arXiv subjects

Ann-Kristin Malz

Publications and source records attributed to Ann-Kristin Malz.

7 recordsLinked to original sources

arXiv2026

Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction

In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.

gr-qc↗

arXiv2026

Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model

Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. However, terrestrial non-Gaussian noise artefacts, commonly known as glitches, reduce the sensitivity to signals and can overlap signals, producing biased astrophysical inferences. We introduce a hierarchical Bayesian model to measure the glitch rate, which improves upon existing trigger-counting methods in its capacity to measure the rate down into the low signal-to-noise regime without contamination from the Gaussian noise background, provided the population is accurately modelled. The framework accommodates any glitch model, and measures the rate with respect to the model chosen: here we use the antiglitch model, so the rate inferred is that of short-duration glitches rather than of all glitches. The methodology builds on standard hierarchical inference, but includes several novel features: hierarchical inference with quantile compression (HIQC), a generic approximation for the recycled hyperlikelihood, and a time-domain rate estimated by fitting basis functions. We validate the methodology using simulated data with injected glitches and then apply it to data from the fourth LIGO-Virgo-KAGRA observing run, demonstrating time-resolved inferences of the glitch rate over a 24 h period. The inferred glitch rate is consistent with estimates from trigger counts, but requires no arbitrary threshold and provides a more fine-grained view of the temporal behaviour. Finally, we demonstrate how our individual-detector rate estimates can be transformed into a coincident glitch probability and utilise this to provide evidence that the retracted gravitational-wave candidate GW230630_070659 is likely a pair of coincident glitches.

gr-qc↗

arXiv2026

Combining gravitational wave search pipelines to find subthreshold signals in GWTC-5.0

The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. However, varying performance complicates their interpretation. We use supervised machine learning combined with conformal prediction, a framework to quantify uncertainties, to merge multi-pipeline information into well-calibrated confidence scores. We demonstrate that this approach is robust across different classifier architectures and remains stable when trained on different simulated datasets. When applied to events across the GWTC catalogue up to and including the second part of the fourth observing run, the framework identifies several subthreshold candidates with elevated confidence, including the binary neutron star candidate GW200311_103121. We examine the reliability of these up-rankings, finding evidence that high-confidence predictions correspond to signal-like events. This framework enables simplified systematic candidate assessment for gravitational wave catalogues and real-time alerts by providing a single, well-calibrated confidence measure per candidate.

gr-qc↗

arXiv2026

Case studies with GPBilby of glitch-contaminated transient gravitational waves

In their fourth observing run, the LIGO--Virgo--KAGRA gravitational-wave observatories have found hundreds of new signals, but many are contaminated by non-Gaussian transient noise artefacts known as glitches. Left unaddressed, glitches can bias parameter inference and lead to misleading astrophysical conclusions. We present a series of case studies using GPBilby, a parameter estimation tool that employs a time-domain likelihood jointly modelling the astrophysical signal with a physical waveform and non-Gaussian noise with a Gaussian process. We first show that when the detector noise is Gaussian, GPBilby produces results consistent with those obtained with the standard Gaussian-noise likelihood, and then consider events affected by non-Gaussian features. For GW231123, the highest-mass binary black hole candidate observed to date, analyses using IMRPhenomXPHM reveal coherent residual structure that leads to measurable shifts in inferred source parameters. In contrast, analyses employing NRSur7dq4 show no significant excess residual power and remain consistent across likelihood choices. This demonstrates that waveform systematics and flexible noise modelling are intrinsically coupled, as the Gaussian process terms can partially absorb coherent waveform mismatches. For GW191109, we find that evidence for spin misalignment remains robust despite glitches in both LIGO detectors. For GW230630_070659, excluded from GWTC-4.0 owing to poor data quality, we find the data to be consistent with a BBH waveform model, with no additional residual power identified by the Gaussian process component. Overall, these results highlight how GPBilby can be used to perform glitch-robust inference and as a tool to understand waveform modelling systematics.

gr-qc↗

arXiv2026

Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification

Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine learning-driven approach that combines results from individual pipelines and utilises conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multi-pipeline detections, such as the sub-threshold binary neutron star candidate GW200311_103121.

gr-qc↗

arXiv2025

Joint inference for gravitational wave signals and glitches using a data-informed glitch model

Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inference analysis with Bilby, we estimate glitch and signal parameters simultaneously. We demonstrate the performance of our method through bias reduction, glitch identification and Bayesian model selection on real glitches. Our results show that this approach effectively removes glitches from the data, significantly improving source parameter estimation and reducing bias.

gr-qc↗

arXiv2025

Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction

With the increasing use of Machine Learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification. When taking a measurement it is not enough to provide the result, we also have to declare how confident we are in the measurement. This is also true when the results are obtained from a ML algorithm, and arguably more so since the internal workings of ML algorithms are often less transparent compared to traditional statistical methods. Additionally, many ML algorithms do not provide uncertainty estimates and auxiliary algorithms must be applied. Conformal Prediction (CP) is a framework to provide such uncertainty quantifications for ML point predictors. In this paper, we explore the use and properties of CP applied in the context of glitch classification in gravitational wave astronomy. Specifically, we demonstrate the application of CP to the Gravity Spy glitch classification algorithm. CP makes use of a score function, a nonconformity measure, to convert an algorithm's heuristic notion of uncertainty to a rigorous uncertainty. We use the application on Gravity Spy to explore the performance of different nonconformity measures and optimise them for our application. Our results show that the optimal nonconformity measure depends on the specific application, as well as the metric used to quantify the performance.

gr-qc↗