Applying Probabilistic Inference to Astronomical Spectroscopy #SciPy2020

How can we derive robust astrophysical parameters from noisy spectroscopy?
Talk at SciPy2020, YouTube video is here: https://youtu.be/ME7kSjPe7mM

Transcript

1. Applying Probabilistic Inference to Astronomical Spectroscopy Michael Gully-Santiago, Research Fellow The University of Texas at Austin Department of Astronomy SciPy2020 NOAO/AURA/NSF
2. NOAO/AURA/NSF The spectrum of a star
3. NOAO/AURA/NSF The spectrum of the Sun
4. NOAO/AURA/NSF The spectrum of a different star
5. NOAO/AURA/NSF Astronomical spectroscopy allows you to measure physical properties of stars.
6. NOAO/AURA/NSF Astronomical spectroscopy allows you to measure physical properties of stars. Fundamental/Intrinsic Properties 1. Temperature 2. Surface Gravity 3. Atomic/Molecular composition
7. NOAO/AURA/NSF Astronomical spectroscopy allows you to measure physical properties of stars. Fundamental/Intrinsic Properties 1. Temperature 2. Surface Gravity 3. Atomic/Molecular composition Extrinsic/Kinematic Properties 1. Radial velocity 2. Projected Rotation Speed
8. NOAO/AURA/NSF Astronomical spectroscopy allows you to measure physical properties of stars. Fundamental/Intrinsic Properties 1. Temperature 2. Surface Gravity 3. Atomic/Molecular composition Special Properties 1. Mass accretion 2. Magnetic fields 3. Dust extinction 4. Starspots 5. Exoplanetary atmospheres 6. Laws of physics themselves Extrinsic/Kinematic Properties 1. Radial velocity 2. Projected Rotation Speed
9. NOAO/AURA/NSF Astronomical spectroscopy allows you to measure physical properties of stars. Fundamental/Intrinsic Properties 1. T eff 2. log g 3. [Fe/H], [α/Fe], C/O Special Properties 1. dm/dt 2. B 3. A V 4. f spot 5. R p /R star (λ) 6. log gf, etc. Extrinsic/Kinematic Properties 1. RV 2. v sin i .
10. NOAO/AURA/NSF Some of the best tools for astronomical spectral analysis are built in Python.
11. github.com/jonathansick/awesome-astronomy (some) Python Tools for astronomical spectral analysis
12. github.com/jonathansick/awesome-astronomy (some) Python Tools for astronomical spectral analysis
13. - Physics driven model - Removes “telluric artifacts” from Earth’s atmosphere - Python wrapper for Fortran-based LBLRTM TelFit github.com/kgullikson88/Telluric-Fitter Gullikson et al. 2014 SciPy 2015 Talk!
14. - Physics driven model - Removes “telluric artifacts” from Earth’s atmosphere - Python wrapper for Fortran TelFit github.com/kgullikson88/Telluric-Fitter Gullikson et al. 2014
15. wobble github.com/megbedell/wobble - Data driven model (many spectra of same star) - Removes Earth’s absorption spectrum - Yields Precision Radial Velocities - Built with TensorFlow Bedell et al. 2019
16. psoap github.com/iancze/PSOAP Czekala et al. 2017 - Data driven model (many spectra of same star) - Assumes Earth signals are already removed - Yields orbit of Spectroscopic Binaries - Built with SciPy/cython
17. specmatch-emp github.com/samuelyeewl/specmatch-emp - Nearest Neighbor / Template matching model - Has a large library of observed template spectra - Built with SciPy/AstroPy/Pandas Yee et al. 2017
18. specmatch-emp github.com/samuelyeewl/specmatch-emp - Nearest Neighbor / Template matching model - Has a large library of observed template spectra - Built with SciPy/AstroPy/Pandas - Assumes all stars look like “normal” stars in your library Yee et al. 2017
19. New frontiers in astrophysics yield astronomical spectra that look unlike anything we’ve seen before. → Templates are scarce/non-existent. → Ground-truth labelling is difficult/impossible. We have to model our spectra based on astrophysical theory.
20. We want a Python function that takes in 1. A noisy astronomical spectrum 2. A tunable theoretical model for how that spectrum could have been generated and outputs the-cloud-of-physical-properties-consistent-with-that-data
21. fixed tunable probability cloud Czekala et al. 2015 Astronomical spectral inference (analogous to retrievals in Earth Science Literature)
22. Why is spectral inference hard? 1. Physics-based models are expensive to compute. 2. The models are imperfect. 3. The models have possibly many parameters. 4.
23. Why is spectral inference hard? 1. Physics-based models are expensive to compute. 2. The models are imperfect. 3. The models have possibly many parameters. 4. Degeneracies among parameters give rise to similar spectra. 5. The data possess correlated noise (e.g. from Earth’s atmosphere). 6. The noise properties may not be perfectly known.
24. Due to the computational complexity, self-consistent synthetic spectral models are pre-computed on coarsely sampled grids of physical properties. How do you turn a coarse grid into a smooth function? Husser et al. 2013
25. Starfish github.com/iancze/Starfish Czekala et al. 2015 - Physics driven model emulator - Fits all parameters simultaneously - Built with SciPy/cython/sklearn/ multiprocessing
26. Starfish makes spectral inference possible. 1. Physics-based models are expensive to compute. 2. The models are imperfect. 3. The models have possibly many parameters. 4. Degeneracies among parameters give rise to similar spectra. 5. The data possess correlated noise (e.g. from Earth’s atmosphere). 6. The noise properties may not be perfectly known. Spectral emulation Gaussian Processes MCMC & Gibbs Sampling Local covariance kernels Noise scale inference
27. Spectral emulation quantifies the discretization noise from interpolating coarse model grids. - The interpolation occurs on the weights of PCA eigenspectra computed from the grid volume. - These weights tend to be smooth in the model parameters, giving a better reconstruction than linear interpolation of pixels. Czekala et al. 2015
28. Spectral emulation quantifies the discretization noise from interpolating coarse model grids. Emulation mitigates “piling up” at interpolated grid points (e.g. Cottaar et al. 2014) Mean reconstructed model Covariance matrix of each pixel Czekala et al. 2015
29. Gaussian Processes expect correlated residuals arising from a sea of slightly-off line strengths. Non-stationary kernel downweights routine outliers. Instrumental noise alone underestimates residuals. Net effect of Gaussian Process is to avoid overfitting noise spikes. Starfish covariance matrix “Chi-squared” diagonal matrix Czekala et al. 2015
30. Sub-pixel resampling Convolves instrumental and astrophysical sources of line broadening Enables reasonably precise Radial Velocity applications. Czekala et al. 2015
31. Three science cases enabled by Starfish 1. Starspot physical properties 2. Photospheres of embedded protostars 3. Critically evaluating substellar atmospheres
32. 1. Measuring starspot temperature and coverage area on a young star ^Sunspots are seen on the Sun. Giant starspots confound fundamental properties and are difficult to measure. Somers et al. 2015; Roettenbacher et al. 2016
33. We adapted Starfish to infer all the normal stellar parameters, Plus: - Temperature of the spot, T spot - Coverage fraction of spots, f spot 14 total parameters fit with ensemble sampling with emcee, chunking the IGRINS spectrum into 42 segments matched to spectral order; 21 segments shown here → github.com/BrownDwarf/welter
34. We find ~70-85% coverage fraction of starspots on this extremely spotted young star. The spot temperature is ~2700 K surrounded by ~4100 K ambient photosphere. Gully-Santiago et al. 2017
35. 2. Measuring physical properties of a Class 0 protostar. ^Protostars are shrouded in dust and difficult to observe. We used ~8 hours of Keck time on a single protostar to measure its spectrum. Greene, Gully-Santiago, Barsony 2018 github.com/browndwarf/protostars
36. The spectrum is consistent with a large contracting protostar with a ~1200 K disk possessing 4x the emitting area of protostar. github.com/browndwarf/protostars We added 4 new parameters to Starfish: 1. Disk temperature 2. Disk emitting area 3. Extinction A K 4. Extinction power law Informs strategies for JWST.
37. 3. Fundamental properties and physical chemistry of ultracool substars Gully-Santiago et al. in prep. We’ve extended Starfish to Brown Dwarfs using the Sonora-Bobcat synthethic model grid (Marley et al. in prep) cf. github.com/gully/jammer-Gl570D A sea of molecules blanket the spectra of brown dwarfs making them difficult to interpret. Starfish enables retrieval-like analyses with physically self-consistent models.
38. Key limitations of Starfish and path forward 1. Significant barriers to entry have led to high interest but low adoption 2. Tuning the blocked Gibbs sampler is subtle and slow 3. Training the spectral emulator is computationally demanding and slow
39. Key limitations of Starfish and path forward 1. Significant barriers to entry have led to high interest but low adoption 2. Tuning the blocked Gibbs sampler is subtle and slow 3. Training the spectral emulator is computationally demanding and slow 4. Physical extensions reside in undocumented forks 5. Not set up for auto-differentiation 6. No GPU acceleration
40. starfish.readthedocs.io Major overhaul in v0.3.0! By Miles Lucas Graduate Student at UHawaii
41. starfish.readthedocs.io Major overhaul in v0.3.0! By Miles Lucas Graduate Student at UHawaii New API design should encourage even more experimentation.
42. Key limitations of Starfish and path forward 1. Significant barriers to entry have led to high interest but low adoption 2. Tuning the blocked Gibbs sampler is subtle and slow 3. Training the spectral emulator is computationally demanding and slow 4. Physical extensions reside in undocumented forks 5. Not set up for auto-differentiation 6. No GPU acceleration Addressed in v. 0.3!
43. Key limitations of Starfish and path forward 1. Significant barriers to entry have led to high interest but low adoption 2. Tuning the blocked Gibbs sampler is subtle and slow 3. Training the spectral emulator is computationally demanding and slow 4. Physical extensions reside in undocumented forks 5. Not set up for auto-differentiation 6. No GPU acceleration Addressed in v. 0.3! Applying for NASA funding for support
44. Why are GPUs helpful? - The main bottleneck in Starfish is solving the N~1000 Gaussian Process likelihood. - We cannot use celerite* since the Starfish noise matrix is non-stationary. With modern GPUs we can get to N~20,000 pixel spectra *Foreman-Mackey et al. 2017 github.com/dfm/celerite
45. Why is autodiff important? - MCMC Sampling in high dimensions (10+ parameters) is difficult. - Hamiltonian Monte Carlo (e.g. NUTS) overcomes this challenge by using exact gradients - Autodiff dramatically simplifies writing physical extensions Hoffman & Gelman 2011 arxiv.org/abs/1111.4246 ^ Samples from a 250 dimensional correlated Multivariate Normal
46. Why is autodiff important? - MCMC Sampling in high dimensions (10+ parameters) is difficult. - Hamiltonian Monte Carlo (e.g. NUTS) overcomes this challenge by using exact gradients - Autodiff dramatically simplifies writing physical extensions statmodeling.stat.columbia.edu/2017/03/15/ensemble-methods-doomed-fail-high-dimensions/ Emcee begins to fail for ten(s) of parameters
47. github.com/pyro-ppl/numpyro github.com/google/jax Jax and numpyro make it easy to write models for Hamiltonian MC. - Forward and backward mode autodiff - CPU/GPU/TPU support out of the box
48. github.com/gully/TgiF/
49. github.com/gully/TgiF/
51. You can get automatically get uncertainty contours by computing the Hessian with exact autodiff in Jax.
52. github.com/BrownDwarf/fiatlux/ Proof-of-concept: numpyro, Jax, HAPI, and NVIDIA GPUs to constrain the Temperature-Pressure profile of Earth’s atmosphere. hitran.org/hapi/ It works! Ongoing demos: 20+ parameter models.
53. Key ideas 1. Excellent Python frameworks exist for Spectral Inference 2. Spectral emulation unlocks value from pre-computed synthetic grid models 3. Starfish has enabled new applications domains (starspots, brown dwarf physical chemistry, protostars) 4. Future promise of Jax/Numpyro: autodiff & GPUs will allow us to ask new questions at the scientific frontier
54. Thank you: ➔ Jill Cowan, Enthought, and SciPy2020 organizers and sponsors ➔ Ian Czekala (UC Berkeley), Miles Lucas (U Hawaii), and Starfish contributors ➔ Greg Herczeg (KIAA-Beijing), Tom Greene & Mark Marley (NASA Ames), Caroline Morley (UT Austin) for funding Starfish development ➔ Austin Python Users Group, Beijing Python Meetup, SF Python Meetup