Adaptive density estimator for galaxy surveys
Abstract
Galaxy number or luminosity density serves as a basis for many structure classification algorithms. Several methods are used to estimate this density. Among them kernel methods have probably the best statistical properties and allow also to estimate the local sample errors of the estimate. We introduce a kernel density estimator with an adaptive data-driven anisotropic kernel, describe its properties and demonstrate the wealth of additional information it gives us about the local properties of the galaxy distribution.
- Publication:
-
The Zeldovich Universe: Genesis and Growth of the Cosmic Web
- Pub Date:
- October 2016
- DOI:
- 10.1017/S1743921316009947
- Bibcode:
- 2016IAUS..308..242S
- Keywords:
-
- Cosmology: large-scale structure of universe;
- surveys;
- galaxies: statistics