Conveners
AI for data analysis: Contributed Talks
- gianluca inguglia
AI for data analysis: Contributed Talks
- gianluca inguglia
AI for data analysis
- Valerie Domcke (CERN)
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Maximilian Dax06/05/2026, 09:00AI for Data AnalysisTalk
Gravitational-wave (GW) astronomy promises groundbreaking discoveries in the coming decades, but its progress is bottlenecked by the computational challenges of large-scale and real-time data analysis. I will present DINGO, a machine learning approach for fast and accurate GW inference that addresses these challenges. DINGO trains generative neural networks to directly estimate probability...
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Huw Haigh (Austrian Academy of Sciences (AT))06/05/2026, 09:20AI for Data AnalysisTalk
We present a study of deep convolutional autoencoders applied to anomaly detection of GW signals. This initial work focuses on short-duration signals (< 2s), corresponding to mergers that involve, or form, intermediate mass black holes. These burst-like signals are notably difficult to disentangle from both background noise and glitches that may occur during data taking. We utilise the...
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Rahul Srinivasan06/05/2026, 09:40AI for Data AnalysisTalk
Extreme-mass-ratio inspirals (EMRIs) are key gravitational-wave sources for the Laser Interferometer Space Antenna (LISA), but their detection and parameter inference are computationally challenging due to the extreme concentration of posterior distributions within vast prior volumes. In this work, we introduce a novel divide-and-conquer strategy that reformulates global inference as a...
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Dr Eleonora Villa (INAF - IASF Mi)06/05/2026, 10:00AI for Data AnalysisTalk
Pulsar Timing Array data analysis faces severe computational challenges as parameter spaces scale with the number of pulsars. I present two Normalizing Flows (NFs) based strategies to accelerate and improve Bayesian inference for stochastic gravitational wave background (SGWB). First, integrating NFs into the importance nested sampling framework i-nessai yields speedups of one to three orders...
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James Alvey (University of Cambridge)06/05/2026, 10:50AI for Data AnalysisTalk
The Laser Interferometer Space Antenna (LISA) will deliver an unprecedented view of the gravitational-wave universe, but unlocking its scientific potential hinges on a monumental data science challenge: the "global fit". Extracting thousands of overlapping, time-varying signals from complex instrumental noise is a high-dimensional inference problem that pushes the limits of traditional...
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Giada Chiara Badaracco (ETH Zurich (CH))06/05/2026, 11:10AI for Data AnalysisTalk
We present a framework for probing the full geometry of Bayesian posteriors in inverse problems through a noise-conditioned homotopy. By embedding the likelihood in a one-parameter family controlled by a noise-scaling parameter, we construct a continuous deformation from an almost deterministic posterior concentrated at the true parameters to the full noisy posterior.
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Traversing this path... -
Lucia Papalini (University of Pisa & INFN-Pisa)06/05/2026, 11:30AI for Data AnalysisTalk
Third-generation ground-based gravitational wave detectors such as the Einstein Telescope are expected to significantly advance our understanding of compact binary coalescences. One of the most critical challenges in data analysis for the Einstein Telescope is that of overlapping signals. With a tenfold improvement in sensitivity, the Einstein Telescope will be able to detect binary black hole...
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JESUS TORRADO CACHO (Instituto de Estructura de la Materia (IEM-CSIC))06/05/2026, 11:50AI for Data AnalysisTalk
Source inference for deterministic gravitational waves is a computationally demanding task in LISA. In a novel approach, we investigate the capability of Gaussian processes to learn the posterior surface in order to reconstruct individual signal posteriors. We use GPry, which automates this reconstruction through active learning, using a very small number of likelihood evaluations, without the...
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Benedict Armstrong07/05/2026, 13:30AI for Data AnalysisTalk
The detection of long gravitational wave signals in noisy strain data demands models that can efficiently capture long-range temporal structure while remaining computationally tractable. In this talk we introduce Linear Oscillatory State-Space models (LinOSS), a class of sequence models rooted in linear dynamical systems theory, as an alternative to conventional deep learning architectures for...
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Kyungseop Yoon (Massachusetts Institute of Technology)07/05/2026, 13:50AI for Data AnalysisTalk
Fast and accurate parameter estimation of binary neutron star (BNS) mergers, gravitational wave events with electromagnetic counterparts, remains a central challenge in multimessenger astronomy. Building on the State Space Model (SSM) framework presented in the companion talk, we directly regress BNS merger source parameters from raw gravitational wave time series, without sampling-based...
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Giovanni Giarda07/05/2026, 14:10AI for Data AnalysisTalk
Gravitational-wave observations from future space-borne detectors will present a fundamentally new inference challenge: not only we estimate the parameters of each source, but we must simultaneously determine how many sources are present. This is the trans-dimensional Bayesian inference problem, and classical approaches such as Reversible Jump MCMC can take hours to days per analysis.
We...
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Andy Chen (Institute of Physics, National Yang-Ming Chiao Tung University, Hsinchu, Taiwan), Eric Anton Moreno (Massachusetts Institute of Technology (US))07/05/2026, 14:30AI for Data AnalysisTalk
Since the first gravitational-wave detection by ground-based interferometers, after more than a decade of observations has yielded over one hundred compact binary coalescence (CBC) events, whose waveforms can be well-modeled by general relativity. These well-modeled signals enable detection pipelines based on matched filtering, which search for waveform consistency against the CBC template...
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