arXiv subjects

Chris Messenger

Publications and source records attributed to Chris Messenger.

At least 19 recordsLinked to original sources

arXiv2026

Probabilistic kilonova prediction from gravitational wave inferred binary neutron star parameters

Kilonovae provide a key electromagnetic window into binary neutron star mergers, revealing the properties of the merging system while probing r-process nucleosynthesis of the Universe. However, only a few kilonova candidates have been detected to date, with AT2017gfo being the only one associated with a gravitational wave event, GW170817. In this work, we present \textsc{Genova}, a probabilistic framework for predicting kilonova spectra and light curves directly from gravitational wave posterior samples of binary neutron star mergers. This method uses a conditional normalising flow to learn the distribution of rest-frame spectra conditioned on the source-frame component masses, tidal deformabilities, viewing angle and time since merger. Other kilonova model parameters, such as ejecta opacities, are marginalised over during training, so that their effects are propagated into the predicted spectra as predictive uncertainty. In the self-consistency test, the flow model reproduces the median light curves with residuals typically below $\sim 0.1$ mag, and the ratio of the predicted central 68\% interval widths remains predominantly between $0.8$ and $1.4$ over $\sim 0.4$--$8.0$ days. Comparisons with a physically distinct kilonova model show that the probabilistic prediction can remain informative beyond the model used for training. We apply \textsc{Genova} to GW170817/AT2017gfo using multi-band observations, including newly re-reduced $Y$-, $J$-, $K_s$-band photometry from the Visible and Infrared Survey Telescope for Astronomy, which we present in this work. The resulting predictive intervals broadly encompass the observations while capturing both gravitational wave posterior uncertainty and the variation induced by marginalised kilonova model parameters.

astro-ph.HE↗

arXiv2026

Pre-localization of Massive Black Hole Binaries in the Millihertz Band

The space-borne gravitational-wave (GW) detectors will open a new mass and redshift regime, allowing us to observe massive black hole binaries (MBHBs) throughout the Universe. A subset of these systems is expected to produce electromagnetic (EM) counterparts, offering a unique opportunity to follow the continuous evolution of massive black holes through joint GW and EM observations. Realizing this potential, however, requires low-latency, high-throughput data-analysis pipelines that can extract reliable source parameters and sky localizations from space-borne data streams fast enough to trigger EM follow-up. In this work we develop a fast, normalising flow-based inference pipeline designed for early-warning analysis of MBHB signals in a TianQin-like configuration. Our method combines a learned embedding of the detector time series with a neural spline flow (NSF) to perform amortized Bayesian inference, producing posterior samples for the main source parameters in roughly one minute per event. For a representative MBHB whose merger occurs $\sim 15$ minutes after the end of the analyzed GW observation, the pipeline achieves pre-merger sky localizations of order $\sim 20~\mathrm{deg}^2$, recovers the same number of sky modes as a reference parallel-tempered Markov chain Monte Carlo (PTMCMC) analysis, and yields parameter uncertainties of comparable scale, while still operating within a practically useful pre-merger warning window. These results demonstrate that NSF-based inference can deliver accurate, near-real-time parameter estimation for space-borne MBHB GW signals, and that the resulting early-warning localizations are sufficiently precise to make rapid EM follow-up.

gr-qc↗

arXiv2025

Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation

Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthetic image quality is the Fr\'echet Inception Distance (FID) which measures the similarity of two image datasets. In this study we evaluate the relationship between this metric and the improvement which synthetic images, generated by a Progressively Growing Generative Adversarial Network (PGGAN), grant when augmenting Diabetes-related Macular Edema (DME) intraretinal fluid segmentation performed by a U-Net model with limited amounts of training data. We find that the behaviour of augmenting with standard and synthetic images agrees with previously conducted experiments. Additionally, we show that dissimilar (high FID) datasets do not improve segmentation significantly. As FID between the training and augmenting datasets decreases, the augmentation datasets are shown to contribute to significant and robust improvements in image segmentation. Finally, we find that there is significant evidence to suggest that synthetic and standard augmentations follow separate log-normal trends between FID and improvements in model performance, with synthetic data proving more effective than standard augmentation techniques. Our findings show that more similar datasets (lower FID) will be more effective at improving U-Net performance, however, the results also suggest that this improvement may only occur when images are sufficiently dissimilar.

eess.IV↗

arXiv2024

Reconstructing source motion from gravitational wave strain

Searches for un-modelled burst gravitational wave signals return potential candidates for short duration signals. As there is no clear model for the source in these searches, one needs to understand and reconstruct the system that produced the gravitational waves. Here we aim to reconstruct the source motion and masses of a system based only on its gravitational wave strain. We train a normalising flow on two models including circular orbits and random unphysical motion of two point masses. These models are used as a toy problem to illustrate the technique. We then reconstruct a distribution of possible motions of masses that can produce the given gravitational wave signal. We demonstrate the ability to reconstruct the full distribution of possible mass motions from strain data across multiple gravitational wave detectors. This distribution encompasses the degenerate motions that are expected to produce identical gravitational wave strains.

gr-qc↗

arXiv2024

Searching for gravitational waves from stellar-mass binary black holes early inspiral

The early inspiral from stellar-mass binary black holes (sBBHs) can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched filtering technique poses a significant challenge for analyzing this kind of signal, as it requires an impractically high number of templates ranging from $10^{31}$ to $10^{40}$. We propose a search strategy that involves two main parts: initially, we reduce the dimensionality of the simulated signals using incremental principal component analysis (IPCA). Subsequently, we train the convolutional neural networks (CNNs) based on the compressed TianQin data obtained from IPCA, aiming to develop both a detection model and a point parameter estimation model. The compression efficiency for the trained IPCA model achieves a cumulative variance ratio of 95.6% when applied to $10^6$ simulated signals. To evaluate the performance of CNN we generate the receiver operating characteristic curve for the detection model which is applied to the test data with varying signal-to-noise ratios. At a false alarm probability of 5%, the corresponding true alarm probability for signals with a signal-to-noise ratio of 50 is 86.5%. Subsequently, we introduce the point estimation model to evaluate the value of the chirp mass of corresponding sBBH signals with an error. For signals with a signal-to-noise ratio of 50, the trained point estimation CNN model can estimate the chirp mass of most test events, with a standard deviation error of 2.49 $M_{\odot}$and a relative error precision of 0.13.

astro-ph.SR↗

arXiv2023

Quantum state preparation of gravitational waves

We detail a quantum circuit capable of efficiently encoding analytical approximations to gravitational wave signal waveforms of compact binary coalescences into the amplitudes of quantum bits using both quantum arithmetic operations and hybrid classical-quantum generative modelling. The gate cost of the proposed method is considered and compared to a state preparation routine for arbitrary amplitudes, where we demonstrate up to a four orders of magnitude reduction in gate cost when considering the encoding of gravitational waveforms representative of binary neutron star inspirals detectable to the Einstein telescope. We demonstrate through a quantum simulation, that is limited to 28 qubits, the encoding of a second post-Newtonian inspiral waveform with a fidelity compared to the desired state of 0.995 when using the Grover-Rudolph algorithm, or 0.979 when using a trained quantum generative adversarial network with a significant reduction of required gates.

quant-ph↗

arXiv2023

Importance nested sampling with normalising flows

We present an improved version of the nested sampling algorithm nessai in which the core algorithm is modified to use importance weights. In the modified algorithm, samples are drawn from a mixture of normalising flows and the requirement for samples to be independently and identically distributed (i.i.d.) according to the prior is relaxed. Furthermore, it allows for samples to be added in any order, independently of a likelihood constraint, and for the evidence to be updated with batches of samples. We call the modified algorithm i-nessai. We first validate i-nessai using analytic likelihoods with known Bayesian evidences and show that the evidence estimates are unbiased in up to 32 dimensions. We compare i-nessai to standard nessai for the analytic likelihoods and the Rosenbrock likelihood, the results show that i-nessai is consistent with nessai whilst producing more precise evidence estimates. We then test i-nessai on 64 simulated gravitational-wave signals from binary black hole coalescence and show that it produces unbiased estimates of the parameters. We compare our results to those obtained using standard nessai and dynesty and find that i-nessai requires 2.68 and 13.3 times fewer likelihood evaluations to converge, respectively. We also test i-nessai of an 80-second simulated binary neutron star signal using a Reduced-Order-Quadrature (ROQ) basis and find that, on average, it converges in 24 minutes, whilst only requiring $1.01\times10^6$ likelihood evaluations compared to $1.42\times10^6$ for nessai and $4.30\times10^7$ for dynesty. These results demonstrate the i-nessai is consistent with nessai and dynesty whilst also being more efficient.

astro-ph.IM↗

arXiv2022

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

astro-ph.IM↗

arXiv2022

Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders

All-sky searches for continuous gravitational waves are generally model dependent and computationally costly to run. By contrast, SOAP is a model-agnostic search that rapidly returns candidate signal tracks in the time-frequency plane. In this work we extend the SOAP search to return broad Bayesian posteriors on the astrophysical parameters of a specific signal model. These constraints drastically reduce the volume of parameter space that any follow-up search needs to explore, so increasing the speed at which candidates can be identified and confirmed. Our method uses a machine learning technique, specifically a conditional variational auto-encoder, and delivers a rapid estimation of the posterior distribution of the four Doppler parameters of a continuous wave signal. It does so without requiring a clear definition of a likelihood function, or being shown any true Bayesian posteriors in training. We demonstrate how the Doppler parameter space volume can be reduced by a factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.

astro-ph.IM↗

arXiv2022

Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks

Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of-principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability towards different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs towards more realistic EMRI data analysis.

astro-ph.HE↗

arXiv2021

Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts

Coherent WaveBurst is a generic, multidetector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on selected attributes in the multidimensional event attribute space to distinguish between noisy events from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a postprocessing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic gravitational wave burst signal morphologies as well as the astrophysical source such as core-collapse supernova models due to the application of our Gaussian mixture model approach to coherent WaveBurst triggers. The Gaussian mixture model method recovers the gravitational wave signals from massive compact binary coalescences identified by coherent WaveBurst targeted for binary black holes in GWTC-2, with better significance than the all-sky coherent WaveBurst search. No additional significant gravitational wave bursts are observed.

gr-qc↗

arXiv2021

A Pixelated Approach to Galaxy Catalogue Incompleteness: Improving the Dark Siren Measurement of the Hubble Constant

The use of gravitational wave standard sirens for cosmological analyses is becoming well known, with particular interest in measuring the Hubble constant, $H_0$, and in shedding light on the current tension between early- and late-time measurements. The current tension is over $4\sigma$ and standard sirens will be able to provide a completely independent measurement. Dark sirens (binary black hole or neutron star mergers with no electromagnetic counterparts) can be informative if the missing redshift information is provided through the use of galaxy catalogues to identify potential host galaxies of the merger. However, galaxy catalogue incompleteness affects this analysis, and accurate modelling of it is essential for obtaining an unbiased measurement of $H_0$. Previously this has been done by assuming uniform completeness within the sky area of a gravitational wave event. This paper presents an updated methodology in which the completeness of the galaxy catalogue is estimated in a directionally-dependent matter, by pixelating the sky and computing the completeness of the galaxy catalogue along each line of sight. The $H_0$ inference for a single event is carried out on a pixel-by-pixel basis, and the pixels are combined for the final result. A reanalysis of the events in the first gravitational wave transient catalogue (GWTC-1) leads to an improvement on the measured value of $H_0$ of approximately 5% compared to the 68.3% highest-density interval of the equivalent LIGO and Virgo result, with $H_0= 68.8^{+15.9}_{-7.8}$ km s$^{-1}$ Mpc$^{-1}$.

astro-ph.CO↗

arXiv2021

Exploring the sky localization and early warning capabilities of third generation gravitational wave detectors in three-detector network configurations

This work characterises the sky localization and early warning performance of networks of third generation gravitational wave detectors, consisting of different combinations of detectors with either the Einstein Telescope or Cosmic Explorer configuration in sites in North America, Europe and Australia. Using a Fisher matrix method which includes the effect of earth rotation, we estimate the sky localization uncertainty for $1.4\text{M}\odot$-$1.4\text{M}\odot$ binary neutron star mergers at distances $40\text{Mpc}$, $200\text{Mpc}$, $400\text{Mpc}$, $800\text{Mpc}$, $1600\text{Mpc}$, and an assumed astrophysical population up to redshift of 2 to characterize its performance for binary neutron star observations. We find that, for binary neutron star mergers at $200\text{Mpc}$ and a network consisting of the Einstein Telescope, Cosmic Explorer and an extra Einstein Telescope-like detector in Australia(2ET1CE), the upper limit of the size of the 90% credible region for the best localized 90% signals is $0.25\text{deg}^2$. For the simulated astrophysical distribution, this upper limit is $91.79\text{deg}^2$. If the Einstein Telescope-like detector in Australia is replaced with a Cosmic Explorer-like detector(1ET2CE), for $200\text{Mpc}$ case, the upper limit is $0.18\text{deg}^2$, while for astrophysical distribution, it is $56.77\text{deg}^2$. We note that the 1ET2CE network can detect 7.2% more of the simulated astrophysical population than the 2ET1CE network. In terms of early warning performance, we find that a network of 2ET1CE and 1ET2CE networks can both provide early warnings of the order of 1 hour prior to merger with sky localization uncertainties of 30 square degrees or less. Our study concludes that the 1ET2CE network is a good compromise between binary neutron stars detection rate, sky localization and early warning capabilities.

astro-ph.IM↗

arXiv2021

A quantum algorithm for gravitational wave matched filtering

Quantum computational devices, currently under development, have the potential to accelerate data analysis techniques beyond the ability of any classical algorithm. We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. We apply Grover's algorithm to matched-filtering, a signal processing technique that compares data to a number of candidate signal templates. In comparison to the classical method, this provides a speed-up proportional to the square-root of the number of templates, which would make possible otherwise intractable searches. We demonstrate both a proof-of-principle quantum circuit implementation, and a simulation of the algorithm's application to the detection of the first gravitational wave signal GW150914. We discuss the time complexity and space requirements of our algorithm as well as its implications for the currently computationally-limited searches for continuous gravitational waves.

quant-ph↗

arXiv2021

Deep searches for X-ray pulsations from Scorpius X-1 and Cygnus X-2 in support of continuous gravitational wave searches

Neutron stars in low mass X-ray binaries are hypothesised to emit continuous gravitational waves that may be detectable by ground-based observatories. The torque balance model predicts that a higher accretion rate produces larger-amplitude gravitational waves, hence low mass X-ray binaries with high X-ray flux are promising targets for gravitational wave searches. The detection of X-ray pulsations would identify the spin frequency of these neutron stars, and thereby improve the sensitivity of continuous gravitational-wave searches by reducing the volume of the search parameter space. We perform a semi-coherent search for pulsations in the two low mass X-ray binaries Scorpius X-1 and Cygnus X-2 using X-ray data from the \textit{ Rossi X-ray Timing Explorer} Proportional Counter Array. We find no clear evidence for pulsations, and obtain upper limits (at $90\%$ confidence) on the fractional pulse amplitude, with the most stringent being $0.034\%$ for Scorpius X-1 and $0.23\%$ for Cygnus X-2. These upper limits improve upon those of Vaughan et al. (1994) by factors of $\sim 8.2$ and $\sim 1.6$ respectively.

astro-ph.HE↗

arXiv2021

Nested Sampling with Normalising Flows for Gravitational-Wave Inference

We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings.

gr-qc↗

arXiv2020

Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models

Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.

gr-qc↗

arXiv2020

A robust machine learning algorithm to search for continuous gravitational waves

Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set and at a 1% false alarm probability we showed that at 95% efficiency a fully-automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10 Hz$^{-1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.

astro-ph.IM↗