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Josh Spitz's comments on v2

  • For separating pion and proton tracks, is it correct that only the
    mean dE/dx is used? Why don't you consider the full dE/dx vs range
    profile? This should work well to distinguish pions from protons.
    Perhaps this issue is taken care of by the BDTG step.

    We do use the full dE/dx vs range profile in the selection.
    This is described in section 2.1 when we talk about the "MIP-like" requirement.
    The track is flagged as MIP-like is the full dE/dx vs range profile is consistent
    with expectations from muon or pion. Though this does not work well for exiting
    particles. We added the following sentence for clarification:

    this means that the track was either labeled as a muon or a pion based on the full dE/dx vs range profile.
    There are two calorimetry related variables in the BDTG: the number of "MIP-like"
    tracks and the average dEdx of the pion candiate track.
  • Some numbers in Table 1 don't pass a simple sanity check. (Contained
    and reconstructed as MIP-like track + Not contained and reconstructed
    as MIP-like track) 37+76!=100.

    They are not supposed to add up to 100% since they are independent samples.
    Contained and reconstructed as MIP-like track + contained and reconstructed as non-MIP-like
    track should add up to 100%.
    The purpose is to demenstrated most of the contained protons are flagged as protons and most
    of the exiting protons as flagged as MIP-like particles.
  • What does "Non-resonant events" refer to in Figure 2?

    GENIE caterizes events into 5 different channels: QE, resonance pion production,
    DIS, coherent pion production and MEC. Here "non-resonant events" means all channels except
    resonance pion production. This is now clarified in the note:

    "Non-resonant events" means the events produced in one of the CC channels: QE, DIS,
    coherent pion production and MEC.
  • I notice that no unfolding is used for any of the measurements
    reported (muon momentum, muon angle wrt neutrino direction, pion angle
    wrt neutrino direction, and angle between muon and pion). In order to
    justify not unfolding, you must show the reconstructed and true
    distributions in terms of all of these variables for simulated events
    and demonstrate that unfolding is not necessary.

    We do unfolding in this analysis in the sense that the numerator of efficiency
    is calculated using the reconstructed quantity while the demoninator of
    efficiency is calculated using the truth quantity. It is a standard approach to
    correct for both efficiency and smearing. Note we have the following sentence in
    the note:
    Using reconstructed information in the numerator and truth information in the
    denominator, the process is able to take into account and correct for detector smearing.
  • Also, it is important to see what the expected resolutions are in
    terms of the reconstructed variables.

    We have added the following plots in the note to show the resolution
    of the kinematic variables.


  • Is the efficiency reported in Figure 6, top right in consideration of
    all analysis cuts?

    No, this is the definition of the efficiency in the note:
    The detection efficiency is defined as the ratio between the distribution
    of the truth value of a pion variable (momentum) in all truth CC 1$\pi$
    events when the pion is reconstructed as a track and the distribution of
    the same variable in all truth CC 1$\pi$ events, requiring in both cases
    only the neutrino interaction vertex to be contained in the ArgoNeuT TPC
    fiducial volume.
    It is basically the track reconstruction efficiency. We tweaked the definition
    a little to make it clear.
  • Why are true NC events considered when reporting efficiency (and in
    Table 2)? Instead of NC+CC, shouldn't the denominator be "number of
    true charged current events with an interaction vertex featuring a
    single pion, with no kaon or neutral pion and no requirement on the
    number of nucleons"?

    We include NC events in Table 2 to show the event composition. Most of
    the NC events are removed by the MINOS matching cuts. NC events are not
    included in the calculation of efficiency. See the efficiency definition
    in the above response.
  • From Table 2, I notice that a lot more neutrino events are removed by
    the "MINOS match" requirement than antineutrino events. Is this
    understood?

    In the antineutrino beam, neutrinos events have higher energy, thus more DIS
    interactions with lots of tracks. It is hard to reconstruct the muon track in
    the busy enviroment and that's why there are fews neutrino events passing MINOS
    matching cut.
  • In Figure 9, it seems that the ratio of signal:background is much
    lower with neutrinos than antineutrinos. According to the text, this
    seems to simply be an issue with the number of events simulated--and
    this is corrected by applying a weighting factor of 0.25 to neutrino
    background events. However, it doesn't seem like this was propagated
    to the plots in Figure 9(?)

    The neutrino events are mostly DIS interactions due to the higher energy.
    For the anti-neutrino events, roughly 1/3 of the events are resonance pion production.
    This is the reason anti-neutrinos have much higher signal to background
    ratio compared with neutrinos. We apply a weight in the BDT training so
    the training won't completely focus on the background event. Figure 9 shows
    the real signal/background predictions from GENIE without any weights/tuning,
    which just reflects the proor modeling of pion production.
  • How are correlations between bins accounted for in the
    TFractionFitter procedure? I don't fully understand the procedure. In
    particular, it is not clear to me why one needs to invoke a root class
    to normalize the distributions to match the normalization of the data.

    TFractionFitter takes into account both data and Monte Carlo statistical
    uncertainties. The way in which this is done is through a standard likelihood
    fit using Poisson statistics; however, the template (MC) predictions are
    also varied within statistics, leading to additional contributions to the
    overall likelihood.
    We actually tried both TFractionFitter and another method to just scale
    signal and backgorund templates and got consistent results.
  • Just to reiterate my previous comment: the flux normalization
    uncertainties for neutrinos and antineutrinos should be treated more
    carefully. Given central values and a correlation matrix from MINERvA,
    this should be fairly simple to extract. Even though this is a
    statistics-dominated measurement, I think it is important to get the
    largest systematic correct, especially since I don't think it is very
    difficult to do so (given info from MINERvA).

    Based the MINERvA flux paper (https://arxiv.org/abs/1607.00704), we have
    changed the flux normalization uncertainty from 11% to 9.7% for neutrino flux
    and 7.8% for antineutrino flux. The systematic errors reduced from +18.7-18.6%
    to +18.2-18.0 for neutrinos and from +11.6-13.4% to +9.1-10.0% for antineutrinos.