Classifying variables in the Kepler data set - Revision history

Rebull at 17:59, 13 December 2011

2011-12-13T17:59:39Z

Rebull at 05:39, 12 August 2010

2010-08-12T05:39:15Z

Rebull at 02:42, 12 August 2010

2010-08-12T02:42:17Z

Rebull at 02:36, 12 August 2010

2010-08-12T02:36:04Z

Rebull: /* Time domain astronomy */

2010-08-11T07:25:21Z

Time domain astronomy

Rebull at 07:24, 11 August 2010

2010-08-11T07:24:16Z

Rebull at 07:23, 11 August 2010

2010-08-11T07:23:03Z

Rebull: Created page with '''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the 2010 Sagan Exoplanet Workshop. This group project was entitled "Identifying and Classifying Varia…'

2010-08-11T07:17:33Z

Created page with '''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the 2010 Sagan Exoplanet Workshop. This group project was entitled "Identifying and Classifying Varia…'

New page

''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the 2010 Sagan Exoplanet Workshop. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''

Every star has a story, told by its light curve. How do you listen to 150,000 stories at the same time? How can you (easily and accurately) tell which stories are interesting? In the figures below, one is a transit, one is noise, and one is indicative of stellar rotation, but they all have similar statistical properties. How can you tell without looking at each one by hand?

[[image:keplervar1.gif]][[image:keplervar2.gif]][[image:keplervar3.gif]]

The goals for this exercise are to learn about variability statistics, periodograms, machine learning classification schemes, and the utility of ancillary information.

@@ Line 26: / Line 26: @@
 Use:
 *Kepler data set
-*NStED standard variability statistics
+*NASA Exoplanet Archive standard variability statistics
-*NStED periodogram and visualization tools
+*NASA Exoplanet Archive periodogram and visualization tools
 *Harvard Time-Series Center classification tool
 *Or, “roll your own code”

← Older revision		Revision as of 05:39, 12 August 2010
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−	''Based on materials originally developed by Peter Plavchan (~~NStED~~/IPAC) for the [http://nexsci.caltech.edu/workshop/2010/ 2010 Sagan Exoplanet Workshop]. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''	+	''Based on materials originally developed by Peter Plavchan (NExScI/IPAC/Caltech) for the [http://nexsci.caltech.edu/workshop/2010/ 2010 Sagan Exoplanet Workshop]. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''

	'''NOTE: this page is still under construction'''		'''NOTE: this page is still under construction'''

← Older revision		Revision as of 02:42, 12 August 2010
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	''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the [http://nexsci.caltech.edu/workshop/2010/ 2010 Sagan Exoplanet Workshop]. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''		''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the [http://nexsci.caltech.edu/workshop/2010/ 2010 Sagan Exoplanet Workshop]. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''
		+
		+	'''NOTE: this page is still under construction'''

	=Introduction=		=Introduction=

← Older revision		Revision as of 02:36, 12 August 2010
Line 1:		Line 1:
−	''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the 2010 Sagan Exoplanet Workshop. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''	+	''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the [http://nexsci.caltech.edu/workshop/2010/ 2010 Sagan Exoplanet Workshop]. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''

	=Introduction=		=Introduction=

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 ==Time domain astronomy==
-Astronomers have learned a lot from exploring the frequency domain, but have under-sampled the time domain in sample size, wavelength coverage, and cadence.
+All of spatially unresolved astronomy can be thought of as the exploration of a two-dimensional space:  frequency and time.  Astronomers have learned a lot from exploring the frequency domain, but have under-sampled the time domain in sample size, wavelength coverage, and cadence. The future of time-domain astronomy is bountiful: LSST, Pan-STARRS, Kepler, CoRoT, YSOVAR, radial velocities, ASTrO/TESS/PLATO,  etc.
-The future of time-domain astronomy is bountiful: LSST, Pan-STARRS, Kepler, CoRoT, YSOVAR, radial velocities, ASTrO/TESS/PLATO,  etc.
+''Every'' star is variable at some level. Once you have a variability time-scale, you have clues to its physical mechanism.
-''Every'' star is variable at some level.
+Astronomers trip over themselves in the time-domain, because the analysis can be tricky.  The signatures of non-ideal observations in time-series data are often detectable, often un-avoidable, and always annoying.  For example:
 *CoRoT unavoidably moves into Earth's shadow
 *When observing from ground-based telescopes, your object rises and sets (or at least the Sun rises), introducing 1-day period aliasing

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 *Classify the light curves
-==Questions to answer==
+==Questions you can address by working through this project==
 *What time-scales are seen, e.g., what physical mechanisms are of interest?  Ex: eclipses, flares, rotation, asteroseismology, young stars
 *What are the amplitudes of variability? (and how to identify them) Ex:  How can you tell the difference between a 0.1 mag amplitude sinusoid variable with a V= 9 and 0.01 mag photon noise, vs. a V=16 object with 0.1 mag photon amplitude noise?

@@ Line 1: / Line 1: @@
 ''Based on materials originally developed by Peter Plavchan (NStED/IPAC) for the 2010 Sagan Exoplanet Workshop. This group project was entitled "Identifying and Classifying Variables in the Kepler Data Set."''
 Every star has a story, told by its light curve.  How do you listen to 150,000 stories at the same time? How can you (easily and accurately) tell which stories are interesting? In the figures below, one is a transit, one is noise, and one is indicative of stellar rotation, but they all have similar statistical properties. How can you tell without looking at each one by hand?
@@ Line 6: / Line 8: @@
 The goals for this exercise are to learn about variability statistics, periodograms, machine learning classification schemes, and the utility of ancillary information.