One of the most intriguing components of galaxies is the warm-hot (~10^5-10^6 K) gas extending into the circumgalactic medium (CGM) of galaxies. The mass of this gas reservoir likely contains more mass than the stars in their parent galaxy, and its characterization is thus crucial to the understanding of galaxy formation and evolution. However, despite its importance, the properties of the warm-hot gas are poorly constrained so far. Aspera is a NASA-funded UV SmallSat Mission lead by the University of Arizona and expected to launch in 2025. The goal of Aspera is, for the first time, to detect and map the warm-hot gas around nearby galaxies through the detection and characterization of OVI line emission at 1032 Angstrom.

The focus of this poster is the development of a data processing pipeline for Aspera. The payload consists of two parallel channels each fed by a long-slit spectrograph illuminating a common Microchannel Plate detector providing a time-resolved event list of incident photons for each observation. The mission operations will apply a step-and-stare concept where individual galaxies are targeted at one or several spatial fields to spatially sample the galaxies’ environments. The Aspera data pipeline's goal is to reconstruct for each target calibrated 3-dimensional (2D spatial x 1D spectral) data cubes from several individual slit pointings around each target galaxy.

The ASPERA data pipeline will take care of applying the calibration metrics (derived from on-ground and in-orbit calibration activities) to the recorded photon event lists, extract a flux- and wavelength calibrated spectrum for each observation, and combine the spectra from individual slit pointings to reconstruct the final 3D data product. The raw data, as well as pipeline-produced data products will be archived through MAST. The pipeline will be written in Python and will be made publicly available to users.

This poster will lay out the planned structure of the pipeline, discuss individual steps to be applied to the science data during the processing, and present the format and content of our planned data products. We will put emphasis on the image cube reconstruction process, which will use an interpolation method to resample the spatially irregular sampled pointing observations onto a spatially regular gridded image cube. We will further present the generation of mock observations through an instrument simulator that will allow us to test and verify the pipeline.