Studying very high redshift Galaxies through Gravitational Telescopes
This program aims to study the formation and evolution of high redshift galaxies strongly lensed by a massive cluster acting as a gravitational lens. I plan to study the assembly history of galaxies at 2 < z < 5 through very de...
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Información proyecto HIGHZLENS
Líder del proyecto
UNIVERSITY OF DURHAM
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
164K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
This program aims to study the formation and evolution of high redshift galaxies strongly lensed by a massive cluster acting as a gravitational lens. I plan to study the assembly history of galaxies at 2 < z < 5 through very detailed analysis of highly magnified arcs with Integral Field Spectroscopy, in order to compare the local star formation rate and spectrophotometric properties at a spatial resolution of 0.1-0.2 kpc, allowed by the gravitational lensing effect which stretches the observed physical scales. I will use the new PanSTARRS survey to look for more examples of highly magnified arcs in galaxy-galaxy events. I will perform a large survey for very high redshift lensed galaxies (6<z<12) identified as near-infrared dropouts and undertake the spectroscopic follow-up using the forthcoming multi-object spectrographs at high resolution. By comparing the number counts, luminosity functions and spectroscopic results with theoretical expectations from semi-analytical models, I will build a complete picture of the star formation history in the early Universe, well ahead of the commissioning of the next generation of large-diameter (30m) telescopes such as the ELTs, or the JWST. All this research will be performed using the latest instruments on large-diameter telescopes accessible to the Durham group (Gemini, ESO observatories, Magellan), including the available Integral Field Units, and the space telescope. The programme includes technical training on reduction and analysis of 3D datasets, project management, collaboration with different groups and presentation/publication of scientific results.