2017
DOI: 10.1016/j.nima.2017.06.030
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Charged particle tracking without magnetic field: Optimal measurement of track momentum by a Bayesian analysis of the multiple measurements of deflections due to multiple scattering

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Cited by 14 publications
(28 citation statements)
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“…How devise a segmentation-free, optimal, unbiased, momentum measurement from the amazing ability of Kalman filters to (statistically) decipher the (detector precision) uncorrelated and the (multiple scattering) correlated interleaved contributions in the track trajectory statistics ? We develop an optimal method to measure the electron momentum from the multiple measurement of multiple scattering induced deflections, based on a Bayesian analysis of the innovation residues of a set of momentum-dependent Kalman filters applied to the track [13]. 1 Let's define s, the track-momentum-dependent multiple-scattering-angle average variance per unit track length:…”
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confidence: 99%
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“…How devise a segmentation-free, optimal, unbiased, momentum measurement from the amazing ability of Kalman filters to (statistically) decipher the (detector precision) uncorrelated and the (multiple scattering) correlated interleaved contributions in the track trajectory statistics ? We develop an optimal method to measure the electron momentum from the multiple measurement of multiple scattering induced deflections, based on a Bayesian analysis of the innovation residues of a set of momentum-dependent Kalman filters applied to the track [13]. 1 Let's define s, the track-momentum-dependent multiple-scattering-angle average variance per unit track length:…”
mentioning
confidence: 99%
“…The distribution of the probability density function p(s) is shown in Fig. 1 for one simulated 50 MeV/c track [13]. The most probable value of p is then obtained from the most probable value of s: p = p 0 ∆x/lX 0 s.…”
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confidence: 99%
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