Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset

@article{Johnston2016GenerationOA,
  title={Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset},
  author={Kyle B. Johnston and Hakeem M. Oluseyi},
  journal={ArXiv},
  year={2016},
  volume={abs/1601.03769}
}
View PDF on arXiv
11 Citations
Background Citations
2
Methods Citations
3

11 Citations

Advanced Astroinformatics for Variable Star Classification
TLDR
It is proposed that a complete machine learning strategy for use in the upcoming era of big data from the next generation of big telescopes, such as LSST, must consider this type of design integration.
Automatic Survey-invariant Classification of Variable Stars
TLDR
A full Probabilistic model is proposed that represents the joint distribution of features from two surveys, as well as a probabilistic transformation of the features from one survey to the other, and represents the features of each domain as a Gaussian mixture and models the transformation as a translation, rotation, and scaling of each separate component.
On the use of logistic regression for stellar classification
TLDR
An alternative supervised classification method scarcely exploited in astronomy, Logistic Regression, that has been applied successfully in other scientific areas, particularly biostatistics is applied in this article in order to derive membership probabilities for potential T Tauri star candidates from ultraviolet-infrared colour-colour diagrams.
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.
Variable Star Classification Using Multi-View Metric Learning
TLDR
Both the vector and matrix-variate versions of the multi-view learning framework perform favorably --- demonstrating the ability to discriminate variable star categories.
A detection metric designed for O’Connell effect eclipsing binaries
We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification…
Improved water cycle algorithm with probabilistic neural network to solve classification problems
TLDR
The results of the experiment substantiated that the WCA and E-WCA are capable of improving the weight parameters of the PNN, thereby imparting improved performance with respect to convergence speed and classification accuracy, compared with the initial PNN classifier.
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
TLDR
The proposed algorithm based on water cycle that includes the evaporation, condensation and precipitation (WC-ECP), which is a population based metaheuristic is used in the paper to improve the accuracy in the feed forward neural network (PNN-probabilistic neural network) and improvise the accuracy of the categorization and the speed of the convergence.
Self-taught Learning: Image Classification Using Stacked Autoencoders
TLDR
Rigorous experiments show that the self-taught learning approach is doing well against the baseline model (where no transfer learning has been used) even when there are limited number of labeled data instances in the target domain.
Image classification with caffe deep learning framework
TLDR
The convolutional neural networks model of the winner of ilsvrc12 competition is implemented and the method distinguishes 1.2 million images with 1000 categories in success.
...
...

References

SHOWING 1-10 OF 49 REFERENCES
AUTOMATED CLASSIFICATION OF PERIODIC VARIABLE STARS DETECTED BY THE WIDE-FIELD INFRARED SURVEY EXPLORER
TLDR
It is described a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases and shows that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification.
Variable Star Signature Classification using Slotted Symbolic Markov Modeling
Random forest automated supervised classification of Hipparcos periodic variable stars
TLDR
An evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types using random forests and a multistage scheme involving Bayesian network and Gaussian mixture methods leads to statistically equivalent results.
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.
Machine-assisted discovery of relationships in astronomy
High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically…
Automated classification of periodic variable stars{Improved methodology for the automated classification of periodic variable stars}
We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and…
The vvv templates project towards an automated classification of vvv light-curves i. Building a database of stellar variability in the near-infrared
TLDR
The VVV Templates Project is described, which describes the construction of this comprehensive database of infrared stellar variability, which will be used as a training-set for the machine-learning algorithms that will automatically classify the light-curves produced by VVv.
AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA
We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict…
Flashes in a star stream: Automated classification of astronomical transient events
TLDR
This work is exploring a variety of novel techniques, mostly Bayesian, to respond to the challenges of automated, rapid classification of transient events detected in the modern synoptic sky surveys, using the ongoing CRTS sky survey as a testbed.
Light curve analysis of Variable stars using Fourier decomposition and Principal component analysis
Context. Ongoing and future surveys of variable stars will require new techniques to analyse their light curves as well as to tag objects according to their variability class in an automated way.…
...
...