Backward filter for Two-filter Smoothing #788
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As @beomki-yeo discovered in acts-project/traccc#788, constant reverse iterators cannot be currently compared to non-constant reverse iterators as is required by the C++ standard in section [container.requirements.general]. This commit adds the necessary comparison code, and adds some tests for the new behaviour.
As @beomki-yeo discovered in acts-project/traccc#788, constant reverse iterators cannot be currently compared to non-constant reverse iterators as is required by the C++ standard in section [container.requirements.general]. This commit adds the necessary comparison code, and adds some tests for the new behaviour.
As @beomki-yeo discovered in acts-project/traccc#788, constant reverse iterators cannot be currently compared to non-constant reverse iterators as is required by the C++ standard in section [container.requirements.general]. This commit adds the necessary comparison code, and adds some tests for the new behaviour.
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| @@ -123,9 +126,10 @@ TEST_P(CkfSparseTrackTelescopeTests, Run) { | |||
| // Finding algorithm configuration | |||
| typename traccc::finding_config cfg; | |||
| cfg.ptc_hypothesis = ptc; | |||
| cfg.chi2_max = 30.f; | |||
| cfg.chi2_max = 200.f; | |||
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After I set the B field to z-axis and Telescope planes set along the x-axis, chi2 cut value of 30 for the CKF selection did not work for a few measurements :/
I believe the cut value of 30 should be OK in most cases from the distribution of chi2 of the Kalman Fitter (See the plot below). The change to 200 is just to cover the outliers which happen very rarely
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If I read the ACTS implementation correctly it just rewinds the direction and uses the usual KF formalism but then sets That does not seem correct to me 🤔 since we would use measurements twice without accounting this in the covariance. Have you looked at this @beomki-yeo? I suppose you use the formalism provided in the reference? |
If I read the code correctly, And It seems the ACTS backward filter does not inflate covariance. I inflated the covariance of both forward and backward propagator. In the various references, I found that there are two boundary conditions:
The second condition sounds a bit weird because there is no inverse matrix for a zero matrix but I interpreted this as a big diagonal initial covariance matrix. |
Yeah it seems some measurements are used twice; |
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This PR implements the two-filters smoother where a smoothed state is calculated by taking a weighted average between filtered state from the forward filter and predicted state from the backward filter (See Eq (3.38) of Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors)
Following changes are made to implement the two-filters method:
However, the default smoothing algorithm will still be the RTS smoother (i.e. smoothing algorithm which is already implemented) until we improve the two-filters method with the DirectNavigator of ACTS. The DirectNavigator is required to make sure that forward and backward filter propagate through the same set of surfaces.