Skip to main content
SearchLoginLogin or Signup

Photometry in Crowded Fields: Parametric Models vs. a Data-Driven Approach

Presentation #302.09 in the session Computation, Data Handling, Image Analysis — iPoster Session.

Published onJun 29, 2022
Photometry in Crowded Fields: Parametric Models vs. a Data-Driven Approach

The problem of disentangling flux from objects in a crowded image is central to many problems in observational astronomy. Rather than fitting a parametric generative model (e.g., a Sersic model for galaxies) to the data, we consider component separation to be the primary task. Once separated from both neighbors and background, an object is easier to characterize. Given a Gaussian prior in the Npixel-dimensional space for each component we can perform a joint inference on all components, conditional on them summing to the data. Despite replacing operations on Npixel-dimensional vectors with operations on Npixel by Npixel covariance matrices, this approach is computationally tractable and gives satisfactory results for crowded stellar fields on complex backgrounds. Performance is relatively robust to PSF uncertainty, mis-centering, and masked pixels. Such an approach generalizes to more complex objects in images (galaxies), and to other vector spaces (spectra).

Comments
0
comment

No comments here