Pablo de la Cruz
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Pablo de la Cruz has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- David L. Donoho
Department of Statistics, Stanford University, Stanford, CA 94305-4065, USA
2 shared publications - Enn Saar
Tartu Observatoorium, Tõravere 61602, Estonia
2 shared publications - Jean Luc Starck
Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM
2 shared publications - Silvestre Paredes
Department of Applied Mathematics and Statistics, Technological University of Cartagena, Cartagena 30203, Spain
2 shared publications - Simon C. Reynolds
Blackett Laboratory, Imperial College London, SW7 2AZ, UK
2 shared publications - Vicent J. Martinez
Observatori Astronòmic, Universitat de València, C/ Catedrático José Beltrán 2, 46980 Paterna, Spain
2 shared publications
Latest Publications
- 1 December 2005
- Published by American Astronomical Society in The Astrophysical Journal
- Vol. 634 (2) , 744-755
Morphology of the Galaxy Distribution from Wavelet Denoising
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments and hotspots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provide us with a unique and more accurate morphological description.
- 1 December 2005
- Published by American Astronomical Society in The Astrophysical Journal
- Vol. 634 (2) , 744-755
Morphology of the Galaxy Distribution from Wavelet Denoising
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments and hotspots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provide us with a unique and more accurate morphological description.
- Open Access
Morphology of the galaxy distribution from wavelet denoising
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments and hotspots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provide us with a unique and more accurate morphological description.
Open AccessMorphology of the galaxy distribution from wavelet denoising
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments and hotspots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provide us with a unique and more accurate morphological description.