Fast” methods include: • Continuous wavelet transform (CWT, e.g., Damaini et al. 1997) • “MRfilter” – Haar wavelet transform (Stark) • Bayesian Blocks – 2D/3D generalization of Scargle’s 1D method • Optimal filter – 2D analog of Weiner filter • All of these methods still need a separate algorithm for identifying candidate sources.

16. Open Issues & Conclusions • LAT data require computational intensive analysis. • Uncertainties in the Galactic diffuse model limit how well other discrete components can be characterized using likelihood. • Extended vs point sources: • Maximum likelihood and ratio test for extended emission parameters – however, see Protossav et al. (2002) • For Gaussian PSFs, CWT gives a clear prescription. • Deconvolution of Galactic diffuse emission: • Use EGRET data, then 1st year survey. • EMC2 applicable? • Generalization to full Celestial sphere? • Energy dependent PSF?