1 citations · 1 across the 3 of their papers we have counts for
14 papers
Population synthesis and detection prospects for Galactic long-period transients with LISA
Arthur G. Suvorov, Nikolaos Karnesis, Valeriya Korol
The recently-discovered long-period radio transients represent a puzzling new class of astrophysical sources, some of which are thought to be compact binary systems emitting a puls…
Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly
Argyro Sasli, Nikolaos Karnesis, Minas Karamanis +5
Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixt…
Inferring the population properties of galactic binaries from LISA's stochastic foreground
Federico De Santi, Alessandro Santini, Alexandre Toubiana +2
Galactic binaries are expected to be the most numerous LISA sources and to produce a stochastic gravitational-wave foreground whose spectral shape encodes information about the und…
Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis
Argyro Sasli, Minas Karamanis, Nikolaos Karnesis +4
Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a ro…
Statistics of time and frequency-averaged spectra in gravitational-wave background searches
Quentin Baghi, Nikolaos Karnesis, Jean-Baptiste Bayle
Time series analysis from gravitational-wave detectors often relies on the assumption that time chunks, or frequency bins, are uncorrelated. We discuss the validity of this approxi…
Effect of noise characterization on the detection of mHz stochastic gravitational waves
Nikolaos Karnesis, Quentin Baghi, Jean-Baptiste Bayle +1
Pulsar timing arrays' hint for a stochastic gravitational-wave background (SGWB) leverages the expectations of a future detection in the millihertz band, particularly with the LISA…