In 2026, LSST Discovery Alliance launched a special call for proposals to help the U.S. astronomy community seize the scientific opportunities presented by the first public Rubin Observatory data products. Made possible through the generous support of the Heising-Simons Foundation, the program provided fast-turnaround grants to accelerate early science with Rubin Observatory’s Legacy Survey of Space and Time (LSST).
The fourteen funded projects span astrophysics, cosmology, Solar System science, and scientific software. Together they are developing community data products, machine learning tools, software, catalogs, and scientific methodologies that will help researchers throughout the Rubin community make effective use of LSST data from the earliest stages of the survey.
In addition to research funding, each award includes tailored software engineering support through Project Dovetail, which pairs scientists with professional software engineers to strengthen research software, improve scientific workflows, and accelerate discovery. This combination of scientific expertise and professional software engineering enables researchers to focus on the science while building robust, reusable tools for the broader Rubin ecosystem.
Galaxy–Galaxy Lensing with LSST DP2: Halo Mass Constraints and Groundwork for Year-1 Cosmology
Principal Investigator: Alexandra Amon
Institution: Princeton University
Award Amount: $120,000
This project will establish galaxy–galaxy lensing as a flagship early Rubin LSST science result and as a foundation for Year-1 weak-lensing cosmology. Using Rubin Data Preview 2 imaging together with DESI spectroscopic data, the team will measure the distribution of dark matter around galaxies and produce early LSST constraints on the connection between galaxies and their dark matter halos.
The project will also validate the shear catalogs, redshift calibration, and analysis pipelines needed for Rubin’s first weak-lensing cosmology results. By delivering both early science measurements and a road-tested analysis framework, this work will help prepare the Rubin Dark Energy Science Collaboration for Year-1 cosmological analyses while demonstrating Rubin’s power for galaxy formation and dark matter studies.
Maximizing Early LSST Science with Deep Learning Photometric Redshifts (Cal-PITA)
Principal Investigator: Brett Andrews
Institution: University of Pittsburgh
Award Amount: $49,132
Photometric redshifts are a foundational data product for Rubin LSST science, enabling researchers to estimate distances to billions of galaxies from imaging observations alone. This project will develop and validate deep learning approaches for producing accurate photometric redshifts using Rubin’s earliest datasets.
The team will adapt modern machine learning techniques for Rubin data, evaluate their performance across a range of galaxy populations, and release software and documentation to facilitate community use. The resulting tools will strengthen scientific analyses in cosmology, galaxy evolution, and large-scale structure while providing an important resource for the Rubin community as survey data volumes rapidly expand.
Identifying Variable Active Galactic Nuclei in Dwarf Galaxies Using Rubin LSST
Principal Investigator: Vivienne Baldassare
Institution: Washington State UniversityAward Amount: $90,000
This project will search for variable active galactic nuclei in dwarf galaxies using Rubin’s early data releases, helping to identify previously unknown populations of low-mass black holes. Understanding these systems is essential for testing theories of how the first massive black holes formed and evolved over cosmic time.
The project will combine Rubin variability measurements with complementary datasets to develop robust methods for identifying candidate active galaxies and will release software and analysis products that can support future studies across the Rubin community. These methods will help unlock one of Rubin Observatory’s most exciting opportunities: discovering black holes in galaxies that have previously been too faint or too difficult to study.
Solar System First Data Sprints for Early Rubin Science
Principal Investigator: Colin Chandler
Institution: University of Washington
Award Amount: $27,000
The Rubin Solar System Science Collaboration has spent years preparing for the Observatory’s first discoveries. This project will organize a series of intensive data sprints and community workshops that bring together planetary scientists, software developers, and students to analyze Rubin’s earliest Solar System observations.
Building on the collaboration’s successful Readiness Sprint model, these events will provide hands-on training, accelerate software development, and foster collaboration around Rubin’s rapidly growing catalog of asteroids, comets, and trans-Neptunian objects. The project will help ensure that the Solar System community is prepared to capitalize on Rubin’s extraordinary early discoveries.
Rapid, Physically Motivated Supernova Light Curve Modeling for Early Rubin Science (ezNova)
Principal Investigator: Paul Duffell
Institution: Purdue UniversityAward Amount: $12,341
This project develops ezNova, an efficient radiation-transfer code that enables physically realistic modeling of Type II-P supernova light curves using Rubin LSST observations. Designed to bridge the gap between simplified fitting tools and computationally intensive simulations, ezNova will allow researchers to model large numbers of supernovae on practical timescales.
The project will incorporate improved opacity calculations, validate the software against established radiative transfer models, and release the code, documentation, and example analyses for community use. The resulting software will provide an important resource for interpreting Rubin’s unprecedented sample of core-collapse supernovae.
Identifying Likely Host Galaxies for Tidal Disruption Events
Principal Investigator: Decker French
Institution: University of Illinois, Urbana ChampaignAward Amount: $11,984
Tidal disruption events (TDEs), in which stars are torn apart by massive black holes, provide a unique opportunity to study otherwise quiescent black holes across the Universe. This project will use Rubin imaging together with archival multiwavelength observations to identify galaxies that are especially likely to host future TDEs.
The team will produce a publicly available catalog of likely host galaxies that can be incorporated into Rubin alert brokers, enabling astronomers to identify promising TDE candidates more rapidly and efficiently. The project will also release the software used to generate the catalog, providing a valuable community resource for transient astronomy during Rubin operations.
Planet Formation and Planetary Defense: Probing the Limits of Asteroid Cohesion and Internal Strength in the LSST Era
Principal Investigator: Sarah Greenstreet
Institution: University of WashingtonAward Amount: $119,950
Rubin Observatory will discover millions of asteroids, creating unprecedented opportunities to study their physical properties while improving planetary defense capabilities. This project will use Rubin’s early observations to identify rapidly rotating asteroids, investigate their internal structure and strength, and develop methods for rapidly assessing the impact probability of newly discovered near-Earth asteroids.
The project will extend recent discoveries of unusually fast-rotating asteroids into the Rubin era while implementing near-real-time impact assessment tools suitable for Rubin’s discovery rate. Together, these efforts will advance both fundamental understanding of planet formation and practical approaches to identifying potentially hazardous asteroids.
A General-Purpose Weak Lensing Catalog for Rubin Observatory
Principal Investigator: Arun Kannawadi
Institution: Duke University
Award Amount: $50,000
Weak gravitational lensing is one of Rubin Observatory’s primary probes of dark matter and dark energy. This project will develop and publicly release a general-purpose weak lensing shear catalog for Rubin Data Preview 2 that complements the Observatory’s planned data products while providing the community with an independently validated resource.
The project will demonstrate the scientific utility of the catalog through galaxy cluster mass measurements, make the resulting catalog available through the Rubin Science Platform, and contribute software improvements back to the Rubin Science Pipelines. By providing an accessible, community-oriented weak lensing product early in the Rubin era, this work will accelerate a broad range of cosmological studies.
Discovering hostless TDEs at z > 1: A new probe of black hole growth with LSST
Principal Investigator: Mitchell Karmen
Institution: Johns Hopkins University
Award Amount: $50,000
Rubin Observatory will make it possible to discover tidal disruption events (TDEs) at distances that have never before been accessible, opening a new window on the growth of supermassive black holes in the early Universe. This project will develop machine learning methods to identify high-redshift, “hostless” TDEs that would be missed by existing discovery techniques.
The team will build an automated discovery pipeline, obtain spectroscopic confirmation of the first high-redshift Rubin TDEs, and produce a public catalog spanning a broad range of cosmic history. These discoveries will provide new constraints on how the first supermassive black holes formed and evolved.
The First Cometary Outbursts in the Legacy Survey of Space and Time
Principal Investigator: Michael Kelley
Institution: University of Maryland, College ParkAward Amount: $47,035
Cometary outbursts provide rare opportunities to probe material buried beneath comet surfaces and better understand the formation and evolution of the Solar System. This project will use Rubin’s early observations to identify, characterize, and analyze cometary outbursts while developing tools that can also be applied to asteroid impacts and other transient Solar System phenomena.
The team will produce methods for detecting outbursts in Rubin data, investigate the physical processes responsible for these dramatic events, and release software that enables the broader community to study comet activity throughout the Rubin survey.
DeepDISC Photometric Redshift Catalog for LSST Data Preview 2
Principal Investigator: Xin Liu
Institution: University of Illinois, Urbana-Champaign
Award Amount: $70,000
This project will prepare a machine learning framework for estimating photometric redshifts from Rubin LSST observations, enabling astronomers to determine galaxy distances efficiently from imaging data alone. Accurate photometric redshifts are essential for many Rubin science investigations, including cosmology, galaxy evolution, and studies of large-scale structure.
The team will refine and validate the DeepDISC framework for early Rubin datasets while producing software and documentation that can be adopted by the broader community. The resulting tools will support scientific analyses across multiple Rubin Science Collaborations and help maximize the scientific return from Rubin’s earliest observations.
Searches for Extra-Galactic Planets with Archival Data
Principal Investigator: Ryan Oelkers
Institution: University of Texas, Rio Grande Valley
Award Amount: $49,918
This project will combine nearly three decades of archival Hubble Space Telescope and James Webb Space Telescope observations with Rubin LSST data to search for the first known exoplanets beyond the Milky Way. The team will develop software that merges archival and Rubin photometry into long-baseline light curves capable of revealing planetary transits in nearby satellite galaxies.
In addition to producing an initial catalog of candidate extragalactic exoplanets, the project will publicly release the photometry software and provide interactive visualization tools that can support a wide range of future Rubin science beyond exoplanet searches.
Wandering Massive Black Holes in Rubin LSST
Principal Investigator: Charlotte Ward
Institution: Penn State UniversityAward Amount: $116,588
Not all massive black holes remain at the centers of galaxies. This project will search Rubin LSST data for evidence of “wandering” massive black holes—objects displaced from galactic nuclei by galaxy mergers and other dynamical processes. Detecting these systems would provide important insights into galaxy assembly, black hole evolution, and the growth of structure across cosmic time.
The project will combine Rubin observations with advanced analysis techniques to identify promising candidates, characterize their variability, and develop methods that can be applied throughout the Rubin survey. The resulting software and analysis framework will support future investigations of one of the least-explored populations of massive black holes.
AI-Powered Archaeology of the Cosmic Web with Dwarf Galaxies
Principal Investigator: Charlotte Welker
Institution: City University of New York, City TechAward Amount: $40,000
This project will develop machine learning methods to identify and characterize the large-scale structure of the Universe in Rubin LSST’s earliest data releases. By combining Rubin observations with state-of-the-art artificial intelligence techniques, the team will investigate how galaxies trace the underlying cosmic web and develop tools that can be applied throughout the Rubin survey.
The project will produce open software and analysis workflows that enable researchers to study cosmic structure more efficiently while preparing the community for increasingly large Rubin datasets. These tools will help transform early Rubin observations into new insights about galaxy evolution and the growth of cosmic structure.