First search for ultralight dark matter with a space-based gravitational-wave antenna: LISA Pathfinder
Andrew L. Miller, Luis Mendes

TL;DR
This study conducted the first search for ultralight dark photon dark matter using LISA Pathfinder data, applying gravitational-wave search methods to set new upper limits on dark matter coupling in space-based observations.
Contribution
It demonstrates the application of ground-based gravitational-wave search techniques to space-based data for the first time, establishing a proof-of-concept for detecting persistent, monochromatic signals like dark matter.
Findings
No evidence of dark photon dark matter was found.
Set upper limits on dark photon/baryon coupling strength.
Validated robustness of search methods against data non-Gaussianities and gaps.
Abstract
We present here results from the first-ever search for dark photon dark matter that could have coupled to baryons in LISA Pathfinder, the technology demonstrator for a space-based gravitational-wave antenna. After analyzing approximately three months of data taken by LISA Pathfinder in the frequency range Hz, corresponding to dark photon masses of eV/, we find no evidence of a dark-matter signal, and set upper limits on the strength of the dark photon/baryon coupling. To perform this search, we leveraged methods that search for quasi-monochromatic gravitational-wave signals in ground-based interferometers, and are robust against non-Gaussianities and gaps in the data. Our work therefore represents a proof-of-concept test of search methods in LISA to find persistent, quasi-monochromatic signals, and shows our ability to…
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.
Taxonomy
TopicsRadio Astronomy Observations and Technology · Pulsars and Gravitational Waves Research · Computational Physics and Python Applications
