arXiv subjects

Nikolaos Karnesis

Publications and source records attributed to Nikolaos Karnesis.

At least 19 recordsLinked to original sources

arXiv2026

Effect of noise characterization on the detection of mHz stochastic gravitational waves

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 space mission. However, finding an SGWB with a single orbiting detector is challenging: It calls for cautious modelling of instrumental noise, which is also mainly stochastic. It was shown that agnostic noise reconstruction methods provide robustness in the detection process. We build on previous work to include more realistic instrumental simulations and additional degrees of freedom in the noise inference model and analyze the impact of LISA's sensitivity to SGWBs. Particularly, we model the two main types of noise sources with separate transfer functions and power spectral density spline fitting. We assess the detectability bounds and their dependence on the flexibility of the noise model and on the prior probability, allowing us to refine previously reported results.

gr-qc↗

arXiv2026

Population synthesis and detection prospects for Galactic long-period transients with LISA

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 pulse once per orbit. If this interpretation is correct, the orbital period--and therefore the gravitational-wave frequency--is directly encoded in the radio signal, which typically lies in the millihertz band. In this work, we explore whether these systems can be detected by the Laser Interferometer Space Antenna (LISA). Assuming that this phase-locking between radio pulses and orbital motion applies broadly across the population, we construct synthetic source catalogues informed by both observations and theoretical models of related systems, such as cataclysmic variables. We estimate that between $\sim 0.05\%$ and $\sim 6\%$ of long-period transients will be detectable within four years of observation with LISA, depending on astrophysical assumptions. For detectable systems, we find injected frequencies are recoverable to one part in $\sim 10^{5}$, amplitudes to within a factor $\sim 2$, and the sky positions to within $\sim$30 square degrees. Our results demonstrate that gravitational-wave observations can provide direct evidence for the binary nature of these sources and, importantly, can guide future radio surveys by predicting pulse periods, sky positions, and orbital properties. The mismatch between extracted frequency derivatives and that imposed by gravitational-wave decay is also computed to show that the likelihood of electromotive losses driving orbital evolution can be assessed for each detectable candidate.

astro-ph.HE↗

arXiv2026

Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly

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 mixture modeling, and reversible-jump Markov chain Monte Carlo solves it exactly but mixes slowly. Learned proposals are well established at fixed dimension, but whether they can accelerate the dimension-changing moves themselves has remained largely untested. We show that the answer has a structural origin: the optimal proposal for the dimension-changing birth move is a different object in different phases of the run. While the fit is being assembled it must match the current residual - a state-dependent quantity no state-independent network can represent - but at equilibrium it degenerates to the posterior's single-component marginal, which is exactly the distribution an adaptive normalizing flow learns from the sampler's own history. A learned state-independent birth proposal is therefore useless in one phase and optimal in the other. Controlled experiments confirm the attribution: applied with an exact Metropolis--Hastings correction that leaves the target invariant for any network, the learned births leave acceptance rates unchanged yet accelerate model-order mixing - in a ten-seed benchmark they meet a pre-specified stopping rule in six of ten runs, typically several times sooner, where a strong hand-tuned baseline meets it in one (one-sided p=0.03) - and an isolation experiment shows the same flow deployed within-model buys nothing. Making no domain-specific assumptions, the same sampler counts sources in a noisy image and reconstructs signals across scientific domains, including gravitational waves from ground- and space-based detectors and a scalp EEG recording. We release the method as HyperWave, an open-source package.

physics.data-an↗

arXiv2026

Inferring the population properties of galactic binaries from LISA's stochastic foreground

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 underlying population. Extracting this information with standard hierarchical methods is challenging due to the high dimensionality of the problem and the computational cost of global-fit analyses. We present a simulation-based inference framework to measure the population properties of galactic binaries directly from the reconstructed foreground. Adopting an astrophysically agnostic parametrization in the observable space -- defined by signal amplitude, frequency, and frequency derivative -- we generate synthetic catalogs and foreground spectra using a global-fit-inspired subtraction algorithm. We then train a neural posterior estimator to map spectra to population parameters. We validate our method on simulated data and recover population parameters with good accuracy, including the total number of binaries. As a by-product, we present a GPU-accelerated version of the subtraction algorithm, which delivers a ~100X speed-up compared to previous implementations in the literature. Our results demonstrate that LISA's stochastic foreground alone carries significant information about the Galactic binary population and provide a practical step toward joint inference from resolved and unresolved sources.

astro-ph.HE↗

arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

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 robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗

arXiv2026

Statistics of time and frequency-averaged spectra in gravitational-wave background searches

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 approximation in the context of searches for stochastic gravitational-wave backgrounds. We examine the impact of averaging over time and frequency, a reduction technique commonly employed to minimize the computational expense of likelihood evaluations. We introduce an analytical tool based on Fisher information to quantify the error in parameter inference arising from ignoring these effects. Finally, we address the issue of locally stationary processes and optimal time chunking.

gr-qc↗

arXiv2025

Extraction of gravitational wave signals from LISA data in the presence of artifacts

The Laser Interferometer Space Antenna (LISA) mission is being developed by ESA with NASA participation. As it has recently passed the Mission Adoption milestone, models of the instruments and noise performance are becoming more detailed, and likewise prototype data analyses must as well. Assumptions such as Gaussianity, stationarity, and data continuity are unrealistic, and must be replaced with physically motivated data simulations, and data analysis methods adapted to accommodate such likely imperfections. To this end, the LISA Data Challenges have produced datasets featuring time-varying and unequal constellation armlength, and measurement artifacts including data interruptions and instrumental transients. In this work, we assess the impact of these data artifacts on the inference of Galactic Binary and Massive Black Hole properties. Our analysis shows that the treatment of noise transients and gaps is necessary for effective parameter estimation, as they substantially corrupt the analysis if unmitigated. We find that straightforward mitigation techniques can significantly if imperfectly suppress artifacts. For the Galactic Binaries, mitigation of glitches was essentially total, while mitigations of the data gaps increased parameter uncertainty by approximately 10%. For the Massive Black Hole binaries the particularly pernicious glitches resulted in a 30% uncertainty increase after mitigations, while the data gaps can increase parameter uncertainty by up to several times. Critically, this underlines the importance of early detection of transient gravitational waves to ensure they are protected from planned data interruptions.

gr-qc↗

arXiv2024

An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called ``Erebor,'' designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog $\sim12000$ Galactic Binaries ($\sim8000$ as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

gr-qc↗

arXiv2024

The interacting double white dwarf population with LISA; stochastic foreground and resolved sources

In this work, we investigate the impact of tidal torques and mass transfer on the population of double white dwarfs (DWDs) that will be observed with LISA. Starting from a distribution of DWDs at formation predicted by numerical simulations, we use a semi-analytical model to evolve DWDs under different hypotheses for the efficiency of tidal coupling and the birth spins of white dwarfs. We then estimate the stochastic foreground and the population of resolvable binaries for LISA in each scenario. Our predicted DWD binary distribution can differ substantially from the distribution expected if only gravitational waves (GWs) are considered. If white dwarfs spin slowly, then we predict an excess of systems around a few mHz, due to binaries that outspiral after the onset of mass transfer. This excess of systems leads to differences in the confusion noise, which are most pronounced for strong tidal coupling. In that case, we find a significantly higher number of resolvable binaries than in the GW-only scenario. If instead white dwarfs spin rapidly and tidal coupling is weak, then we find no excess around a few mHz, and the confusion noise due to DWDs is very small. In that scenario, we also predict a subpopulation of outspiralling binaries below 0.1 mHz. Using the Fisher matrix approximation, we estimate the uncertainty on the GW-frequency derivative of resolvable systems. We estimate that, even for non-accreting systems, the mismodelling error due to neglect of effects other than GWs is larger than the statistical uncertainty, and thus this neglect would lead to biased estimates for mass and distance. Our results highlight the need for flexible tools in LISA data analysis. Because our semi-analytical model hinges upon a simplistic approach to determining the stability of mass accretion it will be important to deepen our comprehension of stability in mass-transferring DWD binaries.

astro-ph.SR↗

arXiv2024

Characterization of non-Gaussian stochastic signals with heavier-tailed likelihoods

Future Gravitational Wave observatories will give us the opportunity to search for stochastic signals of astrophysical, or even cosmological origins. However, parameter estimation and search will be challenging, mostly due to the overlap of multiple signal components, as well as the potentially partially unknown properties of the instrumental noise. In this work, we propose a robust statistical framework based on heavier-tailed likelihoods for the characterization of stochastic gravitational-wave signals. In particular, we use the symmetric hyperbolic likelihood, which allows us to probe the signal spectral properties and simultaneously test for any departures from Gaussianity. We demonstrate this methodology with synthetic data from the future LISA mission, where we estimate the potential non-Gaussianities induced by the unresolved Ultra Compact Galactic Binaries.

gr-qc↗

arXiv2024

Magnetic Field Simulation and Correlated Low-Frequency Noise Subtraction for an In-Orbit Demonstrator of Magnetic Measurements

In recent years, nanosatellites have revolutionized the space sector due to their significant economic and time-saving advantages. As a result, they have fostered the testing of advanced instruments intended for larger space science missions. The case of MELISA is presented in this work. MELISA is a magnetic measurement instrument which aims at demonstrating the in-orbit performance of AMR sensors featuring dedicated noise reduction techniques at sub-millihertz frequencies. Such low frequency ranges are relevant for future space-borne gravitational wave detectors, where the local magnetic environment of the satellite might yield a significant contribution to the overall noise budget of the observatory. The demanding magnetic noise levels required for this bandwidth, down to 0.1 mHz, make measurements arduous. To explore sensing solutions within the H2020 European Commission Programme with the involvement of ESA, the functional performance of MELISA-III will be validated in-orbit. During operations, there is the possibility to measure the low-frequency magnetic contribution stemming from orbiting the Earth's magnetic field, impeding the characterization of the intrinsic performance of the sensor. With the objective of minimizing excess noise during the in-flight operations, the present research aims to simulate the environmental magnetic conditions in LEO to identify and subtract undesired contributions to the measurements. The in-orbit long-term magnetic fluctuations are replicated using a Helmholtz coil system. A fluxgate magnetometer allows the correlation of the generated field with the payload measurements, leading to the subsequent subtraction. Proving the effect of this approach will facilitate the noise characterization of magnetic sensors in LEO, paving the way for the in-orbit validation of MELISA-III for use in magnetically demanding missions with long integration times.

astro-ph.IM↗

arXiv2024

Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA

Neutron star-white dwarf (NS+WD) binaries offer a unique opportunity for studying NS-specific phenomena with gravitational waves. In this paper, we employ the binary population synthesis technique to study the Galactic population of NS+WDs with the future Laser Interferometer Space Antenna (LISA). We anticipate approximately $\mathcal{O}(10^2)$ detectable NS+WDs by LISA, encompassing both circular and eccentric binaries formed via different pathways. Despite the challenge of distinguishing NS+WDs from more prevalent double white dwarfs in the LISA data (especially at frequencies below 2 mHz), we show that their eccentricity and chirp mass distributions may provide avenues to explore the NS natal kicks and common envelope evolution. Additionally, we investigate the spatial distribution of detectable NS+WDs relative to the Galactic plane and discuss prospects for identifying electromagnetic counterparts at radio wavelengths. Our results emphasise LISA's capability to detect and characterise NS+WDs and to offer insights into the properties of the underlying population. Our conclusions carry significant implications for shaping LISA data analysis strategies and future data interpretation.

astro-ph.HE↗

arXiv2024

Neural density estimation for Galactic Binaries in LISA data analysis

The future space based gravitational wave detector LISA (Laser Interferometer Space Antenna) will observe millions of Galactic binaries constantly present in the data stream. A small fraction of this population (of the order of several thousand) will be individually resolved. One of the challenging tasks from the data analysis point of view will be to estimate the parameters of resolvable galactic binaries while disentangling them from each other and from other gravitational wave sources present in the data. This problem is quite often referred to as a global fit in the field of LISA data analysis. A Bayesian framework is often used to infer the parameters of the sources and their number. The efficiency of the sampling techniques strongly depends on the proposals, especially in the multi-dimensional parameter space. In this paper we demonstrate how we can use neural density estimators, and in particular Normalising flows, in order to build proposals which significantly improve the convergence of sampling. We also demonstrate how these methods could help in building priors based on physical models and provide an alternative way to represent the catalogue of identified gravitational wave sources.

gr-qc↗

arXiv2024

LISA Definition Study Report

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to distant massive black-hole mergers and the expansion of the Universe. This definition study report, or Red Book, presents a summary of the very large body of work that has been undertaken on the LISA mission over the LISA definition phase.

astro-ph.CO↗

arXiv2023

Exploring the Potential for Detecting Rotational Instabilities in Binary Neutron Star Merger Remnants with Gravitational Wave Detectors

We explore the potential for detecting rotational instabilities in the post-merger phase of binary neutron star mergers using different network configurations of upgraded and next-generation gravitational wave detectors. Our study employs numerically generated post-merger waveforms, which reveal the re-excitation of the $l=m=2$ $f$-mode at a time of $O(10{\rm})$ms after merger. We evaluate the detectability of these signals by injecting them into colored Gaussian noise and performing a reconstruction as a sum of wavelets using Bayesian inference. Computing the overlap between the reconstructed and injected signal, restricted to the instability part of the post-merger phase, we find that one could infer the presence of rotational instabilities with a network of planned 3rd-generation detectors, depending on the total mass and distance to the source. For a recently suggested high-frequency detector design, we find that the instability part would be detectable even at 200 Mpc, significantly increasing the anticipated detection rate. For a network consisting of the existing HLV detectors, but upgraded to twice the A+ sensitivity, we confirm that the peak frequency of the whole post-merger gravitational-wave emission could be detectable with a network signal-to-noise ratio of 8 at a distance of 40Mpc.

gr-qc↗

arXiv2023

Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data

In recent years, the field of Gravitational Wave Astronomy has flourished. With the advent of more sophisticated ground-based detectors and space-based observatories, it is anticipated that Gravitational Wave events will be detected at a much higher rate in the near future. One of the future data analysis challenges is performing robust statistical inference in the presence of detector noise transients or non-stationarities, as well as in the presence of stochastic Gravitational Wave signals of possible astrophysical and/or cosmological origin. The incomplete knowledge of the total noise of the observatory can introduce challenges in parameter estimation of detected sources. In this work, we propose a heavy-tailed, Hyperbolic likelihood, based on the Generalized Hyperbolic distribution. With the Hyperbolic likelihood we obtain a robust data analysis framework against data outliers, noise non-stationarities, and possible inaccurate modeling of the noise power spectral density. We apply this methodology to examples drawn from gravitational wave astronomy, and in particular to synthetic data sets from the planned LISA mission.

gr-qc↗

arXiv2023

Eryn : A multi-purpose sampler for Bayesian inference

In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built {\tt Eryn}, a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the LISA global fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases.

astro-ph.IM↗

arXiv2023

The Laser Interferometer Space Antenna mission in Greece White Paper

The Laser Interferometer Space Antenna (LISA) mission, scheduled for launch in the mid-2030s, is a gravitational wave observatory in space designed to detect sources emitting in the millihertz band. LISA is an ESA flagship mission, currently entering the Phase B development phase. It is expected to help us improve our understanding about our Universe by measuring gravitational wave sources of different types, with some of the sources being at very high redshifts $z\sim 20$. On the 23rd of February 2022 we organized the 1$^\mathrm{st}$ {\it LISA in Greece Workshop}. This workshop aimed to inform the Greek scientific and tech industry community about the possibilities of participating in LISA science and LISA mission, with the support of the Hellenic Space Center (HSC). In this white paper, we summarize the outcome of the workshop, the most important aspect of it being the inclusion of $15$ Greek researchers to the LISA Consortium, raising our total number to $22$. At the same time, we present a road-map with the future steps and actions of the Greek Gravitational Wave community with respect to the future LISA mission.

gr-qc↗