What tools (or self-rolled code) do people prefer when making big publishable figures of spectra? The example given was an echelle spectrum, where the spectrum is loooong, and has many spectral orders to stack onto one or more pages of the journal.
Since this is a shamelessly self-generated Ask AstroBetter, I will award a beer for the best answer (or source code.)
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TEDxMidAtlantic recently, where I gave a talk about space telescopes and modern astrophysics. It was thrilling (and frightening) to talk to an enthusiastic crowd of 800 people at a professionally-produced event. The next speaker, my favorite chef Jose Andres, even began his talk by riffing on mine: “I’m happy we’re finding water on distant planets. With water, I can cook — I’ll have a job forever.”
I was amazed at how excited the TEDx crowd was, in person and on Twitter, by dark energy. I would have thought this was old hat — the discovery was 1998, the Nobel prize was 2011. People are clued in, right? Nope, not at all. I was mobbed by people who wanted to know about Dark Energy. What is it? Can we find it on Earth? What happens if the Universe flies apart? Apparently even folks in bars want to learn about dark energy.
So, what can we learn from this?
These, I would argue, are the three most important recent discoveries in astrophysics. Everybody should know them, right? One was even the 2011 Nobel Prize in Physics. But I’m finding that people *don’t* know this stuff, and are blown away when they find out.
This is really helpful for me to know, because I’m tempted to talk about my own research in more detail than a public audience wants. So I’m now trying a new approach of the Really Big Picture, and then add a dash of “honest-to-god actual-research” near the end. Audiences are actually fine with the bulge mass / black hole mass relation, if it’s explained clearly. And they like seeing a little data, especially raw images from Hubble or Spitzer, and how we turn those into results. But we need to keep the big picture out in front, because the public doesn’t know it yet.
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And for my last twitter review post, here are some miscellaneous quotes or paraphrases from several different talks that I found interesting, humorous, provocative, or inspiring.
What were your favorite moments of AAS 219?
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Continuing with my twitter review of AAS 219 sessions:
This was a panel discussion where panel members included heads of postdoc fellowship programs and successful ex-postdocs who have moved on to faculty, research staff, industry, and education positions. Overall the discussion was interesting but rather open-ended. Here are my tweets, with some additional explanation in a couple of cases.
If you attended the session, what were your questions and take-away messages?
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