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fix: Propagate initial vertex time variance for AMVF w/o time #2936
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@felix-russo do you have an opinion on this? |
@andiwand Alternatively one could make sure that a very large positive value to the initial for sigma_tt is present in the initial cov matrix (or set it to a very large value) and check/set that the other terms in the last row/column of the covariance matrix to 0 and just call the KF update on it. |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #2936 +/- ##
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Coverage 48.84% 48.84%
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Files 495 495
Lines 28911 28909 -2
Branches 13734 13732 -2
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Hits 14122 14122
Misses 4890 4890
+ Partials 9899 9897 -2 ☔ View full report in Codecov by Sentry. |
📊: Physics performance monitoring for d8af9fbphysmon summary
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@pbutti We are actually forwarding the initial covariance into the vertex fit which, like you described, has a big diagonal covariance. To me it seems good enough to use the initial cov. What do you think? |
Looks very good to me! |
I will go ahead and update the reference here |
🔴 Athena integration test results [b50dd08]🔴 Some tests have failed!Please investigate the pipeline! |
Athena failures unrelated. |
…roject#2936) Logs like ``` 21:16:17 VertexPerfor WARNING Nonpositive variance after vertex fit: Var(T) = 0 <= 0. ``` are gone after this change. To me it seems more sensical to propagate the initial time variance instead of hard setting it to `0` which causes the cov matrix to be invalid.
…roject#2936) Logs like ``` 21:16:17 VertexPerfor WARNING Nonpositive variance after vertex fit: Var(T) = 0 <= 0. ``` are gone after this change. To me it seems more sensical to propagate the initial time variance instead of hard setting it to `0` which causes the cov matrix to be invalid.
Logs like
are gone after this change.
To me it seems more sensical to propagate the initial time variance instead of hard setting it to
0
which causes the cov matrix to be invalid.