Photometric Supernova Classification With Machine Learning

@article{Lochner2016PhotometricSC,
  title={Photometric Supernova Classification With Machine Learning},
  author={Michelle Lochner and Jason D. McEwen and Hiranya V. Peiris and Ofer Lahav and Max K. Winter},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
  year={2016}
}
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the… Expand

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References

SHOWING 1-10 OF 68 REFERENCES
Machine Learning Classification of SDSS Transient Survey Images
TLDR
The results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise. Expand
Semi-supervised learning for photometric supernova classification★
We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base ofExpand
A simple and robust method for automated photometric classification of supernovae using neural networks
A method is presented for automated photometric classificat ion of supernovae (SNe) as TypeIa or non-Ia. A two-step approach is adopted in which: (i) the SN lightcurve flux measurements in eachExpand
Statistical classification techniques for photometric supernova typing
Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curvesExpand
Effect of training characteristics on object classification: An application using Boosted Decision Trees
TLDR
An application of a particular machine-learning method (Boosted Decision Trees, BDTs using AdaBoost) to separate stars and galaxies in photometric images using their catalog characteristics, with results being of wider use to other machine learning techniques. Expand
Supernova Photometric Classification Challenge
We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark EnergyExpand
Machine learning for transient discovery in Pan-STARRS1 difference imaging
Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. TheseExpand
Results from the Supernova Photometric Classification Challenge
We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion toExpand
Towards automatic classification of all WISE sources
The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levelsExpand
Toward Characterization Of The Type IIP Supernova Progenitor Population: A Statistical Sample Of Light Curves From Pan-STARRS1
In recent years, wide-field sky surveys providing deep multiband imaging have presented a new path for indirectly characterizing the progenitor populations of core-collapse supernovae (SNe):Expand
...
1
2
3
4
5
...