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The Hidden Potential of the “Gamma’’ Data.

The Hidden Potential of the “Gamma’’ Data. The Milagro Gamma-Hadron separation is bases on simple collective variables (no topology). The optimization of the core/angle fitters is for the typical event.

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The Hidden Potential of the “Gamma’’ Data.

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  1. The Hidden Potential of the “Gamma’’ Data. • The Milagro Gamma-Hadron separation is bases on simple collective variables (no topology). • The optimization of the core/angle fitters is for the typical event. • It may be possible to re-optimize the gamma/hadron separation and reconstruction for the “best” events (large, high energy). • We have archived ~50TB of raw data from Summer 2005-Present -->Future A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  2. VERITAS spent 10hrs looking at J2019+37. Reported at APS that they don’t see it, but can’t rule out Milagro. There is a population of sources for which Milagro, largely due to our HE response is more sensitive than VERITAS. If we can improve our sensitivity at HE we can do some really important science. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  3. HESS Galactic Source Spectra Milagro Sensitivity A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  4. 10TeV in Milagro Motivation for this work Better core locator? Better curvature/sampling? Better angle fitter? Optimize on big showers >10 TeV. Don’t let CPU be a limitation -> Minuit OK -> Max Likelihood. --> Take a systematic approach. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  5. Gamma-Hadron Separation Gamma-Hadron Variables: X2 = nB2/mxPE A4 = (fOR+fAS)nFit/mxPE Interpretation: nFit ~ nB2 ~ nAS ~ N X2 = N/mxPE A4 ~ N2/mxPE + OR term A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  6. “Idealized” Gamma-Hadron Separation S = Shower Size = Integral under core fit Pi = Amplitude of ith non-correlated hit in bottom layer Ri = Distance of ith bottom layer hit to core P(S,Pi,Ri) = Probability of observing a hit of amplitude Pi at radius Ri from a shower with size S. Size should be something like GeV at ground level. Likelihood (/h) = ∏ Pgamma(S,Pi,Ri)/Phadron(S,Pi,Ri) Take care to make sure that bottom layer hits are independent. Smoothing? Local maxima. Similar to A4? More hits => bigger S. Larger R => more OR’s hit. Get P’s from gamma MC and hadron data/MC. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  7. Gaussian Core Fitter • The limitations of the Gaussian core fitter. • Amplitude computed, but not saves. I fixed that. • Fitter includes lots of black magic: • Different starting positions • Levenberg-Marquardt Method • Several passes. • Starting points from on AS/OR COM. • OR/AS not normalized. • No Zeros • Variable width • Output not suitable for amplitude • measurement A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  8. Gaussian Core Fitter • Generated a sample of 10 TeV showers thrown from zenith. • Fluctuations in the depth of the first interaction dominate the PE fluctuations at observation level. • Fixed the energy on the ground level ( 400-600 GeV ). • PE distributions are log-normal. • Plotted log10(PEs) vs. Rcore for three PMT types (AS,MU,OR) • Fit log10(PE) slices for fixed R to Gaussian to get mean,sigma. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  9. Gaussian Core Fitter Mean - AS Mean - MU Mean - OT A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  10. Sigma -AS Sigma -MU Sigma -OR A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  11. Max. Likelihood Core Fitter Do fits in log(amp) space. Fix width. Sure it varies, but not much and not for the big events at large R. Minuit fit: 3 parameters x,y,A A (in GeV) is natural parameter. Get predicted amp’s and sigmas from fits to distributions on previous page. Result: Can fit cores out to ~180m! Poorer fits for shorter distances. Subtle point: Pond PMTs are making redundant measurements. Re-sampling the same blobs of energy. Played with 2 contribution from pond PMTs. Still no zeros. Potential to double collection area! Code is functional and in Milinda (Beta). Red = Gaussian Blue = Likelihood Red = Gaussian Blue = Likelihood A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  12. New Estimate of Sampling and Curvature Correction • Use same 10 TeV sample thrown from zenith. • MC now includes time offset between Corsika injection times and time of FI • Can now get “absolute” shower from time. T=0 A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  13. R = 140 m R = 75 m R = 5 m time time time Curvature/Sampling • These plots are for 5PE pulses. • Sampling and curvature both depend on R. • Not surprising, Tony taught us this. • Fits to Gaussian with different sigma to the left and right of the mean. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  14. 1 PE 2.5 PE 5 PE 9 PE >100 PE 75 PE 37 PE 19 PE Curvature/Sampling • Curvature is not linear -- more parabolic • Curvature depends on amplitude. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  15. 1 PE 2.5 PE 5 PE 9 PE >100 PE 75 PE 37 PE 19 PE Curvature/Sampling • These are sigmas. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

  16. New Likelihood Angle Fitter • Fitter is not there yet. • Must account for noise. • Some time distributions don’t fit well. • First test is an idealized --> How well can we do test. • How much improvement can we expect (hope for?) • 2x (?) from core fitter. • Angle improvement ? Time will tell. • Milagro is the most sensitive instrument at E>20 TeV. • Most of the code is in place, but not ready yet. A. Smith UMD, April 20-21, 2007 Milagro collaboration meeting

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