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H2020Doctoral network2020–2024

BiD4BEST · Big Data applications for Black hole Evolution STudies

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2020-03-01 → 2024-02-29
EU contribution
€3,503,443
Participants
10
Scheme
MSCA-ITN

Lines connect the coordinator with its partners.

Results in brief

Big Data applications for Black hole Evolution STudies

The overall objective was to train doctoral researchers in the formation of supermassive black holes and their impact on the evolution of galaxies. The study focused on four scientific Work Packages with ambitions that these researchers would acquire outstanding academic expertise, a solid theoretical framework and master state-of-the-art data-science tools in machine learning (ML) and statistical analysis. WP1-INFANCY The early black hole growth in highly starforming, dust-enshrouded galaxies WP2-ADOLESCENCE Feedback and Outflows, The effects of AGN on their host galaxies WP3-ADULTHOOD Galaxy properties and AGN WP4-Bridging theoretical models to key observables The project has ensured the creation in Europe of a critical mass of experts in supermassive black hole physics with comprehensive scientific, computational, mathematical, statistical and soft skills. Our ESRs have published 18 papers, contributed to 189 conferences and highlighted to the public the field of supermassive black hole evolution.

Data: CORDIS, © European Union

Project objective

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.

Original text from CORDIS.

Participants

  • UNIVERSITY OF SOUTHAMPTON · SOUTHAMPTONCoordinatorUnited Kingdom
  • ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA · BolognaItaly
  • ETHNIKO ASTEROSKOPEIO ATHINON · ATHINAGreece
  • FUNDACION DONOSTIA INTERNATIONAL PHYSICS CENTER · Donostia San SebastianSpain
  • INSTITUTO DE ASTROFISICA DE CANARIAS · SAN CRISTOBAL DE LA LAGUNASpain
  • LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN · PlaneggGermany
  • SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI DI TRIESTE · TriesteItaly
  • UNIVERSITEIT LEIDEN · LeidenNetherlands
  • UNIVERSITY OF BATH · BATHUnited Kingdom
  • UNIVERSITY OF DURHAM · DURHAMUnited Kingdom

Links

Data: CORDIS, © European Union