1 / 16

Cross-section systematics

Cross-section systematics. D. A. Petyt 15 th Dec 2004. Broad aims of this study: Evaluate the effect of cross-section uncertainties on the all-event CC analysis (selection efficiencies, energy resolution, parameter measurement errors) using NEUGEN reweighting package.

strom
Télécharger la présentation

Cross-section systematics

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. Cross-section systematics D. A. Petyt 15th Dec 2004 Broad aims of this study: • Evaluate the effect of cross-section uncertainties on the all-event CC analysis (selection efficiencies, energy resolution, parameter measurement errors) using NEUGEN reweighting package. • Develop a procedure that uses ND data to constrain systematics in FD oscillation fit.

  2. The CC-like sample • A CC-like event is defined by the following cuts: • At least 1 reconstructed track with trkpass=1 • Pid parameter>-0.4 (>-0.1 in ND) • Selected sample consists of: • 55.4% (61.1%) DIS • 25.9% (23.0%) RES • 16.5% (13.2%) QEL • 2.2% (2.7%) NC (numbers in parentheses are for dmsq=0.002,s2t=1.0) NCQELRESDIS

  3. Event reweighting ma_qel+10% ma_res+10% • Cross-section weighting is performed on an event-by-event basis using the NeugenInterface package. The cross-section parameters that can be changed are: • ma_qel, ma_res, RES-DIS acceptance factors, PDFs • Events are reweighted according to the following parameters: • True enu, initial_state, CC/NC, flavour, target nucleus and the following kinematic variables: • q2 (QEL) • q2, W (RES) • x,y (DIS) Disfact-25%

  4. Shape & normalisation weight • The plot at right shows the weighting factors for QE,RES&DIS events as a function of visible energy calculated for the following parameters: • ma_qel + 5% • ma_res + 5% • Disfact + 5% • The QEL (and to a lesser extent, the RES) weights are approximately flat versus Evis. • DIS weights much smaller than QEL/RES QEL RESDIS Visible energy (GeV)

  5. Overall CC-like normalisation These factors are #weighted/#unweighted events in the three event categories

  6. CC efficiencies/purities Reconstruction efficiencies not included in these numbers

  7. “Error band” for ma_qel10% 19.5e20 p.o.t FD only nominal Note asymmetry. Protons? unoscillated 10% dmsq=0.002,s2t=1 NC included NC subtracted

  8. “Error band” for ma_res10% 19.5e20 p.o.t Lots of NC RES in this bin?

  9. “Error band” for disfact10% 19.5e20 p.o.t

  10. Fits with ma_qel10% Dm2=0.002, sin22q=1 6.5e20 p.o.t. 90%CL Far-only fits – fit with ‘unweighted’ MC Nominal ma+10%ma-10%

  11. Fitting cross-section uncertainties • Cross-section uncertainties can be treated as nuisance parameters in oscillation fit. • Define c2 as a function of oscillation parameters and cross-section parameters. Minimise chisq with respect to cross-section parameters to yield dmsq,s2theta contours • Can also apply ‘penalty terms’ to c2 in order to constrain the values of these nuisance parameters. FD c2 therefore looks like this: • Can add additional c2term for ND which depends only on the nuisance parameters. The idea here is that the ND will help to constrain these parameters since they will, in general, be correlated with dmsq,s2t in FD-only fits.

  12. FD fit – ma_qel 6.5e20 p.o.t unconstrained • Simulated oscillation signal with dmsq=0.002, s2t-1 • 3 parameter fit: dmsq, s2t, ma_qel • Plots show ma_value that minimises chisq for each dmsq,s2t point • ‘band’ structure evident – positive correlation between ma_qel and oscillation probability s2theta dmsq smA=5%

  13. Parameter measurement 6.5e20 p.o.t nominal sma=5%sma=10% unconstrained note positive correlation

  14. ND-FD fit – constrained ma_qel FD onlyND only ND+FD 6.5e20 p.o.t FD, ~10000 ND snarls

  15. ND-FD fit – unconstrained ma_qel FD onlyND only ND+FD

  16. Future work • Need to look at other cross-section parameters. Only considered QEL weights so far and these affect just ~15% of the total CC-like dataset • What are the correlations/degeneracies between the various parameters? To what extent can ND data resolve them? How much ND data will be required? • Should increase size of FD dataset. At what pot value do systematic errors exceed statistical errors? • Resolve question mark hanging over DIS weights • Can then perform a MDC-style study with 3 of the 4 systematics in place (the 3 cross-section parameters) to test the fitting machinery - in advance of tackling the real MDC once beam weighting code is available.

More Related