LINCC Frameworks Incubator Awardees


The LINCC Frameworks Incubator Program supports teams of researchers to expand early-stage analysis software being developed as a part of LINCC Frameworks using their own scientific investigations. Here are four recent awardees with a description of their exciting projects. Interested in applying? View the Call for Proposals.

Supernova Template Fitting for the Age of LSST

Primary Investigator: Kaylee de Soto, Harvard University

Transient astrophysics probes the evolution of the Cosmos on truly human timescales. Of particular interest are supernovae, the explosive death of stars. The Vera Rubin Observatory’s LSST will discover thousands of supernovae (and other transients) every night. Given these high event rates, rapid inference tools are essential in quick classification and determining appropriate follow-up strategies. In this project, we will enable real-time fitting of transient-like light curves discovered with the Rubin Observatory using a simple parametric model. We will compare the use of variational inference, MCMC, and nested sampling in terms of accuracy, precision, and computational cost/speed for these models. Using a neural network, the posteriors of the fitted parameters of our empirical model will be mapped to physical parameters. This method of feature extraction will be incorporated as a filter in the ANTARES Broker.

Partnering with the LINCC Frameworks team will enable us to scale up these inference techniques to LSST data rates and provide a flexible community tool. Via module design, we hope our framework can incorporate user-defined models and new inference techniques as they are developed. This work will require a combination of algorithmic, scalability, productionization, and machine learning experience.

Optimizing an LSST Solar System Simulator

Primary Investigator: Meg Schwamb, Queen’s University Belfast

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover more than six million new Solar System bodies. This is an order of magnitude more objects than are currently known today in each of the Solar System’s small body reservoirs. LSST will go beyond just discovery; with a 10-year baseline, the survey will be able to measure broad-band optical colors and phase curves and capture episodes of cometary activity, orbit changes, rotational breakup events, and rotational brightness variations. Planetesimals are the bricks and mortar left over after the construction of planets. Their compositions, shapes, densities, rotation rates, and orbits help reveal their formation history, the conditions in the planetesimal-forming disk, and the processes active in the Solar System today. LSST will transform our current view of the Solar System and let us peer back into the Solar System’s past like never before.

The LSST Solar System Science Collaboration (SSSC) has identified key software products/tools that the Rubin user community must develop to achieve the planetary community’s LSST science goals. Near the top of the SSSC’s software roadmap is a Solar System survey simulator to enable comparisons of model small body orbital and size/brightness distributions to LSST discoveries. For the past several years, we have been developing an open-source community LSST Solar System Survey Simulator that takes a model Solar System small body population and uses the pointing history, observation metadata, and expected Rubin Observatory detection efficiency to output what LSST should find so that the numbers and types of simulated detections can be directly compared to the number and types of real small bodies found in the actual LSST survey.  We have developed use cases/user stories and a design for the overall code architecture that works for all Solar System populations that the SSSC/planetary community will want to compare to models and orbital/size/compositional maps, but we are struggling with scaling up our algorithms to LSST data rates and optimizing the code. Partnering with experts in data structures, databases, scalability, productionization, and code architecture through this LINCC Incubator will enable us to truly make the LSST Solar System Survey Simulator a real open-source, community-wide tool.

DeepDISC LSST: photo-z

Primary Investigator: Grant Merz, University of Illinois Urbana Champaign

With the help of the LINCC Frameworks team, we will implement our image-based models within RAIL and construct a pipeline for testing and comparing our framework to existing photo-z codes. We will test our implementation with DESC DC2 data and construct datasets for other image-based methods to use. We will provide a public repo with a modular design and community-oriented tools to promote the use of image-based photo-z methods.

With the first light of the Vera C. Rubin Observatory on the horizon, astronomers will be faced with unprecedented amounts of data at never-before-seen depths for ground-based observations. Photometric redshift, or photo-z, estimation is an important task in survey pipelines for cosmological analyses, as spectroscopic redshift measurements are too costly for large surveys such as LSST. Existing frameworks for photo-z estimation include template fitting methods, as well as machine/deep learning models. Image-based models are able to incorporate colors as well as morphology information into predictions.  In this project, we will implement image-based deep learning models for photo-z estimation within our DeepDISC framework. DeepDISC uses instance segmentation models to simultaneously detect, deblend, and classify objects within an image. It is an efficient method for extracting catalog-level information on large cutouts. We will adapt our existing models for photo-z estimation and interface them with LSST software RAIL, designed to facilitate the comparison of photo-z codes.

Integrating Robust Cross-Matching from the LSST: UK into the LINCC Framework

Primary Investigator: Tom J Wilson, University of Exeter

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will revolutionize many aspects of astronomy all by itself, but there are important outstanding questions for which the real benefits will come from the combining of LSST data with other datasets. However, the LSST will probe such depths, and unprecedented levels of source crowding, in the night sky that standard cross-matching algorithms, used to determine counterparts between two photometric catalogs, will begin to break down. The sheer density of detected LSST sources across the entire southern sky means that there will be something like, at minimum, two and upwards of ten randomly placed objects nearby each and every LSST source, which will confuse counterpart identification. Worse still, the levels of object crowding observed, especially in the Galactic plane, mean that the positions of objects recorded in catalogs begin to be affected by even fainter, undetected sources hiding in their footprints. To that end, as part of an in-kind contribution, we have developed novel cross-matching algorithms tuned for handling the crowded sky that the Rubin Observatory will reveal.

This Incubator program will investigate the integration of these cross-matches into the LINCC Framework’s software, offering robust counterparts in the crowded LSST sky. We will work with LINCC’s experts in catalog storage, database partitioning and querying, and scalable spatial analysis to create an efficient and effective way to run, store, and serve this new method of determining counterpart assignments to the wider Rubin community, maximizing the scientific return of LSST.