1. 1 CC Update Status of the PAN –Integration of “standard” all-event analysis with Mad Analysis update –Resolving parameter degeneracies in the ND –To do list… D. A. Petyt 16 th Feb 2005

2. 2 PAN update Standard all-event analysis is now incorporated into the Mad framework –MadAnalysis::CreatePAN() method modified to include all variables required by the standard analysis –MadDpAnalysis methods added: MakeMyFile() – creates PDFs (6 1-dimensional histograms) from a list of input files ReadPIDFile() – reads in PDFs to be used in PID calculation PID() - calculates PID parameter for a single event Code will be committed to CVS this week –Need to co-ordinate with Chris re: MadAnalysis changes

3. 3 Using the code A macro CCSTD.C is provided to allow users to produce PDF histograms and PAN ntuples for both near and far events: /////////////////////////////////////////////////////////// // STD analysis controls // Un-comment next line to create the PDFs necessary for CC/NC // separation // anal->MakeMyFile(Tag); // These three lines are used to create the STD PAN // - edit pidfile to point to the PID file you created with the // anal->MakeMyFile() method string pidfile = "DPHistos_r12_far.root"; anal->ReadPIDFile(pidfile); anal->CreatePAN(Tag); ////////////////////////////////////////////////////////// Run with: loon –bq ‘Mad/macros/CCSTD.C(“ ”,” ”)’

4. 4 Results – ND PDFs

5. 5 Analysis update – event reweighting In my collaboration meeting talk, I showed how ND data could be used to constrain the value of ma_qel, which is treated as a free parameter in the FD oscillation fit However, there are several unknown systematic parameters to be determined: ma_qel, ma_res, disfact + beam parameters. –How well can these parameters be determined? What are the degeneracies? –What variables & event samples should be used in the ND fits? The plot at right shows the example of a degeneracy between ma_qel and ma_res when a fit is made to the visible energy spectrum of ND cc-like events. 68% CL 90% CL

6. 6 Cause of the degeneracy The Evis distribution is not particularly suitable for distinguishing between ma_qel and ma_res since the weights are only weakly energy dependent (see plot at right) The plot below shows how combinations of ma_qel and ma_res can conspire to produce an energy distribution that is indistinguishable from the nominal values 5% increase in ma,disfact Neutrino energy weight nominal weighted Ma_qel=1.15 Ma_res=0.88 Ma_qel=1.15, ma_res=0.88          QEL RES DIS

7. 7 Use of additional information Use of additional kinematic variables - such as q 2 and y – can help to isolate the effects of cross-section weights on QEL, RES and DIS events –In addition, event sub-samples (such as a pure QEL sample) should also be useful here (i.e. Ed’s shower energy=0 sample) I have chosen to use the 2 dimension reco_enu, reco_y distribution, where reco_y is calculated as reco_eshw/reco_enu –This is roughly analogous to the K2K method of fitting to their measured p    to estimate the QE/non-QE composition of their beam QEL RES DIS

8. 8 E_nu vs reco_y distributions, all events nominal ma_qel=1.15, nominal ma_res ma_res=0.88, nominal ma_qel ma_qel=1.15, ma_res=0.88

9. 9 Difference between weighted and nominal distributions Relative (% change) Absolute (#events) ma_qel=1.15, nominal ma_res ma_res=0.88, nominal ma_qel ma_qel=1.15, ma_res=0.88 Effects do not cancel

10. 10 2D fit to ma_qel, ma_res Fit the two dimensional distribution (reco_enu, reco_y) to obtain values of ma_qel and ma_res. The shape information evident in the reco_enu, reco_y distributions breaks the degeneracy between the two cross-section parameters Fits use ~21000 ND events 68% CL 90% CL E_reco, reco_y fit Fit to E_nu Fit to E_nu,y

11. 11 Other topics NC contamination, although small in total (~3%) is significant in the lowest energy bins. The plots at right show how the presence of NC events ‘flatten out’ the oscillation dip and result in a less precise measure of the oscillation parameters and they should therefore be subtracted from the cc-like sample For the Mock Data Challenge, can be achieved by simply predicting the number of NC events for a given E_vis bin and set of cross-section/beam parameters. This subtraction will provide some additional ‘wiggle room’ for the fit – the size of this effect is currently being studied. Note that the composition of NC “cc- like” sample is ~50% RES, ~50% DIS. No NC subtraction Perfect NC subtraction

12. 12 The next few weeks The critical pieces of the analysis are the reweighting routines. I think these are pretty close to completion – Chris and Hugh have done a lot of good work here. –Cross-section reweighting is in place. There are a couple of known bugs, but these should be fixed this week. We’ll need to re-create the PAN ntuples to provide the extra variables that the latest version of the reweight package needs. –See Chris’s talk for the status of the beam weights. There is an issue we need to address about the non-availability of FD beam weighting information. The analysis code is in fairly good shape although it will need some development to handle the beam weights when they are available. –Barring any hitches, I’d estimate we’d need a couple of weeks to tune up the analysis procedure before turning to the analysis of the mock data itself.