38 citations · 91 across the 10 of their papers we have counts for
4 papers · 1 filter
Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV
J. B. Cabral, M. Lares, S. Gurovich +2
As most of the modern astronomical sky surveys produce data faster than humans can analyze it, Machine Learning (ML) has become a central tool in Astronomy. Modern ML methods can b…
Automatic Catalog of RRLyrae from 14 million VVV Light Curves: How far can we go with traditional machine-learning?
Juan B. Cabral, Felipe Ramos, Sebastián Gurovich +1
The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. The overwhelming number of sources under analysis request the use of autom…
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
J. B. Cabral, B. Sánchez, F. Ramos +3
Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facil…
Corral Framework: Trustworthy and Fully Functional Data Intensive Parallel Astronomical Pipelines
Juan B. Cabral, Bruno Sánchez, Martín Beroiz +4
Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via d…