gully
@gully.bsky.social
interpretable machine learning for atmospheric and astronomical data analysis, near-IR spectra, climate tech, stars & planets; bikes, Austin, diving off bridges into the ocean.
Very cool paper! Maps out the starspots in exquisite detail, among the best constraints on spot physical properties anywhere. Exoplanets make great scanning reticles for stellar surface features, in addition to their other virtues.
BEHEMOTH of a planet + starspot paper led by @matthewsplanet.bsky.social and @afeinstein20.bsky.social. 🧪🔭 #stellarastro Murphy uses JWST observations of 20 Myr old sun-like star V1298 Tau and its planets to map the stellar surface and its effects on planet measurements. 😍 arxiv.org/abs/2606.16782
FOMO on Cool Stars in Japan right now! What are the cool new talks? Best splinter sessions? Need to know!
Exoplanet atmospheres 🤝 starspots, nice, curious to see the tomographic analysis of the spots
This is all cool because planets are cool, but what about the spots? Well... Stay tuned for a follow-up paper in the next few weeks where we fully characterize the spots on this young solar analog to see how they differ from the Sun 👀🌟
Would adore talking physics-based models in Sydney at NeurIPS! How to make the stars align…
With NeurIPS and the AIP conferences simultaneously in Sydney the week of 7-11 December, would anyone be interested in a differentiable models for physics & astronomy workshop day or two the week before or after?
I pitched Kepler/K2 advertising swag that simply said “THERE ARE SO MANY PLANETS” in all caps. There are!
This was one month after I started working at NASA in the Kepler Science Office. At this point I'd been looking for planets for AWHILE: 2 PhD projects and a postdoc, tens of thousands of stars, with nothing to show except stubbornness and a highly trained eye. This thumb drive had SO MANY PLANETS.
Just did a double take looking at the size of that echelle grating, whoa
Successful Echelle installation 🎉 The heart of HARPS3 is looking amazing 😍
I think astronomers are particularly effective at the use (and acceptance) of pathological scenarios for the purpose of illustration and education. We don’t mind conjuring impossibly weird planets as what-if scenarios. I sense that other fields can view pathological examples as distracting.
If you haven’t seen 3Blue1Brown videos yet, you are missing out! His latest one is on M.C. Escher’s picture frame distortion is absolutely breathtaking, using visuals to introduce complex analysis in what feels a very intuitive way. All his videos are excellent and well worth a browse.
A free, cool idea I was excited about at Kepler/K2: There exists a transformation to make nonstationary stellar variability into stationary. Deep Kernel Learning was the fad at the time, but I’m not sure anyone ever pulled this off successfully. The hope was to make a physics based transformation
I just stopped by UT Austin to return some long lost inventory, and was delighted to see so many familiar faces and hear about the great work going on! 🔭 🪐 my grad school instrumentation memos are actively being used—surreal!
I was about to propose a feature then did YAGNI and stopped, where do I collect my software engineering award.
China 5 year plan includes “Aim to double non-fossil energy in 10 years”, already plateauing emissions ☀️💨⚡️🔋⚛️🌋
New planet detection technique: Look for stars that disappear entirely (black holes in interior, cf Bellinger) then look for light echo plateaus from the reflected light of planets. Apparently ~100 M dwarfs have disappeared consistent with this mechanism. You’d have a few light minutes of signal.
Currently reading the Project Hail Mary book and there’s a ton of astrophysics in it—no spoilers but early on there’s infrared spectroscopy, ALMA, and more. Curious to hear what other astronomers think!
Neat work on precision PSF modeling of Gaia! Little known fact: we captured Kepler driftscan PSFs during downtime in the K2 mission. PSF modeling is great boon for low SNR science, extended objects, etc.
Interesting paper out today discussing improvements in the point spread function model of @esa.int's Gaia satellite in preparation for Gaia DR4. Every time that I read about this instrument I am blown away by how technical and complicated its data is to process ☄️ #galactic #astromethods
Good band name, or maybe a softball team composed in part by punk rock astronomers with a fastball
misspelled radial velocities as radical velocities
Very cool opportunity to make differentiable physics models for EPRV, work with Ben and co, and live in a cool place. Pretty much a dream job, apply!
Very happy to advertise a Postdoctoral Research Associate in Exoplanet Science position with me and Christian Schwab at Macquarie University in Sydney. We'll be working on applying machine learning & differentiable physics models to extremely precise radial velocity surveys.
Still want a JAX for science conference, where all my JAX scientists at
The sheer joy I felt just now discovering that there is a new Dan Foreman Mackey video about JAX.
standing room only for physics? faith in humanity restored ✨👏
I still feel like differentiable models are under appreciated, it’s like a magic super power for interpretability and extensibility. Maybe LLM-assisted coding can bring autodiff to more science teams? Still a mental leap to adoption.
Everything becomes so much easier when you can forward model your data
Looking back on this talk about autodiffable spectral models, there is still so much promise, I’d love to see what folks have been up to in this space! speakerdeck.com/gully/blase-...
blasé: An interpretable transfer learning approach to cool star échelle spectroscopy
Comparison of échelle spectra to synthetic models has become a computational statistics challenge, with over ten thousand individual spectral lines affe…
speakerdeck.com