Pith. sign in

REVIEW 2 cited by

Improving Detection of Gravitational wave Microlensing Using Repeated Signals Induced by Strong Lensing

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.03308 v3 pith:4XVAMPOU submitted 2021-10-07 astro-ph.HE gr-qc

Improving Detection of Gravitational wave Microlensing Using Repeated Signals Induced by Strong Lensing

classification astro-ph.HE gr-qc
keywords microlensinggravitationalinferencemassstellareffectsenhanceimprints
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Microlensing imprints by typical stellar mass lenses on gravitational waves are challenging to identify in the LIGO and Virgo frequency band because such effects are weak. However, stellar mass lenses are generally embedded in lens galaxies such that strong lensing accompanies microlensing. Therefore, events that are strongly lensed in addition to being microlensed may significantly improve the inference of the latter. We present a proof of principle demonstration of how one can use parameter estimation results from one strongly lensed signal to enhance the inference of the microlensing effects of the other signal with the Bayesian inference method currently used in gravitational wave astronomy. We expect this to significantly enhance our future ability to detect the weak imprints from stellar mass objects on gravitational-wave signals from colliding compact objects.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Identifying lensed gravitational waves with physics-informed posterior learning

    gr-qc 2026-07 conditional novelty 6.0

    Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.

  2. Parameter inference of millilensed gravitational waves using neural spline flows

    gr-qc 2025-05 conditional novelty 6.0

    Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.