Efficient lineshape estimation by ghost spectroscopy

Fuente: arXiv
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Autores principales: Gianani, Ilaria, Soto, Luis L. Sanchez, Goldberg, Aaron Z., Barbieri, Marco
Formato: Preprint
Publicado: 2023
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author Gianani, Ilaria
Soto, Luis L. Sanchez
Goldberg, Aaron Z.
Barbieri, Marco
author_facet Gianani, Ilaria
Soto, Luis L. Sanchez
Goldberg, Aaron Z.
Barbieri, Marco
contents Recovering the original spectral lineshapes from data obtained by instruments with extended transmission profiles is a basic tenet in spectroscopy. By using the moments of the measured lines as basic variables, we turn the problem into a linear inversion. However, when only a finite number of these moments are relevant, the rest of them act as nuisance parameters. These can be taken into account with a semiparametric model, which allows us to establish the ultimate bounds on the precision attainable in the estimation of the moments of interest. We experimentally confirm these limits with a simple ghost spectroscopy demonstration.
format Preprint
id arxiv_https___arxiv_org_abs_2301_08123
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Efficient lineshape estimation by ghost spectroscopy
Gianani, Ilaria
Soto, Luis L. Sanchez
Goldberg, Aaron Z.
Barbieri, Marco
Quantum Physics
Data Analysis, Statistics and Probability
Optics
Recovering the original spectral lineshapes from data obtained by instruments with extended transmission profiles is a basic tenet in spectroscopy. By using the moments of the measured lines as basic variables, we turn the problem into a linear inversion. However, when only a finite number of these moments are relevant, the rest of them act as nuisance parameters. These can be taken into account with a semiparametric model, which allows us to establish the ultimate bounds on the precision attainable in the estimation of the moments of interest. We experimentally confirm these limits with a simple ghost spectroscopy demonstration.
title Efficient lineshape estimation by ghost spectroscopy
topic Quantum Physics
Data Analysis, Statistics and Probability
Optics
url https://arxiv.org/abs/2301.08123