AIDA is an implementation and extension of the MISTRAL myopic deconvolution method developed by Mugnier et al. (2004) (see J. Opt. Soc. Am. A 21:1841-1854). The MISTRAL approach has been shown to yield object reconstructions with excellent edge preservation and photometric precision when used to process astronomical images. AIDA improves upon the original MISTRAL implementation. AIDA, written in Python, can deconvolve multiple frame data and three-dimensional image stacks encountered in adaptive optics and light microscopic imaging.

ODNet uses a convolutional neural network to examine frames of a given observation, using the flux of a targeted star along time, to detect occultations. This is particularly useful to reliably detect asteroid occultations for the Unistellar Network, which consists of 10,000 digital telescopes owned by citizen scientists that is regularly used to record asteroid occultations. ODNet is not costly in term of computing power, opening the possibility for embedding the code on the telescope directly. ODNet's models were developed and trained using TensorFlow version 2.4.

EXOTIC (EXOplanet Transit Interpretation Code) analyzes photometric data of transiting exoplanets into lightcurves and retrieves transit epochs and planetary radii. The software reduces images of a transiting exoplanet into a lightcurve, and fits a model to the data to extract planetary information crucial to increasing the efficiency of larger observational platforms. EXOTIC is written in Python and supports the citizen science project Exoplanet Watch. The software runs on Windows, Macintosh, and Linux/Unix computer, and can also be used via Google Colab.