An asteroid discovery algorithm — designed to uncover near-Earth asteroids for the Vera C. Rubin Observatory’s upcoming 10-year survey of the night sky — has identified its first “potentially hazardous” asteroid, a term for space rocks in Earth’s vicinity that scientists like to keep an eye on.
The roughly 600-foot-long asteroid, designated 2022 SF289, was discovered during a test drive of the algorithm with the ATLAS survey in Hawaii. Finding 2022 SF289, which poses no risk to Earth for the foreseeable future, confirms that the next-generation algorithm, known as HelioLinc3D, can identify near- Earth asteroids with fewer and more dispersed observations than required by today’s methods.
In conversation with James Davenport and 2022 DiRAC Research Prize recipients read more about Vera C. Rubin Observatory and important role of the scientists at the UW’s DiRAC Institute.
Stellar variability is a limiting factor for planet detection and characterization, particularly around active M-type stars. Here we revisit one of the most active stars from the Kepler mission.
In partnership with the news team at the Massachusetts Institute of Technology, the UW News office has posted a story about a rare and mysterious star system discovered by a team of astronomers and reported in a paper published this morning in Nature. The researchers report that the system appears to be a “black widow binary” — consisting of a rapidly spinning neutron star, or pulsar, that is circling and slowly consuming a smaller companion star, just as its arachnid namesake does to its mate.
Washington state’s NASA Space Grant program at the UW invites you, as a faculty member conducting research in a STEM area, to participate our 2022 Summer Undergraduate Research Program (SURP). The application period for students closes on Friday, April 8, 2022.
SURP is an excellent way to extend your summer funding through WA Space Grant’s contribution of half of the selected student’s award payment. Faculty contribution per student is $2,750 full-time and $1,375 part-time.
If you already have a student working with you, we encourage you to apply for the program along with that student. If you are looking for new students, you can apply and we will help to match you with a qualified undergraduate.
Through SURP, WA Space Grant seeks to increase research opportunities for undergraduates on NASA-related STEM projects, and we particularly welcome applications from students with traditionally marginalized genders and from underrepresented minoritized communities. UW undergraduate students in good academic standing who are interested in research in science, technology, engineering or mathematics fields are eligible to apply. Applicants must be U.S. citizens.
DiRAC members Joachim Moeyens and Zeljko Ivezić, aided by a DiRAC guest researcher Vedrana Ivezić, led a multi-institutional team of scientists who produced and analyzed simulated SPHEREx spectra of asteroids.
SPHEREx is a 2-year NASA space mission scheduled for launch in less than 3 years. SPHEREx will deliver the first all-sky spectral survey at about 100 spectral channels in the infrared wavelength range 0.8-5.0 micron. The team estimated that SPHEREx dataset will be transformative: high-quality spectra will be obtained for close to 10,000 asteroids, representing an increase over our current sample size by more than an order of magnitude.
With its additional LSST expertise, DiRAC will be well positioned to lead cutting-edge studies of asteroid taxonomy and photometric variability, and thus contribute to our understanding of the formation and evolution of our SolarSystem.
Supernovae are the explosions of stars that can be seen across vast distances, appearing as new bright points of light in optical images of the sky, even when the original star was far too faint to be detected.
When different types of stars explode (e.g., low-mass and high-mass) they cause supernovae with a variety of characteristics (e.g., brightness, color, duration). When two or more supernovae (explosions of stars) occur in the same galaxy, we say they have the same “parent galaxy” and are “sibling supernovae”. The characteristics of sibling supernovae can thus be compared knowing that, since they have the same distance from Earth and come from similar environments, any differences between them are more likely to be related to the type of star that exploded. Sibling supernovae are thus very useful for obtaining a better understanding of both supernovae and their parent galaxies.
Since the average supernova rate for a Milky Way-type galaxy is just one per century, a large imaging survey is required to discover an appreciable sample of sibling supernovae. In this paper we present 10 sibling supernovae in 5 parent galaxies from the wide-field Zwicky Transient Facility (ZTF).
For each of these families we analyze the supernova’s location within the parent galaxy, finding agreement with expectations that supernovae from more massive stars are found nearer to their parent galaxy’s core, and in regions of more active star formation.
We also present an analysis of the relative rates of core collapse and thermonuclear sibling supernovae, finding a significantly lower ratio than past samples due to the unbiased nature of the ZTF.
Melissa Graham currently works for Rubin Observatory as the Lead Community Scientist for the Community Engagement Team and as a Science Analyst for the Data Management team. Her main research focus is supernovae, especially those of Type Ia.
A tenet of modern cosmology is the existence of the “cosmic web”, a vast filamentary structure formed via the collapse of matter due to gravity. This structure is ubiquitous in cosmological simulations yet challenging to observe due to its diffuse nature.
Recently, a new technique was developed which is inspired by the growth and movement of Physarum polycephalum slime mold to map the cosmic web of a low redshift (z < 0.01) sub-sample of the SDSS spectroscopic galaxy catalog (Burchet et al. 2020, Elek et al. 2020). However, this limited volume limits the statistics and observational techniques that can be applied to this slime mold generated density field.
Matthew Wilde, UW graduate student, applied this algorithm to the classic SDSS and eBOSS surveys which expands the volume mapped to z < 0.5. The slime mold density map shown in the accompanying image was recently released as a value added catalog (VAC) and released by the SDSS DR17 team (Abdurro’uf et al. 2021, Wilde et al. in prep.), and will allow astronomers a new way to explore the cosmic web. Applications of this density map include constraining the role of environment on galaxy evolution and the role of galactic feedback, more efficient follow up observing strategies for gravitational wave targets, and interesting tests of cosmological constants and dark matter candidates.
Trans-Neptunian objects provide a window into the history of the solar system, but they can be challenging to observe due to their distance from the Sun and relatively low brightness.
In the recently published paper, Sifting through the Static: Moving Object Detection in Difference Images, DiRAC researchers report the detection of 75 moving objects that could not be linked to any other known objects, the faintest of which has a VR magnitude of 25.02 ± 0.93 using the Kernel-Based Moving Object Detection (KBMOD) platform.
They recover an additional 24 sources with previously known orbits and place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer solar system. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. They describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics-processing unit filter, new coadded stamp generation, and a convolutional neural network stamp filter, which allow KBMOD to take advantage of difference images.
These enhancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.
DiRAC researchers are heavily involved in building the Vera C. Rubin Observatory, a new facility that is currently under construction in Chile. This observatory will feature the 8.4 meter Simonyi Survey Telescope and the world’s largest CCD camera which will scan the entire visible sky every three nights. It will discover and observe millions of supernovae which are powerful explosions of stars that can outshine an entire galaxy for a brief period of time.
A particular type of supernovae called “Type Ia” can be used to map out how the universe has expanded since the big bang. This led to the discovery of dark energy which was awarded the Nobel Prize in 2011. The Rubin Observatory will discover over 100 times as many Type Ia supernovae then have been observed by all surveys to date and will dramatically improve our understanding of the universe.
Extracting scientific results from this large deluge of data is a big challenge. In his paper, DiRAC Fellow Kyle Boone discusses a new statistical model called ParSNIP that can be used to distinguish Type Ia supernovae from others and improve our maps of the universe. This novel work combines recent advances in computer science and deep learning with physics models of how light propagates through the universe. The resulting hybrid model is the first one that can empirically describe how the emitted light spectrum from any kind of supernova evolves over time.
This foundational work has many applications. ParSNIP will be used to identify the different kinds of supernovae that the Rubin Observatory finds, and it can do this with over twice the performance of previous models. It will also be used to hunt for new unknown kinds of supernovae in the large Rubin dataset. ParSNIP will use all of the millions of supernovae that the Rubin Observatory discovers to measure the properties of dark energy in contrast to current methods that can only use less than a tenth of the full sample. This work will transform supernova science with the Rubin Observatory and help to extract the full scientific potential.
About
Kyle Boone is DiRAC Postdoctoral Fellow. Kyle’s research focuses on developing novel statistical methods for astronomy and cosmology. He is particularly interested in using Type Ia supernovae to probe the accelerated expansion of the universe that we believe is due to some form of “dark energy”. One aspect of his research focuses on identifying Type Ia supernovae among the millions of astronomical transients that upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will discover.
