PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 13, 2026Astronomy and Astrophysics0 citationsOpen Access

Observing double white dwarfs with the Lunar GW Antenna

View Full Paper
GBG. BenettiMBM. BranchesiJHJ. Harms

Key Points

Key points are not available for this paper at this time.

Abstract

Context . The Lunar Gravitational Wave Antenna (LGWA) is a proposed gravitational-wave detector that will observe in the decihertz (dHz) frequency region. In this band, binary white dwarf systems are expected to merge, emitting gravitational waves. Detecting this emission opens new perspectives for understanding the Type Ia supernova progenitors and for investigating dense matter physics. Aims . In this paper, we present the capabilities of LGWA to detect and localize short-period double white dwarfs in terms of sky locations and distances. The analysis employs realistic spatial distributions and merger rates, as well as binary-mass distributions informed by population-synthesis models. Methods . The simulated population of double white dwarfs was generated using the SEBA stellar-evolution code, coupled with dedicated sampling algorithms. The performance of the LGWA detector, both in terms of signal detectability and parameter estimation, was assessed using standard gravitational-wave data analysis techniques, including Fisher matrix methods, as implemented in the GWFISH and LEGWORK codes. Results . The analysis indicates that, over 10 years of observation, LGWA could detect approximately 30 monochromatic Galactic sources and ten extragalactic mergers, demonstrating the unique potential of decihertz gravitational-wave detectors to access and characterize extragalactic double white dwarfs (DWDs) populations. This will open new avenues for understanding Type Ia supernova progenitors and the physics of DWDs.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Benetti et al. (2026) studied this question.

synapsesocial.com/papers/6a0ed1031c5e2d2319f9ea4fhttps://doi.org/10.1051/0004-6361/202556940
Ask AI
Helpful
Bookmark
Share
View Full Paper