1 / 11

Introduction Sensitivity and Error Analysis Results of Weighting Analysis and Errors

Weighting Analysis of MC Data and the Studies of Uncertainties (Milagro Collaboration Meeting, Chuan Chen). Introduction Sensitivity and Error Analysis Results of Weighting Analysis and Errors. Introduction. Results from Andy’s memo (06-27-05)

dewey
Télécharger la présentation

Introduction Sensitivity and Error Analysis Results of Weighting Analysis and Errors

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Weighting Analysis of MC Data and the Studies of Uncertainties(Milagro Collaboration Meeting, Chuan Chen) Introduction Sensitivity and Error Analysis Results of Weighting Analysis and Errors

  2. Introduction • Results from Andy’s memo (06-27-05) • For MC data events are weighted in energy E and position r, so statistical errors are introduced. (Uncertainties in Weighted Monte Carlo Data, R. Ellsworth, 05-27-03) • Using GEANT4 Monte Carlo data.

  3. Error Analysis • Error of each event that passes the cuts is ewgt2 x rwgt2 • The total error of the detected events in the ith angle is • The events in the ith zenith angle is • The total error is • For example, the number of gamma events from Crab is 14.669±0.195 events/day for NFit =20 and X2=2.5 and the multiplicity number is 50.

  4. Weighting Analysis • The weight of each slice is the ratio of the expected number of signal events to the number of background events passing the cuts. Since the weight is relative, the weight for slice 1 is defined to be 1.0 and the other weights are computed relative to it. The weight for the ith slice is • After propagation of uncertainties the error of weight of ith slice is

  5. Results • The weight and error assigned to each slice of different spectrums of Crab and Cygnus is in the tables below. Table 1. Weights and errors of 7 slices for different spectrums of Crab region

  6. Results (cont) Table 2. Weights and errors of 7 slices for different spectrums of Cygnus region

  7. Weight vs Slice# Fig 1. Weight vs Slice# for Crab Region

  8. Weight vs Slice# (cont) Fig 2. Weight vs Slice# for Cygnus Region

  9. Compare Results Table 3. Weight results from real data and MC data. Error of MC results is included.

  10. Data Results Compare to MC Results Fig 3. Data results compare to MC results

  11. Weighting Matrix Table 4. Weighting matrix of Crab for different X2 cuts and NFIT cuts

More Related