Unsupervised machine learning for transient discovery in deeper, wider, faster light curves
| dc.contributor.author | Lochner, Michelle | |
| dc.contributor.author | Webb, Sara | |
| dc.contributor.author | Muthukrishna, Daniel | |
| dc.date.accessioned | 2021-02-08T09:02:18Z | |
| dc.date.available | 2021-02-08T09:02:18Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers’ ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the ASTRONOMALY package. As proof of concept, we evaluate 85 553 min-cadenced light curves collected over two ∼1.5 h periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. | en_US |
| dc.identifier.citation | Lochner, M. et al. (2020). Unsupervised machine learning for transient discovery in deeper, wider, faster light curves. Monthly Notices of the Royal Astronomical Society,498(3), 3077–3094, | en_US |
| dc.identifier.issn | 1365-2966 | |
| dc.identifier.uri | https://doi.org/10.1093/mnras/staa2395 | |
| dc.identifier.uri | http://hdl.handle.net/10566/5849 | |
| dc.language.iso | en | en_US |
| dc.publisher | Oxford University Press | en_US |
| dc.subject | Methods: data analysis | en_US |
| dc.subject | Methods: observational | en_US |
| dc.subject | Techniques: photometric | en_US |
| dc.subject | Unsupervised machine | en_US |
| dc.title | Unsupervised machine learning for transient discovery in deeper, wider, faster light curves | en_US |
| dc.type | Article | en_US |