Main Research Fields

I am a postdoctoral researcher specialized in the use of machine and deep learning techniques for the exploitation of large astronomical datasets. My research focuses on three main areas:

• M dwarfs and ultracool dwarfs parameter determination: Developing deep transfer learning approaches using autoencoder neural networks to estimate atmospheric parameters (effective temperature, surface gravity, metallicity, and rotational velocity) of M dwarfs from high-resolution CARMENES spectra, bridging the gap between synthetic models and observed data. Applying the same methodology to low-resolution spectroscopy to determine the effective temperature of ultracool dwarfs.

• Detailed morphology of galaxies in clusters: Using visual morphology classifications, obtained with the Zoobot deep learning classifier, to study the evolution of different galaxy morphologies in high density environments in the Euclid survey.

• Identification and characterisation of low-mass objects: Enabling Virtual Observatory methodologies to efficiently identify ultracool dwarf candidates in wide-field surveys.

Feel free to explore my personal space: https://pmb-research.vercel.app/