A High-Time Resolution Search for Compact Objects using Fast Radio Burst Gravitational Lens Interferometry with CHIME/FRB
Zarif Kader, Calvin Leung, Matt Dobbs, Kiyoshi W. Masui, Daniele, Michilli, Juan Mena-Parra, Ryan Mckinven, Cherry Ng, Kevin Bandura, Mohit, Bhardwaj, Charanjot Brar, Tomas Cassanelli, Pragya Chawla, Fengqiu Adam Dong,, Deborah Good, Victoria Kaspi, Adam E. Lanman

TL;DR
This paper presents a novel high-time resolution method using CHIME/FRB data to search for gravitational lensing signatures in fast radio bursts, aiming to detect or constrain compact objects like primordial black holes.
Contribution
The study introduces a new dechannelization and matched filtering technique to identify phase-coherent gravitational lensing signals in FRB data at nanosecond resolution.
Findings
No significant gravitational lensing detections in 172 FRB events.
Constraints on the abundance of compact objects in the mass range $10^{-4}-10^{4} M_{\odot}$.
Method demonstrates high sensitivity to lensing signatures at nanosecond timescales.
Abstract
The gravitational field of compact objects, such as primordial black holes, can create multiple images of background sources. For transients such as fast radio bursts (FRBs), these multiple images can be resolved in the time domain. Under certain circumstances, these images not only have similar burst morphologies but are also phase-coherent at the electric field level. With a novel dechannelization algorithm and a matched filtering technique, we search for repeated copies of the same electric field waveform in observations of FRBs detected by the FRB backend of the Canadian Hydrogen Mapping Intensity Experiment (CHIME). An interference fringe from a coherent gravitational lensing signal will appear in the time-lag domain as a statistically-significant peak in the time-lag autocorrelation function. We calibrate our statistical significance using telescope data containing no FRB signal.…
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.
