BiD4BESt

A deep dive into supermassive black holes

Supermassive black holes have a mass somewhere in the region of millions or billions times that of the sun, and many questions remain about how they formed and subsequently evolved. Researchers in the BiD4BESt network are using a variety of methods to learn more about the evolution of supermassive blackholes, as Professor Francesco Shankar explains.

The relationship between supermassive black holes and their host galaxies, and how that relationship has evolved across cosmic time, is the subject of intense debate in the astrophysics field. Supermassive black holes are typically found at the centre of galaxies and have a mass in the region of millions or billions times that of the sun, now researchers in the BiD4BESt project aim to learn more about how these objects formed and subsequently evolved. “We have brought together the best modellers and observers in Europe in the project, who work intensively on this topic. By joining forces, our aim is to answer questions around the formation and evolution of supermassive black holes, which are some of the most pressing questions in astronomy,” says Professor Francesco Shankar, the coordinator of the project. A black hole doesn’t emit light, in fact it absorbs it, yet Professor Shankar says it is still possible to detect evidence of their existence. “Matter will form a structure before it actually falls into a black hole because of the conservation of angular momentum. This structure has the shape of a disc, it’s called an accretion disc,” he outlines.

Supermassive black holes

Friction among the particles in the accretion disc generates heat, leading to the emission of energy from the disc. This creates a highly luminous region called an Active Galactic Nucleus (AGN), which provides evidence of the presence of a supermassive black hole. “The existence of an AGN should tell us that there is an underlying supermassive black hole,” says Professor Shankar. There are 13 early stage researchers (ESRs) in the project who are investigating different questions around how these supermassive blackholes formed and subsequently evolved, from their infancy, to adolescence, through to adulthood. “The infancy of many of these supermassive black holes was in the very distant past - their mass today is an indicator of that. Adolescence is the period when the black hole started growing very fast, for which a lot of gas was required,” continues Professor Shankar. “Models also suggest that while growing up at the centre of their galaxies, supermassive black holes possibly had a lot of kinetic and thermal energy that could have had profound consequences on the surrounding galaxy, allegedly contributing to the halting of the star formation in the host galaxy.”

Researchers in the project are using data from a variety of state-of-the-art facilities, including both ground-based telescopes and space missions, to investigate how these black holes started interacting with galaxies. The data from these observations is multi-wavelength in nature, spanning the red and far infra-red, Professor Shankar and his colleagues hope to build a fuller picture of how AGN have evolved over time. “It’s like putting together a puzzle. If you want to get the full picture, you need all the different pieces,” he outlines.

The data gathered from ground-based facilities is an important part of this, with techniques like adaptive optics used to account for the distorting effect of the earth’s atmosphere on light. This is not an issue with space-based telescopes, yet black holes are often shrouded by dust,

The infancy of many of these supermassive black holes was in the very distant past – their mass today is an

indicator of that. Adolescence is the period when the black hole started growing very fast, for which a lot of gas was required.

whole electromagnetic spectrum. “When a black hole is ‘naked’ – so essentially when you cannot really see the AGN – it’s very difficult to observe, unless you can determine its position and mass by dynamical measurements. Otherwise we rely on when these black holes are active as AGN. When they are active, they radiate energy at all wavelengths within the electromagnetic spectrum,” explains Professor Shankar. By bringing together data from a variety of different wavelengths, including x-rays, infraa factor which must also be taken into consideration. “Stars like our own sun tend to emit light in the optical and the UV range, depending on their mass. This is especially the case in their early stages, when they are bright and young stars,” says Professor Shankar. When a galaxy is dust-enshrouded, light in the UV and optical range is absorbed by the intervening material, then usually re-emitted in the infra-red. In addition, X-rays most probably originating from the very inner regions

around the central supermassive black hole, can also more efficiently be transmitted through thick layers of dust. “Infrared, X-rays, and even radio wavelengths, can all be used to probe the presence of AGN buried in dust-enshrouded galaxies, thus enriching our knowledge on supermassive black hole evolution in the early stages of galaxy formation, and more generally pushing the detection of AGN, and thus supermassive black holes, into the realm of the early Universe,” continues Professor Shankar. “We use x-rays and infra-red telescopes in order to build a fuller picture, as the AGN itself will emit in x-rays and the infra-red as well.” A further source of data is numerical simulations, which complements the astronomical images from telescopes. This data then needs to be processed in order to gain deeper insights into the behaviour of different variables. “We use different statistical techniques for this task, while we also make intensive use of machine learning in the project. One of the most popular ways in which machine learning is used in this field – especially regarding astronomical images – is to try and train a machine on images from numerical simulations,” outlines Professor Shankar. The processes leading to these numerical simulations are already known, which provides a good opportunity for researchers to train machine learning tools. “We know the full history of a specific galaxy or AGN in a numerical simulation. For example, if it merged with another galaxy in the past, then we we would know it from the simulation,” explains Professor Shankar. “This merger might have left a clear signature in the morphology of the galaxy. It might also have destabilised the gas inside the galaxy.”

This is a topic that researchers are exploring in BiD4BESt, with ESRs working to train neural networks to recognise merger signatures in images taken from numerical simulations. These machines could then be used on images of real galaxies, to detect how many have signatures indicating merger activity,

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The Big Data applications for Black hole Evolution Studies

Project Objectives

The overall objective of the BiD4BEST project is to train doctoral researchers in one of the most crucial open question in astrophysics, namely that of how supermassive black holes formed and their impact on the evolution of galaxies. This study is structured as four distinct scientific Work Packages, with the ambition that these researchers will acquire outstanding academic expertise and master state-of-the-art datascience tools in machine/deep learning and statistical analysis, as well as deep knowledge of the theoretical framework.

Project Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860744.

Project Partners

https://www.bid4best.org/consortium/

Contact Details

Project coordinator Professor Francesco Shankar Professor of Astrophysics, School of Physics & Astronomy, University of Southampton B46, West Highfield Campus, University Road, SO17 1BJ T: +02380 593172 (int 23172) E: f.shankar@soton.ac.uk W: https://www.bid4best.org/ : @BiD4BEStITN

Prof. Francesco Shankar

Professor Francesco Shankar is the BiD4BESt ITN Consortium project coordinator. He is Professor of Astrophysics and the CHEP professional development lead, University of Southampton. He is a fellow of The Alan Turing Institute, The Higher Education Academy and The Royal Astronomical Society.

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as well as in investigating other questions. “We can try to find a connection between AGN activity and mergers for example. This is one of the clearest examples of how we use different types of datasets and statistical tools – in this specific case neural networks – to try and pin down the processes that shaped galaxies and black holes in the past,” says Professor Shankar. The main scientific aim in the project is to investigate the different phases of the co-evolution of supermassive blackholes and their host galaxies, starting from the initial stages when they started growing within dust-enshrouded galaxies. “We are investigating the idea that these supermassive blackholes may have been formed by many stellar black holes combining together,” outlines Professor Shankar.

“ESRs in BiD4BEST are exploring a number of relevant aspects of the supermassive black hole and galaxy co-evolution from both the observational and theoretical side. Just to give a few highlights, ESRs are busy: 1) in extracting empirical evidence for the supermassive black hole “feedback” effects on their host galaxies by detecting signature of their powerful winds directly triggered by the central active galactic nuclei, 2) in building full cosmological models of supermassive black hole evolution inclusive of obscuration, number of mergers, evolution of their “spin”, 3) in estimating their gas accretion rates through cosmic time and across different environments, and many more…”.

This project has provided for the first time to our knowledge an estimate of the full determination of the black hole demography, Professor Shankar says. A very promising route to the formation of supermassive black holes has been identified in the project, which Professor Shankar says can give a full determination of black hole demography. “Our results suggest that there is a high peak of black holes of a given mass, then a drop, then it picks up again at another specific mass,” he says. ”This will be important to make solid predictions for future deep space missions hunting for the origin of supermassive black holes.”

A number of important results on black hole evolution have been gained during the project, while at the same time this research has also opened up new questions. New collaborations among the partners in BiD4BESt have been established, which will open up further training opportunities for the ESRs, an important aspect of the project. “ We provide scientific training in areas like machine learning to the students, while we also have connections with industry,” outlines Professor Shankar.

The ESRs have had the opportunity to undertake secondments with the project’s industrial partners, part of preparing them for their future careers, whether in academia or industry. “The students were able to use their mathematical and statistical skills to help companies solve some of the problems they face,” continues Professor Shankar. “In the project we aim to both provide scientific training to the ESRs, and also to show how these techniques and skills can be used outside academia.”