Talks

The next generation of gravitational wave detectors will produce data at a scale and complexity that renders traditional matched-filtering and parameter estimation pipelines insufficient as standalone tools. We argue that the field must shift toward representation-aware learning — building models that acquire rich, physics-informed embeddings of gravitational wave signals rather than optimising narrowly for a single downstream task. Inspired by contrastive multimodal frameworks such as CLIP, we introduce GWCLIP: a model trained to align gravitational wave strain data with their corresponding physical descriptions — waveform families, source parameters, and detector context — through contrastive objectives across a large and diverse signal corpus. This shared embedding space enables zero/few-shot classification, rapid similarity search across catalogs, and robust transfer to downstream tasks including glitch rejection, denoising and parameter inference with minimal labeled data. We demonstrate that GWCLIP embeddings capture physically meaningful structure, clustering signals by source parameters in a geometry that generalises across detector configurations.

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The LISA space mission, set to launch in the mid 2030s, will open a new window on the "gravitational wave universe". Thanks to its exceptional sensitivity in the low frequency band ~10⁻⁴–10⁻¹ Hz, it will observe a variety of sources all at the same time: from massive black hole binaries to extreme mass ratio inspirals and Galactic compact binaries. Among these, double white dwarf binaries, emitting nearly monochromatic signals, are expected to dominate the mHz band. Of the ~10⁷ binaries in the Milky Way, only a small fraction—ranging from tens to thousands—will be individually resolvable by LISA, while the unresolved population will produce a stochastic foreground ("confusion noise") that must be accounted for in the analysis of other sources. Different astrophysical formation and evolution channels are expected to leave distinct imprints on this confusion noise. However, extracting population-level information from this signal remains a challenging and computationally expensive task within the standard Global Fit framework. In this work, we present a machine learning approach based on Simulation-Based Inference to directly link the confusion noise to the underlying astrophysical population. We develop fast simulators to efficiently generate synthetic catalog realizations, and train a Neural Posterior Estimator to learn the mapping from the observed foreground to the parameters describing the binary population. We show that this approach can efficiently recover key population properties, bypassing some of the limitations of traditional inference pipelines. Our results highlight the potential of modern AI-driven inference methods for gravitational-wave data analysis, and represent a step towards integrating Simulation-Based Inference within the LISA Global Fit framework. This work is presented in detail in arXiv:2602.18560

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Accurate signal models and the disruptive presence of transient noise artifacts (glitches) impose key limitations on the sensitivity of gravitational-wave detectors. Efficient simulation of both is essential for data analysis. Fast generation of gravitational-wave signals is required for detection and parameter estimation, where waveforms are evaluated repeatedly within inference pipelines. At the same time, realistic modeling of glitches is needed to represent unknown noise populations and to stress test search and inference methods. This talk compares state-of-the-art approaches for fast simulation of both gravitational-wave signals and glitches. We focus on generative deep learning methods that learn data distributions and enable rapid generation in the time domain. These methods speed up simulations compared to traditional approaches while preserving key features of signals and noise, enabling more efficient and realistic data analysis.

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At the European Bioinformatics Institute (EMBL-EBI), we support bioinformatics efforts through research, data services, and training. Training plays an essential role in shaping how biodata scientists design analyses, access data, and utilise computational infrastructure. An aim of EMBL-EBI Training is not only to teach bioinformatic tools, but also their responsible use. In this talk, we present ongoing work to embed green computing principles across our activities. This includes: (1) developing new training content, such as the "Sustainable computing in science" on-demand course published in January 2026; (2) integrating sustainability into already existing courses; and (3) exploring ways to monitor and reduce the impact of our live courses, both computationally and operationally. We aim to share practical lessons and open discussion on encountered challenges, such as audience prioritisation, addressing gaps in trainer expertise, and integrating sustainability as standard practice rather than an add-on, making it a core component of scientific training.

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Research Software Engineers (RSEs) collaborate with researchers to develop and maintain software, helping to embed best practices that improve reliability and reduce inefficiencies in research workflows. As awareness grows of the environmental impact of computational research, a new specialism - Green RSE- is emerging. Green RSEs integrate environmental sustainability into software development, ensuring environmental considerations are addressed alongside performance and usability. But despite its potential to significantly reduce the carbon footprint of research computing, the role is still loosely defined and lacks formal recognition or clear career pathways. To address this gap, the Green RSE Special Interest Group has been established in collaboration with the UK Society of Research Software Engineering. This initiative aims to build a community, share best practices, and define the skills and responsibilities of Green RSEs. Early findings highlight how Green RSEs can support sustainable software practices across disciplines and contribute to reducing the environmental impact of computational research.

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Reducing the environmental impact of research computing requires reliable data on the carbon implications of resource use. To understand the current state of computing resource accounting, we conducted a landscape review of hardware-use accounting across the UK's national Digital Research Infrastructure, focussed on data needed for operational decision-making and sustainability reporting. In this talk, we will present our findings on community requirements, gaps in current accounting practices, and possible solutions for improvement. We invite discussion on how these findings can inform broader efforts to reduce the environmental impact of research computing.

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UK Research and Innovation is committed to achieving net zero emissions and to supporting environmental sustainability within the research it funds. This includes improving the sustainability of UKRI's Digital Research Infrastructure (DRI) and use of digital resources. This presentation will outline UKRI's approach to achieving this, focussing on the action being taken in four key areas: Funding, Procurement, Training and Engagement, and Monitoring and Reporting."

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One of the main challenges in adopting green software practices is the limited understanding and resources on the topic. Which components have the most emissions? How can researchers minimise their footprint and engage others? We argue that structured community-building is a critical yet underexplored mechanism for accelerating green computing adoption. This talk highlights recent community-building activities in green computing, including the Environmentally Sustainable Computational Science forum that connects 170+ members on an online platform, the Green Algorithms Initiative that aims to study and improve carbon calculators and impact measurement tools, and the Green DiSC certification scheme providing a sustainability roadmap for digital research groups. Drawing on these experiences, we reflect on what has worked to grow and sustain engagement, share lessons on lowering participation barriers, and outline priorities for strengthening the green computing ecosystem ahead.

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In the face of growing environmental impacts of computing, there is a legitimate request from researchers to identify what they can do about it. Green DiSC is an open access sustainability certification framework enabling researchers, labs and institutions to tackle the environmental impacts of their computing activities. It will be an opportunity to discuss the scheme, demonstrate how it can work, and showcase how it supports more environmentally sustainable computational research. We will also start by reflecting back on the inception of the scheme, and look ahead what's coming next.

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