We present spatially resolved Star Formation Histories and metallicity evolution on nearby galaxies. We use Convolutional Neural Networks with a combination of MUSE optical spectroscopy and HST photometry in the UV range. Combined with the high-resolution CO emission information from the PHANGS catalogue, this analysis will allow to infer the timescales for star formation and cloud destruction in different galaxy environments, providing clues about the dominant mechanisms of stellar feedback
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...
This thesis explores the chemical signatures of galaxy formation and evolution using a software pack...
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...
We present spatially resolved Star Formation Histories and metallicity evolution on nearby galaxies....
We present an artificial neural network design in which past and present-day properties of dark matt...
How do galaxies form? How do they evolve? These are the fundamental questions driving the current de...
We simulate the formation and evolution of galaxies with a self-consistent 3D hydrodynamical model i...
In this thesis, I study the integrated and spatially resolved (kilo-parsec scale) properties of star...
We present a new method for inferring galaxy star formation histories (SFH) using machine learning m...
Using a revolutionary combination of high spatial resolution MUSE and ALMA data we examine, in unpre...
We study the chemical properties of the stellar populations in eight simulations of the formation of...
Using a revolutionary combination of high spatial resolution MUSE and ALMA data we examine, in unpre...
Numerous studies have demonstrated the ability of Convolutional Neural Networks (CNNs) to classify l...
In this Thesis, I study the formation of late-type galaxies and the role that feedback from stars ...
HGC wishes to thank the UKRI Science and Technology Facilities Council for funding this research, un...
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...
This thesis explores the chemical signatures of galaxy formation and evolution using a software pack...
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...
We present spatially resolved Star Formation Histories and metallicity evolution on nearby galaxies....
We present an artificial neural network design in which past and present-day properties of dark matt...
How do galaxies form? How do they evolve? These are the fundamental questions driving the current de...
We simulate the formation and evolution of galaxies with a self-consistent 3D hydrodynamical model i...
In this thesis, I study the integrated and spatially resolved (kilo-parsec scale) properties of star...
We present a new method for inferring galaxy star formation histories (SFH) using machine learning m...
Using a revolutionary combination of high spatial resolution MUSE and ALMA data we examine, in unpre...
We study the chemical properties of the stellar populations in eight simulations of the formation of...
Using a revolutionary combination of high spatial resolution MUSE and ALMA data we examine, in unpre...
Numerous studies have demonstrated the ability of Convolutional Neural Networks (CNNs) to classify l...
In this Thesis, I study the formation of late-type galaxies and the role that feedback from stars ...
HGC wishes to thank the UKRI Science and Technology Facilities Council for funding this research, un...
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...
This thesis explores the chemical signatures of galaxy formation and evolution using a software pack...
Star formation and feedback in astrophysical simulations remains a longstanding challenge when attem...