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feat: Track parameters lookup estimation examples #3823

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@ssdetlab ssdetlab commented Nov 6, 2024

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.

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@github-actions github-actions bot added Component - Examples Affects the Examples module Track Finding labels Nov 6, 2024
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github-actions bot commented Nov 6, 2024

📊: Physics performance monitoring for bee4354

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Quality Gate Failed Quality Gate failed

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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


/// @brief Lookup table for track parameters estimation
/// in the track estimation algorithm
using Lookup = std::unordered_map<Acts::GeometryIdentifier, LookupGrid>;
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This is a fairly generic name.


void ActsExamples::TrackParamsLookupAccumulator::addTrack(
const Acts::CurvilinearTrackParameters& ipTrackParameters,
const Acts::CurvilinearTrackParameters& refTrackParameters,
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Do these need to be restricted to curvilinear? I guess they need to be consistent across all tracks being averaged, but they couldn't they be any type of bound parameters?

/// reference layer track parameters for a given position
/// in the track estimation algorithm
using LookupAccumGrid =
Acts::Grid<std::vector<Acts::CurvilinearTrackParameters>, LookupAxis,
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You could avoid storing all individual tracks by having one grid where you sum up all of the parameters and one where you count the number of tracks.

m_cfg(std::move(config)) {
// Iterate over the reference layers and create
// track parameter accumulators
for (const auto& [geoId, refSurface] : m_cfg.refLayers) {
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Could you check the surface type in the constructor as well?

m_inputSimHits.initialize(m_cfg.inputHits);
}

ActsExamples::TrackParamsLookupEstimation::~TrackParamsLookupEstimation() {
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Can you do this in the finalize method instead of the destructor?

/// Virtual destructor
~JsonTrackParamsLookupWriter() override = default;

/// Write out the material map
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material map?

Comment on lines +35 to +49
Acts::Vector4 fourPosition = Acts::Vector4::Zero();
Acts::Vector3 direction = Acts::Vector3::Zero();
Acts::ActsScalar qOverP = 0;
for (const auto& track : tracks) {
fourPosition += track.fourPosition();
direction += track.direction();
qOverP += track.qOverP();
}
fourPosition /= tracks.size();
direction /= tracks.size();
qOverP /= tracks.size();

return Acts::CurvilinearTrackParameters(
fourPosition, direction, qOverP, std::nullopt,
tracks.front().particleHypothesis());
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I am always a bit skeptical about plain averaging track parameters

  • direction will not be normalized anymore
  • does it really mean something to average qOverP?

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3 participants