Descripción del proyecto
La astronomía avanza en las técnicas de extracción de datos masivos
Los datos masivos ya están presentes en numerosos ámbitos y dominios, pero la extracción eficiente y automatizada de datos sigue planteando un reto. Asimismo, es necesaria la automatización de las técnicas de aprendizaje automático en estos procesos. Eso es precisamente lo que están haciendo los astrónomos. Colaborar con ellos aportaría beneficios en diversos ámbitos. Con el apoyo de las acciones Marie Skłodowska-Curie, el equipo del proyecto SUNDIAL pretende establecer una red dedicada al desarrollo de algoritmos novedosos. Dichos algoritmos se utilizarán para estudiar las vastas bases de datos generadas por los telescopios modernos, lo que permitirá comprender mejor la formación y evolución de las galaxias. El equipo del proyecto también se centra en la formación de jóvenes científicos tanto en informática como en astronomía. Se espera que esta colaboración única produzca múltiples y sofisticadas aplicaciones para la sociedad.
Objetivo
Though Big Data has become common in many domains nowadays, the challenges to develop efficient and automated mining of the ever increasing data sets by new generations of data scientists are eminent. These challenges span wide swathes of society, business and research. Astronomers with their high-tech observatories are historically at the forefront of this field, but obviously, the impact in e.g. commercial applications, security, environmental monitoring and experimental research is immense. We aim to contribute to this general discussion by training a number of young scientists in the fields of computer science and astronomy, focussing on techniques of automated learning from large quantities of data to answer fundamental questions on the evolution of properties of galaxies. While these techniques will lead to major advances in our understanding of the formation and evolution of galaxies, we will also promote, in collaboration with industry, much more general applications in society, e.g. in medical imaging or remote sensing. We have put together a team of astronomers and computer scientists, from academic and private sector partners, to develop techniques to detect and classify ultra-faint galaxies and galaxy remnants in a deep survey of the Fornax cluster, and use the results to study how galaxies evolve in the dense environment of galaxy clusters. With a team of young researchers we will develop novel computer science algorithms addressing fundamental topics in galaxy formation, such as the huge dark matter fractions inferred by theory, and the lack of detected angular momentum in galaxies. The collaboration is unique - it will develop a platform for deep symbiosis of two radically different strands of approaches: purely data-driven machine learning and specialist approaches based on techniques developed in astronomy. Young scientists trained with such skills are highly demanded both in research and business.The duration of the project was originally 48 months, till 31/3/2021. An extension of 6 months till 30/9/2021 was granted. The project therefore last 54 months which is indicated in all related project activities
Ámbito científico
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesastronomyastrophysicsdark matter
- natural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactions
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesphysical sciencesastronomyphysical cosmologygalaxy evolution
Programa(s)
Régimen de financiación
MSCA-ITN-ETN - European Training NetworksCoordinador
9712CP Groningen
Países Bajos