1. Source detection at Saclay Look for a fast method to find sources over the whole sky Provide list of positions, allowing to run maximum likelihood locally Jean Ballet and Régis Terrier, CEA SaclayLAT consortium, 29/09/04 Tests on DC1 data: 6 days data set Work in Galactic coordinates 4 energy bands (32 MeV / 100 MeV / 316 MeV / 1 GeV / 10 GeV) Pixel adapted to each band (0.5° / 0.3° / 0.2° / 0.1°) Cartesian projection around the Galactic plane Polar projection (r = 90-b or 90+b, θ=l) around the poles

2. Source detection using wavelets. Description Iterative algorithm: Select relevant scales Wavelet Transform Threshold for each scale Detect relevant strucure to compute multiresolution support M Reconstruct solution S Compute residuals Wavelet Transform on residuals Detect structures belonging to M Reconstruct and update solution S Iterate until convergence Can be applied to continuous WT (reconstruction via wavelet packets) dyadic WT (a-trou algorithm) Tests using MR1 software package (developped by J.L. Starck). Actual source detection on the smoothed image with SExtractor.

3. 100 MeV – 1 GeV Source detection using wavelets. Maps DC1 sky with mr_filter using iterative filter, Poisson noise, 4 sigma threshold 100 MeV – 1 GeV: keep scales 0.8° and 1.6°. 135 candidate sources 1 GeV – 10 GeV: keep scales 0.4° and 0.8°. 210 candidate sources 1 GeV – 10 GeV

4. Source detection using wavelets. Results Sources found using the mr_filter (yellow crosses) compared with the 3 rd EGRET catalogue (green circles). The image is the sky at 100 µm (to show the Galaxy). 0.1 – 10 GeV: 108 correct identifications, 27 spurious 1 – 10 GeV: 168 correct identifications, 48 spurious, one duplicate Together: 205 correct identifications, 75 spurious

5. Source detection using wavelets. Summary Weak points: Finds many spurious sources. Because the detection bears on wavelet coefficients (not on sources directly), raising the threshold does not give very good results. Maximum likelihood is necessary to weed out the false detections. Already existing maintained package, immediately available, fast Good detecting power Method already used in other contexts (for example, XMM large scale survey of M. Pierre et al.) Can detect extended sources as well (if any) Strong points: In progress: Wavelet filtering in 3-D (X, Y, E) currently being developed as a general tool. Not clear this will be available soon enough for GLAST.

6. Source detection using optimal filter. Description Idea: Determine optimal filter using (known) power density spectrum of the background (Galactic diffuse emission) and Point Spread Function. Generalisation of the matched filter technique (Vio et al., A&A 391, 789). PSF averaged over off-axis angle and energy. Source detection in Poisson regime: compute probability that local photon distribution follows background shape (like wavelet transforms do). Example: 316 MeV to 1 GeV band

7. Source detection using optimal filter. Results Threshold such that probability times number of resolution elements is 1 0.032 – 0.1 GeV: 29 correct sources, 6 spurious, 2 duplicates 0.1 – 0.316 GeV: 52 correct sources, 6 spurious 0.316 – 1 GeV: 86 correct sources, 5 spurious 1 – 10 GeV: 108 correct sources, 7 spurious, 4 duplicates Together: 166 correct sources, 24 spurious Below: Raw map + sources (Galactic plane). Above: Filtered map (truncated at 10 sigma)

8. Source detection using optimal filter. Summary Strong points: Optimal filter varies a lot with energy. Split into even more energy bands ? Need to combine likelihood images. Not the same structure in latitude (sharper) and longitude near the Galactic plane. Use different filter in both directions ? Optimal filter depends on amplitude of background structures (balance with Poisson noise). Not the same in plane and at poles. Use smaller latitude intervals ? Galactic power density spectrum must be extrapolated to shorter wavelengths (currently masked by Poisson noise) Open issues: Simple method, handy to experiment on pixel size, sky projections, … Reasonably fast (1 hour on my laptop for the whole sky with 0.1° pixels) Reasonably powerful Gives direct source significance

9. Source detection at Saclay Several open issues: Would like to reduce pixel size to 0.05° above 1 GeV. Becomes RAM hungry Get significances of sources after wavelet detection ? Much easier for setting threshold, also useful for setting position error. Adding likelihoods in several energy bands at map level will require storing even more data in parallel, and could become CPU consuming. Need reasonable approximations to avoid full Poisson probability computation at all pixels. Methods exist … work quite fast … give a reasonable list of sources … but there is still a long way to go. Jean Ballet and Régis Terrier, CEA SaclayLAT consortium, 29/09/04