Data Mining & Exploration

PHOTOMETRIC REDSHIFTS: Photometric redshift estimation with Machine Learning methods.

QSO CLASSIFICATION: photometric candidate QSO classification with Machine Learning methods.

GLOBULAR CLUSTERS: Machine Learning methods to identify Globular Clusters in external galaxies.

AGN CLASSIFICATION: application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad types.

STRADIWA: Sky transient object classification.

VIALACTEA: a suite of next-generation 3D-visualization tools that will integrate visual analytics, on-the-fly handling of multi-SED radiative transfer modelling and data mining/machine-learning technologies to build a multi-wavelength Galactic Plane survey.

KiDS science: Data mining methods for peculiar object classification and photometric redshifts within the complete KiDS DR2 data release.

BIG-SKY-EARTH: European COST Action project boosting the interaction between disciplines by clustering relevant common solutions developed within research and industries.

HIPPOCAMPAL SEGMENTATION: The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. In this project we study different Machine Learning techniques for feature selection and classification from MRI images.

EUCLID: DAME members are involved in Science Ground Segment projects (Data Quality Control Tools, Supernovae pipeline, photometric redshifts).

Large Synoptic Survey Telescope: The project will conduct a 10-year survey of the sky that will address key questions about the structure and evolution of the universe.

RIVER ZOO: A public survey for Solar System drainage networks classification by Deep Learning.