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Ultrafast AI Agents for Fast Radio Transient Discovery
Ultrafast AI Agents for Fast Radio Transient Discovery
We are developing a novel fast radio transient discovery agent able to direct radio interferometer response in microseconds to maximize signal-to-noise ratio and trigger detailed data capture. This requires deploying a reinforcement learning agent on a field-programmable gate array (FPGA). This real-time system represents a major leap in autonomous, high-speed astronomical discovery, laying the groundwork for large-scale, next-generation radio surveys that can operate beyond human reaction limits.
Strategizing follow-up of electromagentic counterparts to gravitational waves
Strategizing follow-up of electromagentic counterparts to gravitational waves
We are developing, Sybil, a novel AI agent to identify kilonovae. The agent will use low-latency alert data from the International Gravitational Wave Network and the Interplanetary Network, and transient photometry from public surveys, ZTF and Rubin, to make follow-up recommendations in real time that maximizes a designated objective. The agent will be trained using reinforcement learning, which is necessary to ensure that the recommended strategies are optimal despite incomplete and evolving light curve information and given that constraints on physical models will only be available after the event is complete.
Random sample of ZTF SN Ia LCs augmented in real-time given the goal of minimizing SALT2 parameter uncertainties
Improving distance estimates using Type Ia supernovae
Improving distance estimates using Type Ia supernovae
Even as surveys have produced an unprecedented sample of thousands of spectroscopically confirmed SNe Ia, driving down statistical uncertainties, systematic uncertainties remain a major roadblock toward precision cosmology. As LSST promises to plough deeper at the expense of temporal resolution, it is important to have light-curves that maximize information for cosmology.
We design a novel framework for performing real-time science-driven follow-up and demonstrate it for strategizing photometric augmentation of Zwicky Transient Facility (ZTF) SNe Ia light-curves given the goal of minimizing SALT2 parameter uncertainties. We find a median improvement of 2-6% for SALT2 parameters and 3-11% for photometric redshift with 2-7 additional data points in g, r and/or i compared to random augmentation (Sravan et al. 2021).
Recommender Engine For Intelligent Transient Tracking
Recommender Engine For Intelligent Transient Tracking
While next-generation surveys like the Rubin Observatory (https://www.lsst.org/), monitoring the sky with unprecedented spatial and temporal resolution, are leading to transformative advances in our scientific understanding, the resulting data deluge have rendered traditional human-guided data collection and inference techniques impractical. In order to maximize the science potential of data collected and limited follow-up resources, autonomous systems reacting in real-time to simultaneously maximize diverse science goals are needed.
REFITT is a novel AI system that strategizes and coordinates transient follow-up in real-time, given the goal of most optimally augmenting the alert stream for science inference. Right: Follow-up recommendations on a random night of Rubin Observatory operations given 32,000 core-collapse supernova events of interest (Sravan et al. 2020).
Single and Binary Stripped-Envelope Supernova Progenitors
Single and Binary Stripped-Envelope Supernova Progenitors
The mechanisms driving removal of envelopes of stripped-envelope supernova progenitors is a key challenge to our understanding of massive star evolution. Comparing population-scale detailed stellar evolution models with a wide variety of observational constraints (serendipitous progenitor photometry, multi-band SN light-curves, X-ray/radio observations) not only constrain evolutionary channels but can also reveal unobserved populations.
Right: Probability distribution of progenitor and companion properties of SN 2016gkg (Sravan et al. 2018).
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