Dr. Stephen Portillo, DiRAC Postdoctoral Fellow, coauthored paper “Photometric Biases in Modern Surveys” published in March 2020.
Many surveys use maximum-likelihood (ML) methods to fit models when extracting photometry from images. We show that these ML estimators systematically overestimate the flux as a function of the signal-to-noise ratio and the number of model parameters involved in the fit.
Paper published by DiRAC Researcher, Dr. Sarah Greenstreet. Dr. Greenstreet is a joint postdoctoral fellow with the Asteroid Institute, a program of B612, and the DiRAC Institute at the University of Washington. Her research interests include the study of orbital dynamics and impacts of small bodies in the Solar System.
Months ago, a team of scientists led by Sarah Greenstreet (B612 Asteroid Institute and University of Washington) conducted a study in which they made predictions for the crater count they expected to find on MU69’s surface. Greenstreet and collaborators used observations of Pluto and Charon’s surfaces and models of known Kuiper-belt populations to explore the bombardment of MU69 over the solar system’s life span and calculate the number of craters of different sizes its surface should host.
The authors’ results were intriguing: they found that, despite getting bombarded for 4+ billion years, MU69 should be marred by very few craters. Greenstreet and collaborators estimate that MU69 should have only ~25–50 craters larger than ~200 m in size, which is the smallest size we’re likely be able to see with the full-resolution New Horizons images.
Read full article provided by aasnova.org here.
Dr. Melissa L. Graham, LSST Research Scientist and DiRAC Fellow, coauthored paper on a large Hubble survey of supernovae “Delayed Circumstellar Interaction for Type Ia SN 2015cp Revealed by an HST Ultraviolet Imaging Survey”.
DiRAC Researcher, Gwendolyn Eadie, published a paper in the Astrophysical Journal. In a series of three papers, Eadie et al. developed a hierarchical Bayesian method to estimate the Milky Way Galaxy’s mass given a physical model for the potential, a measurement model, and kinematic data of test particles such as globular clusters (GCs) or halo stars in the Galaxy’s halo.
Science has become a big-data endeavor. But scientists are not universally adept in “data science” — the computing and statistical skillsets needed to handle, sort, analyze and draw conclusions from big data. The shortage of know-how in data science can hamper research, medicine and even private industry
A new paper led by Daniela Huppenkothen, Associate Director of DiRAC, was just published in the Proceedings of the National Academy of Sciences on how we can learn these skills by working collaboratively. With a team of researchers from the University of Washington, New York University and the University of California, Berkeley she developed an interactive workshop in data science for researchers at multiple stages of their careers. The course format, called “hack week,” blends elements from both traditional lecture-style pedagogy with participant-driven projects. The most recent was a neuroscience-themed event held in July on the UW campus organized by Ariel Rokem, a data scientist with the UW eScience Institute. As the team reports in a their paper published Aug. 20, participants rated the hack weeks as opportunities to learn about new concepts, foster new connections, share data openly, and develop skills and work on problems that will positively affect their day-to-day research lives.
Read more about this in the full UW press release
DiRAC Researcher and NSF Postdoctoral Fellow, James Davenport, is a coauthor on a recent paper studying “Boyajian’s Star”, aka the Most Mysterious Star in the Universe! KIC 8462852, as the star is officially known, has been observed to undergo dramatic changes in brightness over several days, as well as smaller long-term variations. Both of these forms of variability have been unexplained so far, but this latest paper (including Davenport and UW grad student , Brett Morris) finds that clumpy dust surrounding KIC 8462852 is the most likely explanation.
For more information, see this UW Press Release