feat: Track parameters lookup estimation examples

[ssdetlab]

The PR adding the simulation based track parameter estimation.

The algorithm runs the Fatras simulation of the detector setup. A set of grids is imposed onto the user-defined reference layers of the tracking detector (e.g. first tracking layers sensitive surfaces). CurvilinearTrackParameters at the vertex and the reference tracking layers are recorded for the simulated particles and stored into the bins of the grids.

After the simulation is finished, the contents of the grids' bins are averaged and the grids containing the correspondence between the particles' intersection points at the reference layers, track parameters at the vertex and track parameters at the reference layers are constructed.

The constructed grids are stored into the json files. The grids are then readout and used for track parameters estimation in seeding algorithms, e.g. connected to the PathSeeder.

This PR contains the lookup generation part of the algorithm. When the interfaces, realisation and the general idea are agreed upon, the second part with the validation of the estimated lookups is going to be put up.

[coderabbitai]

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[github-actions]

📊: Physics performance monitoring for 3c21b70

Full contents

physmon summary

• ✅ Particles fatras
• ✅ Particles geant4
• ✅ Particles ttbar
• ✅ Vertices ttbar
• ✅ Truth tracking (KF)
• ✅ Truth tracking (GSF)
• ✅ Truth tracking (GX2F)
• ✅ Truth tracking (KF refit)
• ✅ Truth tracking (GSF refit)
• ✅ CKF finding performance | trackfinding | single muon | truth smeared seeding
• ✅ CKF fitting performance | trackfinding | single muon | truth smeared seeding
• ✅ CKF track summary | trackfinding | single muon | truth smeared seeding
• ✅ Seeding trackfinding | single muon | truth estimated seeding
• ✅ CKF finding performance | trackfinding | single muon | truth estimated seeding
• ✅ CKF fitting performance | trackfinding | single muon | truth estimated seeding
• ✅ CKF track summary | trackfinding | single muon | truth estimated seeding
• ✅ Seeding trackfinding | single muon | default seeding
• ✅ CKF finding performance | trackfinding | single muon | default seeding
• ✅ CKF fitting performance | trackfinding | single muon | default seeding
• ✅ CKF track summary | trackfinding | single muon | default seeding
• ✅ Seeding trackfinding | single muon | orthogonal seeding
• ✅ CKF finding performance | trackfinding | single muon | orthogonal seeding
• ✅ CKF fitting performance | trackfinding | single muon | orthogonal seeding
• ✅ CKF track summary | trackfinding | single muon | orthogonal seeding
• ✅ Seeding trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF fitting performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF track summary | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ Ambisolver finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ IVF notime | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ AMVF gauss notime | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ AMVF grid time | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ Seeding trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF finding performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF fitting performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF track summary | trackfinding | ttbar with 200 pileup | default seeding
• ✅ Ambisolver finding performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ AMVF gauss notime | trackfinding | ttbar with 200 pileup | default seeding
• ✅ AMVF grid time | trackfinding | ttbar with 200 pileup | default seeding

[paulgessinger]

This is really could. A few thoughts:

• Could the logic be split up so that the accumulation code itself is reusable and in Core and the orchestration is in the examples?
• this is not tied to the gen2 geometry is it?
• Can this be generalized to bound parameters or do you need it to be curvilinear? In particular I imagine this could be useful in non-telescope setups, where you might want to use e.g. perigee parameters

[sonarqubecloud]

Quality Gate passed

Issues
2 New issues
0 Accepted issues

Measures
0 Security Hotspots
54.1% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[acts-project-service]

✅ Athena integration test results [6ff7d49]

✅ All tests successful

status	job	report
🟢	report_pull_request
🟢	run_art_test: test_run4_acts_vertex_PU200
🟢	run_art_test: test_run4_ttbar_PU200
🟢	run_art_test: test_data18_13TeV_1000evt
🟢	run_art_test: test_ttbarPU40_reco
🟢	run_workflow_tests_run4_mc
🟢	run_workflow_tests_run2_mc
🟢	run_workflow_tests_run2_data
🟢	run_workflow_tests_run3_mc
🟢	run_workflow_tests_run3_data
🟢	test_ActsDumpGeometryIdentifiers
🟢	test_ActsCheckObjectCountsWorkflowHgtd
🟢	test_ActsCheckObjectCountsCachedWorkflow
🟢	test_ActsCheckObjectCountsWorkflow
🟢	test_ActsEFTrackFit
🟢	test_ActsPersistifySeeds
🟢	test_ActsBenchmarkWithSpot
🟢	test_ActsAnalogueClustering
🟢	test_ActsWorkflowHeavyIons
🟢	test_ActsWorkflowFastTracking
🟢	test_ActsWorkflowCached
🟢	test_ActsWorkflow
🟢	test_ActsValidateAmbiguityResolution
🟢	test_ActsValidateResolvedTracks
🟢	test_ActsValidateTracks
🟢	test_ActsValidateActsCoreSpacePoints
🟢	test_ActsValidateActsSpacePoints
🟢	test_ActsValidateSeeds
🟢	test_ActsValidateOrthogonalSeeds
🟢	test_ActsValidateClusters
🟢	test_ActsPersistifyEDM
🟢	test_ActsGSFRefitting
🟢	test_ActsKfRefitting
🟢	test_ActsExtrapolationAlgTest
🟢	test_ActsITkTest
🟢	run_unit_tests