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Alice Spadaro (L2IT)5/6/26, 3:10 PMAI for Data AnalysisPoster
We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a...
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Alexandre Göttel (School of mathematical sciences, University of Nottingham)5/6/26, 3:10 PMAI for Data AnalysisPoster
Bayesian analyses have long been at the forefront of GW data analyses, mostly using nested sampling. With even state-of-the-art simulation-based-inference methods working in a Bayesian regime, they are here to stay. This has over time lead to discussions about new GW discoveries, be it general-relativity precession, eccentricity, or population-level model comparisons, to be held almost...
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Warren Bristol (University of Arizona)5/6/26, 3:10 PMAI for Detector OperationsPoster
Recent work on identifying suitable sites for the next-generation gravitational wave observatory, Cosmic Explorer (CE), includes site visits, efforts to build relationships with relevant communities surrounding promising sites, characterizing costs of construction, and finding suitable sites based on scientific thresholds. The common theme amongst these complementary efforts is Geographic...
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Philipp Horak (University of Victoria)5/6/26, 3:10 PMAI for Detector OperationsPoster
Beam loss events at SuperKEKB represent a major operational challenge, threatening both the Belle II detector and accelerator components while significantly impacting data-taking rates, with diamond doses reaching several thousand mrad per event.
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The current abort system relies on loss monitors positioned outside the beam pipe and at the interaction point. However, for Sudden Beam Loss (SBL)... -
Sharvari Tatachar (Georgia Institute of Technology)5/6/26, 3:10 PMAI for Detector OperationsPoster
This project builds on DeepClean, a machine learning framework for subtracting instrumental and environmental noise from gravitational-wave detector data using auxiliary “witness” channels. While effective, DeepClean’s convolutional architecture limits its ability to scale across varying channel configurations and frequency bands.
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We propose a hybrid CNN–transformer architecture that... -
Rizchel Masong5/6/26, 3:10 PMAI for Data AnalysisPoster
As next-generation observatories push the boundaries of sensitivity and data throughput, the challenge of isolating faint, unmodeled astrophysical transients from non-Gaussian terrestrial noise has become a universal bottleneck. Both radio astronomy and gravitational-wave (GW) physics face highly analogous data environments: continuous, high-rate time-series streams plagued by instrumental...
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Maria Theodora Folina (Democritus University of Thrace & CERN)5/6/26, 3:10 PMAI for Detector OperationsPoster
Accurate classification of instrumental and environmental noise glitches is essential for gravitational-wave detector characterisation and data quality assurance in LIGO. We present GravCloud, a hybrid deep-learning framework that interprets one-dimensional transient noise burst glitches transformed into a multi-dimensional pointcloud representation, allowing noise transients to be analysed in...
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Valéria Carvalho5/6/26, 3:10 PMPoster
The equation of state (EoS) of neutron star matter encodes the relationship between pressure and density at supranuclear densities, fundamentally governing the star’s structure and observable macroscopic properties, such as mass, radius, and tidal deformability. In this work, we apply Neural Posterior Estimation (NPE) with conditional normalising flows to infer the EoS from synthetic...
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Alexandra Eleni Koloniari (Aristotle University of Thessaloniki)5/6/26, 3:10 PMAI for Data AnalysisPoster
We examine the robustness of two common sensitivity metrics for GW detection pipelines, using AresGW model 1, an ML-based detection code. By analyzing the number of detected waveform injections in real detector noise for multiple month-long datasets and the sensitive distance at different false-alarm-ratio (FAR) thresholds, we investigate how these sensitivity metrics fluctuate due to dataset...
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Estuti Shukla (Penn State University)5/6/26, 3:10 PMAI for GW SimulationPoster
In general relativity, determining whether two spacetime metric solutions expressed in different gauge describe the same physical scenario poses a significant challenge. This study proposes a novel approach to addressing this problem within the context of numerical relativity by leveraging neural networks. Specifically, we introduce the first implementation of neural networks trained to learn...
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Anna-Malin Lemke5/6/26, 3:10 PMAI for Data AnalysisPoster
In summer 2023, multiple PTA collaborations reported evidence for a gravitational wave background in the nanohertz regime. Searching for anisotropies is considered a promising way to discriminate between an astrophysical and a cosmological signal origin, but existing methods face key limitations: Bayesian approaches are computationally expensive, while frequentist methods rely on a Gaussian...
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Alexandra Wernersson5/6/26, 3:10 PMAI for Data AnalysisPoster
Constructing template banks for matched filtering gravitational wave searches requires placing waveforms densely across a curved parameter space, where distances are defined by a position dependent metric. Evaluating these distances is computationally expensive, and the non-uniform curvature of the space makes uniform template placement suboptimal.
We propose a neural network approach that...
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William Cook5/6/26, 3:10 PMAI for GW SimulationPoster
Accurate Gravitational Wave models require data from Numerical Relativity simulations of compact object mergers to inform them of the waveform behaviour during the non-linear merger phase of the binary evolution. Such simulations require large computational resources to reach valuable resolutions, and are time consuming to perform, restricting their ability to fully explore the parameter space...
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Ludovica Carbone (University of Milano-Bicocca)5/6/26, 3:10 PMPoster
Accurate localisation of continuous gravitational waves (CGWs) from supermassive black hole binaries (SMBHBs) remains one of the key challenges in Pulsar Timing Array (PTA) data analysis. Traditional searches based on the $\mathcal{F}_e$ statistic provide a robust analytic framework, but the resulting sky maps are strongly affected by the PTA antenna pattern, which redistributes signal power...
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Thomas SAINRAT5/6/26, 3:10 PMAI for Data AnalysisPoster
Plug-and-Play methods have been introduced as a generalization of
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variational-based regularization techniques for image-related inverse
problems. They combine a Gaussian denoising task with a tractable
likelihood to achieve state-of-the-art image reconstruction. The
denoising component is flexible and typically implemented using neural
networks. We present an application of this method to... -
Liu Tao (AstroParticule & Cosmologie, Université Paris Cité)5/6/26, 3:10 PMAI for Detector OperationsPoster
Precise sensing and control of spatial mode content is essential for the performance of precision optical systems, particularly interferometric gravitational-wave detectors, where misalignment and mode mismatch can lead to significant optical losses and degraded quantum noise suppression. Conventional approaches, including heterodyne wavefront sensing and phase camera techniques, are effective...
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Josephine Prochnow5/6/26, 3:10 PMAI for Data AnalysisPoster
Artificial intelligence (AI) is increasingly applied in scientific research, but its growing computational demands raise concerns regarding reliability, transparency, and environmental impact.
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The PEARLS project: Precision in Energy-aware AI Research for Low-carbon Solutions, is an ErUM-Data consortium between DESY and the University of Freiburg and aims to address these challenges.
Within... -
Maria Isfan5/6/26, 3:10 PMAI for Data AnalysisPoster
Quantum computing and machine learning are two cutting-edge domains, in continuous evolution. Their intersection is quantum machine learning, an area with great potential of enhanced data analysis due to quantum advantage. As gravitational waves astronomy is progressing rapidly, the need for fast and robust data analysis tools increases.
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In our work, we present a quantum neural network data... -
Dr Priscilla Canizares (University of Cambridge)5/6/26, 3:10 PMAI for Data AnalysisPoster
The gravitational-wave (GW) detections reported by the LIGO-Virgo-KAGRA (LVK) collaboration have so far been consistent with quasi-circular compact binary coalescences (CBCs). Nevertheless, a small fraction of binaries driven to merge through dynamical interactions in dense stellar environments or in field triples may retain measurable orbital eccentricity when entering the sensitive frequency...
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