The Experts below are selected from a list of 43095 Experts worldwide ranked by ideXlab platform
Yisok Oh - One of the best experts on this subject based on the ideXlab platform.
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quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces
IEEE Transactions on Geoscience and Remote Sensing, 2004Co-Authors: Yisok OhAbstract:A semiempirical polarimetric backscattering model for bare soil surfaces is inverted directly to retrieve both the volumetric soil moisture content M/sub v/ and the rms surface height s from multipolarized radar observations. The rms surface height s and the moisture content M/sub v/ can be read from Inversion diagrams using the measurements of the cross-polarized backscattering coefficient /spl sigma//sub vh//sup 0/ and the copolarized ratio p(=/spl sigma//sub hh//sup 0///spl sigma//sub vv//sup 0/). Otherwise, the surface parameters can be estimated simply by solving two equations (/spl sigma//sub vh//sup 0/ and p) in two unknowns (M/sub v/ and s). The Inversion Technique has been applied to the polarimetric backscattering coefficients measured by ground-based polarimetric scatterometers and the Jet Propulsion Laboratory airborne synthetic aperture radar. A good agreement was observed between the values of surface parameters (the rms height s, roughness parameter ks, and the volumetric soil moisture content M/sub v/) estimated by the Inversion Technique and those measured in situ.
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an empirical model and an Inversion Technique for radar scattering from bare soil surfaces
IEEE Transactions on Geoscience and Remote Sensing, 1992Co-Authors: Yisok Oh, K SarabandiAbstract:Polarimetric radar measurements were conducted for bare soil surfaces under a variety of roughness and moisture conditions at L-, C-, and X-band frequencies at incidence angles ranging from 10 degrees to 70 degrees . Using a laser profiler and dielectric probes, a complete and accurate set of ground truth data was collected for each surface condition, from which accurate measurements were made of the rms height, correlation length, and dielectric constant. Based on knowledge of the scattering behavior in limiting cases and the experimental observations, an empirical model was developed for sigma degrees /sub hh/, sigma degrees /sub vv/, and sigma degrees /sub hv/ in terms of ks (where k=2 pi / lambda is the wave number and s is the rms height) and the relative dielectric constant of the soil surface. The model, which was found to yield very good agreement with the backscattering measurements of the present study as well as with measurements reported in other investigations, was used to develop an Inversion Technique for predicting the rms height of the surface and its moisture content from multipolarized radar observations. >
Simon Birrer - One of the best experts on this subject based on the ideXlab platform.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
Astronomy and Astrophysics, 2019Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form-lens-modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. In addition, sparse regularisation avoids over-fitting the data and does not require the use of an adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of the arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated into future lens-modelling Techniques iterating over the lens mass parameters.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form lens modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. in addition, sparse regularisation avoids over-fitting the data and does not require the use of any adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated in future lens modelling Technique iterating over the lens mass parameters. The python package corresponding to the algorithms described in this article can be downloaded via the github platform at this https URL.
R. Joseph - One of the best experts on this subject based on the ideXlab platform.
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SLITronomy: towards a fully wavelet-based strong lensing Inversion Technique
2021Co-Authors: A. Galan, A. Peel, R. Joseph, F. Courbin, J L StarckAbstract:Strong gravitational lensing provides a wealth of astrophysical information on the baryonic and dark matter content of galaxies. It also serves as a valuable cosmological probe by allowing us to measure the Hubble constant independently of other methods. These applications all require the difficult task of inverting the lens equation and simultaneously reconstructing the mass profile of the lens along with the original light profile of the unlensed source. As there is no reason for either the lens or the source to be simple, we need methods that both invert the lens equation with a large number of degrees of freedom and also enforce a well-controlled regularisation that avoids the appearance of spurious structures. This can be beautifully accomplished by representing signals in wavelet space. Building on the Sparse Lens Inversion Technique (SLIT), in this work we present an improved sparsity-based method that describes lensed sources using wavelets and optimises over the parameters given an analytical lens mass profile. We apply our Technique on simulated HST and E-ELT data, as well as on real HST images of lenses from the Sloan Lens ACS (SLACS) sample, assuming a lens model. We show that wavelets allow us to reconstruct lensed sources containing detailed substructures when using both present-day data and high-resolution images from future thirty-meter-class telescopes. Wavelets moreover provide a much more tractable solution in terms of quality and computation time compared to using a source model that combines smooth analytical profiles and shapelets. Requiring very little human interaction, our pixel-based Technique fits into the effort to devise automated modelling schemes. It can be incorporated in the standard workflow of sampling analytical lens model parameters. The method, which we call SLITronomy, is freely available as a new plug-in to the modelling software Lenstronomy.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
Astronomy and Astrophysics, 2019Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form-lens-modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. In addition, sparse regularisation avoids over-fitting the data and does not require the use of an adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of the arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated into future lens-modelling Techniques iterating over the lens mass parameters.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form lens modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. in addition, sparse regularisation avoids over-fitting the data and does not require the use of any adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated in future lens modelling Technique iterating over the lens mass parameters. The python package corresponding to the algorithms described in this article can be downloaded via the github platform at this https URL.
J L Starck - One of the best experts on this subject based on the ideXlab platform.
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SLITronomy: towards a fully wavelet-based strong lensing Inversion Technique
2021Co-Authors: A. Galan, A. Peel, R. Joseph, F. Courbin, J L StarckAbstract:Strong gravitational lensing provides a wealth of astrophysical information on the baryonic and dark matter content of galaxies. It also serves as a valuable cosmological probe by allowing us to measure the Hubble constant independently of other methods. These applications all require the difficult task of inverting the lens equation and simultaneously reconstructing the mass profile of the lens along with the original light profile of the unlensed source. As there is no reason for either the lens or the source to be simple, we need methods that both invert the lens equation with a large number of degrees of freedom and also enforce a well-controlled regularisation that avoids the appearance of spurious structures. This can be beautifully accomplished by representing signals in wavelet space. Building on the Sparse Lens Inversion Technique (SLIT), in this work we present an improved sparsity-based method that describes lensed sources using wavelets and optimises over the parameters given an analytical lens mass profile. We apply our Technique on simulated HST and E-ELT data, as well as on real HST images of lenses from the Sloan Lens ACS (SLACS) sample, assuming a lens model. We show that wavelets allow us to reconstruct lensed sources containing detailed substructures when using both present-day data and high-resolution images from future thirty-meter-class telescopes. Wavelets moreover provide a much more tractable solution in terms of quality and computation time compared to using a source model that combines smooth analytical profiles and shapelets. Requiring very little human interaction, our pixel-based Technique fits into the effort to devise automated modelling schemes. It can be incorporated in the standard workflow of sampling analytical lens model parameters. The method, which we call SLITronomy, is freely available as a new plug-in to the modelling software Lenstronomy.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
Astronomy and Astrophysics, 2019Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form-lens-modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. In addition, sparse regularisation avoids over-fitting the data and does not require the use of an adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of the arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated into future lens-modelling Techniques iterating over the lens mass parameters.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form lens modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. in addition, sparse regularisation avoids over-fitting the data and does not require the use of any adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated in future lens modelling Technique iterating over the lens mass parameters. The python package corresponding to the algorithms described in this article can be downloaded via the github platform at this https URL.
F. Courbin - One of the best experts on this subject based on the ideXlab platform.
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SLITronomy: towards a fully wavelet-based strong lensing Inversion Technique
2021Co-Authors: A. Galan, A. Peel, R. Joseph, F. Courbin, J L StarckAbstract:Strong gravitational lensing provides a wealth of astrophysical information on the baryonic and dark matter content of galaxies. It also serves as a valuable cosmological probe by allowing us to measure the Hubble constant independently of other methods. These applications all require the difficult task of inverting the lens equation and simultaneously reconstructing the mass profile of the lens along with the original light profile of the unlensed source. As there is no reason for either the lens or the source to be simple, we need methods that both invert the lens equation with a large number of degrees of freedom and also enforce a well-controlled regularisation that avoids the appearance of spurious structures. This can be beautifully accomplished by representing signals in wavelet space. Building on the Sparse Lens Inversion Technique (SLIT), in this work we present an improved sparsity-based method that describes lensed sources using wavelets and optimises over the parameters given an analytical lens mass profile. We apply our Technique on simulated HST and E-ELT data, as well as on real HST images of lenses from the Sloan Lens ACS (SLACS) sample, assuming a lens model. We show that wavelets allow us to reconstruct lensed sources containing detailed substructures when using both present-day data and high-resolution images from future thirty-meter-class telescopes. Wavelets moreover provide a much more tractable solution in terms of quality and computation time compared to using a source model that combines smooth analytical profiles and shapelets. Requiring very little human interaction, our pixel-based Technique fits into the effort to devise automated modelling schemes. It can be incorporated in the standard workflow of sampling analytical lens model parameters. The method, which we call SLITronomy, is freely available as a new plug-in to the modelling software Lenstronomy.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
Astronomy and Astrophysics, 2019Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form-lens-modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. In addition, sparse regularisation avoids over-fitting the data and does not require the use of an adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of the arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated into future lens-modelling Techniques iterating over the lens mass parameters.
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sparse lens Inversion Technique slit lens and source separability from linear Inversion of the source reconstruction problem
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: R. Joseph, J L Starck, F. Courbin, Simon BirrerAbstract:Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form lens modelling Technique, we consider linear Inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the Inversion to represent arbitrarily small and complex luminous structures in the lens and source. in addition, sparse regularisation avoids over-fitting the data and does not require the use of any adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of arbitrary choice of initial parameters. Our Inversion Technique for a fixed mass distribution can be incorporated in future lens modelling Technique iterating over the lens mass parameters. The python package corresponding to the algorithms described in this article can be downloaded via the github platform at this https URL.
