Seeing the Sky. Visualization & Astronomers

Transcription

1 Seeing the Sky Visualization & Astronomers Alyssa A. Goodman Harvard Smithsonian Center for Astrophysics & Radcliffe Institute for Advanced

2 WorldWide Telescope F= Gm 1m2 R 2 glue multidimensional data exploration

3 Paper of the Future Data Cognition Language* Pictures Communication * Language includes words & math

4 Why Galileo is my Hero Explore-Explain-Explore Notes for & re-productions of Siderius Nuncius 1610

5 Galileo s New Order, A WorldWide Telescope Tour by Goodman, Wong & Udomprasert 2010 WWT Software Wong (inventor, MS Research), Fay (architect, MS Reseearch), et al., now open source, hosted by AAS, Phil Rosenfield, Director see wwtambassadors.org for more on WWT Outreach WorldWide Telescope

6 Galileo s New Order, A WorldWide Telescope Tour by Goodman, Wong & Udomprasert 2010 WWT Software Wong (inventor, MS Research), Fay (architect, MS Reseearch), et al., now open source, hosted by AAS, Phil Rosenfield, Director see wwtambassadors.org for more on WWT Outreach WorldWide Telescope

7 Paper of the Future Data Cognition Language* Pictures Communication * Language includes words & math

8 enabled by d3.js (javascript) outputs d3po Cognition Communication [demo] [video] Many thanks to Alberto Pepe, Josh Peek, Chris Beaumont, Tom Robitaille, Adrian Price-Whelan, Elizabeth Newton, Michelle Borkin & Matteo Cantiello for making this posible.

9 Centuries from Galileo to Galileo

10 WorldWide Telescope F= Gm 1m2 R 2 glue multidimensional data exploration

11 glue multidimensional data exploration WorldWide Telescope

12 glue multidimensional data exploration WorldWide Telescope

13 Data, Dimensions, Display 1D: Columns = Spectra, SEDs or Time Series 2D: Faces or Slices = Images 3D: Volumes = 3D Renderings, 2D Movies 4D: Time Series of Volumes = 3D Movies

14 Data, Dimensions, Display Spectral Line Observations Loss of 1 dimension Mountain Range No loss of information

15 Data, Dimensions, Display mm peak (Enoch et al. 2006) sub-mm peak (Hatchell et al. 2005, Kirk et al. 2006) 13CO (Ridge et al. 2006) mid-ir IRAC composite from c2d data (Foster, Laakso, Ridge, et al.) Optical image (Barnard 1927)

16 3D Viz made with VolView

17 LETTERS NATURE Vol January D PDF High-Dimensional data in a Paper on its way to the Future [demo/video] a c 8 T mb (K) d 8 T mb (K) Self-gravitating leaves v z y (dec.) y (dec.) v z x (RA) x (RA) Self-gravitating structures All structure CLUMPFIND segmentation data, CLUMPFINDtypicallyfindsfeatures on a limitedrangeofscales, abovebutclosetothephysicalresolution ofthedata, and itsresults can be overly dependent on input parameters. By tuning CLUMPFIND s two free parameters, the same molecular-line data set 8 can be used to show either that the frequency distribution of clump mass is the same as the initial mass function of stars or that it follows the much shallower mass function associated with large-scale molecular clouds (Supplementary Fig. 1). Four years before the advent of CLUMPFIND, structure trees 9 were proposed as a way to characterize clouds hierarchical structure v z y (dec.) x (RA) 0 Figure 2 Comparison of the dendrogram and CLUMPFIND featureidentification algorithms as applied to 13 CO emission from the L1448 region of Perseus. a, 3D visualization of the surfaces indicated by colours in the dendrogram shown in c. Purple illustrates the smallest scale selfgravitating structures in the region corresponding to the leaves of the dendrogram; pink shows the smallest surfaces that contain distinct selfgravitating leaves within them; and green corresponds to the surface in the data cube containing all the significant emission. Dendrogram branches corresponding to self-gravitating objects have been highlighted in yellow over the range of T mb (main-beam temperature) test-level values for which the virial parameter is less than 2. The x y locations of the four selfgravitating leaves labelled with billiard balls are the same as those shown in Fig. 1. The 3D visualizations show position position velocity (p p v) space. RA, right ascension; dec., declination. For comparison with the ability of dendrograms (c) to track hierarchical structure, d shows a pseudodendrogram of the CLUMPFIND segmentation (b), with the same four labels used in Fig. 1 and in a. As clumps are not allowed to belong to larger structures, each pseudo-branch in d is simply a series of lines connecting the maximum emission value in each clump to the threshold value. A very large number of clumps appears in b because of the sensitivity of CLUMPFIND to noise and small-scalestructurein the data. In the online PDF version, the 3D cubes (a and b) can be rotated to any orientation, and surfaces can be turned on and off (interaction requires Adobe Acrobat version or higher). In the printed version, the front face of each 3D cube (the home view in the interactive online version) corresponds exactly to the patch of sky shown in Fig. 1, and velocity with respect to the Local Standard of Rest increases from front (20.5 km s 21 ) to back (8 km s 21 ). b Click to rotate using 2D maps of column density. With this early 2D work as inspiration, we have developed a structure-identification algorithm that abstracts the hierarchical structure of a 3D (p p v) data cube into an easily visualized representation called a dendrogram 10. Although well developed in other data-intensive fields 11,12, it is curious that the application of tree methodologies so far in astrophysics has been rare, and almost exclusively within the area of galaxy evolution, where merger trees are being used with increasing frequency 13. Figure 3 and its legend explain the construction of dendrograms schematically. The dendrogram quantifies how and where local maxima of emission merge with each other, and its implementation is explained in Supplementary Methods. Critically, the dendrogram is determined almost entirely by the data itself, and it has negligible sensitivity to algorithm parameters. To make graphical presentation possible on paper and 2D screens, we flatten the dendrograms of 3D data (see Fig. 3 and its legend), by sorting their branches to not cross, which eliminates dimensional information on the x axis while preserving all information about connectivity and hierarchy. Numbered billiard ball labels in the figures let the reader match features between a 2D map (Fig. 1), an interactive 3D map (Fig. 2a online) and a sorted dendrogram (Fig. 2c). A dendrogram of a spectral-line data cube allows for the estimation of key physical properties associated with volumes bounded by isosurfaces, such as radius (R), velocity dispersion (s v ) and luminosity (L). The volumes can have any shape, and in other work 14 we focus on the significance of the especially elongated features seen in L1448 (Fig. 2a). The luminosity is an approximate proxy for mass, such that M lum 5 X 13CO L 13CO, where X 13CO cm 2 K 21 km 21 s (ref. 15; see Supplementary Methods and Supplementary Fig. 2). The derived values for size, mass and velocity dispersion can then be used to estimate the role of self-gravity at each point in the hierarchy, via calculation of an observed virial parameter, a obs 5 5s v 2 R/GM lum. In principle, extended portions of the tree (Fig. 2, yellow highlighting) where a obs, 2 (where gravitational energy is comparable to or larger than kinetic energy) correspond to regions of p p v space where selfgravity is significant. As a obs only represents the ratio of kinetic energy to gravitational energy at one point in time, and does not explicitly capture external over-pressure and/or magnetic fields 16, its measured value should only be used as a guide to the longevity (boundedness) of any particular feature. Intensity level Local max Test level Local max Merge Local max Merge Leaf Trunk Figure 3 Schematic illustration of the dendrogram process. Shown is the construction of a dendrogram from a hypothetical one-dimensional emission profile (black). The dendrogram (blue) can be constructed by dropping a test constant emission level (purple) from above in tiny steps (exaggerated in size here, light lines) until all the local maxima and mergers are found, and connected as shown. The intersection of a test level with the emission is a set of points (for example the light purple dots) in one dimension, a planar curve in two dimensions, and an isosurface in three dimensions. The dendrogram of 3D data shown in Fig. 2c is the direct analogue of the tree shown here, only constructed from isosurface rather than point intersections. It has been sorted and flattenedfor representation on a flat page, as fully representing dendrograms for 3D data cubes would require four dimensions. Macmillan Publis hers Limited. All rights res erved Leaf Branch Leaf Goodman et al. 2009, Nature, cf: Fluke et al. 2009

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19 Why AstronomicalMedicine? Keith Perseus z is depth into head z is line-of-sight velocity

20 Why AstronomicalMedicine? Composite X-ray CT/MRI composite CT Astronomy & Medicine both rely on Optical high-dimensional, Radio big, wide, MRI data for insight. SPECT chandra.harvard.edu/photo/2014/m106/ Chang, et al. 2011, brain.oxfordjournals.org/content/134/12/3632

21 glue multidimensional data exploration WorldWide Telescope

22 Wide Data mm peak (Enoch et al. 2006) sub-mm peak (Hatchell et al. 2005, Kirk et al. 2006) 13CO (Ridge et al. 2006) mid-ir IRAC composite from c2d data (Foster, Laakso, Ridge, et al.) Optical image (Barnard 1927)

23 Wide Data Temperature Foreground amplitudes from Commander, Planck Data [Feb 2015]

24 WorldWide Telescope Big DAta

25 BIG DATA and Human-Aided Computing mark bubbles machinelearning algorithm (Brut) example here from: Beaumont, Goodman, Kendrew, Williams & Simpson 2014; based on Milky Way Project catalog (Simpson et al. 2013), which came from Spitzer/GLIMPSE (Churchwell et al. 2009, Benjamin et al. 2003), cf. Shenoy & Tan 2008 for discussion of HAC; astroml.org for machine learning advice/tools

26 BIG DATA and Human-Aided Computing mark neurons machinelearning algorithm (RF+CRF) example here from: Kaynig...Lichtman...Pfister et al. 2013, Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images ; cf. Shenoy & Tan 2008 for discussion of HAC; astroml.org for machine learning advice/tools (Note: RF=Random Forest; CRF=Conditional Random Fields.)

27 Big and Wide Data Movie: Volker Springel, formation of a cluster of galaxies. Millenium Simulation requires 25TB for output.

28 glue multidimensional data exploration WorldWide Telescope

29 LETTERS NATURE Vol January D PDF High-Dimensional data in a Paper on its way to the Future a c 8 T mb (K) d 8 T mb (K) Self-gravitating leaves v z y (dec.) x (RA) Self-gravitating structures All structure CLUMPFIND segmentation data, CLUMPFINDtypicallyfindsfeatures on a limitedrangeofscales, abovebutclosetothephysicalresolution ofthedata, and itsresults can be overly dependent on input parameters. By tuning CLUMPFIND s two free parameters, the same molecular-line data set 8 can be used to show either that the frequency distribution of clump mass is the same as the initial mass function of stars or that it follows the much shallower mass function associated with large-scale molecular clouds (Supplementary Fig. 1). Four years before the advent of CLUMPFIND, structure trees 9 were proposed as a way to characterize clouds hierarchical structure v z y (dec.) x (RA) 0 Figure 2 Comparison of the dendrogram and CLUMPFIND featureidentification algorithms as applied to 13 CO emission from the L1448 region of Perseus. a, 3D visualization of the surfaces indicated by colours in the dendrogram shown in c. Purple illustrates the smallest scale selfgravitating structures in the region corresponding to the leaves of the dendrogram; pink shows the smallest surfaces that contain distinct selfgravitating leaves within them; and green corresponds to the surface in the data cube containing all the significant emission. Dendrogram branches corresponding to self-gravitating objects have been highlighted in yellow over the range of T mb (main-beam temperature) test-level values for which the virial parameter is less than 2. The x y locations of the four selfgravitating leaves labelled with billiard balls are the same as those shown in Fig. 1. The 3D visualizations show position position velocity (p p v) space. RA, right ascension; dec., declination. For comparison with the ability of dendrograms (c) to track hierarchical structure, d shows a pseudodendrogram of the CLUMPFIND segmentation (b), with the same four labels used in Fig. 1 and in a. As clumps are not allowed to belong to larger structures, each pseudo-branch in d is simply a series of lines connecting the maximum emission value in each clump to the threshold value. A very large number of clumps appears in b because of the sensitivity of CLUMPFIND to noise and small-scalestructurein the data. In the online PDF version, the 3D cubes (a and b) can be rotated to any orientation, and surfaces can be turned on and off (interaction requires Adobe Acrobat version or higher). In the printed version, the front face of each 3D cube (the home view in the interactive online version) corresponds exactly to the patch of sky shown in Fig. 1, and velocity with respect to the Local Standard of Rest increases from front (20.5 km s 21 ) to back (8 km s 21 ). b Click to rotate using 2D maps of column density. With this early 2D work as inspiration, we have developed a structure-identification algorithm that abstracts the hierarchical structure of a 3D (p p v) data cube into an easily visualized representation called a dendrogram 10. Although well developed in other data-intensive fields 11,12, it is curious that the application of tree methodologies so far in astrophysics has been rare, and almost exclusively within the area of galaxy evolution, where merger trees are being used with increasing frequency 13. Figure 3 and its legend explain the construction of dendrograms schematically. The dendrogram quantifies how and where local maxima of emission merge with each other, and its implementation is explained in Supplementary Methods. Critically, the dendrogram is determined These almost entirely by the are data itself, and it has negligible sensitivity to algorithm parameters. To make graphical presentation possible on paper and 2D screens, we flatten the dendrograms of 3D data (see Fig. dead 3 and its legend), by sorting their branches to not cross, which eliminates dimensional information on the x axis while preserving all information about connectivity and hierarchy. Numbered panels! billiard ball labels in the figures let the reader match features between a 2D map (Fig. 1), an interactive 3D map (Fig. 2a online) and a sorted dendrogram (Fig. 2c). That s A dendrogram of a spectral-line data not cube allows for the estimation of key physical properties associated with volumes bounded by isosurfaces, such as radius (R), velocity dispersion (s v ) and luminosity (L). The volumes good can have any shape, and in other work 14 we focus on the significance of the especially elongated features seen in L1448 (Fig. 2a). The luminosity is an approximate proxy for mass, such that M lum 5 X 13CO L 13CO, where X 13CO cm 2 K 21 km 21 s (ref. 15; see Supplementary Methods and Supplementary Fig. 2). The derived values for size, mass and velocity dispersion can then be used to estimate the role of self-gravity at each point in the hierarchy, via calculation of an observed virial parameter, a obs 5 5s 2 v R/GM lum. In principle, extended portions of the tree (Fig. 2, yellow highlighting) where a obs, 2 (where gravitational energy is comparable to or larger than kinetic energy) correspond to regions of p p v space where selfgravity is significant. As a obs only represents the ratio of kinetic energy to gravitational energy at one point in time, and does not explicitly capture external over-pressure and/or magnetic fields 16, its measured value should only be used as a guide to the longevity (boundedness) of any particular feature. Intensity level Local max Test level Local max Merge Local max Merge Leaf Trunk Figure 3 Schematic illustration of the dendrogram process. Shown is the construction of a dendrogram from a hypothetical one-dimensional emission profile (black). The dendrogram (blue) can be constructed by dropping a test constant emission level (purple) from above in tiny steps (exaggerated in size here, light lines) until all the local maxima and mergers are found, and connected as shown. The intersection of a test level with the emission is a set of points (for example the light purple dots) in one dimension, a planar curve in two dimensions, and an isosurface in three dimensions. The dendrogram of 3D data shown in Fig. 2c is the direct analogue of the tree shown here, only constructed from isosurface rather than point intersections. It has been sorted and flattenedfor representation on a flat page, as fully representing dendrograms for 3D data cubes would require four dimensions. Macmillan Publis hers Limited. All rights res erved enough. Leaf Branch Leaf Goodman et al. 2009, Nature, cf: Fluke et al. 2009

30 Linked Views of High-dimensional Data John Tukey 2D 3D Data Abstraction 100 Statistics figure, by M. Borkin, reproduced from Goodman 2012, Principles of High-Dimensional Data Visualization in Astronomy

31 John Tukey s Legacy PRIM-9 PRIM-H XGobi GGobi RGGobi

32 Linked Views of High-dimensional Data (in Python) Glue glue multidimensional data exploration video by Tom Robitaille, lead glue developer glue created by: C. Beaumont, M. Borkin, P. Qian, T. Robitaille, and A. Goodman, PI

33 Linked Views of High-dimensional Data (in Python) Glue glue multidimensional data exploration video by Chris Beaumont, glue developer glue created by: C. Beaumont, M. Borkin, P. Qian, T. Robitaille, and A. Goodman, PI

34 your handout built-in plug-in standard data loaders custom data loaders Data File 1 Data File 2 Data File N define new variables, import/export insights, interactive plots for the web, save state, all from GUI link data files attributes custom buttons, features highlight live or algorithmic selections with Boolean logic? standard 1D, 2D & 3D plots custom plots +options user config.py file (loaders, colors, plot types, +) access to all matplotlib functions through built-in IPython terminal run & interact with glue from Jupyter notebook & other tools glueviz.org

35 What is visualization (and all this software) for? INSIGHT CONTEXT PATTERN RECOGNITION EVALUATION Spatial Ideas Algorithms Non-Spatial Outliers Errors

36 glue multidimensional data exploration glueing together the Milky Way

37 glue multidimensional data exploration Logan airport (and my FBI file)

38 JupYter lab: Glue in the browser in the browser Video courtesy of Maarten Breddels, consulting developer

39 Yes, more on this visualization challenge! Nah, maybe later glue multidimensional data exploration

40 a visualization saga

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46 glue multidimensional data exploration WorldWide Telescope

47 The challenge of 3D Selection A state-of-the-art 3D model of the stars & gas near the Orion nebula, created at Orion (un)plugged, Vienna, Expert builders (~20 total) include: Joao Alves, John Bally, Alyssa Goodman & Eddie Schlafly. (cf. Image & Meaning workshops by Felice Frankel) YouTube video explanation; WWT Tour

48 The challenge of 3D Selection

49 The challenge of 3D Selection

50 WorldWide Telescope F= Gm 1m2 R 2 glue multidimensional data exploration

51 Literature as (a filter for) Data Many thanks to Alberto Pepe, August Muench, Thomas Boch, Jonathan Fay, Michael Kurtz, Alberto Accomazzi, Julie Steffen, Laura Trouille, David Hogg, Dustin Lang, Christopher Stumm, Chris Beaumont & Phil Rosenfield for making this all work!

52 Bringing Dead Data Back to Life

53 ADS All-Sky Survey & Astronomy Rewind 1. Images Extracted from Journal Articles putting articles and images (back) on the Sky 2. Missing coordinate metadata added back to images, either your handout 0. ADS All-Sky Survey offers (filtered) article density layer on the Sky historical images automatically, applying astronometry.net to wide-field optical images, or 3. Solved images returned to ADS & Astronomy Image Explorer via Astronomy Rewind Zooniverse Citizen Science Project 4. New button in Astronomy Image Explorer offers image-incontext, using AAS WorldWide Telescope in the browser recent images ADS 1992 click entries on the timeline to try out services WorldWide Telescope 2008 Zooniverse 2009 Astrometry.net 2011 ADS All Sky Survey 2014 Astronomy Image Explorer 2014 Astronomy Rewind 2017

54 Who, How, and Who s Paying? The ADS All Sky Survey was first funded via a 2012 grant from the NASA ADAP program to Seamless Astronomy, in collaboration with CDS, Astrometry.net and Microsoft Research. your handout Help us create a database of astroreferenced old Astronomy images. Learn more Get started Articles-on-the-Sky was first deployed in 2014, using APIs from WWT (Microsoft Research, now AAS) and CDS (Aladin) 1 person is talking about Astronomy Rewind right now. Join in ASTRONOMY REWIND STATISTICS Images-on-the-Sky relies on the astrometry.net, Zooniverse, IOP/AAS Astronomy Image Explorer and WorldWide Telescope platforms, and it is funded by the American Astronomical Society, in addition to the NASA ADAP grant. These projects rely on open source sofware, primarily hosted on GitHub. 100% Complete 201 1, Volunteers Classifications Subjects Completed Subjects WORDS FROM THE RESEARCHER "Your contributions unlock the information from old astronomy journals. Thank you and enjoy the images!" ABOUT ASTRONOMY REWIND This project is part of an ongoing NASA-funded effort aimed at turning the SAO/NASA Astrophysics Data System (ADS) into a data resource. The result will be a database of astro-referenced images, i.e., images of the sky for which coordinates, orientation, and pixel scale will be publicly available through NASA data archives, the Astronomy Image Explorer, and World Wide Telescope, thanks to your help! PI to contact for more information Alyssa Goodman, Harvard agoodman@cfa.harvard.edu Projects Collections Build a Project About Us Education Our Team Zooniverse Talk Daily Zooniverse Blog! " + How to Build Publications Project Policies Acknowledgements Contact Us Privacy Policy Jobs System Status Security

55 Seeing the Sky Visualization & Astronomers Alyssa A. Goodman Harvard Smithsonian Center for Astrophysics & Radcliffe Institute for Advanced

56 To continue the conversation Creativity & Collaboration: Revisiting Cybernetic Serendipity National Academy of Sciences Sackler Colloquium, March 13-14, 2018, Washington, DC Role/Play: Collaborative Creativity and Creative Collaborations National Academy of Sciences Sackler Student Fellows Symposium, March 12, 2018, Washington DC 10qviz.org with Arzu Çöltekin (beta 2017, release 2018) Creativity & Collaboration at NAS March 2018 with Ben Shneiderman, Maneesh Agrawala, Roger Malina, Youngmoo Kim & Donna Cox