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.
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.
Where: PAB, B305, 3rd floor
When: March 13, 2019 @ 12:00 pm
Astropy Overview, Update, and Discussion
Dr. Kelle Cruz is an Associate Professor of Physics and Astronomy at the Hunter College and a Research Associate at the American Museum of Natural History (AMNH). Her research interests include the study of very low mass stars and brown dwarfs. She received both her Bachelors and PhD from the University of Pennsylvania, where she was an NSF Graduate Research Fellow. Prior to joining the Hunter faculty, she was an NSF Astronomy and Astrophysics Postdoctoral Fellow at AMNH and a Spitzer Postdoctoral Fellow at Caltech. She is passionate about open science practices and resource sharing among scientists. She is the founder and Editor-in-Chief of the AstroBetter blog and wiki and serves on the Coordination Committee of the Astropy Project, the core software suite for Astronomy in the Python programming language. She is currently serving on the Board of the American Astronomical Society. Prior to being elected to the Board, she served as the Chair of the Committee on Employment from 2010-2017. She also recently started ScienceBetter Consulting, a small business dedicated to serving the needs of the scientific community.
When: February 22, 2019 @ 11:00am
Where: PAB, 3rd floor, B305
Disentangling Overlapping Astronomical Sources using Spatial, Spectral, and Temporal Information
We present a powerful new algorithm that combines spatial and spectral information to separate photons from overlapping X-ray sources. We use a Bayesian hierarchical modeling approach to simultaneously infer the number of overlapping sources, to probabilistically separate the photons among the sources, to fit the parameters describing the individual sources, and to coherently quantify the associated uncertainties. The advantages and utility of combining spatial and spectral information are demonstrated through a simulation study and a data analysis. Since many sources vary in intensity over time, our most recent work additionally aims to model the arrival times of the detected photons. We illustrate that this temporal information can further separate sources and allow us to detect transient phenomena.
Dr. Jones’ research interests include astrostatistics, hierarchical Bayesian modeling, machine learning methods, Monte Carlo methodology, and more. He has contributed work in exoplanet detection, light curve classification, and disentangling overlapping sources in spatial and spectral analysis.
When: January 14, 2019 @ 12:30pm
Where: PAB, eScience Data Studio, 6th floor
Maximizing LSST science with probabilistic data products
LSST will produce massive catalogs including detected objects down to unprecedented floors in signal-to-noise ratio, opening the door to a new space of potential discoveries, from illuminating the dark energy accelerating the expansion of the universe to revealing the physical processes underlying transients and variable stars. The anticipated deluge of uncertainty-dominated data, however, demands an unprecedented degree of statistical rigor. Posterior probabilities that quantify complex uncertainties are appropriate successors to the conventional point estimates of physical parameters that suffice for more informative data. In contrast with traditional science analysis pipelines for point estimates and Gaussian errors, inferential infrastructure compatible with probabilistic data products remains underdeveloped. I present mathematically self-consistent techniques for validating, storing, and using such probabilities in the context of photometric redshifts with applications in cosmology. A statistically principled propagation of information will enable us to use every part of the animal and do the best science possible with LSST.
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”.
The supernova explosions of white dwarf stars are thought to be caused by the influence of a binary companion star. Most evidence points towards a merger with another white dwarf star, but accreting too much mass from a sun-like or red giant star could also cause a supernova. It is very difficult to directly observe the companion star caught in a supernova explosion, but luckily, some companion stars are very messy. Red giants are so big and bright that material blows off their surface and is scattered around the vicinity like litter in the wind. If the fast-moving front of material ejected by a supernova hits this residual gas, it sends a shock through the material that creates luminous ultraviolet emission. This has only been seen a few times before, when the residual gas was very close to the explosion, and it was unknown how often these stellar systems had gas littering their environment, which had been blown out to further distances, and thus gone unnoticed. By patiently waiting, and using HST to take NUV snapshots of the sites of ~70 supernovae at 1-3 years after the explosion, we caught one white dwarf explosion in a stellar system with a litterbug companion star: Supernova 2015cp. Our team used ground- and space-based facilities (including Keck, VLT, VLA, HST, and Swift) to obtain additional data and study SN 2015cp in detail. Our analysis limits the fraction of white dwarf explosions with messy companion stars to be <6%.
December 2018 Newsletter
It is a great pleasure to welcome you to the first DIRAC Institute newsletter. It is hard to believe that the institute is only a year old and how much has happened over the last 12 months.
In January a team of DIRAC scientists made use of the University of Washington’s 3.5m telescope in New Mexico to measure the shape of the interstellar asteroid 1I/‘Oumuamua (the first asteroid or comet we have discovered that originated from another solar system). In May, 450 people packed Kane Hall to hear our inaugural DIRAC Public Lecture by Nobel Laureate Saul Perlmutter, who described how surveys such as the Zwicky Transient Facility and the Large Synoptic Survey Telescope can revolutionize our understanding of the universe. And in just the last few weeks we streamed the detection of over one million new, variable, or moving objects detected by the Zwicky Transient Factory telescope in a single night.
You can read more about these discoveries in the articles below. I hope you will be able to join us for some of the lectures and events that we will be hosting at the DIRAC Institute in 2019.
As we wrap up this year I did want to ask one thing of you. We are starting a new initiative at DIRAC to bring in students from under-represented universities and colleges around the country to spend the summer working with our researchers. In Seattle, and at the University, we are fortunate to live in a dynamic and entrepreneurial community with access to many skilled and talented researchers. We want to share that knowledge with students who do not have access to these resources.
As you consider your charitable donations this year please consider supporting DIRAC by sponsoring one of the our 2019 Summer Fellows.
If you have already made your gift, thank you for your generosity.