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 email@example.com 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.
Modern sky surveys are producing astronomical catalogs with billions of stars and galaxies. What is often important for science is cross-correlating these catalogs and finding the matching objects in several catalogs so that new insights can be gained from all observations at once. This operation, commonly known as ‘cross-matching’, can be extremely computationaly expensive because of the large number of comparisons that need to be performed.
DIRAC team has designed and implemented a system called AXS, or Astronomical Extensions for Spark, that comprises a new cross-matching approach that significantly outperforms other such systems and is capable of cross-matching multi-billion catalogs in tens of seconds on commodity hardware. AXS also contains other functionalities useful to astronomers and is based on Apache Spark, an industry-standard, open-sourced, distributed data processing system.”
During the last week of June, Asteroid Day celebrated their Fifth Anniversary in 2019, with events in 192 countries, and once again broadcasted their six-hour Asteroid Day LIVE TV show from Luxembourg, hosting innovators, astronauts, planetary scientists, celebrities and asteroid experts.
Scientists from the DiRAC Institute and LSST, Dr. Lynne Jones and Prof. Mario Jurić, were among the panel guests. The program featured two short films about team’s work and current research. “2017 The LSST – Making Census of the Solar System” and “2019 New Era of Cosmic Discovery“.
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.
When: April 12, 2019 @ 11:00am
Where: PAB, 6th floor, WRF Data Studio, eScience Seminar Room
Quantifying Discovery in Astro/Particle Physics: Frequentist and Bayesian Perspectives
David A van Dyk, Statistics Section, Department of Mathematics, Imperial College London
Statistical discovery questions in astrophysics and high-energy physics often involve mathematical subtleties that mean standard methods (e.g., chi-square) are inappropriate and can lead to misleading results. At the same time Bayesian and classical statistical techniques can lead researchers to differing conclusions. Moreover modern computational strategies are typically infeasible under extreme discovery criteria (4 sigma or more). This talk explores the statistical challenges that arise in the quantification of discovery and suggests a strategy that combines Bayesian and classical statistical techniques to tackle these challenges.
Where: PAB, WRF Data Science Studio, 6th floor
When: March 26, 2019 @ 11:00 am
Separating wheat from chaff: photometric classification in the age of LSST
The Large Synoptic Survey Telescope will generate a data deluge: millions of transients and variable sources will need to be classified from their light curves. Photometric classification has long been a problem of interest in the astronomical community, but the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) brings a wide range of models together, simulated under LSST-like conditions for the first time. PLAsTiCC was delivered to the community through a Kaggle challenge, designed to stimulate interest in time-series photometric classification and deliver methodologies that will advance the LSST science case. I will give an overview of the road to PLAsTiCC, the models and the validation of the data, discuss some of the results from PLAsTiCC, and discuss the science impact of classification on photometric cosmology with Type Ia supernovae.