Active Physics & Astronomy Computing & AI

Time-domain Analysis to study the Life-cycle and Evolution of Supermassive black holes

In plain English

AI plain-English summary

Supermassive black holes are about to get a lot less mysterious, thanks to a new European training network that will teach a generation of PhD students how to read the flickering light these cosmic giants produce. The problem is straightforward: new telescopes—including the Vera Rubin Observatory and the Einstein Probe, both coming online within two years—will flood astronomers with time-domain data, tracking how black holes’ surroundings change over hours, days, or years. Current methods cannot keep up. This network trains researchers to apply data-science techniques to those observations, build better theoretical models, and map the chaotic environments just outside black holes’ event horizons. If it succeeds, Europe gains a cohort of scientists who can handle the coming data deluge—and who also bring transferable skills in data analysis and machine learning to industry. That matters for digital transformation and for developing technologies that improve energy efficiency or environmental monitoring. This is fundamentally curiosity-driven research. It will not fix a pothole or speed up a phone. But understanding how supermassive black holes grow and shape galaxies—and training the people who can do that work—is the kind of fundamental science that, a generation from now, often turns out to underpin something nobody predicted.

View original technical description
The proposed network aspires to provide high quality doctorate training to a core group of young researchers in one of the most visible and fast-developing areas of astrophysics, the lifecycle of supermassive black holes and their impact on the evolution of galaxies. The key innovative aspects of the proposed training include (i) the leveraging of time-domain astronomy observations from state-of-the-art facilities to map the inner environments of supermassive black holes, (ii) the use of novel analysis methods from the discipline of data science to maximise the information gain from the observations, (iii) the development of new models and theories to interpret the data and learn about physics, (iv) the extensive interaction with the industrial sector to promote key technical and complementary skills that are essential for future market leaders. Training in the proposed science theme is needed now to exploit the increasing influx of time-domain observations and prepare for the imminent explosion in data volume and quality as new dedicated facilities (e.g. Vera Rubin telescope, Einstein probe), with strong European involvement, see first light in the next 1-2 years. The ultimate goal of the network is to enhance the human capital of Europe, thereby contributing to major societal priorities, such as the digital transformation through new technologies, or the competitiveness and environmental neutrality of the European economy through innovative solutions.

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Researchers

Andrew Young (Principal Investigator)

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Original classification

Training Grant

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