COIN-Focus #1: RESSPECT – France – 2019
The Cosmostatistics Initiative (COIN) is an international network which aims to create an interdisciplinary environment where collaborations between astronomers, statisticians and machine learning experts can flourish. The group utilizes a management model which can find parallel in technological start-ups: based on a dynamic, non-hierarchical and people-centric approach.
ELEPHANT: ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients
ELEPHANT represents an effective strategy to filter extragalactic events within large and complex astronomical alert streams. There are many applications for which this pipeline will be useful, ranging from transient selection for follow-up to studies of transient environments. We find that less than 2% of all analyzed transients are potentially hostless. Among them, approximately 10% have a spectroscopic class reported on TNS, with Type Ia supernova being the most common class, followed by SLSN. Among the hostless candidates retrieved by our pipeline, there was SN 2018ibb, which has been proposed to be a PISN candidate; and SN 2022ann, one of only five known SNe Icn. When no class is reported on TNS, the dominant classes are QSO and SN candidates, the former obtained from SIMBAD and the latter inferred using the Fink ML classifier.
Active Learning for Supernova Photometric Classification
Active Learning is a class of algorithms that aims to minimize labeling costs by identifying a few, carefully chosen, objects which have high potential in improving a given machine learning classifier. In this project, we show how Active Learning can be used as a tool for optimizing the construction of spectroscopic samples for supernova photometric classification.
Gaussian Mixture Models
Here, we show how to use a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and the WHAN diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the log [OIII]/H-beta, log [NII]/H-alpha, and log EW(H-alpha) optical parameters. We demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results; hence being a precious tool for maximum exploitation of the ever-increasing astronomical surveys.
Representativeness in Machine Learning applications for photometric redshifts
We present two galaxy catalogues built to enable a more demanding and realistic test of photo-z methods. We demonstrate the potential of these catalogues by submitting them to the scrutiny of different photo-z methods, including machine learning (ML) and template fitting approaches. Our catalogues represent the first controlled environment allowing a straightforward implementation of such tests.