Fix MC test: use CSMS model and regenerate reference with n=10000#69
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Fix MC test: use CSMS model and regenerate reference with n=10000#69
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The MC regression test was failing for two reasons: 1. Model mismatch: the Python reference data was generated with CSMS but the Julia test was running with DIPOLE. 2. Too few events: n=100 events produced 30-60% statistical noise in the median exit energy when comparing across different RNGs (numpy vs MersenneTwister), making the test unreliable even when the physics agrees. Fix: switch the Julia MC testset to CSMS and regenerate python_reference.json with n=10000 events (primaries only, slicing out[:n_events] to match the Julia run_mc convention of one result per injected event). Python and Julia now agree at the 1-4% level on all three test cases. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Summary
DIPOLEtoCSMSto match the model used when generatingpython_reference.jsonrun_mcconventionRoot cause
The test was failing for two compounding reasons:
Model mismatch:
generate_python_reference.pyusesCrossSections('CSMS')but the Julia MC testset was runningCrossSections(DIPOLE).Too few events: n=100 produced 30-60% statistical noise in the median exit energy when comparing across different RNGs (numpy vs MersenneTwister), making the test unreliable even when the physics agrees.
A third issue was identified but does not affect this fix: Python
run_MCappends secondary particles to its output (exit_fraction > 1), while Juliarun_mcreturns one result per injected event. The reference is now generated by slicingout[:n_events]to compare primaries only.Test plan