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This commit fixes the chi2 analysis so that it is no longer biased.
Previously, the chi2 analysis pull plots showed a consistent bias. At
first, I thought this was due to the fact that the posterior wasn't
gaussian, but even after switching to percentile plots based on the
algorithm outlined in "Validating Bayesian Inference Algorithms with
Simulation-Based Calibration", I was still seeing a bias. I finally
tracked it down to the fact that I was applying the energy scale
parameters to the data instead of the Monte Carlo. Therefore, in this
commit I update the posterior to now apply the energy scale parameters
to the Monte Carlo instead of the data. This has the slight disadvantage
that the final histograms will be binned in the biased energy, but
that's not really a big deal.
In addition, this commit contains several other updates:
- switch to plotting percentile plots based on the algorithm in
"Validating Bayesian Inference Algorithms with Simulation-Based
Calibration" instead of pull plots
- apply both the energy scale and resolution at the individual particle
level, i.e. there is no longer an energy resolution term for electron
+ muon fits
- separate pull plots and coverage plots. Previously I was making both
the p-value coverage plots and the pull plots at the same time.
However, the pull plots shouldn't have anything to do with the GENIE
weights whereas the p-value coverage plots should draw samples
weighted by the GENIE weights. In addition, for the pull plots we draw
new truth parameters on every iteration whereas for the p-value
coverage plots we only draw them once.
- switch to using KDEMove() for the MCMC since I think it samples
multimodal distributions a lot better than the default emcee move.
- I now correct for the reconstruction energy bias in plot-michel and
plot-muons
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This commit updates the criteria for selecting stopping muons from:
- calibrated nhit < 4000
- udotr < -0.5
to
- reconstructed kinetic energy < 10 GeV
The previous criteria were intended to remove through going atmospheric
events but produced a strong bias in the comparison due to the nhit cut
and an energy bias in the data relative to the Monte Carlo. The new cut
does a good job of cutting through going muons but doesn't produce the
same bias.
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- added a cos(theta) cut
- plot the energy and angular distribution of stopping muons
- fix bug in calculating Michel normalization constant
- plot legend for energy resolution plot
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stopping muons
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- use pd.Series.where() instead of DataFrame.loc() to speed things up in
tag_michels
- don't set y limits when plotting bias and resolution for stopping
muons
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- add get_multinomial_prob() function to stats.py
- add plot_hist2_data_mc() function to do the normal particle id plot
but also print p values
- other small bug fixes
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- only look at muons with nhit < 4000 and udotr < -0.5
- switch from energy1 -> ke
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This commit adds a first draft of a script to plot the michel energy
distribution and particle id histograms for data and Monte Carlo and to
plot the energy bias and resolution for stopping muons.
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