Big Data Astronomy Workshop

March 1-2, 2018

Faculty of Engineering

University of Concepción





During the last couple of years data coming from astronomical instruments has grown from giga to petabytes. As an example, the Large Synoptic  Survey Telescope (LSST) will produce approximately 15 terabytes of raw data per night, and about 60 petabytes over its ten years of operation. This poses the challenge of developing new data processing tools to address the astronomical data deluge.

The aim of the workshop is to bring together data scientists working on big data astronomy problems. All topics related to big data Astronomy are welcome including machine learning for Astronomy, pipeline development, astrostatistics, astronomical databases, and astronomical data orchestration among others.


Organizing Committee

Guillermo Cabrera-Vives (UdeC)

Pablo Estévez (U. Chile)

Pierluigi Cerulo (UdeC)





Faculty of Engineering, University of Concepción

Edmundo Larenas 219

Auditorium 105, 1st floor.





11:30 Welcome

12:00 The Astronomy Department at University of Concepción and stellar variability in the LSST

Ronald Mennickent

12:30 Deep Learning in Astronomy

Guillermo Cabrera-Vives


13:00 Lunch


14:45 Extragalactic distance scale based on the Tip of the Red Giant Branch

Marek Gorski

15:10 Colours and Stellar Population Properties of Brightest Cluster Galaxies at Redshifts z < 0.4 in the Sloan Digital Sky Survey

Pierluigi Cerulo

15:35 The zoology of galaxy morphologies and the need for automatic classification

Monserrat Martínez

15:45 Morphological prediction of galaxies with neural networks

Manuel Pérez


16:00 Coffee break


17:00 Discussion: Big-Data Astronomy and the opportunities for the Chilean community

Pablo Estévez, Francisco Förster, Neil Nagar





9:30 The APOGEE survey

Sandro Villanova

9:55 ALeRCE, a Chilean astronomical alert broker

Francisco Förster

10:20 Robust Period Estimation using Mutual Information for Multi-band Light Curves

Pablo Huijse


10:45 Coffee Break


11:30 KBMOD: Detecting Solar System objects below the single-image noise floor

Hayden Smotherman

11:50 Analysis of neural networks with mutual information

Ignacio Reyes

12:10 Photometric supernovae classification using deep recurrent neural network

Cristobal Donoso

12:20 Deep Learning for Image Sequence Classification of Astronomical Events

Rodrigo Carrasco


12:40 Final remarks