Smooth stochastic density field reconstruction

Abstract

We introduce a method for generating a continuous, mass-conserving and high-order differentiable density field from a discrete point distribution such as particles or halos from an N-body simulation or galaxies from a spectroscopic survey. The method consists on generating an ensemble of point realizations by perturbing the original point set following the geometric constraints imposed by the Delaunay tessellation in the vicinity of each point in the set. By computing the mean field of the ensemble we are able to significantly reduce artifacts arising from the Delaunay tessellation in poorly sampled regions while conserving the features in the point distribution. Our implementation is based on the Delaunay Tessellation Field Estimation (DTFE) method, however other tessellation techniques are possible. The method presented here shares the same advantages of the DTFE method such as self-adaptive scale, mass conservation and continuity, while being able to reconstruct even the faintest structures of the point distribution usually dominated by artifacts in Delaunay-based methods. Additionally, we also present preliminary results of an application of this method to image denoising and artifact removal, highlighting the broad applicability of the technique introduced here.

arXiv commentSubmitted to MNRAS

References in corpus (14)

• The Sloan Digital Sky Survey: Technical Summary
• The Seventh Data Release of the Sloan Digital Sky Survey
• Properties of galaxies reproduced by a hydrodynamic simulation
• Detecting filamentary pattern in the cosmic web: a catalogue of filaments for the SDSS
• A Cosmic Watershed: the WVF Void Detection Technique
• The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution
• NEXUS: Tracing the Cosmic Web Connection
• Tracing the Dark Matter Sheet in Phase Space
• The Spine of the Cosmic Web
• SDSS DR7 superclusters. The catalogues
• The Cosmic Web, Multi-Stream Flows, and Tessellations
• Unfolding the Hierarchy of Voids
• Structural Analysis of the SDSS Cosmic Web I.Nonlinear Density Field Reconstructions
• Sub-Megaparsec Individual Photometric Redshift Estimation from Cosmic Web Constraints

Cited by in corpus (3)

• Encoding large scale cosmological structure with Generative Adversarial Networks
• Chemodynamic evidence of pristine gas accretion in the void galaxy VGS 12
• Predicting large scale cosmological structure evolution with generative adversarial network-based autoencoders