The DIRAC Institute in the Department of Astronomy at the University of Washington is seeking applicants with a strong research record in the development of statistical techniques or algorithms for analyzing large astrophysical data sets for two postdoctoral positions.
AstroML: The first position is to help in the development of the second edition of astroML (http://astroml.org) a popular Python-based machine learning package for astrophysics. New components we are incorporating within astroML include methodologies from deep learning and hierarchical bayesian statistics. Special emphasis will be placed on building a broader community and making astroML a sustainable open-source project. The successful candidate will lead these activities, including the application of the new codes to dataset available to UW researchers.
Time Series Data: The second position is to develop new approaches for analyzing astronomical time series data using modern computational frameworks. The goal of this framework will be to enable science with the ZTF and LSST data sets. Promising applicants should possess an interest in time domain science and experience or interest in the use of databases and large scale compute platforms such as Spark, Dask, or similar. Good Python skills, and experience with machine learning libraries, image processing of astronomical images, or astronomical databases are desirable.
The DIRAC Institute is a newly formed center for data intensive astrophysics at the University of Washington. The Institute consists of six faculty and senior fellows, and over 20 postdoctoral researchers and research scientists. It has active research programs in Cosmology, Solar System science, Milky-Way structure, the Variable and Transient universe, andAstronomical Software.
The University of Washington is a partner in the Zwicky Transient Facility (ZTF) project, a new time-domain survey which will begin operations in early 2018. The UW is a founding partner of the LSST project, and leads the construction of its time domain and solar system processing pipelines. Other research activities at UW/DIRAC include topics in extragalactic science, as well as the understanding the structure, formation, and evolution of the Milky Way using large surveys (SDSS, WISE, PanSTARRS PS1, and others).
A Ph.D. degree in astronomy, physics, computer science, or a related subject is required. The initial appointment is for two years, renewable up to three years, and offers competitive salary and benefits. The appointments are available immediately and are expected to start no later than September 2018.
Applicants should submit a curriculum vitae, description of research interests (with links to Github if relevant) and arrange for three letters of reference to be submitted to Nikolina Horvat at firstname.lastname@example.org with subject line “DIRAC postdoc application (your name)”. Applications will be accepted until the positions are filled, to assure full consideration, please send your application by Dec 31st 2017
For detailed information about the benefits available through the University of Washington, including dental, medical and disability insurance, retirement, and childcare centers, see the University of Washington benefits page: https://www.washington.edu/admin/hr/benefits/.
The DIRAC Institute is a community of people with diverse interests and areas of expertise, engaged in the understanding of our universe through the analysis of large and complex data sets. We are an open, ethical, highly engaged and collaborative community based on trust, transparency and mutual respect. We believe in providing a welcoming and inclusive environment, in the importance of quality of life, in embracing diversity, in making a difference and having fun.
When: November 9, 2018 @ 11:00am
Where: PAB, WRF Data Science Studio, 6th floor, seminar room (C607)
Stellar tidal disruptions flares: new tools for black hole astrophysics
We invite applications for up to two DIRAC Research Associates in the Department of Astronomy at the University of Washington. These 3-year positions are available to promising, early-career researchers who
Applicants should demonstrate excellence in research that either complements or builds upon ongoing research directions within the DIRAC Institute. Successful candidates will work with the current DIRAC faculty and researchers to develop their own programs of research and innovation.
The DIRAC Institute is a recently formed center for data intensive astrophysics at the University of Washington. The Institute consists of six faculty and senior fellows, and over 20 postdoctoral researchers and research scientists. The University of Washington is a partner in the Zwicky Transient Facility (ZTF) project, a new time-domain survey now in routine operations. The UW is a founding partner of the LSST project, and leads the construction of its time domain and solar system processing pipelines.
Qualifications: A Ph.D or foreign equivalent degree in astronomy, physics, computer science, or a related subject is required. The initial appointment is for two years, renewable for a third year, and offers competitive salary and benefits. The appointment is expected to start no later than September 2019.
Application Instructions: Applications should be submitted online through the UW Interfolio FS module . Applicants should submit a cover letter, curriculum vitae, research statement (maximum length 3 pages, addressing both current work and laying out research ideas for the 3-year fellowship) as well as contact information for three letters of recommendation. Candidates should address in their application how their work potentially complements, connects to, or extends current research efforts at DIRAC, or indicate how they would benefit from expertise available at DIRAC.
We encourage candidates to highlight work and achievements related to data-intensive astronomy that they are enthusiastic about, but do not easily fit within traditional metrics of academic success (e.g. open-source software development, development of teaching or outreach materials, diversity work, etc).
Please apply by December 10th, 2018 for full consideration, but applications will be accepted until the positions are filled.
Quanta Magazine spoke with prof. Mario Jurić about how the swell of data is changing what it means to be an astronomer.
Kathryn McKeough is a Ph.D. candidate in the Department of Statistics at Harvard University. She does research as a member of both the CHASC: Astrostatistics Group and the Sports Analytics Lab at Harvard. Her Tuesday, October 23rd, 2018 talk at 12pm will be on Defining Regions that Contain Complex Astronomical Structures.
Katy is a Ph.D. candidate in the Department of Statistics at Harvard University. She does research as a member of both the CHASC: Astrostatistics Group and the Sports Analytics Lab at Harvard. Her Tuesday 12pm talk will be on Defining Regions that Contain Complex Astronomical Structures.
Defining Regions that Contain Complex Astronomical Structures
Astronomers are interested in delineating boundaries of extended sources in noisy images. An example is finding outlines of a jet in a distant quasar. This is particularly difficult for jets in high redshift, X-ray images where there are a limited number of pixel counts. Using Low-counts Image Reconstruction and Analysis (LIRA), Stein et al. 2015 and McKeough et al. 2016 propose and apply a method where jets are detected using previously defined regions of interest (ROI). LIRA, a Bayesian multi-scale image reconstruction, has been tremendously successful in analyzing low count images and extracting noisy structure. However, we do not always have supplementary information to predetermine ROI and the size and shape can greatly affect flux/luminosity. LIRA is also unaware of correlations that may exist between adjacent pixels in the real image. In order to group similar pixels, we impose a successor or post-model on the output of LIRA. We adopt the Ising model as a prior on assigning the pixels to either the background or the ROI. From the posterior of this model, we are able to delineate probabilistic boundaries. This method has been applied to the jet data as well as simulations and appears to be capable of picking out meaningful ROIs.
DIRAC Researcher, Gwendolyn Eadie, published a paper in the Astrophysical Journal.
Published on September 24, 2018.
Estimating the Milky Way’s Mass via Hierarchical Bayes: A Blind Test on MUGS2 Simulated Galaxies
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. The Galaxy’s virial mass was found to have a 95% Bayesian credible region (c.r.) of (0.67, 1.09) × 1012 . In the present study, we test the hierarchical Bayesian method against simulated galaxies created in the McMaster Unbiased Galaxy Simulations 2 (MUGS2), for which the true mass is known. We estimate the masses of MUGS2 galaxies using GC analogs from the simulations as tracers. The analysis, completed as a blind test, recovers the true M 200 of the MUGS2 galaxies within 95% Bayesian c.r. in 8 out of 18 cases. Of the 10 galaxy masses that were not recovered within the 95% c.r., a large subset have posterior distributions that occupy extreme ends of the parameter space allowed by the priors. A few incorrect mass estimates are explained by the exceptional evolution history of the galaxies. We also find evidence that the model cannot describe both the galaxies’ inner and outer structure simultaneously in some cases. After removing the GC analogs associated with the galactic disks, the true masses were found more reliably (13 out of 18 were predicted within the c.r.). Finally, we discuss how representative the GC analogs are of the real GC population in the Milky Way.
DIRAC Institute has the 1st Astronomy Open-source Sprint, Sep 28, 2018 at the University of Washington.