The analysis and modeling of microlensing events has always been a computationally-intensive and time-consuming task, requiring a powerful computer cluster as well as well sampled lightcurves. While the number of interesting events with adequate data remained fairly low, it has been practical to perform a careful interactive analysis of each one, often with the aid of a powerful computer cluster. Even so, a number of challenges remain, particularly concerning the analysis of triple lenses.
This is expected to change with next-generation surveys, especially with the launch of WFIRST. This mission is expected to detect thousands of microlensing events, including hundreds of planetary events. Clearly, our analysis techniques need an upgrade to fully exploit this dataset, and we encourage people from outside the current microlensing community to bring in diverse expertize.
Release date: late January/early February each year
Submission deadline: October 31 of the same year, 23:59 UTC.
Each dataset will be made available from this website coincidently will the annual microlensing conference, together with a link for the submission of entries, and documentation describing the data products and metrics required for the evaluation of all entries.
The exact parameters of the simulated lightcurves will be known only to the simulator, who will keep them secret until the submission deadline.
All entries will be judged by a panel of experts drawn from the microlensing community.
Although entries will be assessed on the accuracy of their models, the emphasis of the challenge is on stimulating novel approaches to the challenge problems. For example, an entry that tests a new solution to a particular element of the modeling or classification process is encouraged, even if it doesn't provide a "complete" solution in every case.
A number of software packages for microlensing modeling have been made publicly-available and we recommend that entrants explore them. Details of these can be found under the Software Tab
Tiffany Meshkat from IPAC has kindly developed a Python notebook tutorial which provides an introduction to three public microlensing packages: PyLIMA, MulensModel and muLAn. This is a great way to get started if you're new to the field! Click here to download a tarball of the notebook, data and instructions, or visit the IPAC website