The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

@article{Malz2019ThePL,
  title={The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals},
  author={Alex I. Malz and Ren{\'e}e Hlo{\vz}ek and T. Allam and Anita Bahmanyar and Rasel Biswas and Mi Dai and Llu{\'i}s Galbany and Emille E. O. Ishida and S. W. Jha and D. O. Jones and Richard Kessler and Michelle Lochner and Ashish A. Mahabal and Kaisey S. Mandel and J. R. Mart{\'i}nez-Galarza and Jason D. McEwen and Daniel Muthukrishna and G. Narayan and Hiranya V. Peiris and Christina Peters and Kara A. Ponder and C. N. Setzer},
  journal={The Astronomical Journal},
  year={2019}
}
Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. ... 

Figures and Tables from this paper

On the Classification and Feature Relevance of Multiband Light Curves
TLDR
This paper performed classification of multiband astronomical light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) data set via boosted neural nets, boosted decision trees, and a voted classifier for 14 astronomical categories through a feature ranking method using a neural network.
Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
  • Kyle Boone
  • Physics, Computer Science
    The Astronomical Journal
  • 2019
TLDR
The results suggest that spectroscopic campaigns used for training photometric classifiers should focus on typing large numbers of well-observed, intermediate redshift transients instead of attempting to type a sample of transients that is directly representative of the full dataset being classified.
On the cosmological performance of photometrically classified supernovae with machine learning
TLDR
This paper investigates the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classification Challenge, released in 2010, and proposes a threshold selection method to construct the final catalogs.
A Machine Learning Technique to Classify LSST Observed Astronomical Objects Based on Photometric Data
TLDR
This work has proposed an automated classification method using the simulated, photometric light curves in to 14 different classes that performs reasonably well for most of the classes while others still offer a little room for improvement.
Considerations for Optimizing the Photometric Classification of Supernovae from the Rubin Observatory
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all SNe discovered.
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
TLDR
Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy.
Photometric classification of Hyper Suprime-Cam transients using machine learning
TLDR
The potential use of machine learning for SN type classification purposes for spectroscopic follow-up and the development of a machine learning algorithm using a deep neural network with highway layers is discussed.
Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
TLDR
A new hierarchical structure is developed and a new set of classification features are proposed, enabling the accurate identification of subtypes of cepheids, RR Lyrae and eclipsing binary stars in CRTS data.
A survey on machine learning based light curve analysis for variable astronomical sources
TLDR
This survey reviews important developments in light Curve analysis over the past years, summarizes the basic concepts in machine learning and their applications in light curve analysis and concludes perspectives and challenges for light curveAnalysis in the near future.
Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification
TLDR
An initial study to essentially determine SN type with as few data points as possible, which identifies whether it is a Type Ia or CCSNe with better than 80\% accuracy, but with several biases in the data that will require addressing in future work.
...
1
2
...

References

SHOWING 1-10 OF 52 REFERENCES
Kernel PCA for Type Ia supernovae photometric classification
TLDR
This work proposes the use of Kernel Principal Component Analysis combined with k = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classication.
Detecting Quasars in Large-Scale Astronomical Surveys
TLDR
A classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey is presented and its performance on a manually labeled training set is evaluated.
Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning
TLDR
The feasibility of machine learning classification in a high-$z SN survey with application to real SN data is demonstrated and the differences between classifying simulated SNe, and real SN survey data are investigated.
Photometric Supernova Classification With Machine Learning
TLDR
A multi-faceted classification pipeline, combining existing and new approaches, finds that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up.
Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry
We identify 885,503 type 1 quasar candidates to i 3.5 than the traditional mid-IR selection "wedges" and to 2.2 3. This catalog paves the way for luminosity-dependent clustering investigations of
Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
TLDR
The inner workings of a framework, based on machine-learning algorithms, that captures expert training and ground-truth knowledge about the variable and transient sky to automate the process of discovery on image differences, and the generation of preliminary science-type classifications of discovered sources are presented.
Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey
TLDR
A process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey and a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy are described.
Towards an Automated Classification of Transient Events in Synoptic Sky Surveys
TLDR
The development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys and an automated follow-up recommendation engine that suggest those measurements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects.
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 Energy
K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4
We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, automated data analysis techniques are required. Here
...
1
2
3
4
5
...