1. Point Source Subtraction Bart Pindor University of Melbourne

2. Objectives Remove Bright EG point sources prior to polynomial EOR FG subtraction Issue: Mode-mixing / ‘Frizz’ RTS will Peel some bright sources as part of CML It will be possible/necessary to remove further sources after imaging Characterize residuals/effect on (the other) PS

3. Importance of PS Subtraction Liu et al. 2008

4. MWA Beam > 1%

5. Matched Filters Generally; positions, fluxes, spectra unknown Matched filter is the optimal linear S/N estimator of source amplitudes Peaks in filtered maps used to identify PS

6. Matrix Filters Extension of Matched Filters to multi-channel Developed for Planck CMB maps (Herranz et al. astro-ph/08082884 Incorporates cross-channel correlations but allows for independent spectral slopes Each filtered image estimates source fluxes

7. Matrix Filters MTXF applied to MAPS + RTS 8s snapshot Beam is a single simulated position-independent point source Peak pixel is the inferred position

8. Matrix Filters ISSUES: Centroiding Efficient beam reconstruction Iteration Diffuse sky model

9. Matrix Filters Aim for frizz-free

10. OOB – oh oh? Sidelobes will appear from sources outside of the primary beam Can we detect/locate sources in combined maps? Use of MWA survey or other (GMRT?) data to locate relevant source? What is the accuracy of calibration solution in that direction?

11. Open Issues / Simulation To-Do: Ionosphere HEALPIX pixelization Integration, co-addition, registration Rotation synthesis Peeling levels and residuals Calibration / Beam Accuracy OOB 8 minutes * 200 Channels * 5000 images

12. Open Issues / Simulation To-Do: Ionosphere HEALPIX pixelization Integration, co-addition, registration Rotation synthesis Peeling levels and residuals Calibration / Beam Accuracy OOB 8 minutes * 200 Channels * 5000 images = 136 years!