Science Catalyst Grants maximize the scientific impact of Rubin Observatory’s LSST by supporting bold ideas, inclusive participation, and interdisciplinarity. Below are our 2021 awardees.
Peculiar Velocities Cosmology with LSST Type Ia Supernovae
Awardee: Bastien Carreres
This award will support Bastien Carreres, PhD student, who is using measurements of Supernovae to understand the late-time universe, its expansion, and the role of gravity. Thanks to this award, he will attend the next in-person LSST-DESC collaboration meeting, present his work, and visit collaborators in the US for a month-long period. This is a great opportunity for him to build a strong international network and bring his project to maturity.
Joint Modelling of Astrophysical Systematics for Cosmology with LSST Weak Lensing and Galaxy Clustering
Awardees: Danielle Leonard, Francois Lanusse, Jonathan Blazek, Niko Sarcevic
We are working to develop new tools that jointly model the intrinsic alignment of galaxies, photometric redshift uncertainties, and galaxy bias using shared parameters. This will enable more robust cosmological constraints with LSST data.
Cluster Dark Matter Maps with the World’s Largest Survey Telescope
Awardee: John Peterson
We will prepare to make the most detailed dark matter maps using weak lensing of galaxy clusters with the Rubin observatory. We will assess the weak lensing systematics derived from a simulated stack of Rubin observations using the Photon Simulator (PhoSim) of a cluster of galaxies using modern analysis pipelines.
Going from Thousands to Millions: Visualizing the Solar System in the Era of the Rubin Observatory
Awardees: Meg Schwamb, Grigori Fedorets
The Vera C. Rubin Legacy Survey of Space and Time (LSST) will discover and monitor over 5 million Solar System objects. In total, this will generate a database of approximately a billion photometric and astrometric measurements. With the impending release of a full mock database of Solar System detections representative of 10 years of LSST operations, an undergraduate summer intern based at Queen’s University Belfast, collaborating with Dr. Meg Schwamb and Dr. Grigori Fedorets, will develop an opensource python package to visualize the orbits and properties of millions of data points to enable the planetary community and the LSST Solar System Science Collaboration (SSSC) to dive into the LSST Solar System data.
Approaches to Mitigating the Galaxy-Galaxy Blend Problem in LSST
Awardees: Camille Avestruz, Jeffrey Regier, Ismael Mendoza
Ismael works on two complementary approaches to mitigating the blending problem in LSST DESC analyses, namely: (1) community software to train and evaluate deblending algorithms and (2) a fully probabilistic algorithm that enables uncertainty propagation in galaxy measurements. The proposal encompasses a v1.0 code release for the Blending Toolkit (a DESC tool), the journal publication and code release of BLISS (a deep neural network approach to probabilistic deblending), and a DESC note illustrating BLISS incorporated into the Blending Toolkit.
Galaxy Bias Modeling for LSST
Awardees: David Alonso, Anze Slosar, Andrina Nicola
In this project, we will make use of realistic cosmological simulations to characterize a set of novel galaxy bias models, quantifying the range of scales over which they can be used given the expected statistical power of LSST, quantifying the corresponding achievable cosmological constraints with each of them. The most promising bias schemes resulting from this exercise will also be incorporated into the DESC analysis pipelines.
Stress-Testing Multimodal Photometric Redshift Posteriors in the Extrapolative Regime
Awardees: Peter Hatfield, Alex Malz, Natalia Stylianou
This project will test Machine learning photometric redshift codes on a realistic LSST-like data set under scenarios where training and test data have dramatically different distributions in color-magnitude space to investigate if multimodal posterior PDFs can be recovered.
Searching for Black Holes with Gravitational Microlensing with Rubin LSST
Awardees: Jessica Lu and Natasha Sarah Abrams
We propose to explore how the Rubin survey can be used to search for microlensing events, particularly free-floating black holes in the Milky Way when they gravitational lens a background star. We will estimate the impact of various survey scenarios on microlensing searches, using population synthesis models to estimate candidate black hole yields with Rubin and testing microlensing event identification pipelines from ZTF on simulated Rubin data.
Artificial Intelligence Solutions to Transient Discovery in Rubin LSST
Awardee: Tatiana Acero Cuellar
Tatiana Acero Cuellar, Master’s student at Universidad Nacional de Colombia and Summer Intern at the University of Delaware, is leveraging Artificial Intelligence to simplify the process of detecting transients in astrophysical images. Traditionally, we need to subtract a sky template from each survey image to see if anything has changed brightness, but this is an extremely computationally expensive process, especially for Rubin LSST, which will collect 500-800 3.2 Gigapixel images every night! Tatiana is training neural networks to bypass this expensive process altogether.
A MAF Classifier for Fast Transients and Related Cadence Considerations
Awardee: Ming Lian
Rubin LSST will be able to discover thousands of transients every night. Some of them will be rare, unusual, or even entirely unknown phenomena. Particularly interesting are those transients that change rapidly, but to truly understand the nature of phenomena that change on hours to days time scales, the Rubin LSST observations will have to be augmented by timely follow-up photometry and spectroscopy at other telescopes. Astrophysicists distinguish transients from one another based on their color and rate of brightness change. Ming Lian, a graduate student at the University of Delaware, is mapping the known transients in the space of color and rate of change to allow us to test if simulated Rubin observing strategies allow us to recognize those unusual transients from the rest immediately.
Investigating Galaxy Intrinsic Alignment and its Mitigation with LSST-DESC Tools
Awardees: Mustapha Ishak Boushaki, Leonel Medina Varela, Eske Pedersen
We will adapt and use LSST-DESC tools such as TXPipe and Core Cosmology Library in order to extract intrinsic alignments of galaxies from precursory data sets such as DES and KIDS surveys. Removing this astrophysical systematic will provide more accurate measurements of cosmic shear from LSST.
Discovering True Anomalies via Proper Motion
Awardees: Will Clarkson, Fabio Ragosta, Maria Teresa Botticella
True anomalies – objects and even populations whose existence have heretofore not been predicted from observation and/or theory – are among the most important potential scientific contributions of the Rubin Observatory LSST survey. This award will support the finalization of investigations into the sensitivity of the LSST to true anomalies discovered and measured by virtue of their kinematics and explore the implications for survey strategies that maximize sensitivity to these objects.
Student Development of the Solar System Notification Alert Processing System (SNAPS)
Awardees: Michael Gowanlock, David E. Trilling, Daniel R. Kramer
We will complete our first round of development on SNAPS, the Solar System Notification Alert Processing System, with several tasks related to connecting our existing back-end database to our prototype front-end web server. The result will be the release of SNAPS v1.0, a fully functional tool that serves value-added data on ZTF-detected Solar System objects.
Simulating the Impact of Blending on Crowded Field Photometry
Awardees: Catalina Vanessa Zamora, Stephen Portillo, Andrew J. Connolly
Characterizing the performance of the LSST Science Pipelines in crowded stellar fields is critical to Milky Way and Local Volume science with Rubin Observatory. We are simulating and fitting images of pairs of point sources with varying separations, flux ratios, and background levels to produce fitting formulae for the impact of blending in crowded stellar fields.
The New Photometric Model of M-Dwarf Flares for LSST
Awardees: Matthew Graham, Kostya Novoselov, Gautham Narayan Qifeng Cheng, Ved Shah Gautam
Award: $5,000 x 2
Students Qifeng Cheng and Ved Shah Gautam will work on the second version of the LSSTC Enabling Science Supported- PLAsTiCC project, working in close conjunction with the Dark Energy Science Collaboration, Transient and Variable Star Science Collaboration and community alert broker teams. They will be simulating LSST alerts from Changing-Look AGN (Qifeng) and M-dwarf flares (Ved) for a public classification challenge starting in Fall 2021, as well as developing machine-learning methods to classify the PLAsTiCC alerts.
Bridging the Gap: Analysis of Clustering and Weak Lensing using Precursor Data Sets (2021-02)
Awardees: Andrina Nicola, Eusebio Sánchez, Ignacio Sevilla, David Sánchez, Francisco Javier Sánchez
Understanding our current model of the Universe is one of the main goals of the Vera Rubin Observatory Legacy Survey of Space and Time. In our project, we will help pave the way for this study using state-of-the-art pipelines and data sets close to those the project will employ in a few years.
Building Deep Learning Engine for AGN Light-Curves
Awardees: Iva Čvorović‐Hajdinjak, Isidora Jankov, Nemanja Rakić, Paula Sánchez Sáez, Dragana Ilic, Andjelka B. Kovačević, Viktor Radović, Robert Nikutta
The LSST Exploring transient optical sky-science opportunity No. 14 focuses on LSST light curves (LC) of active galactic nuclei (AGN) for photometric reverberation mapping (PhotoRM). We are building a deep learning engine (DLE) for AGN-LC nonparametric modeling and implementing the PhotoRM procedure to respond to the LSST operations, be adaptable to non-AGN LC, and be tested on LSST Data Previews.
LSST Precursor Study of Globular Clusters Around Low Surface Brightness Galaxies
Awardees: Alex Drlica-Wagner, Burçin Mutlu-Pakdil, Louise Gagnon
The Rubin Observatory LSST will open an unprecedented window into the low surface brightness Universe. The masses and distances of low surface brightness galaxies can be estimated from the abundances and luminosity functions of the globular cluster populations that they host. This grant will support an undergraduate student in investigating the potential for the Rubin Observatory LSST to detect and measure globular clusters associated with low-surface-brightness galaxies by developing and testing tools on precursor data from the Dark Energy Survey.
Robust Census of Long-Period Solar System and Interstellar Objects with LSST
Awardees: Antonio Vanzanella, Laura Inno, Tansu Daylan
We will develop a machine learning framework to identify the faint, slow-moving objects in the presence of systematics from artificial satellites and reconstruct their trajectories in order to reveal their dynamics and bulk characteristics. Our machine-learning pipeline will enable the characterization of the underlying occurrence of long-period SS bodies using LSST.
Creating a Bayesian Inference Engine to Enable Solar System Science in Year One Operations of LSST
A Solar System LSST survey simulator is one of the Solar System Science Collaboration’s highest software priorities in preparation for Year One of LSST Operations. With support from the LSSTC Enabling Science Committee, University of Canterbury (New Zealand) student Rosemary Dorsey will construct a Python-based Bayesian inference engine as a general-purpose post-processing module for the prospective SSSC LSST survey simulator — available for use by all SSSC members prior to first light.
Consistent Redshifts from Consistent Colors: A Preliminary Investigation with DESC DC2 Image Simulations
Awardees: Arun Kannawadi, Robert Lupton
Support a student project in understanding the ability of photometric redshift codes in handling consistent colors. The colors are largely independent of the observing conditions but have a definition of galaxy color that is different from what is more commonly used.
Virtual Internship in Rubin/LSST Science to Provide Research Experience to Undergraduate Students in Colombian Institutions
Awardees: Andrés A. Plazas Malagón, Michael Strauss, Luis Henry Quiroga, Javier Gonzalez Sanchez, Nicolás Garavito-Camargo, Craig Lage
This award partially funded the virtual research program RECA Internship 2022, which seeks to give research experience to undergraduate students from Colombian institutions in astronomy, physics, engineering, and related areas to help further their careers and make them more competitive for graduate school in astronomy internationally. The students worked on five projects related to LSST science, and their results were presented in a report and a Jupyter notebook that will serve as a tutorial for future LSST users. In addition to gaining research experience, the students also participated in technical development activities (e.g., a one-week Python boot camp, remote observing at telescopes in the Canary Islands) as well as professional development lectures (including talks about equity, diversity, and inclusion in astronomy and the state of astronomy in Colombia). This astronomy research internship for undergraduate students is one of the first of its kind (paid) in Colombia, if not the first, and served to promote the development of astronomy in Colombia and South America and strengthen the links between Rubin/LSST and the South American astronomical community in preparation for Rubin operations and LSST science.
Detecting Tidal Features to Uncover Galaxy Interactions
Awardees: Sarah Brough, Francois Lanusse
A final-year undergraduate student in Australia will develop a Machine Learning algorithm to identify and classify tidal features around galaxies in LSST. The student will be co-supervised by members of the Galaxies and Informatics and Statistics Science Collaborations.
Intrinsic Alignment Mock Catalogs for Model Exploration and Pipeline Validation
Awardees: Nick Van Alfen, Danielle Leonard, Francois Lanusse, Jonathan Blazek, Niko Sarcevic
This project will produce simulated galaxy catalogs with realistic galaxy shapes and orientations. These catalogs, which will be available to the LSST community, will enable a range of studies to better understand the “intrinsic alignments” of galaxies and to validate mitigation strategies needed for robust cosmology with weak lensing and galaxy clustering.
Translating a TESS-Tested Eclipsing Binary Preclassifier to LSST: Testing Detection Efficiencies with the PLAsTiCC Dataset
Awardees: Kevin Covey, James Davenport, Erin Howard
We will test methods to detect and classify EBs in LSST light curves robustly. We will first adapt an existing pre-classifier that detects and flags EB candidates in TESS light curves, using the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) dataset to optimize the code’s metrics and thresholds for LSST data. We will also use the synthetic EBs in the PLAsTiCC data to build out the architecture for a Convolutional Neural Network that can detect EBs in the LSST dataset to be ready for training with LSST commissioning data.
Identifying and Analyzing the Properties of Clusters of Galaxies in DPO
Awardees: Michel Aguena, Julia Gschwend, Christophe, Luiz Costa
LineA has created a project to improve the performance of WaZP, a galaxy cluster finder, and to study the evolution of the red-sequence of clusters, using an upgraded version of the portal infrastructure developed by LineA’s team to host science workflows. The plan is to select two undergraduate students in physics and computer science to participate in our efforts to adapt and scale our infrastructure to the LSST data using DC2 as a test case.
An LSST Observing Strategy Metric for Photometric Redshifts
Awardees: Melissa Graham, Murtaza Jafry
An LSST Observing Strategy Metric for Photometric Redshifts Melissa Graham and Murtaza Jafry, University of Washington. This student research project will build on existing software to provide an analysis of the photometric redshift quality expected from various LSST observing strategies that is robust, detailed, and presented in a journal publication. This work will also produce PZ-related software that will be available for all to use and should ultimately help optimize the extragalactic science impact of the LSST.
Clone of Alert Broker Filtering for an LSST-like Deep Drilling Survey
Awardees: Melissa Graham and Thomas Kennedy
Alert Broker Filtering for an LSST-like Deep Drilling Survey Melissa Graham and Thomas Kennedy, University of Washington. This student research project will contribute to the characterization and validation of alerts from the new Rubin-pathfinder program “Deep Drilling in the Time Domain with DECam,” to the scientific analysis of this rich dataset of detections at different depths and timescales and the program’s first results paper.
A New Tool for the Bolometric Luminosity and Color Evolution of Supernovae and Other Extragalactic Transients
Selecting an LSST Platinum Galaxy Sample
Catalog Distortion Correction for an LSST-Like Imager
Hyper-resolution, Sub-band Image Deconvolution for LSST
Leveraging Solar System Science with Advanced Dynamical Characterization