Tostada Granos y Bliss

Here is my first post about one of my most rewarding hobbies: coffee roasting.

I started drinking coffee in college. I tried coffee as a teenager, but never really liked the stuff that my parents would buy from the store. Mostly this was your regular low quality brand. Sometimes they would buy Kona blends, and almost always they bought decaf. A lot of people like Kona, but I don’t, and it wasn’t until much later that I developed a taste for other coffees.

Because I had never really enjoyed coffee, I mostly stuck with mocha drinks if I went to coffee shops with friends. But, somewhere along the way I switched to just drinking regular black coffee. The more I payed attention to it, the more I enjoyed it. I think that is part of why I enjoy it - to fully appreciate coffee it takes time, patience, and focus.

A few years ago I started roasting my own coffee at home. I found an online store that fortunately had a physical store-front in my city.  That store actually specialized in home-brew beer kits, but they also sold coffee for home roasting. I purchased their introductory kit online, and went to the store to get some green coffee beans.

The roasting kit is actually very simple. It is a stove-top whirly pop popcorn maker and a thermometer. That’s it. I punched a hole in the top of the popcorn maker with a corkscrew so I could insert the thermometer. The thermometer itself is actually pretty special - it goes up to 500 F. I’ve never seen one in a store, although they must exist. If you look for a thermometer like this you will probably find candy thermometers, but that isn’t what this is. This one is thin and more like a meat thermometer that goes up to 500 F. It’s actually quite important for the process.

Anyways, I ended up getting some green coffee beans from that store and I dumped them into the popcorn popper pot and started roasting. The first thing I noticed was steam! Lots of steam! And then smoke! Lots of smoke! and then chaff! Lots of chaff! It was a bit of a mess. And it was delicious. The aroma of freshly roasted coffee is intoxicating.

Since I started roasting my own coffee I have learned a lot more about what goes into a good cup. I bought a book and started reading it; although I still haven’t actually finished it. I may write more about coffee roasting in the future, including my dreams for running my own café, but this time I just wanted to say . . .

I miss coffee roasting!!!!

Since living in Chile, I haven’t been able to roast my own coffee. That’s mostly because I don’t know anyone who sells green coffee beans. There is one decent coffee roaster here in Concepcion, and he wouldn’t sell me green beans. I was/am very disappointed. There are many things I miss about the US, and coffee roasting is probably number 3 on the list (the first two being a person and a cat).

You can probably already tell that I enjoy coffee from my chosen background image for this blog. Although, I have to say that that is a sorry-looking batch of roasted coffee. My coffee comes out much more evenly. You can see in the background image that some beans are barely roasted at all, while others are charred. tsk tsk.

I’ve included an image of some coffee that I roasted a long time ago (and subsequently ground up, soaked in hot water, and drank to my delight). Le sigh. I miss good coffee. Also, I am including an image of an antique coffee grinder that I saw in an historical German house outside the town of Frutillar in the south of Chile. Both are original photos, so . . . copyright me?

Until next time, chao!

Kyle D Hiner

Coffee-Roaster Extraordinaire

Proposaling!

Foreword: I actually wrote this post a few months ago while I was working on some proposals for telescope time. Also, yes, I was thinking of starting a blog a few months ago, but didn't commit to it until recently.

Proposals are an interesting beast. In astronomy, the proposals for funding and those for data are typically decoupled. In some cases (e.g., when apply for Hubble Space Telescope), support funding comes with the granted time for data acquisition. These applications are rare, and highly sought after.

I have a fellowship sponsored by the Chilean national government. This fellowship pays my salary and some other costs such as travel and publication costs associated with doing science. However, there is no guarantee that I will actually get time on telescopes in order to collect the data necessary for the project that the fellowship is intended to fund! Thus, in addition to the initial fellowship proposal, I have to write many observing proposals in order to get data. One would think that I am decently good at it by now.

In my experience, every proposal is a snarling jabberwocky that needs to be wrestled and tamed until it fits in a cage of predetermined size and material.

These times are often great for learning a lot about my science, as they require some level of reading. I need to know what I’m talking about right? They are also exciting times, because I think a lot about the projects that I am doing or want to do. But my main point for this post is this:

The amount of time you invest in a proposal needs to be carefully considered. I know people who work to the very last minute on proposals. The idea here is that every change you think of can only make the proposal better, and if that one change makes it good enough to get time, then it is worth making. But I think a little bit differently . . . in part, because that method leads to tremendous amounts of stress, late hours, and sweating bullets trying to get the proposal submitted in the last 10 minutes before the deadline and oh my god, did the website crash or is it just my internet - freak out! I have a typo!

No. Here’s the way I look at it:

The “goodness” of a proposal is a function of the amount of time/effort one spends on it. However, that function is not a power-law, not a quadratic function, not even linear. That function quickly closes in on an asymptote (see figure). And no matter how much you work on that proposal it’s never going above that asymptote. Now, somewhere on the “goodness” scale is a cut-off for how good your proposal has to be in order to be granted time, and many factors go into determining that critical value. As long as your proposal reaches that level, then it is “good enough” - you get the time, you get the data, you go on with your life. And it may in fact be that no matter how much time and effort you dump into your proposal, it will never meet that critical value.

Furthermore, it is nearly impossible to predict what that value is. You’ll go insane if you try to squeeze every last moment into proposaling. So, try not to go down the path of infinite time/effort. At some point there is nothing more you can do to make your proposal get time.

At least that’s what I try to do. Inevitably, I get sucked into the vortex of proposal deadlines.





Figure: Blue - your proposal "goodness". Green - the maximum "goodness" your proposal will ever have (determined by quality of proposed science). Red - critical value at which your proposal will make it past review and be granted time (determined by your writing ability and the mood of the review committee).

Kyle D Hiner
More blogs to come

Epilogue: Since the writing of that initial post I've found out the results of the applications I had submitted. One of my proposals was successful, while two others were denied time. I could probably do an analysis to see if I actually beat the odds, but that would require me doing some more research than I really care to do at this point. In any case, I (and my research group) was granted 3 nights of observing with the Magellan telescope at Las Campanas Observatory, Chile. That's a big deal. Each night costs about $30k to run the telescope. Effectively, I was given ~$90k to do some research. There's more to say on that topic, but this post is long enough already.

Astro-Reports: The Teacup AGN

Recently I read an article regarding the “Teacup AGN”, so named because of its apparent shape in visual images. Although, if you ask me, it doesn’t look much like a teacup, even if it does have a loop.

This is a very interesting case that is reminiscent of Hanny’s Voorwerp galaxy. Both cases exhibit extended emission line regions, which are very large swaths of gas that are ionized. Ionization occurs when diffuse gas is illuminated by very energetic photons. The “ionizing photons” interact with the gas, give energy to electrons, and kick the electrons off of their atoms. Subsequently, an electron freed in this manner occasionally falls back down onto an atom. When it does so, it gives off energy by emitting another photon. These are the photons that we see when we observe the gas.

If you take a spectrum of the gas, you’ll be able to identify discrete bright spots - what we call emission lines. Some of those emission lines show up stronger than others. This is the result of various physical conditions such as the density of the gas, and the light that is causing it to be ionized. What that means is that if you observe the emission line intensities and their ratios, you can infer things about the ionizing source. In the Teacup AGN, that ionizing source is an active galactic nucleus. I can write more about them later.

In the paper I read recently, the investigators (Gagne, et al.) measured the spectrum at discrete locations along the extended emission line region (the ionized gas). Then, they predicted properties of the ionizing source based on all those individual data points. Specifically, they calculated the luminosity (or brightness) of the active nucleus that would be needed to create all the emission line strengths and ratios that they observed at the specific locations in the galaxy. And because light has a finite travel speed, this allowed them to track the luminosity of the source over time.

By analogy, the analysis worked, because each spot in the gas acted like a photograph of the active nucleus. The "photographs" from the outer edges of the galaxy showed a very bright source and those from the inner regions showed a dim source. Because the "photos" from the inner region are more recent than those from the outer regions, it can be inferred that the active nucleus dimmed over that time period.

The authors’ main result is that the illuminating source decreased its brightness by a factor of 100 over the course of 46,000 years. That is not that long relative to the lifetime of stars and galaxies. The result, suggests that active nuclei can turn on and off rather quickly.



Kyle D Hiner
Learning to crawl blog.

Python Pt 1

Python pt. 1 

In the past couple months, I have been trying to get more experienced with the python programming language. I do not have a very strong programming background. I took a C/C++ course in undergraduate, but at the graduate level, I’ve had to teach myself IDL. I never had a statistics course, or a computational methods course. Therefore, I feel quite behind when it comes to scientific computing and data science. In order to rectify this, and to make myself a stronger candidate for positions outside academia, I am trying to develop my skill set.

Python is a completely free programing language that anyone with time and desire can learn. Python is available for nearly all operating systems (Linux/Unix, OS X, Windows). Fortunately, there are plenty of freely available tools to help one learn python. I have decided to work through The Python Tutorial and I am using version 2.7.5. This is not the most recent release of python, but I don’t really want to dig around in my computer to see if I have the most recent release somewhere or not.

After spending some time with the python tutorial, I had also learned about many other free resources for python online. I decided to take a look at the Udacity course Intro to Computer Science, which gives the basics of programming using the python language. I was already familiar with a lot of the basic logical statements like loops and conditions from my previous experience in programming (C/C++ class and working knowledge of IDL), but the course was a good intro to the syntax of python, and showed off some of its features.

The main objective in the course is to build a web search engine, which I found very interesting. There are obviously many good search engines that already exist, which makes the task of building one very accessible to a general audience, if a bit less practical. Probably no one is going to substitute their homemade search engine in place of google, but it is nice to know that one could, given sufficient resources, and without an incredible amount of sophistication.

Actually building the search engine was broken down into parts - first building a web crawler that searches web pages for links to other web pages. Then one has to build up an index of pages and match that index with keywords on the page. Finally, to make the engine practical, one needs to build a ranking system that returns pages in an order that, hopefully, matches the user’s intentions. The ranking system implemented in the Intro to Computer Science course is called PageRank, and is the ranking system that Larry Page used in the early days of google. It basically ranks pages based on the number of other useful pages that link to that page, as a kind of “popularity” measure of web pages.

It took me about two months to actually complete the free-access version of the Intro to Computer Science course on Udacity. I found the exercise quite useful, and I’d like to move to primarily using python for my research purposes, when I can. That is a big move, and opens another can of worms that I won’t get into - mainly because I don’t have experience doing it yet.

I enjoyed the course so much that I started working on another Udacity course: Intro to Data Science, which gives an overview of some other very interesting software packages including Pandas for Python, SQL, and API. I’ve only worked through the first two lessons so far, but I am really enjoying it. I will write another post on the topic after completing more of the course.

Kyle D Hiner, Ph.D.

Hello World!

Hello World!

Let me introduce myself. My name is Kyle Hiner, and I am a post-doctoral scholar working in Chile. I earned my PhD from UC Riverside in 2012, and I study astronomy. I have many interests that go beyond astronomy, and I will definitely post about them as this blog progresses.

This is my first blog post, in which I will outline a set of goals to achieve/maintain during the course of this blog. The main purpose of this blog is for me to work on communicating various things to a general audience, and to participate in larger discussions that are occurring in the big wide world. Mostly I will try to stick with topics that I know about or am interested in. I may post on something that I have an interest but no knowledge, or knowledge but no interest. Hopefully we'll keep the later to a minimum.

Post frequency:
I will make an attempt to publish at least two posts per month.

Post length/depth:
I will post at least 3 paragraphs on every topic. I will make an attempt to include relevant data and citations when appropriate.

Post subject matter:
Astronomy, data science, independent learning, academia and higher education, other professional activities, US expat living/working, personal activities, and probably a bunch of other things that strike my fancy.


Well I believe that covers the very basics. I know you don't know anything about me yet, but stay tuned. There just might be more to come.

Kyle D Hiner, PhD
Newborn Blogger