LISA Data Research Articles

Observing kinematic anisotropies of the stochastic background with LISA

We propose a diagnostic tool for future analyses of stochastic gravitational wave background signals of extra-galactic origin in LISA data. Next-generation gravitational wave detectors hold the capability to track unresolved gravitational waves bundled into a stochastic background. This composite background contains cosmological and astrophysical contributions, the exploration of which offers promising avenues for groundbreaking new insights into very early universe cosmology as well as late-time structure formation. In this article, we develop a full end-to-end pipeline for the extraction of extra-galactic signals, based on kinematic anisotropies arising from the galactic motion, via full-time-domain simulations of LISA's response to the gravitational wave anisotropic sky. Employing a Markov-Chain-Monte-Carlo map-making scheme, multipoles up to ℓ=2 are recovered for scale-free spectra in the case of a high signal-to-noise ratio. We demonstrate that our analysis is consistently beating sample variance and is robust against statistical and systematic errors. The impact of instrumental noise on the extraction of kinematic anisotropies is investigated, and we establish a detection threshold of Ω GW ≳ 5 × 10-8 in the presence of instrument-induced noise. Potential avenues for improvement in our methodology are highlighted.

Clock synchronization and light-travel-time estimation for space-based gravitational-wave detectors

Abstract Space-based gravitational-wave detectors, such as LISA, record interferometric measurements on widely separated satellites. Their clocks are not synced actively. Instead, clock synchronization is performed in on-ground data processing. It relies on measurements of the so-called pseudoranges, which entangle the interspacecraft light travel times with the clock desynchronizations between emitting and receiving spacecraft. For interspacecraft clock synchronization, we need to isolate the differential clock desynchronizations, i.e., disentangle the pseudoranges. This further yields estimates for the interspacecraft light travel times, which are required as delays for the laser frequency noise suppression via time-delay interferometry. Previous studies on pseudorange disentanglement apply various simplifications in the pseudorange modeling and the data simulation. In contrast, this article derives an accurate pseudorange model in the barycentric celestial reference system, complemented by realistic state-of-the-art LISA data simulations. Concerning pseudorange disentanglement, this leads to an a priori under-determined system. We demonstrate how on-ground orbit determinations, as well as onboard transmission and on-ground reception time tags of the telemetry data, can be used to resolve this degeneracy. We introduce an algorithm for pseudorange disentanglement based on a nonstandard Kalman filter specially designed for clock synchronization in systems where pseudorange measurements are conducted in different time frames. This algorithm achieves interspacecraft clock synchronization and light travel time estimation with submeter accuracy, thus fulfilling the requirements of time-delay interferometry.

The interacting double white dwarf population with LISA: Stochastic foreground and resolved sources

Aims. We investigate the impact of tidal torques and mass transfer on the population of double white dwarfs that will be observed with LISA. Methods. Our Galactic distribution of double white dwarfs is based on the combination of a cosmological simulation and a binary population synthesis model. We used a semi-analytical model to evolve double white dwarf binaries considering ten different hypotheses for the efficiency of tidal coupling and three hypotheses for the birth spins of white dwarfs. We then estimated the stochastic foreground and the population of resolvable binaries for LISA for these different combinations. Results. Our predicted double white dwarf 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 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 double white dwarfs is very low. 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 find that, even for non-accreting systems, the mismodelling error due to neglecting effects other than GWs is larger than the statistical uncertainty, and thus this neglect would lead to biased estimates for mass and distance. Conclusions. Our results suggest that the population of double white dwarfs is likely to be different from what is expected in the standard picture, which highlights 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 double white dwarf binaries.

Neural density estimation for Galactic binaries in the LISA data analysis

Neural density estimation for Galactic binaries in the LISA data analysis

GPU-accelerated semicoherent hierarchical search for stellar-mass binary inspiral signals in LISA

Searching for gravitational waves from stellar-mass binary black holes with LISA remains a challenging open problem. Conventional template-bank approaches to the search are impossible due to the prohibitive number of templates that would be required. This paper continues the development of a hierarchical semicoherent stochastic search, extending it to a full end-to-end pipeline that is then applied to multiple mock LISA data streams which include simulated noise. Particle swarm optimization is used as a stochastic search algorithm, tracking multiple maxima of a semicoherent search statistic defined over source parameter space. The pipeline is accelerated by the use of graphical processing units (GPUs). No prior information from observations by ground-based detectors is used; this is necessary in order to provide advance warning of the merger. We find that the pipeline is able to detect sources with signal-to-noise ratios as low as ρ∼17, we demonstrate that these searches can directly seed coarse parameter estimation in cases where the search trigger is loud. An example of how the false-alarm probability can be estimated for this type of GW search is also included. Published by the American Physical Society 2024

Signal identification of the stochastic gravitational wave background in the Mock LISA Data Challenge

Signal identification of the stochastic gravitational wave background in the Mock LISA Data Challenge

Low-dimensional signal representations for massive black hole binary signals analysis from LISA data

The space-based gravitational wave observatory LISA will provide a wealth of information to analyze massive black hole binaries with high chirp masses, beyond $10^5$ solar masses. The large number of expected MBHBs (one event a day on average) increases the risk of overlapping between events. As well, the data will be contaminated with non-stationary artifacts, such as glitches and data gaps, which are expected to strongly impact the MBHB analysis, which mandates the development of dedicated detection and retrieval methods on long time intervals. Building upon a methodological approach we introduced for galactic binaries, in this article we investigate an original non-parametric recovery of MBHB signals from measurements with instrumental noise typical of LISA in order to tackle detection and signal reconstruction tasks on long time intervals. We investigated different approaches based on sparse signal modeling and machine learning. In this framework, we focused on recovering MBHB waveforms on long time intervals, which is a building block to further tackling more general signal recovery problems, from gap mitigation to unmixing overlapped signals. To that end, we introduced a hybrid method called SCARF (sparse chirp adaptive representation in Fourier), which combines a deep learning modeling of the merger of the MBHB with a specific adaptive time-frequency representation of the inspiral. Numerical experiments have been carried out on simulations of single MBHB events that account for the LISA response and with realistic realizations of noise. We checked the performances of the proposed hybrid method for the fast detection and recovery of the MBHB.

Global analysis of LISA data with Galactic binaries and massive black hole binaries

Global analysis of LISA data with Galactic binaries and massive black hole binaries

Self-forced inspirals with spin-orbit precession

We develop the first model for extreme mass-ratio inspirals with misaligned angular momentum and primary spin, and zero eccentricity—also known as quasispherical inspirals—evolving under the influence of the first-order in mass-ratio gravitational self-force. The forcing terms are provided by an efficient spectral interpolation of the first-order gravitational self-force in the outgoing radiation gauge. In order to speed up the calculation of the inspiral, we apply a near-identity (averaging) transformation to eliminate all dependence of the orbital phases from the equations of motion while maintaining all secular effects of the first-order gravitational self-force at postadiabatic order. The resulting solutions are defined with respect to “Mino time”; thus, we perform a second averaging transformation, so the inspiral is parametrized in terms of Boyer-Lindquist time, which is more convent for LISA data analysis. We also perform a similar analysis using the two-timescale expansion and find that using either approach yields self-forced inspirals that can be evolved to subradian accuracy in less than a second. The dominant contribution to the inspiral phase comes from the adiabatic contributions, so we further refine our self-force model using information from gravitational wave flux calculations. The significant dephasing we observe between the lower and higher accuracy models highlights the importance of accurately capturing adiabatic contributions to the phase evolution. Published by the American Physical Society 2024

Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.462) by comparing our approach with various methods. As a result, the Aquila hash-q optimizer is the proposed matching technique to retrieve the document in a time-efficient manner for the user query without collision.

Detection and mitigation of glitches in LISA data: A machine learning approach

Detection and mitigation of glitches in LISA data: A machine learning approach

Search for Extreme Mass Ratio Inspirals Using Particle Swarm Optimization and Reduced Dimensionality Likelihoods

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long-lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH, where the latter contribute a time-independent amplitude to the waveforms. Additionally, we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio’s maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with a duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. However, the ranges used by both the LISA Data Challenge (LDC) and Mock LISA Data Challenge (MLDC) to generate their simulated signals are still wider than the those we currently employ in our direct searches. Consequently, we discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency, accuracy, and generality of the EMRI search algorithm.

Measuring scalar charge with compact binaries: High accuracy modeling with self-force

Using the self-force approach, we present the premier first-post-adiabatic accuracy formalism for modeling compact binaries in theories with a massless scalar field nonminimally coupled to gravity. We limit the binary secondary to being a non-spinning compact body with no scalar dipole (we will address the spinning and scalar dipole cases in an upcoming paper). By producing an ansatz for the scalar charged point particle action, we derive first- and second-order perturbative field equations and equations of motion for the secondary compact object. Under our assumptions, implementing this formalism will produce sufficiently accurate waveform templates for precision measurements of the scalar charge of the secondary with LISA data on extreme-mass-ratio inspirals. Our formalism is consistent with almost general scalar-tensor theories of gravity. Implementing our formalism builds on self-force models in general relativity; we show the incorporation into the two-timescale formalism is straightforward. Excitingly, implementation poses no significantly more challenging barriers than computing first-post-adiabatic waveforms in general relativity. Published by the American Physical Society 2024

Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization

Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that represents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from the first application of PSO to one of the most difficult of these challenges, namely the search for the Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with the minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, signal-to-noise ratio (SNR), and breadth, within the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful for a search region ranging over ≳10σ for the majority of parameters and ≳200σ for one, with σ being the SNR-dependent Cramer–Rao lower bound on the parameter estimation error and 30≤SNR≤50. This is in the vicinity of the search ranges that the current hierarchical schemes can identify. Directions for future improvement, including computational bottlenecks to be overcome, are identified.

Fast resolution of Galactic binaries in LISA data

Fast resolution of Galactic binaries in LISA data

Can gravitational-wave memory help constrain binary black-hole parameters? A LISA case study

Besides the transient effect, the passage of a gravitational wave also causes a persistent displacement in the relative position of an interferometer's test masses through the nonlinear memory effect. This effect is generated by the gravitational backreaction of the waves themselves, and encodes additional information about the source. In this work, we explore the implications of using this information for the parameter estimation of massive binary black holes with LISA. Based on a Fisher analysis for nonprecessing black hole binaries, our results show that the memory can help to reduce the degeneracy between the luminosity distance and the inclination for binaries observed only for a short time ($\ensuremath{\sim}\mathrm{few}\text{ }\mathrm{hours}$) before merger. To assess how many such short signals will be detected, we utilized state-of-the-art predictions for the population of massive black hole binaries and models for the gaps expected in the LISA data. We forecast from tens to few hundreds of binaries with observable memory, but only $\ensuremath{\sim}\mathcal{O}(0.1)$ events in 4 years for which the memory helps to reduce the degeneracy between distance and inclination. Based on this, we conclude that the new information from the nonlinear memory, while promising for testing general relativity in the strong field regime, has probably a limited impact on further constraining the uncertainty on massive black hole binary parameters with LISA.

Unified model for the LISA measurements and instrument simulations

LISA is a space-based mHz gravitational-wave observatory, with a planned launch in 2034. It is expected to be the first detector of its kind, and will present unique challenges in instrumentation and data analysis. An accurate preflight simulation of LISA data is a vital part of the development of both the instrument and the analysis methods. The simulation must include a detailed model of the full measurement and analysis chain, capturing the main features that affect the instrument performance and processing algorithms. Here, we propose a new model that includes, for the first time, proper relativistic treatment of reference frames with realistic orbits; a model for onboard clocks and clock synchronization measurements; proper modeling of total laser frequencies, including laser locking, frequency planning and Doppler shifts; better treatment of onboard processing and updated noise models. We then introduce two implementations of this model, LISANode and LISA Instrument. We demonstrate that TDI processing successfully recovers gravitational-wave signals from the significantly more realistic and complex simulated data. LISANode and LISA Instrument are already widely used by the LISA community and, for example, currently provide the mock data for the LISA Data Challenges.

Rapid search for massive black hole binary coalescences using deep learning

The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emissions. Low latency detection of the massive black hole mergers provides a start point for a global-fit analysis to explore the large parameter space of signals simultaneously being present in the data but at great computational cost. To alleviate this issue, we present a deep learning method for rapidly searching for signals of massive black hole binaries in gravitational wave data. Our model is capable of processing a year of data, simulated from the LISA data challenge, in only several seconds, while identifying all coalescences of massive black hole binaries with no false alarms. We further demonstrate that the model shows robust resistance to a wide range of generalization cases, including various waveform families and updated instrumental configurations. This method offers an effective approach that combines advances in artificial intelligence to open a new pathway for space-based gravitational wave observations.

Uncovering gravitational-wave backgrounds from noises of unknown shape with LISA

Detecting stochastic background radiation of cosmological origin is an exciting possibility for current and future gravitational-wave (GW) detectors. However, distinguishing it from other stochastic processes, such as instrumental noise and astrophysical backgrounds, is challenging. It is even more delicate for the space-based GW observatory LISA since it cannot correlate its observations with other detectors, unlike today's terrestrial network. Nonetheless, with multiple measurements across the constellation and high accuracy in the noise level, detection is still possible. In the context of GW background detection, previous studies have assumed that instrumental noise has a known, possibly parameterized, spectral shape. To make our analysis robust against imperfect knowledge of the instrumental noise, we challenge this crucial assumption and assume that the single-link interferometric noises have an arbitrary and unknown spectrum. We investigate possible ways of separating instrumental and GW contributions by using realistic LISA data simulations with time-varying arms and second-generation time-delay interferometry. By fitting a generic spline model to the interferometer noise and a power-law template to the signal, we can detect GW stochastic backgrounds up to energy density levels comparable with fixed-shape models. We also demonstrate that we can probe a region of the GW background parameter space that today's detectors cannot access.

Detectability and parameter estimation of GWTC-3 events with LISA

Multiband observations of coalescing stellar-mass black holes binaries could deliver valuable information on the formation of those sources and potential deviations from General Relativity. Some of these binaries might be first detected by the space-based detector LISA and, then, several years later, observed with ground-based detectors. Due to large uncertainties in astrophysical models, it is hard to predict the population of such binaries that LISA could observe. In this work, we assess the ability of LISA to detect the events of the third catalogue of gravitational wave sources released by the LIGO/Virgo/KAGRA collaboration. We consider the possibility of directly detecting the source with LISA and performing archival searches in the LISA data stream, after the event has been observed with ground-based detectors. We also assess how much could LISA improve the determination of source parameters. We find that it is not guaranteed that any event other than GW150914 would have been detected. Nevertheless, if any event is detected by LISA, even with a very low signal-to-noise ratio, the measurement of source parameters would improve by combining observations of LISA and ground based detectors, in particular for the chirp mass.