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.
When: Monday, March 8th, 2021
Where: Join Zoom Meeting: https://washington.zoom.us/j/96005249441
A key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as color, apparent magnitude, and morphology. This task, known as cataloging, is challenging for several reasons: many astronomical objects are much dimmer than the sky background, labeled data are generally unavailable, overlapping astronomical objects must be resolved collectively, galaxy shapes are complex, and the datasets are enormous. In this talk, I present a new approach to cataloging based on inference in a fully specified probabilistic model. To approximate the posterior distribution, I propose a procedure based on variational inference that employs a deep neural network to “amortize” the computational cost over regions of sky. For crowded starfields, the proposed method is as much as 10,000 times faster than existing methods, while producing more accurate results. Preliminary results indicate that the proposed method is also effective at cataloging heavily blended galaxies.
DiRAC Researchers featured in Science Node
“Stories of the single-handed contributions made by astronomy’s greats conjure images of lone figures leaning over telescopes, peering into space. But, in truth, the field is quite collaborative — necessarily so, given the diverse expertise needed to support its increasingly complex technologies.
Such is the case with a 2022 digital sky survey, which is set to capture a full image of the Southern Hemisphere every three to four nights for ten years. The project, known as the Legacy Survey of Space and Time (LSST), will be run by Vera C. Rubin Observatory’s team of 170, using an 8m telescope on top of Cerro Pachón mountain in Chile.”
Read the full article here.
When: Monday, February 8th, 2021, 2020
There are many types of interplanetary trajectories; e.g. 2-impulse Hohmann transfer (Mars and Venus missions) , impulsive + gravity assist (Galileo & Cassini), impulsive + low-thrust electric propulsion (Dawn, NEAR). Each type requires different methods for determining feasible trajectories. But feasibility is not sufficient. Optimization is also required to obtain the best spacecraft mass delivered to the target planet so that scientific instrumentation and/or maneuvering fuel to extend mission life can be maximized. The last 30 years have seen great improvement in the analysis and numerical methods available to optimize such trajectories. In particular, evolutionary and heuristic algorithms, such as genetic algorithms and particle swarm optimization now significantly aid mission design. Optimizing trajectories for complicated multi-flyby missions such as Galileo and Cassini, which took many person-years of work when done at JPL in the 1980’s, have been transformed by our research group into something that can be done in just a few hours on a laptop with very little a priori information, basically just a range of desired dates for departure and arrival and the type of launch vehicle that will be used.
January 14, 2021 | James Urton | UW News
A team led by Dr. James Davenport, research assistant professor of astronomy at the UW and associate director of the UW’s DIRAC Institute, analyzed more than 125 years of observations of HS Hydra – from astro-photographic plates in the late 1800s to 2019 observations by TESS – and showed how this system has changed dramatically over the course of just a few generations. The two stars began to eclipse in small amounts starting around a century ago, increasing to almost full eclipses by the 1960s. The degree of eclipsing then plummeted over the course of just a half century, and will cease around February 2021.
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Astronomers document the rise and fall of a rarely observed stellar dance
The sun is the only star in our system. But many of the points of light in our night sky are not as lonely. By some estimates, more than three-quarters of all stars exist as binaries — with one companion — or in even more complex relationships. Stars in close quarters can have dramatic impacts on their neighbors. They can strip material from one another, merge or twist each other’s movements through the cosmos.
And sometimes those changes unfold over the course of a few generations.
That is what a team of astronomers from the University of Washington, Western Washington University and the University of California, Irvine discovered when they analyzed more than 125 years of astronomical observations of a nearby stellar binary called HS Hydrae. This system is what’s known as an eclipsing binary: From Earth, the two stars appear to pass over one another — or eclipse one another — as they orbit a shared center of gravity. The eclipses cause the amount of light emitted by the binary to dim periodically.
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Continue reading this article by James Urton in the UW press release here.
When: Monday, December 14th, 2020 Where: https://washington.zoom.us/j/91798174425
Comparisons between cosmological parameters from tomographic cosmic shear measurements and the cosmic microwave background reveal some tension between the amount and clustering strength of (predominantly dark) matter. Furthermore, analyses of cosmological weak lensing by different surveys also reveals slight differences in the values of derived parameters. As a result, tomographic weak lensing collaborations have been increasingly focused on the identification and mitigation of systematic biases in their analyses. Arguably the most significant systematic uncertainty arises from the method of estimating tomographic redshift distributions from purely photometric data. In this talk I will explore the methods behind redshift calibration currently being employed by the Kilo Degree Survey (KiDS), discuss the sources of systematic bias in this calibration, and explore some novel methods used to mitigate said biases. In particular, I will focus my discussion on the applications of unsupervised machine learning methods and how they can be used to tackle these problems.
Angus is a Research Fellow in the German Centre for Cosmological Lensing at the Ruhr Universität Bochum, Germany. His undertook his PhD at the University of Western Australia, where he studied the growth and evolution of baryonic mass as a member of the Galaxy and Mass Assembly (GAMA) collaboration. Following his PhD, Angus worked for the Kilo Degree Survey (KiDS) at the Argelander Institute for Astronomy at the Universität Bonn, where he began his work within weak gravitational lensing. For the last 3 years his research has focused on weak lensing survey science, and particularly on optimisation of photometric image reduction and analysis methods, systematics mitigation, and statistical analyses. Beyond weak lensing, Angus is a keen astrostatistician, a passable astronomy outreach presenter, and an enthusiastic but nonetheless mediocre golfer.
DiRAC is pleased to welcome Federica Bianco to the department (virtually) to give a DiRAC Seminar on Monday Nov 9 at 10am PST.
DiRAC Seminars are appropriate for students and researchers at all levels who are interested in astronomy and/or data science, and everyone is welcome to attend.
Beyond the stars: good and bad ways to use our science training to make a better world
Astrophysicists’ claims to fame include practicing one of the oldest professions in the world and having invented data science. By necessity, astrophysicists work next to the data, observing rather than experimenting, and astrophysics taps on our innate curiosity to understand who we are and what is our place in the world that surrounds us. Because of this, those claims have some merits and the involvement of astrophysicists in social problems has a long history. However, reaching beyond your field can be controversial and complex, and today there is a divisive narrative that opposes “staying in your lane” to “using your skills for good.” In this talk, I will share my experience at the interface of astrophysics, data science, and public policy and lessons learned about good and bad ways to use science and data skills to make a better world. I will talk about astrophysics, Urban Science, COVID-19, data ethics, and pedagogy.
I am a Computer Science Engineering student who is very much passionate about building web apps and creating products for people and communities.
I have previously worked/interned at multiple VC funded tech startups and this summer I started working remotely with the Data Engineering team from UW DiRAC institute under Google Summer of Code 2020!
My project revolved around building a Jupyter Notebook/Lab Extension. The extension can be used by the astronomy community to create and configure apache pyspark clusters just on the click of a few buttons. It can be done from within the Jupyter Environment without the need to write multiple lines of cumbersome codes.
Fig 1: The “SparkManager” Extension.
Fig 2: The options to configure an apache pyspark cluster using SparkManager
Fig 3: “spark” variable is injected into the jupyter notebook using SparkManager
UW DiRAC Data Engineering Team
The best part of my summer project was definitely getting to meet the entire team of UW DiRAC and getting to know about the astronomy projects that DiRAC is working on, like the LSST!
I feel lucky to have worked with a very experienced, passionate and welcoming team of engineers and astronomers.
Also it is so fascinating when I realize that we were thousands of miles apart and yet we were working in a very collaborative manner.
It was a great learning experience for me as I learnt a lot about the Jupyter Ecosystem and remote collaborative work.
External Links for further reading
DiRAC is pleased to introduce the new Associate Director of our Institute, Professor James (Jim) Davenport. Davenport received his PhD from the University of Washington in 2015, working on exploring magnetic activity from low-mass stars using NASA’s Kepler mission. He was then awarded a NSF postdoctoral fellowship at Western Washington University, and returned to UW in the inaugural class of DiRAC postdoctoral fellows in 2017.
Prof. Davenport’s research focuses on stars within our own Milky Way, using “time domain” astronomy techniques with large surveys such as NASA’s Kepler and TESS missions, and the ZTF survey. He is best known for exploring the evolution of “magnetic activity” as stars age, particularly in studying the declining rate of powerful stellar flares over time.
In his most recent paper, Prof. Davenport and collaborators from UW used new data from the TESS mission to revisit one of the most prominent flare stars from the original Kepler mission, a small red dwarf named GJ 1243. These datasets give two precise point-in-time estimates of the flare rate for GJ 1243, over a span of 10 years, and found that unlike our Sun, GJ 1243 does not appear to show any variation in its flaring behavior. This has opened new lines of exploration for Prof. Davenport and his group, searching for changes in stellar flare rates over decades of observations.
Davenport currently leads a group of graduate and undergraduate researchers in a range of data-intensive studies of the active lives of nearby stars, including projects on eclipsing binary stars, variability from massive stars, detecting rotating stars, and studying stellar flares. He is currently working on a review of variable star astronomy for the public with TESS. Davenport is also interested in developing methods to search for signs of life in the universe – particularly for intelligent life – using tools developed for “traditional” data intensive astronomy.
For the coming year he will be leading the DiRAC Time Domain research group in their collaborative search for mysterious “dipper” stars from the ZTF survey.
After three years of careful and thoughtful guidance by the outgoing Associate Director, Dr. Daniela Huppenkothen, DiRAC has developed a wonderfully collaborative and productive atmosphere for researchers studying a wide range of topics. Prof. Davenport is excited to take on the role of Associate Director for DiRAC. He hopes to build on this foundation, encouraging new and novel collaboration from researchers and students, and most importantly to foster an inclusive institute that places the value of people above all else.
Prof. Davenport lives north of Seattle with his family, and in the mornings can often be found drinking coffee and writing at Cafe Solstice on the Ave. He also produces a YouTube series called “Astro Vlog” that showcases the work and life of an astronomer, and can be found on Twitter @jradavenport.