A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

Slides from the AAS Splinter talk I gave on machine learning for normal astronomers.

The full talk video was also posted here:
https://www.youtube.com/watch?v=jaB5jghcu20

Full Abstract:
We have entered an era in observational astronomy in which sky surveys routinely release massive datasets. While this wealth of data is critical for determining rates of rare phenomena (e.g. transiting exoplanets or tidal disruption events), it also enables a new kind of data-driven astrophysics (e.g. "hidden" correlations in our data that point towards new or challenging undetandings of physics). Machine learning is simply one tool available to us to discover these new trends or make predictions from our growing volume of data. However, machine learning alone cannot make astrophysical discoveries, and astronomers are still required to interpret astrophysical meaning from our data. Here I will discuss some uses of machine learning in analyzing data from the Kepler and Gaia missions, and attempt to highlight some of the opportunities and limitations in its use.

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James Davenport

January 07, 2019
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Transcript

  1. 1.

    A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

    James R. A. Davenport jradavenport UNIVERSITY OF WASHINGTON !1
  2. 2.

    A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

    jradavenport !2 Number of ADS abstracts including “machine learning”
  3. 4.

    jradavenport !4 *normal, lots of people doing it. And this

    is a good thing! (i.e. don’t be scared!) Machine Learning is Boring*
  4. 5.

    jradavenport !5 (i.e. can run most algorithms on your laptop)

    Yet sample size is big enough that interesting/rare things can be found Most of our work isn’t “big data”
  5. 6.

    jradavenport !6 Our data is becoming better suited for ML

    • big datasets (Mario’s talk), especially Gaia! • easier than ever to get data (Vizier/Xmatch, ADS, journals, Github, Zenodo…) • value-added datasets for surveys (e.g. stellar parameters from SDSS)
  6. 7.

    jradavenport !7 ML is easier than ever to use •

    robust, open source libraries available • many programming languages • many domain (astro) experts & workshops available
  7. 11.
  8. 16.

    jradavenport !16 Question: Which tracks are starspots, how do they

    emerge/decay? Kepler 17 Example 1: Clustering starspot evolution tracks
  9. 17.

    jradavenport !17 Question: Which tracks are starspots, how do they

    emerge/decay? Manual clustering? Kepler 17 Example 1: Clustering starspot evolution tracks
  10. 18.

    jradavenport !18 Question: Which tracks are starspots, how do they

    emerge/decay? Manual clustering? Kepler 17 Example 1: Clustering starspot evolution tracks Aside: “training data” is super 
 important for many problems!
  11. 21.

    jradavenport !21 DBSCAN: Density-based spatial clustering of applications with noise

    Kepler 17 Example 1: Clustering starspot evolution tracks
  12. 22.

    jradavenport !22 DBSCAN: Density-based spatial clustering of applications with noise

    One starspot’s emergence/decay! Kepler 17 See more upcoming work by Kosuke Namekata Example 1: Clustering starspot evolution tracks
  13. 23.

    Time Flux jradavenport !23 Example 2: Modeling a complex stellar

    flare Flare! Question: Is there (quasi-) sinusoidal behavior in the flare decay? https://github.com/RileyWClarke/QPP-GP
  14. 24.

    Time Flux jradavenport !24 Flare! Question: Is there (quasi-) sinusoidal

    behavior in the flare decay? https://github.com/RileyWClarke/QPP-GP versus Example 2: Modeling a complex stellar flare
  15. 25.

    jradavenport !25 Time Flux Only study decay Residual Flux Time

    Could fit with a damped 
 harmonic oscillator Question: Is there (quasi-) sinusoidal behavior in the flare decay? Difficult to classify sinusoidal vs. stochastic,
 & strict vs quasi sinusoid https://github.com/RileyWClarke/QPP-GP Example 2: Modeling a complex stellar flare
  16. 27.

    jradavenport !27 Residual Flux Gaussian Process https://github.com/RileyWClarke/QPP-GP Celerite Use an

    exponential + 
 simple-harmonic-oscillator kernel Example 2: Modeling a complex stellar flare
  17. 28.

    jradavenport !28 https://github.com/RileyWClarke/QPP-GP No Period Candidate Periods Objective search Robust

    uncertainties!
 (with MCMC) Gaussian Process Example 2: Modeling a complex stellar flare
  18. 29.

    jradavenport !29 Example 3: Modeling photometric metallicities From APOGEE [3.4]

    - [4.6] Observation: [Fe/H] gradient in stars https://github.com/jradavenport/ingot/ Gaia DR2 + (WISE + 2MASS) + APOGEE
  19. 30.

    jradavenport !30 From APOGEE [3.4] - [4.6] Observation: [Fe/H] gradient

    in stars We could build a complex 
 polynomial or spline model https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  20. 31.

    jradavenport !31 Observation: [Fe/H] gradient in stars From APOGEE [3.4]

    - [4.6] We could build a complex 
 polynomial or spline model Tedious, and difficult to add
 additional dimensions! https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  21. 32.

    jradavenport !32 Observation: [Fe/H] gradient in stars From APOGEE [3.4]

    - [4.6] We could build a complex 
 polynomial or spline model Tedious, and difficult to add
 additional dimensions! Or use a simple, flexible 
 ML model! https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  22. 33.

    jradavenport !33 KNearestNeighbors Xdata = (G-J, W1-W2)
 Ydata = [Fe/H]

    Data Fit https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  23. 34.

    jradavenport !34 KNearestNeighbors Xdata = (G-J, W1-W2)
 Ydata = [Fe/H]

    Data Fit https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  24. 35.

    jradavenport !35 KNearestNeighbors Result: a simple to use “surface”,
 no

    tweaking for shape/order, 
 extend to addtional dimensions easily 1 Million new stars with no spectra https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities
  25. 36.

    jradavenport !36 KNearestNeighbors Result: a simple to use “surface”,
 no

    tweaking for shape/order, 
 extend to addtional dimensions easily 1 Million new stars with no spectra https://github.com/jradavenport/ingot/ Example 3: Modeling photometric metallicities