Measuring Shapes of Galaxy Images I: Ellipticity and Orientation
arXiv:astro-ph/0303363 · doi:10.1046/j.1365-8711.2003.06735.x
Abstract
We suggest a set of morphological measures that we believe can help in quantifying the shapes of two-dimensional cosmological images such as galaxies, clusters, and superclusters of galaxies. The method employs non-parametric morphological descriptors known as the Minkowski functionals in combination with geometric moments widely used in the image analysis. For the purpose of visualization of the morphological properties of image contour lines we introduce three auxiliary ellipses representing the vector and tensor Minkowski functionals. We study the discreteness, seeing, and noise effects on elliptic contours as well as their morphological characteristics such as the ellipticity and orientation. In order to reduce the effect of noise we employ a technique of contour smoothing. We test the method by studying simulated elliptic profiles of toy spheroidal galaxies ranging in ellipticity from E0 to E7. We then apply the method to real galaxies, including eight spheroidals, three disk spirals and one peculiar galaxy, as imaged in the near-infrared -band (2.2 microns) with the Two Micron All Sky Survey (2MASS). The method is numerically very efficient and can be used in the study of hundreds of thousands images obtained in modern surveys.
Accepted for publication in MNRAS. Revised version contains 20 pages, 17 PostScript figures. Results unchanged; high-resolution figures # 1,6,7,11,13,16 can be obtained from authors
References in corpus (4)
Cited by in corpus (10)
- Morphology of 21cm brightness temperature during the Epoch of Reioinization using Contour Minkowski Tensor
- Search for anomalous alignments of structures in Planck data using Minkowski Tensors
- A model-independent characterisation of strong gravitational lensing by observables
- Core shapes and orientations of core-Sersic galaxies
- Tracking down the origin of superbubbles and supergiant shells in the Magellanic Clouds with Minkowski tensor analysis
- Morphology and Evolution of Simulated and Optical Clusters: A Comparative Analysis
- Measuring Shapes of Galaxy Images II: Morphology of 2MASS Galaxies
- The geometrical meaning of statistical isotropy of smooth random fields in two dimensions
- Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance
- Morphology of dark matter haloes beyond triaxiality