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Add new dEdx calibration and estimator #45016
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151 changes: 151 additions & 0 deletions
151
RecoTracker/DeDx/interface/LikelihoodFitDeDxEstimator.h
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| #ifndef RecoTracker_DeDx_LikelihoodFitDeDxEstimator_h | ||
| #define RecoTracker_DeDx_LikelihoodFitDeDxEstimator_h | ||
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| #include "RecoTracker/DeDx/interface/BaseDeDxEstimator.h" | ||
| #include "DataFormats/TrackReco/interface/DeDxHit.h" | ||
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| class LikelihoodFitDeDxEstimator : public BaseDeDxEstimator { | ||
| public: | ||
| LikelihoodFitDeDxEstimator(const edm::ParameterSet& iConfig){}; | ||
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| std::pair<float, float> dedx(const reco::DeDxHitCollection& Hits) override { | ||
| if (Hits.empty()) | ||
| return {0., 0.}; | ||
| // compute original | ||
| std::array<double, 2> value; | ||
| const auto& chi2 = estimate(Hits, value); | ||
| // try to remove lowest dE/dx measurement | ||
| const auto& n = Hits.size(); | ||
| if (n >= 3 && (chi2 > 1.3 * n + 4 * std::sqrt(1.3 * n))) { | ||
| auto hs = Hits; | ||
| hs.erase(std::min_element(hs.begin(), hs.end())); | ||
| // if got better, accept | ||
| std::array<double, 2> v; | ||
| if (estimate(hs, v) < chi2 - 12) | ||
| value = v; | ||
| } | ||
| return {value[0], std::sqrt(value[1])}; | ||
| } | ||
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| private: | ||
| void calculate_wrt_epsilon(const reco::DeDxHit&, const double&, std::array<double, 3>&); | ||
| void functionEpsilon(const reco::DeDxHitCollection&, const double&, std::array<double, 3>&); | ||
| double minimizeAllSaturated(const reco::DeDxHitCollection&, std::array<double, 2>&); | ||
| double newtonMethodEpsilon(const reco::DeDxHitCollection&, std::array<double, 2>&); | ||
| double estimate(const reco::DeDxHitCollection&, std::array<double, 2>&); | ||
| }; | ||
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| /*****************************************************************************/ | ||
| void LikelihoodFitDeDxEstimator::calculate_wrt_epsilon(const reco::DeDxHit& h, | ||
| const double& epsilon, | ||
| std::array<double, 3>& value) { | ||
| const auto& ls = h.pathLength(); | ||
| const auto& sn = h.error(); // energy sigma | ||
| const auto y = h.charge() * ls; // = g * y | ||
| const auto sD = 2.E-3 + 0.095 * y; | ||
| const auto ss = sD * sD + sn * sn; | ||
| const auto s = std::sqrt(ss); | ||
| const auto delta = epsilon * ls; | ||
| const auto dy = delta - y; | ||
| constexpr double nu(0.65); | ||
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| // calculate derivatives with respect to delta | ||
| std::array<double, 3> val{{0.}}; | ||
| if (h.rawDetId() == 0) { // normal | ||
| if (dy < -nu * s) { | ||
| val[0] = -2. * nu * dy / s - nu * nu; | ||
| val[1] = -2. * nu / s; | ||
| val[2] = 0.; | ||
| } else { | ||
| val[0] = dy * dy / ss; | ||
| val[1] = 2. * dy / ss; | ||
| val[2] = 2. / ss; | ||
| } | ||
| } else { // saturated | ||
| if (dy < s) { | ||
| val[0] = -dy / s + 1.; | ||
| val[1] = -1. / s; | ||
| val[2] = 0.; | ||
| } else { | ||
| val[0] = 0.; | ||
| val[1] = 0.; | ||
| val[2] = 0.; | ||
| } | ||
| } | ||
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| // d/d delta -> d/d epsilon | ||
| val[1] *= ls; | ||
| val[2] *= ls * ls; | ||
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| // sum | ||
| for (size_t k = 0; k < value.size(); k++) | ||
| value[k] += val[k]; | ||
| } | ||
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| /*****************************************************************************/ | ||
| void LikelihoodFitDeDxEstimator::functionEpsilon(const reco::DeDxHitCollection& Hits, | ||
| const double& epsilon, | ||
| std::array<double, 3>& val) { | ||
| val = {{0, 0, 0}}; | ||
| for (const auto& h : Hits) | ||
| calculate_wrt_epsilon(h, epsilon, val); | ||
| } | ||
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| /*****************************************************************************/ | ||
| double LikelihoodFitDeDxEstimator::minimizeAllSaturated(const reco::DeDxHitCollection& Hits, | ||
| std::array<double, 2>& value) { | ||
| int nStep(0); | ||
| double par(3.0); // input MeV/cm | ||
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| std::array<double, 3> val{{0}}; | ||
| do { | ||
| functionEpsilon(Hits, par, val); | ||
| if (val[1] != 0) | ||
| par += -val[0] / val[1]; | ||
| nStep++; | ||
| } while (val[0] > 1e-3 && val[1] != 0 && nStep < 10); | ||
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| value[0] = par * 1.1; | ||
| value[1] = par * par * 0.01; | ||
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| return val[0]; | ||
| } | ||
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| /*****************************************************************************/ | ||
| double LikelihoodFitDeDxEstimator::newtonMethodEpsilon(const reco::DeDxHitCollection& Hits, | ||
| std::array<double, 2>& value) { | ||
| int nStep(0); | ||
| double par(3.0); // input MeV/cm | ||
| double dpar(0); | ||
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| std::array<double, 3> val{{0}}; | ||
| do { | ||
| functionEpsilon(Hits, par, val); | ||
| if (val[2] != 0.) | ||
| dpar = -val[1] / std::abs(val[2]); | ||
| else | ||
| dpar = 1.; // step up, for epsilon | ||
| if (par + dpar > 0) | ||
| par += dpar; // ok | ||
| else | ||
| par /= 2.; // half | ||
| nStep++; | ||
| } while (std::abs(dpar) > 1e-3 && nStep < 50); | ||
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| value[0] = par; | ||
| value[1] = 2. / val[2]; | ||
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| return val[0]; | ||
| } | ||
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| /*****************************************************************************/ | ||
| double LikelihoodFitDeDxEstimator::estimate(const reco::DeDxHitCollection& Hits, std::array<double, 2>& value) { | ||
| // use newton method if at least one hit is not saturated | ||
| for (const auto& h : Hits) | ||
| if (h.rawDetId() == 0) | ||
| return newtonMethodEpsilon(Hits, value); | ||
| // else use minimize all saturated | ||
| return minimizeAllSaturated(Hits, value); | ||
| } | ||
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| #endif |
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minor point, but I think you could probably use an iterator here instead.
I think it would be something like:
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Just had a quick look on how implement this comment but I not sure how with current EDToken (that points to std::vector<reco::Track>). Maybe changing to a edm::View<reco::Track> could allow using iterators with edm::Ref
Ok, I think I could do what the comment suggest by using:
for (auto it = trackCollectionHandle->begin(); it != trackCollectionHandle->end(); ++it) {const auto& j = it - trackCollectionHandle->begin();But not sure what would be the gain compared to the existing code (since both would still rely on the index)