The Star Planet Activity Research CubeSat (SPARCS) will be a 6U CubeSat devoted to photometric monitoring of M dwarfs in the far-ultraviolet (FUV) and near-ultraviolet (NUV) (160 and 280 nm respectively), measuring the time-dependent spectral slope, intensity and evolution of M dwarf stellar UV radiation. The delta-doped detectors baselined for SPARCS have demonstrated more than five times the in-band quantum efficiency of the detectors of GALEX. Given that red:UV photon emission from cool, low-mass stars can be million:one, UV observation of thes stars are susceptible to red light contamination. In addition to the high efficiency delta-doped detectors, SPARCS will include red-rejection filters to help minimize red leak. Even so, careful red-rejection and photometric calibration is needed. As was done for GALEX, white dwarfs are used for photometric calibration in the UV. We find that the use of white dwarfs to calibrate the observations of red stars leads to significant errors in the reported flux, due to the differences in white dwarf and red dwarf spectra. Here we discuss the planned SPARCS calibration model and the color correction, and demonstrate the importance of this correction when recording UV measurements of M stars taken by SPARCS.

In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE) against a ground truth detection mask and processing throughput of the method, as well as qualitatively, examining the situations in which each model performs well and poorly. Detection methods from existing systems Pyradon and ASTRiDE were implemented and tested. A machine learning (ML) image segmentation model was trained on simulated data and used to detect streaks in test data. The ML model performed favorably relative to the traditional methods tested, and demonstrated superior robustness in general. However, the model also exhibited some unpredictable behavior in certain scenarios which should be considered. This demonstrated that machine learning is a viable tool for the detection of satellite streaks in astronomic images, however special care must be taken to prevent and to minimize the effects of unpredictable behavior in such models.

As the search for life in our universe grows, it is important to not only locate planets outside of our solar system, but also to work towards the ability to understand and characterize their nature. Many current research endeavors focus on the discovery of exoplanets throughout the surrounding universe; however, we still know very little about the characteristics of these exoplanets themselves, particularly their atmospheres. Observatories, such as the Hubble Space Telescope and the Spitzer Space Telescope, have made some of the first observations which revealed information about the atmospheres of exoplanets but have yet to acquire complete and detailed characterizations of exoplanet atmospheres. The EXoplanet Climate Infrared TElescope (EXCITE) is a mission specifically designed to target key information about the atmospheres of exoplanets - including the global and spatially resolved energy budget, chemical bulk-compositions, vertical temperature profiles and circulation patterns across the surface, energy distribution efficiency as a function of equilibrium temperatures, and cloud formation and distribution - in order to generate dynamic and detailed atmospheric characterizations. EXCITE will use phase-resolved transit spectroscopy in the 1-4 micron wavelength range to accomplish these science goals, so it is important that the EXCITE spectrograph system is designed and tested to meet these observational requirements. For my thesis, I present my research on the EXCITE mission science goals and the design of the EXCITE spectrograph system to meet these goals, along with the work I have done in the beginning stages of testing the EXCITE spectrograph system in the lab. The primary result of my research work is the preparation of a simple optics setup in the lab to prepare a laser light source for use in the EXCITE spectrograph system - comparable to the preparation of incoming light by the EXCITE telescope system - which successfully yields an F# = 12.9 and a spot size of s = 39 ± 7 microns. These results meet the expectations of the system and convey appropriate preparation of a light source to begin the assembly and testing of the EXCITE spectrograph optics in the lab.