Abstract The learned helplessness theory of depression suggests that a perceived loss of control over stressful events is associated with depression. However, little research has tested this relationship in humans, and there has been little to no discussion of the possible effect of biological sex. The present study examined the relationship between perceived stress controllability and depression in a sample of college students during the COVID-19 pandemic, and whether biological sex moderated this relationship. Participants (ages 18-38, N = 295) were university students who were enrolled in the study near the beginning of the COVID-19 pandemic and assessed across the subsequent eight weeks. Using remote surveys, we assessed stressors the participants had experienced as well as the amount of control they felt they had over each; we also assessed anhedonic depression via surveys. At baseline, there was a significant correlation between perceived stress controllability and depression. Biological sex was not a moderator of this relationship, but planned post hoc analyses revealed baseline perceived stress controllability was significantly associated with depression in females but not in males. Across the eight weeks of the study, there was not a significant relationship between change in perceived stress controllability and change in depression, and biological sex was not a moderator. Planned post hoc analyses, however, showed that there was a significant correlation between change in perceived stress controllability and change in depression in females but not in males. These results suggest a possible relationship between perceived stress controllability and depression in females. However, our results were mixed and therefore further research is necessary to elucidate the nature of the relationship between perceived stress controllability and depression, and the extent to which this is moderated by sex. If this relationship does exist, it could suggest a potential target for therapy, particularly in females. Lay Summary We can locate brand new exoplanets, many light years away, without ever actually seeing them. Better yet, we can classify and learn vital information about the star and planet system from the exoplanet detection methods. One commonly used detection method is the transit method. The transit method captures the slight dimming of a star’s light as a planet crosses in front of it. This decrease in brightness is temporary and regular. Transits provide information about the composition and properties of stars. One such phenomenon is limb-darkening where the brightness of the star appears darker around its edges. Limb-darkening coefficients are calculated using a quadratic relationship between the intensity at a certain point of the star to the intensity at the center of the star. As the coefficients increase, the “limbs” or edges of the star appear darker. The wavelength in which we are measuring the transit also impacts the limb-darkening. At lower wavelengths, the darkening effect is more dramatic. Using these coefficients at different wavelengths we can create theoretical models to show what stars should look like without being able to see them. The James Webb Telescope (JWST) is capturing numerous exoplanets through the transit method. Launched in December 2021, the JWST contains instruments capable of measuring the slightest changes in the brightness of distant stars. The transit data, collected from the JWST, are fit to infer the best limb-darkening coefficients. We also calculate theoretical coefficients using known physical properties of the star. One particular star captured by the JWST is WASP-39, located 700 light years away. WASP-39 is a dwarf star slightly smaller than our Sun. Models of WASP-39 are created using the empirical and theoretical coefficients. These stellar movies show the appearance of the star at various wavelengths and their corresponding limb-darkening coefficients. The appearances are compared to show how different actual data presents from theoretical models. Providing visual models for stars is an easy way to quantify differences, rather than numerical data. For the full text, please visit https://scholar.colorado.edu/concern/undergraduate_honors_theses/z890rv682.