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Rachel Bean

Professor of Astronomy


While the last century has seen revolutionary developments in our understanding of the matter we find on Earth, my research focuses on improving our understanding of types of matter, and physical laws, whose existence and properties have been inferred purely from astronomical measurements.

My work centers on extracting information about cosmological theories using observations of the cosmic microwave background (CMB) and large scale structure (galaxies and clusters of galaxies). I am working to understand the nature of dark energy, the properties of gravity on cosmic scales and the fundamental origins of primordial inflation.

Dark Energy is the name given to the unknown quantity responsible for the accelerated expansion of the universe we observe today. This acceleration runs counter to the deceleration predicted by General Relativity and a universe populated with Standard Model matter. Dark energy could be a strange new type of matter or it could be evidence that we don’t fully understand how gravity behaves on cosmic scales. I am interested in how can we use different types of astrophysical measurements to distinguish¬† between competing theories.

Cosmic inflation is a period of rapid cosmic expansion during the first moments after the universe was conceived. Inflation occurred at energies inaccessible to terrestrial particle physics experiments, however the variations in the CMB temperature and in the distribution of galaxies contain fossil imprints of quantum fluctuations during inflation.  Cosmological observations, therefore, provide a unique window into fundamental physics at these high energy scales.

I am a member of the LSST Dark Energy Science Collaboration (and led the collaboration from 2015-17), the Dark Energy Spectroscopic Instrument (DESI) science team, the NASA Euclid science team and the NASA WFIRST High Latitude Survey Science Investigation Team. I am also involved in a Cornell-led facility, CCAT-prime.

The surveys will create meticulously detailed maps of the images and/or spectral properties of galaxies and galaxy clusters over volumes of space billions of light-years across. Extracting the cosmological information from these surveys is a challenging enterprise, requiring computational infrastructure and algorithms to manage the massive datasets (e.g. LSST will create datasets of hundreds of TBs each night, and tens of petabytes over its lifetime).

My group are developing and implementing techniques that accurately incorporate the phenomenological complexity of the cosmological theories and fully leverage the complementarity of the data from the various surveys, by combining, contrasting and cross-correlating the datasets.

See the INSPIRES catalog for access to a full list of my published papers.

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