Big HI Data and Artificial Intelligence Team Up to Decode the Multiphase ISM in Galaxies

Size: px

Start display at page:

Download "Big HI Data and Artificial Intelligence Team Up to Decode the Multiphase ISM in Galaxies"

Wilfrid Heath
8 years ago
Views:

1 Big HI Data and Artificial Intelligence Team Up to Decode the Multiphase ISM in Galaxies GALFA-HI Claire Murray University of Wisconsin - Madison Robert Lindner (UW Madison), Snežana Stanimirović (UW Madison), W. M. Goss (NRAO), Carl Heiles (UC Berkeley), John Dickey (UTas), Patrick Hennebelle (CEA) + the rest of the 21-SPONGE team

2 Outline 1. Open questions, observations of the multiphase ISM 2. Pros and cons of future big data 3. Autonomous Gaussian Decomposition: turning the cons into pros! 4. Applications to observations and simulations

3 Important Open Questions What are the properties (T s, N(HI), etc ) of HI in all phases?

4 Important Open Questions What are the properties (T s, N(HI), etc ) of HI in all phases? Unconstrained!

5 Important Open Questions How much HI exists in each phase? CNM, WNM, and unstable fractions depend on input physics (e.g. MacLow et al. 2005, Audit & Hennebelle 2005, Hill et al. 2012) Weak turbulence Strong turbulence Pressure Pressure Density Density Audit & Hennebelle 2005

6 Comparing simulations with observations is essential, and difficult! Simula:ons* Observa:ons* Physical*quan::es* Synthe:c*spectra* AGD() Gaussian*components* i, v i, v i, Observed*spectra* Gaussian*components* i, AGD() v i, v i,

7 Better observational constraints 21-SPONGE 21-cm Spectral line Observations of Neutral Gas with the (E)VLA public.nrao.edu, NRAO/AUI/NSF

8 21-SPONGE VLA 21-cm Spectral line Observations of Neutral Gas with the (E)VLA 58 sources: S>3 Jy, b >10 Matching HI emission from Arecibo Observatory Arecibo High-sensitivity HI absorption: στ ~7 x 10-4

9 21-SPONGE VLA 21-cm Spectral line Observations of Neutral Gas with the (E)VLA 58 sources: S>3 Jy, b >10 Matching HI emission from Arecibo Observatory Arecibo High-sensitivity HI absorption: στ ~7 x 10-4 Superior sensitivity allows us to detect unstable/wnm lines in absorption! Millennium Survey (Heiles & Troland 2003) Max T s ~600 K 21-SPONGE Max T s ~ 1500 K Murray et al. 2015, in press

10 21-SPONGE VLA 21-cm Spectral line Observations of Neutral Gas with the (E)VLA 58 sources: S>3 Jy, b >10 Matching HI emission from Arecibo Observatory Arecibo High-sensitivity HI absorption: στ ~7 x 10-4 Unstable fraction ~ 20% Millennium Survey = 48% (upper limits) Millennium Survey (Heiles & Troland 2003) Max T s ~600 K 21-SPONGE Max T s ~ 1500 K Murray et al. 2015, in press

11 21-SPONGE 21-cm Spectral line Observations of Neutral Gas with the (E)VLA Stacked absorption Murray et al. 2014

12 21-SPONGE 21-cm Spectral line Observations of Neutral Gas with the (E)VLA Stacked emission Probes Lyα scattering excitation! Stacked absorption Murray et al. 2014

13 21-SPONGE limited to 10s of sources WNM GASS; McClure-Griffiths et al. 2009

14 HI Absorption Sightlines Visible to SKA-1 WNM ~10 6 spectra, 5 minutes each ~10 years to analyze! McClure-Griffiths et al. 2015

15 To the rescue AUTONOMOUS GAUSSIAN DECOMPOSITION Lindner et al in press (arxiv: )

16 Automatic, efficient decomposition of 1D spectral data into Gaussian functions Good initial guesses are chosen without human interaction Generally applicable to any data set! Lindner et al in press (arxiv: )

17 Autonomous Gaussian Decomposition (AGD) Provides optimized initial guesses for multi-component Gaussian fit 1. Computer vision using 1 st -4 th numerical derivatives 2. Regularization provides smooth derivatives 3. Supervised machinelearning optimizes regularization parameters to maximize guess accuracy Guess criteria:! Python package GaussPy (available upon publication) gausspy.software@gmail.com

18 21-SPONGE AGD Guesses AGD Best-Fit Human Best-fit

19 AGD vs. Human (me) on 21-SPONGE Number of components Noise in resulting best fit Lindner et al in press (arxiv: )

20 Future Applications

21 Simula:ons* Observa:ons* Physical*quan::es* Synthe:c*spectra* AGD() Gaussian*components* i, v i, v i, Observed*spectra* Gaussian*components* i, AGD() v i, v i, 30*

22 AGD Results Blue: 10 4 synthetically- Input:*10k*spectra* Output:*20k*Gaussian* components*(blue)* observed HI absorption Component*space*has*two* lines (Kim et al. 2014) phases*(cnm,*wnm)* Black: 21-SPONGE VLA HI absorption lines Simulated* WNM * (Murray et al. 2015) components*not*seen*in* observa:ons* AGD*results* Gaussian*components* Stacking% result* 21ASPONGE*data:**Murray*et*al.*(2014b,*in*prep)* Simula:on*data:*Kim*et*al.*(2014)* 34*

23 ??

24 Matching Gaussians to Clouds in Simulations Density LOS distance Optical Depth Velocity

25 Matching Gaussians to Clouds in Simulations First statisticallyrobust quantification of cloud-component correspondence!

27 Conclusions 21-SPONGE will constrain the uncertain mass distribution of HI as a function of T s, as the largest high-sensitivity HI absorption survey Autonomous Gaussian Decomposition (AGD) provides optimized initial guesses (and final fits) for multi-component Gaussian models - Python implementation: GaussPy available soon - Generally applicable to any spectral data set AGD results are comparable to human decompositions AGD enables objective, unbiased comparisons between observations and simulations (e.g., 21-SPONGE vs. 3D hydro sims) - Confirms correspondence between physical clouds and Gaussians - Future: T s completeness function, constraints on Lyα photon field and more!

Highlights from the VLA/ANGST Survey

Highlights from the VLA/ANGST Survey Evan Skillman U. Minnesota Gas in Galaxies 2011: From Cosmic Web to Molecular Clouds Kloster Seeon, Germany 16/06/2011 VLA/ANGST The ANGST HST Treasury Project allowed