Welcome to our competition! Before you delve deeper into it, here is an overview:
The task to be solved is a supervised learning one, and more particularly a multi-target regression problem.
Each training datapoint consists of a set of 55 noisy light curves (one per wavelength, each being a timeseries of 300 timesteps) and a set of 6 additional stellar and planetary parameters. All these are real numbers. For more details on what a light curve is and what we are modelling, go to the Science page.
See also the Data Formats section for a detailed description of the data.
The goal is to predict a set of 55 real values (relative radii, one per wavelength) for any given datapoint.
See the Data Formats section of the Documentation page for a detailed description.
The models will be evaluated on a separate test set. These will be provided to you and you are expected to upload your model’s prediction on them. The ground truth (55 targets) for the test set examples will be available after the end of the competition.
See the Scoring System section of the Documentation page for a detailed description.
There is no restriction on the models, algorithms or data preprocessing techniques, neither on the programming languages, environments or tools used for your implementation. You are also free to use data augmentation techniques, pretrained models or any prior domain knowledge not included in the provided dataset. Finally, you are free to choose your own way of splitting the training data between training and validation sets and to use as many of the provided datapoints or features as you wish – or can handle.
The competition will close on the 15th of August 2019 with the winners announced one week later.
Besides the joy of helping advance science, making detecting distant worlds and deciphering their atmospheres easier and solving a challenging applied data science problem, the prize for the two top-ranked solutions is free registration to ECML-PKDD 2019 in Würzburg. The authors of solutions that are useful to us will also be invited to participate in further collaboration in solving the more general problem of denoising lightcurves.