Dr Claire L. Davies is a post doctoral Research Fellow at the University of Exeter. She presently works in the astrophysics research group of Professor Tim J. Harries, having previously spent 4.5 years in the interferometry research group of Professor Stefan Kraus. Before joining Exeter, Claire held a six-month STFC STEP Fellowship at the University of St Andrews, working with Dr Kenneth Wood. Claire received her PhD in Astronomy from the University of St Andrews in 2015. Her thesis (Revolution evolution: tracing angular momentum during star and planetary system formation) was conducted under the supervision of Professor Jane S. Greaves (now at Cardiff University) and Dr Scott G. Gregory (now at the University of Dundee).
Claire's research is focussed on observationally probing how stars and their planetary systems form and evolve. She uses high angular resolution techniques such as optical/infrared long baseline interferometry and scattered light imaging to study the disks that exist around stars as they form. These disks are the birth sites of planetary systems, comprised of the building blocks of planets like those in our own Solar System.
Claire primarily uses data from the CHARA Array, the VLTI, and GPI. She relies on complementary, open-access archival spectroscopy and photometry and has developed SEDBYS, a python-based open-source command-line tool which automates the process of building spectral energy distributions (SEDs) of young stars.
Claire contributes to new and existing codes for use in her analysis of CHARA, VLTI, and GPI data. These include the Monte Carlo radiative transfer code, TORUS (originally develop by Professor Tim J. Harries), RAPIDO (the Radiative transfer and Analytic modelling Pipeline for Interferometric Disk Observations, developed alongside Professor Stefan Kraus, Dr Alexander Kreplin, Dr Edward Hone, and Dr Aaron Labdon), the MIRC-X pipeline (a suite of python scripts for the reduction, archiving, and visualisation of CHARA/MIRC-X data, developed by the MIRC-X team), and gpi-pypeline (a suite of python scripts developed for the reduction, visualisation and analysis of GPI data, originally developed by Dr Evan Rich and PhD student Anna Laws).