I am a third-year graduate student and NSF Graduate Research Fellow in the Department of Astrophysical Sciences at Princeton University. I am fascinated by the process of extracting useful information from noisy data and have worked on a range of data analysis problems in the fields of cosmology and exoplanets. For my thesis, I am working with Jenny Greene on a systematic search for ultra-diffuse galaxies in data from the Hyper Suprime-Cam survey, a 300-night optical/near-infrared survey with the 8.2 meter Subaru Telescope. Before coming to Princeton, I studied physics and astronomy at the Ohio State University, where I worked with Paul Martini and Todd Thompson.
In our recently submitted paper, Tim Brandt and I show how to measure spectral errors and covariances in high-contrast integral-field spectroscopic observations of exoplanets and include them self-consistently in parameter retrievals. Why is this an interesting/important problem? As we show in our paper, we know some component of the noise in such observations will be correlated due to the scaling of diffraction speckles with wavelength, and not accounting for this can potentially bias the inferred parameters. As a demonstration of this effect, let's look at some data:
In the above animation, we step through the wavelength frames of a GPI data cube. There is no planet or disk in this image, so we are looking at regions of empty sky, which are realizations of the spectral noise. The dots in the left panel correspond to the noise spectra in the right panel, and the black line tracks the current wavelength frame of the data cube. It is immediately clear that the noise is highly correlated – you can even pick out a characteristic correlation length by-eye in the right panel.