Method to robustly measure the surface brightness depth of images. This is a notoriously difficult problem, as the depth depends on the spatial scale, the detailed properties of the data, and the method that is used. sbcontrast defines the depth as the contrast between a patch in the image of a particular size and its immediate surroundings. It is therefore the relevant metric for assessing the detection limits for low surface brightness objects of a particular size (or a range of sizes). It is described in Appendix A of Michael Keim's paper, linked below.
Reference: Keim, M., van Dokkum, Pieter, Danieli, Shany, Lokhorst, Deborah, Li, Jiaxuan, Shen, Zili, Abraham, Roberto, Chen, Seery, Gilhuly, Colleen, Liu, Qing, Merritt, Allison, Miller, Tim B., Pasha, Imad, & Polzin, Ava 2022, ApJ, in press (arXiv:2109.09778)
Multi Resolution Filtering (MRF)
Method to remove compact objects from low resolution imaging data. MRF was developed for Dragonfly data but is generally applicable. MRF builds a model of all objects in an image that are not low surface brightness, using a high resolution image as input. This model is then convolved with an internally-generated differential point spread function and subtracted from the (independent) low resolution data, isolating all low surface brightness structures in that image. The original version of MRF can be obtained from Jiaxuan Li's github repository. Johnny Greco is currently building a new version from the ground up in the context of the Dragonfly software suite.
Photometric redshift code, developed and maintained by Gabriel Brammer. EAzY stands for Easy and Accurate photometric redshifts (=z) from Yale (where the code was first developed). The EAzY website contains documentation and download information. Although not all papers that reference the code have used it, this list gives an indication of its popularity in the community.
Reference: van Dokkum, P. G. 2001, PASP, 113, 1420