Like Sands Through the Hourglass, So Are the Days of My Weekend

I don't think I could have asked for a better weekend. Sure, I got next to no work done, but given the chance, I wouldn't change any of my time management decisions from the last two days.  This post probably won't have much science until the very end (and even then it won't be a lot), so if you want to read about my life, continue, but if you just want to get to the stuff about space, feel free to skip a few paragraphs.

It all started, as most weekends do, on Friday night. I had just closed my eyes for a nap when I got a call from my school-year roommate telling me that her best friend from high school was in town.  He lives in Georgia and I hadn't seen him since he came up to visit two years ago, so I promptly jumped right out of bed and ran over to see them. We spent the night talking and listening to music.  And then we got cheesy bread from Domino's.  It was a pretty great night.

On Saturday morning, I woke up to my alarm at 8:00 and realized that this was the first time since I moved into my summer housing that I had the option of sleeping as long as I wanted. Obviously I took it, and didn't wake up until after noon.  I probably would have slept longer but I got a text from my cousin saying that he was in town.  I woke up and got ready to meet him.  We had lunch and then went to meet his friends from college who live around Boston.  I only meant to spend a few hours with him/them, but ended up spending about nine. Oooops. But it was pretty fantastic. And I got some nice feedback on my ideas for my thesis from unbiased parties who don't have any reason to tell me things just to make me feel good!

Today was going to be the day I got stuff done. Did that happen? Not really.  Instead of working on my research in the morning, I slept. Instead of working on my research in the afternoon, I went all the way out to the suburbs of Boston to lose horribly to my cousin and his friends in Super Smash Bros. And instead of working on my research in the evening, I played --and won -- Spades with the rest of the Banneker group at my advisor's house.

Now it's Sunday night, and I haven't really done anything super productive, but I'm really okay with it. But just so I don't feel completely useless, I'm going to make and share a To Do list for this week.

1. Finish testing the script that will take raw Kepler data and "detrend" it. I have no idea why it's called detrending. I just know that it takes raw Kepler data like this and subtracts the telescope's response to return a more-or-less flat line. 
2. Learn how to run light curve fitting algorithms (the rigorous kind, not like what I did in Astro 16 with Excel)
3. Start LaTeXing my report for the Exoplanet project
4. Make a list of Observation IDs to process using HIPE and actually start processing them 
5. Analyze the data that's already been processed. 

It's going to be a busy week, but I'm ready. 

Danger: Galaxies Merging

We all make decisions that we know aren't the best for our health. This summer, I made the decision to do two *very different* research projects with two *very different* research groups.  You've already heard a lot about my first one dealing with exoplanets, but now I want to tell you a bit about the other one. The title above might give you a hint as to what it's about. 

I'm working with Drs. Howard Smith and Matthew on a sample of Ultra-Luminous Infrared Galaxies, trying to study the molecular outflows that happen when they merge.  I don't think I can say too much about this, or show any pictures, because that would basically be making our data and intentions public, but I can say a bit about what I've been doing.

Data Reduction. That's pretty much it. Every once in a while, I get to make a spreadsheet, but then I go right back to reducing data, initializing scripts that can take days to run.  I might sound bitter, but I actually don't mind.  (No sarcasm, I promise.)  Sure, it's not as exciting as the actual analysis that I'll get to do once this is all over, but taking raw data and manipulating it into a workable form is very satisfying. 

                         

This two-internship thing is more than just busy and slightly stressful.  It's confusing, too. I don't mean confusing in the sense that I have no idea what I'm doing with the research --though there definitely is some of that.  I mean confusing in the sense that my whole life plan has kind of been shaken.

I didn't want to go to grad school until about a year ago.  I didn't know I wanted to study astronomy until about two years ago.  So I guess I was due for another life-changing revelation, but it still took me by surprise.  What's changed?  I don't think exoplanets are boring anymore.  Maybe it's the project I'm working on, or the advisor I'm working on it with (no offense to other advisor; he's really great!), or the fact that I finally get to work with exoplanets outside of the classroom in a real research environment where I can more or less give the work my undivided attention. It's most likely a combination of all three. But whatever it is, it means that galaxies -- their formation and evolution -- aren't my only astronomical love anymore. 

Grad school, particularly choosing what I want to study once I get there, just got a whole lot harder, but at least now there's a better chance that I'll enjoy whatever I end up doing.

Why'd You Have to Go and Make Things So Complicated?

You know how professional mathematicians sometimes have trouble doing really basic math, like multiplication? That's kind of how I feel with computers.  I'm not trying to say I'm an expert coder -- few things are farther from the truth than that statement -- but, at least in python, I'm competent.  But you know what I have the hardest time doing?  Opening programs, or downloading things onto my computer, or turning a computer on/off.  This blog post is about my battle with, and eventual triumph over, basic computer functions.

One of the things that separates the Banneker Institute from REU (Research Experience for Undergrads) programs is that we spend three hours every day taking classes.  Topics for those classes range from Interferometry to Proposal Writing, but for the first few weeks, we're learning how to code in Python. Yesterday, our lessons revolved around using Github, which, as far as I can understand it, is like Facebook and Google Docs for coding.  Users put their code up on their profiles for others to see and, if the profile is public, edit.

Github can be used both online and from the terminal (unix) or command prompt (windows).  The catch is that Macs come with Git (the program) already installed, and Windows don't.  And no matter how many times I try to download things onto my computer, or how intuitive the source website tries to be, downloading is always a struggle.  But I did it! At least, I think I did. Typing "git" onto my command line no longer returns a giant red error message, so I'm going to put this one in the success column.

The next basic-but-near-impossible-to-overcome obstacle was put up by the CfA ([Harvard-Smithsonian] Center for Astrophysics).  In order to fit the transit light curves for the planets in Kepler-186, I need to use my advisor's server, which means I have to remotely connect to the CfA's network from my computer.  This is so ridiculously complicated. I guess it makes sense to be super secure; there is some top-notch science being done on that network. But they sure do make it hard for students!  Not so hard, though, that I couldn't figure it out (after about an hour and with lots of help from my advisor and the CfA helpdesk).  Now I can keep doing the science!

Finally, as if the first two weren't enough, I had to download LaTeX.  For those not familiar with LaTeX, it's basically a word processor, but it's really good at formatting things for science writing, especially equations.  This is another one of those programs that generally comes pre-installed on a Mac, but needs to be downloaded onto a Windows machine.  After I downloaded it, I spent a while with my advisor figuring out where the file was on my computer and how to get it to save changes to the template he gave me.  So. Much. Strugglebussing.

I've been told so many times by so many people that most of these problems wouldn't actually be problems if I used a Mac instead of a Windows machine.  I don't want to fall into the trap of operating system elitism because Macs are great, but I'm a Windows person.  My computer, Sinead, and I have been through a lot together, and though I might not always be on Team Windows, I'm not quite ready to betray my allegiances yet.  As I told my advisor today, "If I can do research on a Wondows computer, I can do anything!"  And in case I start to doubt that, I have some inspirational music to bring me back.

Banneker Institute: Week #2

Two weeks ago, I had no idea how much work had to be done with light curve data before you could actually start fitting the light curve. I guess I never really thought much about what needed to be done, but naive little me probably thought that data came to us ready to be plotted, and all that was left to do was fit.  Boy was I wrong!

If you've been following this blog, you know a bit about what I've had to do with my data over the last couple of weeks. If you haven't, you can read those old posts now, or you can keep reading this one for a brief summary (but if you don't go back, you'll miss most of the pretty plots I've been making).

Here's what I've done so far:

1. Fold the data $\rightarrow$  This involves stacking all of a planet's transits on top of each other.  This makes the transits easier to see (just to check that Kepler isn't going crazy and you actually do have transits in your data) and allows you to separate data points that are in a transit from those that are out of one, which leads to...
2. Remove Outliers $\rightarrow$ Only remove points from a list of data points that occur oustside of any transit for any planet. The $\sigma$ value (how many $\sigma$s away from the mean you want to start removing data) chosen is that which, probabilistically, would only remove one good data point.  
3. Separate Planets $\rightarrow$  Make individual files for each planet excluding points that occur in any other planet's transits. The motivation behind this step is probably the least intuitive.  This step is necessary because (a) working with individual files for each planet is easier in the long run and (b) this eliminates the -- incredibly small -- chance that two planets could have overlapping transits. 

But now that that's done, I can (a) be super proud of myself and (b) move on to actually fitting the light curves for each of the planets in my system. I don't know what this entails, exactly, but I'll let you know when I do.

Today's post was pretty short.  I'm sorry about that. But to make it up to you, I'll leave you with a riddle.

You have 12 cubes, and they all look and feel identical, except for one, which is slightly heavier or lighter than the others. Using a mechanical balance only three times, how do you figure out which cube is different and whether it is heavier or lighter??

Feel free to comment below or email me (momctier@gmail.com) with your answers, but I'll be posting the solution next week.

Let's Paint the...Plants...Black??

The first week of my stressful (but fun!) two-internship summer is over, and I couldn't be happier with how well it went! I love the different projects I'm doing and the students and scientists I get to do them with.  But I also love weekends! And this weekend is especially lovable because I'm spending it in New York with my roommate and her family as they celebrate my roommate's brother's graduation.  So I had a 5-hour long train ride, terrible wifi, and a list of scientific articles that I had downloaded before I left. Can you guess what I spent my time doing?

You got it (I'm assuming; I can't really read your mind)! I spent those 5 hours reading about science.  I read about the Meter Size Barrier, the the Meszaros Effect, and the death of galaxies. All of those were interesting, but none were quite as applicable to my senior thesis (more on that in a bit) as an article sent to me by Dr. Sarah Rugheimer--who just graduated from Harvard University this year!--about plant colors on extrasolar planets, and I'll tell you why here.

You may have heard the joke (perhaps on 30 Rock, as I did just this morning) that at Harvard, we don't have majors. We have concentrations.  And if that weren't pretentious enough, we don't even have double concentrations, we have joint concentrations, which require students to write a joint thesis that combines their two fields.  You may have figured out by reading some of the posts on this blog that I'm kind of, sort of, a little bit interested in astronomy.  Yeah, I love astronomy enough that I'm planning on devoting the next 6 to 8 years of my life learning it, and the rest of my adulthood after that putting what I learned to use.  But I don't love it enough to give up my other passion: folklore.  I am what we Harvardians call a joint concentrator in Astrophysics and Folklore & Mythology. I'm the first one, so the two departments don't really know exactly how to deal with it or me, but I've always been a fan of trailblazing.

People always ask me how astrophysics and folklore go together, and I love it when they do because there are SO MANY WAYS.  There are connections as basic as the stories that accompany each constellation and ones as complex as identifying which ancient cultures may have been more knowledgeable about astronomy than we thought based on their creation myths. I'm doing neither of those, and I'm not really doing anything in between them.  Instead (and, yes, I do get an insanely excited smile on my face every time I get to tell someone this), I'm going to write a science fiction novel based on the exoplanet research I do this summer. And I want that novel to be as scientifically accurate as possible, all the way down to the color of the plants.

I know it's common for kids to ask why the sky is blue, but have you ever wondered why plants are green? I mean, beyond the fact that they have chlorophyll, because they haven't always.  Why have plants evolved to have chlorophyll?  That's what I spent part of my train ride learning.

Our sun emits most of its energy in the range of wavelengths we call "visible."  In fact, we call it visible light because our eyes evolved to see wavelengths at the peak of our sun's spectrum. So it makes sense that plants would evolve in a similar way, but it's more complex than that.

If you paid attention in high school science class (or if you didn't, but you did your own reading on the subject), you probably know that plants look green to us because they absorb all the other colors and reflect green.  What's wrong with green? Does it not taste as good as all the other colors or something? The answer, as in most physics/astronomy questions, has to do with photons.

By the time sunlight makes it through Earth's upper atmosphere, most of the photons traveling around are red, and the numbers decrease as you move down the spectrum to blue.  But blue photons don't have it all bad! They're more energetic than their longer-wavelength counterparts. And green is somewhere in the middle--it doesn't have super impressive quantity or quality.  Plants "know this" and have optimized to get photons high in energy and in numbers, so they just don't need the green photons to make their fuel.

But in extrasolar systems, the star might be totally different from ours!  In the system I'm studying this summer, for example, the star is smaller and dimmer than out own sun.  This means that the plants on a planet in the habitable zone won't get as much light, and will need to absorb all the light they can get, so they'll look black!  Isn't that cool?? It gets even cooler when you think about the ways this seemingly insignificant detail could affect the culture on that planet.

In America, we associate the color green with freshness, rebirth, and nature (the good, clean kind of nature).  We have those associations because plants are green! On a planet where plants are black, color symbolism could be totally shifted from our own.  Black = birth. Maybe green = death. Red = peace.  Wouldn't that be so weird and trippy and awesome? It will be for me, and I hope it will be for anyone who ends up reading my thesis.

Out, Liar!!

You know those days when everything goes well and you can't help but smile the whole time? That's what today was.  Why? Because, in the words of my advisor, Dr. David Kipping (or rather, in the words of some video that Dr. Kipping told me about), I scienced the shit out of today!

By the end of yesterday, I had 5 transit light curves, one for each planet in the system I'm studying, Kepler-186.  By the end of today, I had a list of all of "Good" points and a list of all of the "Outliers". An outlier is a point that is so far away from the average of the other points that we can tell it isn't actually a reflection of what's happening in our target. It might be the result of an instrumental error, or a strange spike in stellar luminosity, or a number of different things. Whatever it is, it isn't part of the transit data I'm interested in, so I don't want it.  My other advisor, Dr. John Johnson, explained it like this:

Getting these lists of good and "bad" points was a multi-step process.  First, I had to identify all of the points that occurred outside of at least one transit for any of the planets. In other words, if a point existed outside any transit for planet b, planet c, planet, d, planet e, or planet f, I made a note of it.  It is from this "Out of Transit" list that I can begin to remove outliers.

Why did I have to first remove the in-transit points? Well, the point of a transit is that its points exist par from the mean amount of light coming from the star. If I removed outliers from the entire dataset, I would also remove most of the transit points, but that's the bit that actually leads to scientific results!

Removing outliers is more-or-less straightforward.  Using the "Out of Transit" data points, I find an average flux value.

For our data, this value is essentially 1.  I then find an acceptable sigma value using a "mad" function from the interwebz. The function finds the Median Absolute Deviation, and calls itself a more robust form of finding the standard deviation.  This is necessary because a standard deviation function is too influenced by the outliers. (Those things really do cause so much trouble!)

The last step is to flag, or take note of, all the points that are a certain number of sigmas away from the mean.  I set that certain number equal to 3, because anything less than that means removing way too much of the data. (See this wikipedia page to better understand why.)

But after all of that, I'm left with this plot!

Please excuse the awful y-axis labels. I'm figuring out how to get rid of that "+9.96e-1" bit. 

You can even see some of the transit points in this plot!  They're the black points that fall way outside the mean. 

That was one part of my day.  After that, I spent another two hours researching galaxy mergers. Well, I spent two hours doing things that will eventually lead to learning more about galaxy mergers. I essentially spent that time reducing Herschel data and playing around with the output fits files in python.  I say "playing around" because I haven't actually managed to do anything too useful yet. But I will soon!  And when I do, I'll write about it here.