Imaging Galactic Nebulae: Adaptive Binning using Weighted Voronoi Tessellation


Noise often interferes with the analysis of astronomical observations. Thus, when working with images, it becomes necessary to group pixels together in such a way that the maximum signal-to-noise is achieved with minimum sacrifice of image resolution. We explore an implementation of an adaptive binning technique, the Weighted Voronoi Tessellation (WVT), and apply the algorithm to simulated data in order to verify its efficacy. One application of this is in looking for Strömgren spheres, nebulae with a distinct edge caused by limited ionization power of the radiative source. During the “Epoch of Reionization” the first stars and galaxies emitted photons which ionized the dark, mostly neutral intergalactic gas, increasing star formation and light in the universe. The WVT procedure may make the detection of edges more straightforward, in turn allowing us to identify “edgeless” nebulae, which may be responsible for Reionization, or nebulae with edges, which would predate Reionization. We examined how the WVT procedure can be used on data at multiple wavelengths in order to gain a better understanding of early dwarf galaxies and their faint structures. Application of our process to KCWI images of nebulae with strong [OII] emissions suggest that some of these nebulae lack an edge and elucidate gaseous outflows that might be responsible for Reionization.


Physics '22
CCS Axline Fellow

Faculty Advisor

Crystal Martin