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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.
The BiD4BEST ITN will offer doctoral training in one of the most visible areas of astrophysical research, the formation of supermassive black holes in galaxies. A coordinated research training effort in this field is needed now to mobilise the community in Europe and prepare a core group of young scientists in anticipation of new observational data in the early/mid 2020s from future space missions with strong European involvement. These data will have the quality and volume to yield transformational science on the formation of black-holes in galaxies, as long as the necessary expertise and synergies among observation, theory and data analytics exist within the European astronomy community. We propose to achieve this goal by setting up a research training network that brings together leading scientists in observational and theoretical studies of black holes and galaxies, industrial experts in cutting-edge big-data technologies, and professionals in science dissemination. Together, we will setup doctoral research projects each of which combines state-of-the-art observations, numerical simulations and innovative analytic tools to compare theory with observation and shed light on the physics of black hole formation in the context of galaxy evolution. The training on expertise from different research areas (observational astronomy, theoretical astrophysics) and sectors (academic, industrial) will be achieved by carefully designed secondments, mixed doctoral supervisory committees (academia, industry), well coordinated events for team communication and interaction, as well as network-wide courses on astrophysics and transferable skills. The proposed research training programme aspires to generate individuals that in addition to academic competences, master big-data analytics and have the capacity to apply these technologies to solve problems in different sectors (research, industry, non-academic) by developing innovative products and services.

News

  • 01.12.2019:
    Job adverticement for prokekt manager (Soton/UK) announced

  • 25.05.2019:
    Invitation to prepare Grant agreement

  • 17.01.2019:
    Proposal for BiD4BEST submitted

BiD4BEST (Big Data applications for Black hole Evolution Studies), structured in four distinct scientific work packages, aims to set stringent constraints on the still unsolved and hotly debated issue of the formation and evolution of supermassive black holes: What is the link between (high) star formation and (early) black hole growth, what is the role of orientation in AGN obscuration (WP1)? Is AGN feedback actually occurring and playing any relevant role in shaping galaxies and their scaling relations with black holes (WP2)? What are the physical links between galaxy morphology/environment and AGN triggering (WP3)? How do state-of-the-art theoretical models compare with state-of-the-art observational data (WP4)? Answering these questions necessarily requires gathering large data sets in different wavebands capable of probing the different black hole evolutionary phases. X-ray observations, due to their unique sensitivity can identify AGN among galaxies over a wide range of accretion luminosities, cosmic distances, and levels of obscuration4. The most heavily obscured AGN can be detected at mid-infrared wavelengths, where the obscuring dust thermally peaks. UV/optical data continue providing very large samples of AGN at their peak and/or decaying phase. A comprehensive picture can thus only be obtained by merging AGN samples selected at different wavelengths, with shallow wide-area and deep pencil beam surveys. Multiwavelength wide/deep AGN surveys, properly de-biased by multiple selection effects and compared to the latest numerical, analytic and phenomenological cosmological models, are the main focus of BiD4BEST.

Members of the project are affiliated with:

LMU SOTON SISSA UoB DIPC IAC UniBo UoD NoA
USM LRZ Excellence Cluster Excellence Cluster IMPRS