Supernova Template Fitting for the Age of LSST
PI: Kaylee de Soto (Pennsylvania State 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 strategy. 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 that 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
PI: Meg Schwamb (Queen’s University Belfast)
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover over 6 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 must be developed by the Rubin user community 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.
