Difference between revisions of "Monitoring young stars"
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The IC 2118 team has embarked on a campaign to monitor some of the candidate TTauri stars that we found using Spitzer data. They are using ground-based telescopes to see if the stars vary. | The IC 2118 team has embarked on a campaign to monitor some of the candidate TTauri stars that we found using Spitzer data. They are using ground-based telescopes to see if the stars vary. | ||
| − | ''This page will be filled out more with general information about the project, but you can [[ | + | ''This page will be filled out more with general information about the project, but you can [[IC_2118_Current_Research_Activities| watch them work here]].'' |
=Time series analysis= | =Time series analysis= | ||
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Time series analysis can be very powerful, and is used in many different fields of science, from tree rings to weather to sunspots to variations of young stars. There is a LOT of information out there on the web with information on analysis of time series data, but everything I found jumped into heavy-duty programming, math, and statistics without much explanation - e.g., the information is aimed at professional scientists. Can anyone else find some online resources with basic explanations, or do we need to write something? | Time series analysis can be very powerful, and is used in many different fields of science, from tree rings to weather to sunspots to variations of young stars. There is a LOT of information out there on the web with information on analysis of time series data, but everything I found jumped into heavy-duty programming, math, and statistics without much explanation - e.g., the information is aimed at professional scientists. Can anyone else find some online resources with basic explanations, or do we need to write something? | ||
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| + | Note: [http://homepage.mac.com/dvhscience/SpaceAcademy/Projects/2004-2005/differentialphotometry.html Here is a page] from one of the other teacher teams on reducing their time-series photometry data. | ||
==What are you doing? and Why?== | ==What are you doing? and Why?== | ||
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=Data to play with= | =Data to play with= | ||
| − | [http://web.ipac.caltech.edu/staff/rebull/ic2118/monitoring/ raw ground-based data to practice with] (data taken by Mr. Spuck and students) - images on which you can measure photometry. | + | *[http://web.ipac.caltech.edu/staff/rebull/ic2118/monitoring/ raw ground-based data to practice with] (data taken by Mr. Spuck and students) - images on which you can measure photometry. |
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| − | + | *[http://web.ipac.caltech.edu/staff/rebull/ic2118/monitoring/luisaslightcurves/ luisa's light curves] (data taken by R. Makidon and used by L. Rebull for part of her [http://adsabs.harvard.edu/abs/2001AJ....121.1676R thesis] - just the reduced photometry time series from many 100s of images, for ~5000 stars. Read the "readme.txt" file in order to understand what you are looking at. | |
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| − | + | *raw Spitzer data to play with can be found in the data that comes as part of program 20079. The final paper from these data is [http://adsabs.harvard.edu/abs/2006ApJ...653.1454M here]; note that we did not find any variations that we could say for sure came from the object and not the instrument! Note too that many of the programs looking for planetary transits use the same basic observing strategy, so you can go get and reduce those data too! If NASA gives us funding to keep going in the Warm Mission (after Spitzer runs out of cryogen), there will be many opportunities for more time series analysis using Spitzer data. | |
Latest revision as of 19:03, 13 May 2011
This page assumes you have read the Finding cluster members article.
The IC 2118 team has embarked on a campaign to monitor some of the candidate TTauri stars that we found using Spitzer data. They are using ground-based telescopes to see if the stars vary.
This page will be filled out more with general information about the project, but you can watch them work here.
Contents
Time series analysis
What is time series analysis?
The term "time series analysis" refers to the analysis of any data set where you have many measurements over some amount of time, for example, watching the same star many times per night over many nights.
Time series analysis can be very powerful, and is used in many different fields of science, from tree rings to weather to sunspots to variations of young stars. There is a LOT of information out there on the web with information on analysis of time series data, but everything I found jumped into heavy-duty programming, math, and statistics without much explanation - e.g., the information is aimed at professional scientists. Can anyone else find some online resources with basic explanations, or do we need to write something?
Note: Here is a page from one of the other teacher teams on reducing their time-series photometry data.
What are you doing? and Why?
What you're doing is pretty straightforward - you are looking for variations in the flux you measure from a star. However, many things can affect the flux - the weather (the amount of dust and humidity in the air), the stability of the atmosphere (seeing), the time of night (are you looking through a lot of air as the target rises or sets, or are you looking at it during its highest point during the night), where on the CCD chip you have placed the target (each pixel is calibrated differently). So, in order to limit the influence of these effects, you can take the average magnitude of every star in the frame, and look at how each star varies with respect to that mean. If the seeing gets much worse during one part of the night, that effect will be felt by every star in the frame. So, the mean will change for that frame. When you look at how the brightness of each star varies with respect to each mean for each frame, then you can take out some of those effects, and just look for variations that (you hope) are coming intrinsically from the target.
So what about the periodic ones?
People who know Luisa's research know that she is interested specifically in finding rotation rates in young stars. Finding periodic variations is a special case of finding variations in general. Finding variations in general is easier. To find periodic variations, you have to not only watch while the variation completes at least 2 cycles (to make it believable that you have found periodicity), but also do some relatively fancy math. (If you've heard of Fourier transforms, it's related to that - not quite the same, mind you, but close -- see the scary words here.) You can't just look at the light curves and say a-HAH, I see something periodic! (Try it in the light curves below, and I bet you that no two of you will find the same periodicity, much less find the same ones in the same stars that the math finds.) Right now, we don't know of any generally-available tools that will let you do this analysis, but we are looking. If you know of any, please let us know.
Data to play with
- raw ground-based data to practice with (data taken by Mr. Spuck and students) - images on which you can measure photometry.
- luisa's light curves (data taken by R. Makidon and used by L. Rebull for part of her thesis - just the reduced photometry time series from many 100s of images, for ~5000 stars. Read the "readme.txt" file in order to understand what you are looking at.
- raw Spitzer data to play with can be found in the data that comes as part of program 20079. The final paper from these data is here; note that we did not find any variations that we could say for sure came from the object and not the instrument! Note too that many of the programs looking for planetary transits use the same basic observing strategy, so you can go get and reduce those data too! If NASA gives us funding to keep going in the Warm Mission (after Spitzer runs out of cryogen), there will be many opportunities for more time series analysis using Spitzer data.
