fix: narrowing and implicit conversion fixes done by AI

[asalzburger]

Address Sonar implicit conversion and narrowing findings

*** This has been done using our SonarCloud output by cursor ***

Summary

This change set makes implicit numeric conversions explicit across Core, Alignment, and related headers/sources, targeting SonarCloud rules around implicit conversion and narrowing (integer precision loss, double→float, and std::size_t mixed with narrower index or arithmetic types). The intent is mechanical clarity for static analysis and alignment with strict floating-point warnings (for example -Wfloat-conversion), without altering algorithms.

Motivation

Sonar reported a large cluster of findings where:

• std::size_t (or other wide integers) were used where API types expect int, trajectory index types, or float-backed binning.
• Physical quantities expressed with UnitConstants (typically double) were assigned to float configuration fields without an explicit cast.
• Arithmetic combined signed and unsigned index offsets in ways that are easy for analyzers to flag (for example std::size_t plus a negative int).

Making these conversions explicit documents intent and removes or reduces the corresponding static-analysis noise.

Approach

• Prefer static_cast<T>(…) at boundaries (sizes to index types, integrals to double for division, double to float for stored parameters).
• Use …f literals where a float is clearly intended.
• For axis / neighborhood index math, use static_cast<int>(idx) before adding signed offsets, and static_cast<int>(getNBins()) where a narrow int range is required.
• fastCathetus: promote hypotenuse to double before the difference/sum under sqrt so mixed-type call sites stay well defined.
• hypotSquare was not changed to always return double; doing so altered deduced return types for fastHypot and broke a lambda that mixed fastHypot and scalar returns. Instead, BroadTripletSeedFilter’s getTopR lambda is explicitly -> float with static_cast<float>(fastHypot(…)).

Areas touched (high level)

Area	Examples
Alignment	χ² / ndf averaging: explicit double casts for counts.
Track fitting	Kalman / GSF / smoother / updater: index types and float χ² assignment; fitter result indices aligned with TrackIndexType where applicable.
Event data	Track/space-point containers, proxies: size() / distance → explicit index or uint32_t casts.
Propagator / material	float paths: explicit casts from double or Eigen scalars.
Seeding / Seeding2	Config defaults with UnitConstants, GbTS filter literals, geometry numOfBins / numLayers, graph-seeder floats, triplet/doublet f suffixes.
Surfaces	FacesHelper: anker and half-count geometry use explicit casts.
Track finding	MeasurementSelector, TrackParamsLookupAccumulator: float χ₂ and double promotion for count-weighted parameters.
Utilities	Axis, BinAdjustment (+ volume variant), BinningData, Polynomials (product → double), MathHelpers (fastCathetus).
Vertexing	GaussianGridTrackDensity config: binSize and literals.
Visualization	EventDataView3D, Interpolation3D: loop indices promoted for double math.
Core sources	Greedy ambiguity resolution ratios, ParticleData SEMF terms, seed/space-point reserve, VectorMultiTrajectory / VectorTrackContainer, geometry builders (Cuboid/Cylinder/LayerArray/CylinderVolumeHelper).

Verification

• Local CMake (minimal preset-style) build with -Wfloat-conversion enabled in project flags: ActsCore and ActsAlignment link successfully.

Follow-up (optional)

• Re-run Sonar and, if desired, mark matching issues Resolved in sc_issues.json (same housekeeping as for other rule batches).
• narrowing_issues.tsv (~179 rows) was a one-off extract from an export; refresh or remove from the repo if it should not be tracked.

Related

• Sonar rule families: implicit conversion, narrowing, floating-point precision in assignments.

--- END COMMIT MESSAGE ---

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[github-actions]

📊: Physics performance monitoring for 70f10ca

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
• ✅ ML Ambisolver | 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
• 🔵️ Comparison - Truth tracking (GX2F vs KF)

[paulgessinger]

There's a number of new issues.

[asalzburger]

There's a number of new issues.

Yeah, many of them are:

Replace the redundant type with "auto"

where SonarCloud is giving the opposite recommendation than Athena coding rules, as far as I remember.

[sonarqubecloud]

Quality Gate passed

Issues
7 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
9.4% Duplication on New Code

See analysis details on SonarQube Cloud