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Kaggle-like competitions

A number of data analysis challenges and competitions have been announced in recent years to address difficult and important problems in statistical and computational astronomy. Most involve issues arising in cosmology. Older entries are obtained from the Cosmology meets Machine Learning Uninstitute.

Challenges in interferometric image reconstruction

Image reconstruction in optical interferometry has gained considerable importance for astrophysical studies during the last decade. This has been mainly due to improvements in the imaging capabilities of existing interferometers and the expectation of new facilities in the coming years. However, despite the advances made so far, image synthesis in optical interferometry is still an open field of research. Since 2004, the community has organized a biennial contest to formally test the different methods and algorithms for imagereconstruction. In 2016, we celebrated the 7th edition of the "Interferometric Imaging Beauty Contest". This initiative represented an open call to participate in the reconstruction of a selected set of simulated targets with a wavelength-dependent morphology as they could be observed by the 2nd generation of VLTI instruments. See details here

Challenges in visualization:

The VisIVO Contest 2014 (Visualization for the International Virtual Observatory) is a call to the worldwide scientific community to use technologies provided by the VisIVO Science Gateway to produce amazing images and movies from multi-dimensional datasets coming either from observations or numerical simulations.  The package offers a framework for exploration of large-scale scientific datasets, particularly related to cosmological simulations.    

Challenges in exoplanet detection:

The WFIRST Coronagraph Exoplanets Community Data Challenge seeks participation from teams with spectral retrieval expertise.  The Challenge will run from Aug15 to Nov15 2016, and it will consist of a blind spectral retrieval exercise using simulated extracted spectra for several "known RV” and/or hypothetical “discovery” exoplanets.  The data will be served via the IPAC WFIRST Science Center.  For the first five teams that complete the entire retrieval challenge (all five planets, all requested SNR and spectral resolution parameters) we are offering travel expenses to an exoplanets meeting.  Contact: Margaret Turnbull, SETI Institute, WFIRST Coronagraph SIT Principal Investigator.

The Nearby Earth Astrometric Telescope (a proposed satellite mission) is designed to measure the tiny positional wobble of solar-like stars due to orbiting planets.  NEAT scientists have designed a double-blind contest with realistic simulated time series with and without planetary signals.  


Challenges for weak-lensing galaxy image analysis:

GRavitational lEnsing Accuracy Testing (GREAT3) challenge is underway to test methods of weak lensing data analysis.  This is similar to strong lensing (above) but the background objects are galaxies rather than quasars, and the statistical problem involves measuring subtle shearing of the galaxy shapes.  Details are available here and here

Mapping Dark Matter Kaggle Challenge (a more accessible version of GREAT10)

GREAT10 PASCAL Challenge contains a spatially varying kernel, and a kernel estimation challenge

GREAT08 PASCAL Challenge was the first shear measurement challenge aimed at MLers


Challenges for galaxy morphology classification:

Kaggle and GalaxyZoo joined to present The Galaxy Challenge for automated galaxy morphology classification. The $16,000 prize  has been won by data scientist graduate student Sander Dieleman, who used a 7-layer neural network with 42M parameters.  Code was written in Python with Theano wrappers for GPU implementation. See Kaggle's interview here.  Kaggle sponsored an earlier galaxy imaging competition in 2011. 

Challenges for photometric redshift estimation:

The PHAT challenge here and here.  


Challenges for strong gravitational lensing time delay:

Strong Lens Time Delay Challenge is now open for competition.  Based on simulated LSST data of gravitational lensing of quasars lying behind foreground galaxies, the challenge is to accurately establish delays between the stochastic variations of two lensed quasar images from sparse, irregularly sampled time series.