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Review of motivation Update on QE sample selection Results for a high statistics MC set

An Update on Using QE Events to Estimate the Neutrino Flux and Some Preliminary Data/MC Comparisons for a QE Enriched Sample. Review of motivation Update on QE sample selection Results for a high statistics MC set Data/MC comparisons.

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Review of motivation Update on QE sample selection Results for a high statistics MC set

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  1. An Update on Using QE Events to Estimate theNeutrino Flux and Some Preliminary Data/MC Comparisons for a QE Enriched Sample • Review of motivation • Update on QE sample selection • Results for a high statistics MC set • Data/MC comparisons

  2. The x-sec for DIS events is fairly well known at high energies (~20GeV) and it is easy to select a sample of DIS events at this energy. The DIS x-sec can be 'divided out' of such a sample to give an estimate of the neutrino flux at this energy. The shape of the QE x-sec is well known and is flat down to ~1GeV but the normalization of this x-sec is not so well known. The DIS flux estimate can be used to 'pin' the normalization of the QE x-sec. Using the flat shape of the QE x-sec the neutrino flux can be estimated as a function of energy by again 'dividing out' the x-sec from samples of QE events in a series of reconstructed energy bins. Motivation

  3. QE Sample Selection • I am using a method of maximum likelihood in a series of reconstructed neutrino energy bins from 0-20GeV to identify QE events. • The following plots briefly recap the variables that go into the ML analysis. They correspond to a MC sample that was generated with a flat energy spectrum: Shape for all events reflects total interaction x-sec Asymmetric binning to reflect energy resolution and ensure adequate numbers of events in each bin to make pdfs

  4. The first two variables that go into the likelihood analysis are just the numbers of reconstructed tracks and showers – generally events with no tracks are likely not to be QE and events with no showers are likely to be QE. I also use the reconstructed invariant mass squared: QE Sample Selection Large numbers of DIS events due to large flux out to 20GeV and dominance of DIS x-sec (black=QE, blue=RES and red=DIS) • Now want to consider event topology and PH distributions near to the vertex to try to distinguish between QE (proton), RES (proton+pion) and DIS (pions) events.

  5. QE Sample Selection • I remove the main muon track from an event (if there was one) as well as hits that occur more than 2m in z from the vertex (protons and pions will not travel further than this) and 'crosstalk-like' hits (defined as having PH<1.5pe). • I then define several variables: • The number of high PH hits (>20pe) remaining • The total PH of the remaining hits • Total remaining PH as a fraction of total event PH (similar to y)

  6. QE Sample Selection • Black = QE • Blue = RES • Red = DIS • These two variables are highly correlated and so I combined them into a single 1D pdf using a toy principal components analysis.

  7. The final variable I am using for a pdf is obtained by performing a Hough transform on the remaining hits and taking the height of the peak. • In each case the low energy events are the hardest to discriminate between. • For all energy ranges the RES events are much harder to remove than the DIS events.

  8. PID Results • I then form a QE PID parameter for each energy bin based on the probabilities outputted from the ML analysis in that bin. • Using the first half of the MC events to make the pdfs and running the second half through the analysis gives the following:

  9. Further Work • There are several unfolding methods that could be used to get a flux estimate from a QE sample – these have not been looked into fully yet. Data/MC Comparisons for a QE Enriched Sample • I have run samples of pME data and MC (R1.16) through the MLPID analysis in order to take a first look at some physics distributions for a QE enriched sample. • In what follows all distributions have been normalized using POTs: • Data – 1.21e18 POTs from May after 'good beam' cuts: abs(hornI)>0.1 -2.0<hpos2<0.0 -1.0<vpos2<2.0 closest spill <2.0 • MC – 0.90e18 POTs

  10. pME MC v.s MC • First I used half of the pME MC to construct my pdfs and then ran the remaining half through the MLPID analysis to see what sort of purities to expect: The resulting efficiencies (black) and purities (red) look worse than those for my previous flat energy spectrum sample – am not sure yet why this is the case. Due to flux and x-sec I only had enough statistics to look in the 2-10 GeV range • The following sample of QE-like events using the MC for pdfs and running the data through the analysis should be ~60% QE events.

  11. pME MC v.s Data • Reconstructed neutrino energy: MC seems to be shifted by ~0.5GeV above data. Black = data, Red = MC

  12. pME MC v.s Data • Reconstructed muon energy: MC seems to be shifted by ~0.5GeV above data. Black = data, Red = MC

  13. pME MC v.s Data • Reconstructed shower energy: MC seems to be shifted by ~0.1GeV below data. Black = data, Red = MC

  14. pME MC v.s Data • Reconstructed y: MC seems to be shifted towards slightly lower y. Black = data, Red = MC

  15. pME MC v.s Data • Reconstructed Q^2: MC seems to be shifted towards slightly lower Q^2. Black = data, Red = MC

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