High-resolution spectroscopic visible data were obtained with the Ultraviolet and Visible Echelle Spectrograph on the Very Large Telescope. Our goal was to analyze the data in an effort to detect the presence of sodium in the atmosphere of hot Jupiter exoplanet KELT-10b, as well as characterize the orbit of the planet via the Rossiter-McLaughlin effect. Eighty spectra were collected during a single transit of KELT-10b. After standard spectroscopic calibration using ESO-Reflex, the synthetic telluric modeling software molecfit was applied to remove terrestrial atmospheric effects, and to refine the wavelength calibration. Sodium is recognized by its characteristic absorption doublet located at 5895.924 and 5889.951 Å, which can be seen in the planet atmosphere transmission spectrum and through excess absorption during the transit. The radial velocity of the host star was analyzed by measuring the average shift of absorption features from spectrum to spectrum. Our results indicate a sodium detection in the planet transmission spectrum with a line contrast of 0.66% and 0.43% ± 0.09% for the sodium DII and DI lines, respectively. Excess absorption measurements agree to within one half combined standard deviation between the planet transmission spectrum (0.143% ± 0.020%, a 7σ detection) and during the time series (0.124% ± 0.034%, a 3.6σ detection) in a band 1.25 Å wide. The wavelength grid corrections provided by molecfit were insufficient to determine radial velocities and measure the Rossiter-McLaughlin effect.

The Aurorasaurus project harnesses volunteer crowdsourcing to identify sightings of an aurora (the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter with the caveat that there is a large background level of non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by using volunteers to sift through a pre-filtered list of geolocated tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geolocating tweets, is described and its accuracy (which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings, so care must be taken when relying on crowdsourced identification.