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Novel Evolutionary Model for the Early stages of Stars with Intelligent Systems

Objective

NEMESIS has the ambition to reshape our understanding on the formation of stars by employing artificial intelligence methods to interpret the largest, panchromatic data collection of young stellar objects. Recent evidence suggests that planets form synchronously rather than sequentially to their host stars, indicating a rapid early evolution of star-planet systems. To ascertain these timescales, it is necessary to first determine the characteristic transitions that describe each phase of star formation. The definition of classes for young stellar objects was made possible more than 30 years ago, due to the first space-based infrared sky surveys. Whilst successful in determining global properties, current classification is prone to large uncertainties, and therefore, timescales, which are based on population statistics among different classes in a steady-state evolution, remain dubious.

NEMESIS aims to readjust the current classification scheme and its characteristic timescales so that it is concurrent with the most recent observational and theoretical constraints. To meet these goals NEMESIS will compile the largest, panchromatic dataset comprising of all young stellar objects in nearby star-forming regions, harnessing critical information that resides in data from space missions. It will reprocess and analyze this unique dataset with supervised and unsupervised machine learning algorithms, deep learning neural networks for object detection, clustering and regression analysis of images in order to advance the analysis and interpretation beyond the current state-of-the-art. Ultimately, NEMESIS brings big data techniques and hybrid machine learning methods to systematically analyze and interpret large data volumes in order to answer some of the most persisting questions, paving the path toward data-intensive science applications in modern astrophysics.

Field of science

  • /natural sciences/computer and information sciences/data science/big data
  • /natural sciences/physical sciences/astronomy/stellar astronomy
  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /natural sciences/physical sciences/astronomy/astrophysics
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning

Call for proposal

H2020-SPACE-2020
See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

UNIVERSITAT WIEN
Address
Universitatsring 1
1010 Wien
Austria
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 675 325

Participants (2)

UNIVERSITE DE GENEVE
Switzerland
EU contribution
€ 578 645
Address
Rue Du General Dufour 24
1211 Geneve
Activity type
Higher or Secondary Education Establishments
CSILLAGASZATI ES FOLDTUDOMANYI KUTATOKOZPONT
Hungary
EU contribution
€ 407 383,75
Address
Csatkai Endre Utca 6-8
9400 Sopron
Activity type
Research Organisations