Background Rejection using Angle Fit Quality

1. Background Rejection using Angle Fit Quality • Source analysis has an implicit cut on delAngle • Gamma-ray events must be within search bin • Gamma rays with badly fit angles are lost • Remove all events with bad angle fits • Incremental loss in signal is small • Preferentially remove background • May be additional gains if protons are generically worse fit by a plane Peter Rovegno and David Williams, Milagro Collaboration Meeting

2. How? • Need test for goodness of fit • ChiSq • Comes directly from angle fit for free • Saved in REC data • Not correctly normalized: <c2/n> ~ 0.5 for outrigger reconstruction (was even worse before) • DelEO • Requires two additional angle fits • Not saved in online REC data • milinda has provision for saving in REC data Peter Rovegno and David Williams, Milagro Collaboration Meeting

3. nFit dependence • Don’t just want to keep big, well-fit events • Attempt to take out nFit dependence • ChiSq • Assume <c2/n> ~ 0.5 comes from normalization of errors • Scale by a common factor so that <c2/n> = 1 for MC gamma rays • Calculate confidence level (probability) from c2/n and n • DelEO • For fixed variance per hit, expect DelEO to scale like 1/sqrt(nFit) • Use DelEO x sqrt(nFit) as fit quality measure Peter Rovegno and David Williams, Milagro Collaboration Meeting

4. Procedure • Use the standard outrigger reconstruction, now online, plus DelEO calculation • Use MC gamma rays and real data (from Crab files) • Apply standard event selection • nFit > 20 • Compactness > 2.5 • delAngle < 1.2 or 0.8 (for MC gamma rays only) • Did analysis initially assuming 1.2 degree bin • Realized (and confirmed) smaller bin optimal for new reconstruction • Look at distributions for Prob(c2/n,n) and DelEO x sqrt(nFit) Peter Rovegno and David Williams, Milagro Collaboration Meeting

5. Optimal bin With outriggers Before outriggers 0.75o Plot above from Andy Smith gives 0.7 degree optimal bin with the new reconstruction Found 0.8 degree optimum with this analysis Peter Rovegno and David Williams, Milagro Collaboration Meeting

6. ChiSq — Prob(c2/n,n) Data MC g Peak Q = 1.15 Prob > 0.64 Cut A reasonable Q at the cost of a very hard cut Peter Rovegno and David Williams, Milagro Collaboration Meeting

7. DelEO — DelEO x sqrt(nFit) MC g Data Cut Peak Q = 1.20 DelEO x sqrt(nFit) < 19 A better Q with a better gamma-ray efficiency Peter Rovegno and David Williams, Milagro Collaboration Meeting

8. What Now? • DelEO x sqrt(nFit) the more promising cut – memo very soon • Have not tested this on Crab data • Have not looked at energy dependence • May be most powerful as a new ingredient in the MARS cocktail • Would need to include DelEO calculation for all events online • Have not benchmarked CPU cost — probably less than factor of 2 • Core fit not redone • Angle fit time goes like nhit, so two fits with 1/2 of PMTs ~ one fit with all Peter Rovegno and David Williams, Milagro Collaboration Meeting