feat: VectorHelpers - Add generic eta method

[junggjo9]

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📊: Physics performance monitoring for e1d9282

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)

[sonarqubecloud]

Quality Gate passed

Issues
1 New issue
0 Accepted issues

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

See analysis details on SonarQube Cloud

[benjaminhuth]

Hmm why not double eta(double theta), wouldn't that be even more generic?

[junggjo9]

I was taking the phi implementation as my template here. Yeah, that would be another option

[andiwand]

in the best case we could have both. we have many implementations for eta -> theta and the other way flying around which could be centralized. but a scalar transformation is out of scope for vector helpers I believe

[junggjo9]

Do we already have a central eta implementation?

[benjaminhuth]

Hmm yeah make sense that its not for vector helpers. But I think code like eta(myFancyType) which internally calls myFancyType.theta() is just less transparent as eta(myFancyType.theta())...

So I don't like the phi function either I think :D

[andiwand]

ah we actually have a helper

acts/Core/include/Acts/Utilities/AngleHelpers.hpp

Lines 35 to 89 in 0dda19a

	/// Conversion limits for `double` eta/theta calculations.
	///
	/// Defines safe min/max theta and eta values for `double` precision.
	template <>
	struct EtaThetaConversionTraits<double> {
	/// Minimum theta value for double precision
	static constexpr double minTheta = 1e-12;
	/// Maximum theta value for double precision
	static constexpr double maxTheta = std::numbers::pi - minTheta;
	/// Maximum eta value for double precision
	static constexpr double maxEta = 700.0;
	/// Minimum eta value for double precision
	static constexpr double minEta = -maxEta;
	};
	/// Calculate the pseudorapidity from the polar angle theta.
	///
	/// This function aims to be FPE safe and returns infinity for theta values
	/// outside the floating point precision range.
	///
	/// @param theta is the polar angle in radian towards the z-axis.
	///
	/// @return the pseudorapidity towards the z-axis.
	template <typename Scalar>
	Scalar etaFromTheta(Scalar theta) {
	if (theta <= EtaThetaConversionTraits<Scalar>::minTheta) {
	return std::numeric_limits<Scalar>::infinity();
	}
	if (theta >= EtaThetaConversionTraits<Scalar>::maxTheta) {
	return -std::numeric_limits<Scalar>::infinity();
	}
	return -std::log(std::tan(theta / 2));
	}
	/// Calculate the polar angle theta from the pseudorapidity.
	///
	/// This function aims to be FPE safe and returns 0/pi for eta values outside
	/// the floating point precision range.
	///
	/// @param eta is the pseudorapidity towards the z-axis.
	///
	/// @return the polar angle in radian towards the z-axis.
	template <typename Scalar>
	Scalar thetaFromEta(Scalar eta) {
	if (eta <= EtaThetaConversionTraits<Scalar>::minEta) {
	return std::numbers::pi_v<Scalar>;
	}
	if (eta >= EtaThetaConversionTraits<Scalar>::maxEta) {
	return 0;
	}
	return 2 * std::atan(std::exp(-eta));
	}