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.
Last year, Asteroid Day celebrated their Fifth anniversary, with events in 192 countries. In the next few days, Asteroid Day TV is broadcasting asteroid related programming from Discovery Science, TED, IMAX, BBC, CNN, The European Space Agency (ESA), the European Southern Observatory (ESO) and other top content producers.
DiRAC & Vera C. Rubin Observatory LSST videos are broadcasted under the names:
LSST | Making Census of the Solar System,
LSST | New Era of Cosmic Discovery
Join us for an evening with an astronomer and participate in talks and live conversations about topics that vary from searching for the most mysterious stars in our Galaxy to the Starlink satellites changing our view to the night sky!
UW astronomers will talk about their work and latest discoveries. Astronomy at Home talks are for everyone: astronomy enthusiasts, students, and all who are curious and interested in astronomy and data science in astronomy. The talks will be 20 minutes in length with plenty of time for Q&A. All talks are streamed on YouTube and you can join for live discussion via Zoom.
Tune in on July 23rd at 8:00pm!
Streaming from DIRAC YouTube channel https://dirac.us/yt
Meredith Rawls works with the Rubin Observatory Legacy Survey of Space and Time (LSST) Data Management group at the University of Washington. Meredith is a Research Scientist in the Department of Astronomy and a DIRAC Fellow. She writes software to enable real-time discovery of moving and variable objects in terabytes of nightly data from LSST.
SpaceX has launched over 400 Starlink satellites into low-earth orbit, and they’re just getting started. These satellites are changing our view of the night sky and showing up as bright streaks in telescope images. They have the potential to mess up large ground-based optical telescopes like Vera C. Rubin Observatory, which is designed to find new things going bump in the night and discern subtle brightness patterns. The good news is, SpaceX is working with astronomers to make future Starlinks a whole lot darker. (The bad news is, SpaceX isn’t the only game in town.) I’ll share new results from observations of SpaceX’s so-called DarkSat — a Starlink they literally painted black — to see how much it helped. SpaceX’s mitigation plans should salvage most planned Rubin Observatory science, and should also hide their satellites from the naked eye most of the time. We really hope other companies will follow their lead.
Željko Ivezić | The Greatest Movie of All Time | June 9, 2020
James Davenport | Searching for the Most Mysterious Stars in Our Galaxy | May 28, 2020
When: Thursday, May 14th, 2020 @ 3:30pm Where: via ZOOM
Meeting PW: 505387 Meeting Link: https://washington.zoom.us/j/98852018851?pwd=OG8zK0s4ZlBLeXhYZGNjZE84SkNaQT09 Meeting ID: 988 5201 8851
One of the best laboratories to study strong-field gravity is the inner 100s of kilometers around black holes and neutron stars in binary systems with low-mass stars like our Sun. The X-ray light curves of these binary systems show variability on timescales from milliseconds to months — the shorter (sub-second) variability can appear as quasi-periodic oscillations (QPOs), which may be produced by general relativistic effects. My research looks at QPOs from black holes and neutron stars (as well as coherent X-ray pulsations from neutron stars) by fitting the phase-resolved energy spectra of these signals to constrain their physical origin and track their evolution in time. In this talk, I will introduce why black holes and neutron stars are interesting and discuss state-of-the-art “spectral-timing” analysis techniques for understanding more about them. I will also highlight open-source astronomy research software and the importance of mental wellbeing in academia.
Dr. Abigail Stevens is an NSF Astronomy & Astrophysics Postdoctoral Fellow at Michigan State University and the University of Michigan. She completed her MSc at the University of Alberta in Canada and her PhD at the University of Amsterdam in the Netherlands. Abbie researches variable emission from accreting black holes and neutron stars in X-ray binaries, to study physical processes in strong gravity. She is also involved in the open-source scientific research software community, a Steering Committee member for STROBE-X (a proposed NASA mission), an Affiliated Scientist with NICER (a soft X-ray telescope on the International Space Station), and an advocate for mental wellbeing in academia.
Published paper includes contribution from DIRAC Researchers: Graham Melissa, Connolly Andrew, Morrison Christopher B., Ivezić Željko, Daniel Scott, Jones R. Lynne, Jurić Mario, Yoachim Peter, Bryce Kalmbach J. Published Date: April 2020.
Accurate photometric redshift (photo-zz) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work we use simulated photometry for mock galaxy catalogs to explore how LSST photo-zz estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, WFIRST, and/or CASTOR space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-zz estimates more than adding NIR or UV filters, but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or over-estimated photo-zz. For Euclid, we find that the addition of JHJH 5σ5σ photometric detections can reduce the standard deviation for galaxies with z>1z>1 (z>0.3z>0.3) by ∼20%∼20% (∼10%∼10%), and the fraction of outliers by ∼40%∼40% (∼25%∼25%). For WFIRST, we show how the addition of deep YJHKYJHK photometry could reduce the standard deviation by ≳50%≳50% at z>1.5z>1.5 and drastically reduce the fraction of outliers to just ∼2%∼2% overall. For CASTOR, we find that the addition of its UVUV and uu-band photometry could reduce the standard deviation by ∼30%∼30% and the fraction of outliers by ∼50%∼50% for galaxies with z<0.5z<0.5. We also evaluate the photo-zz results within sky areas that overlap with both the NIR and UV surveys, and when spectroscopic training sets built from the surveys’ small-area deep fields are used.
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. This bias is substantially worse for resolved sources: while a 1% bias is expected for a 10σ point source, a 10σ resolved galaxy with a simplified Gaussian profile suffers a 2.5% bias. This bias also behaves differently depending how multiple bands are used in the fit: simultaneously fitting all bands leads the flux bias to become roughly evenly distributed between them, while fixing the position in “non-detection” bands (i.e., forced photometry) gives flux estimates in those bands that are biased low, compounding a bias in derived colors. We show that these effects are present in idealized simulations, outputs from the Hyper Suprime-Cam fake-object pipeline (SynPipe), and observations from Sloan Digital Sky Survey Stripe 82. Prescriptions to correct for the ML bias in flux, and its uncertainty, are provided.
Dr. Portillo is DIRAC Postdoctoral Fellow and UW Data Science Postdoctoral Fellow in the DIRAC Institute at the University of Washington. In May 2018, Stephen completed his PhD in Astronomy and Astrophysics at Harvard University where he worked under the supervision of Prof. Douglas Finkbeiner on probabilistic cataloguing.
Monday, March 9th, 2020 @ 12:30pm Where: PAB, 6th Floor, eScience Studio, Seminar Rm.
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.
Paper by DIRAC Researcher, Sarah Greenstreet
Published February 2020
Vatira-class near-Earth objects (NEOs) have orbits entirely interior to the orbit of Venus with aphelia 0.307 < Q < 0.718 AU. Recently discovered asteroid 2020 AV2 by the Zwicky Transient Facility on 4 January 2020 is the first known object on a Vatira orbit. Numerical integrations of 2020 AV2‘s nominal orbit show it remaining in the Vatira region for the next few hundred kyr before coupling to Venus and evolving onto an Atira orbit (NEOs entirely interior to Earth’s orbit with 0.718 < Q < 0.983 AU) and eventually scattering out to Earth-crossing. The numerical integrations of 9900 clones within 2020 AV2‘s orbital uncertainty region show examples of Vatira orbits trapped in the 3:2 mean-motion resonance with Venus at semimajor axis a ≈ 0.552 AU that can survive on the order of a few Myr. Possible 2020 AV2 orbits also include those on Vatira orbits between Mercury and Venus that only rarely cross that of a planet. Together the 3:2 resonance and these rarely-planet-crossing orbits provide a meta-stable region of phase space that are stable on timescales of several Myr. If 2020 AV2 is currently in this meta-stable region (or was in the past), that may explain its discovery as the first Vatira and may be where more are discovered. Read.
Dr. Sarah 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.
Monday, February 10th, 2020 @ 12:30pm
PAA, Room A214
Supermassive black holes – with masses of millions to billions of times that of the Sun – reside in the nuclei of galaxies. While black holes are not directly visible, surrounding material becomes extremely luminous before being accreted, creating telltale signatures of black hole activity. In turn, the amount of activity tells us about black hole growth, and about energy injection back into the host galaxies. This so-called black hole feedback is thought to play a role in regulating the rate at which galaxies form new stars, thereby affecting directly their evolution across cosmic time. After a brief overview, I will highlight new findings from a multi-scale analysis of gas ionization and dynamics thanks to 3D spectroscopy with the VLT/MUSE instrument. I will then present observational constraints on the fueling of black holes, and on the extent to which they can change the fate of galaxies from statistical analyses of large datasets derived from SDSS. The latter are paving the way to yet larger experiments such as the Dark Energy Spectroscopic Instrument (DESI), which will yield over 35 million spectra of galaxies and quasars. I will conclude by briefly showcasing how the Astro Data Lab (datalab.noao.edu) and other science platforms play a role in the analysis of large datasets to further our knowledge on supermassive black holes, galaxies, and beyond.
Stephanie joined the NSF’s OIR Lab ASTRO Data Lab team as a staff scientist, coming from a staff scientist position at CEA Saclay in France. She received her PhD in astronomy from the University of Arizona in 2011 under the supervision of the NSF’s OIR Lab’s Mark Dickinson. Her research interests are focused on the evolution of galaxies and supermassive black holes across cosmic time. She brings to the Data Lab team a wealth of experience and ideas in developing and applying new methods for turning large survey data sets into scientific knowledge.
The astroML project was started in 2012 to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy, by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray.
The astroML Python package is publicly available and designed as a repository of statistical routines and machine learning tools for astrophysics. It builds on the scientific Python ecosystem, on well known libraries such as Numpy, Scipy, Scikit-learn, and Astropy; extending the functionality available in these general-purpose libraries.
astroML is designed to be a resource for both researchers and students of astronomy and Python. It is envisioned to be a community resource, with the development and submission of new algorithms, data sets, and examples provided by GitHub’s collaborative coding interface. In addition to being used for astronomical research, several university courses build on astroML, for example at the University of Washington, University of Cambridge, and Drexel University to list a few.
astroML strives to bring the astronomical community closer to the ideals of Reproducible Research, in which research papers are accompanied by well-written code to reproduce, check, and extend the results. With this in mind we share the source code used to generate the figures in both editions of the textbook in a separate GitHub repository.
Updates and news about astroML project can be found here.