Orateur
Description
The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (RESSPECT) project was born from an inter-collaboration agreement between the LSST Dark Energy Science Collaboration (DESC) and the Cosmostatistics Initiative (COIN), and aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this talk, I will give updates on the status of the project, papers and pipeline. I will describe the main results of a dedicated paper where the cosmology metric is described (currently in internal review in both, DESC and COIN) and show details of the queried sample when ran in simulated data.
