A Synoptic VLBI Technique for Localizing Non-Repeating Fast Radio Bursts with CHIME/FRB
Calvin Leung, Juan Mena-Parra, Kiyoshi Masui, Mohit Bhardwaj, P.J., Boyle, Charanjot Brar, Mathieu Bruneault, Tomas Cassanelli, Davor Cubranic,, Jane F. Kaczmarek, Victoria Kaspi, Tom Landecker, Daniele Michilli, Nikola, Milutinovic, Chitrang Patel, Andre Renard, Pranav Sanghavi

TL;DR
This paper presents a novel VLBI technique using CHIME/FRB for precise localization of non-repeating fast radio bursts, achieving arcsecond accuracy through synchronized, high-data-rate interferometry and in-field calibration.
Contribution
It introduces a synoptic VLBI method with synchronized CHIME telescopes for blind FRB detection and localization, demonstrating high-precision results and addressing key technical challenges.
Findings
Successful detection and localization of two FRBs with 2- and 25-arcsecond precision
Implementation of high-data-rate buffering and real-time triggering system
Calibration using in-field pulsar observations to reduce systematic errors
Abstract
We demonstrate the blind interferometric detection and localization of two fast radio bursts (FRBs) with 2- and 25-arcsecond precision on the 400-m baseline between the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and the CHIME Pathfinder. In the same spirit as very long baseline interferometry (VLBI), the telescopes were synchronized to separate clocks, and the channelized voltage (herein referred to as "baseband") data were saved to disk with correlation performed offline. The simultaneous wide field of view and high sensitivity required for blind FRB searches implies a high data rate -- 6.5 terabits per second (Tb/s) for CHIME and 0.8 Tb/s for the Pathfinder. Since such high data rates cannot be continuously saved, we buffer data from both telescopes locally in memory for s, and write to disk upon receipt of a low-latency trigger from the CHIME Fast Radio Burst…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
