Info: You are not logged
You can do unsupervised clustering or see your stars data without login,
but for other tools you need login (User -> Sign in/out),
because tasks are executed in jobs and are private (only owner can see them) NOTE: This project is still in the development.
Please be noted that it can contains bugs. In this case do not hesitate to report it to the author.
lcc version: 1.2.4

Introduction

Welcome on the Web Interface for Light Curves Classifier - package for classification astronomical objects by using
Machine Learning methods and downloading stars from huge astronomical
surveys by using predefined connectors. Light Curves Classifier is Python package
which can be used directly, by command line interface or by this web interface.
It can be found on Github or installed by "pip install lcc".

In order to train classifiers and searching in databases you have to sigin (free and fast), because all these tasks create
a job which is visible just for you without need of waiting on the result (so far all jobs are stored, but in the future some "very old" jobs
will be deleted. Anyway all results can be downloaded.
Also it allows you to create own modules (descriptors, classifiers and even connectors) which can be then used in the Web Interface.
However there are "Unsupervised clustering" and "Showing light curves" section available eve without login.

Workflow

Descriptors

Objects/tools which obtain features for an inspected object from the given data
Example descriptors:

Curves Shape Descriptor

Light curves are transformed into words by SAX and compared to the template light curves.
The dissimilarity of these two light curves is assigned as the feature to the inspected star.

Histogram Shape Descriptor

Histograms of light curves are shifted to have mean magnitude 0 and transformed to have standart deviation 1.
Then it is transformed into words by SAX and compared to the template histograms.
The dissimilarity of these two light curves is assigned as the feature to the inspected star.

Variogram Shape Descriptor

Time series which represent variation of brightness in different time lags.
It is also transformed into SAX and compared with template variogram.

Deciders

Supervised and unsupervised Machine-Learning methods

Acknowledgements

This research was supported by grant COST LD-15113 of the Ministry of Education Youth and Sports of the Czech Republic. The CSS survey is funded by the National Aeronautics and Space Administration under Grant No. NNG05GF22G issued through the Science Mission Directorate Near-Earth Objects Observations Program. The CRTS survey is supported by the U.S. National Science Foundation under grants AST-0909182 and AST-1313422.