Astro2020 Science White Paper

The Next Decade
of Astroinformatics and Astrostatistics

Thematic Areas:                    Planetary Systems     Star and Planet Formation        Formation and Evolution of Compact Objects            Cosmology and Fundamental Physics Stars and Stellar Evolution   Resolved Stellar Populations and their Environments              Galaxy Evolution                Multi-Messenger Astronomy and Astrophysics

Principal Author:

Name: Aneta Siemiginowska Institution: Center for Astrophysics Harvard & Smithsonian
Chair, AAS Working Group on Astroinformatics and Astrostatistics Email:


Gwendolyn Eadie111eScience Institute, University of Washington, Seattle, WA 98195, USA222DIRAC Institute, Department of Astronomy, University of Washington, Seattle, WA 98195, USA333Department of Astronomy, University of Washington, Seattle, WA 98195, USA, Ian Czekala444Department of Astronomy, University of California, Berkeley, CA 94720 USA, Eric Feigelson555Penn State University, University Park, PA 16802, USA, Eric B. Ford555Penn State University, University Park, PA 16802, USA, Vinay Kashyap666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Michael Kuhn 777California Institute of Technology, Pasadena, CA 91109, USA, Tom Loredo888Cornell University, Cornell Center for Astrophysics and Planetary Science (CCAPS) & Department of Statistical Sciences, Ithaca, NY 14853, USA, Michelle Ntampaka999Harvard University, Cambridge, MA 02138, USA, Abbie Stevens101010Department of Physics & Astronomy, Michigan State University, East Lansing, MI 48824, USA111111Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA, Arturo Avelino666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Kirk Borne121212Booz Allen Hamilton, Annapolis Junction, MD, USA, Tamas Budavari131313Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA, Blakesley Burkhart666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Jessi Cisewski-Kehe141414Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA, Francesca Civano666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Igor Chilingarian666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, David A. van Dyk151515Department of Mathematics, Imperial College London, SW7 2AZ, UK, Giuseppina Fabbiano666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Douglas P. Finkbeiner999Harvard University, Cambridge, MA 02138, USA, Daniel Foreman-Mackey161616Flatiron Institute, Center for Computational Astrophysics, New York, NY 10010, Peter Freeman171717Carnegie Mellon University, Pittsburgh, PA, USA, Antonella Fruscione666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Alyssa A. Goodman999Harvard University, Cambridge, MA 02138, USA, Matthew Graham777California Institute of Technology, Pasadena, CA 91109, USA, Hans Moritz Guenther181818Massachusetts Institute of Technology, Kavli Institute for Astrophysics and Space Research, Cambridge, MA 02139, USA, Jon Hakkila191919Department of Physics & Astronomy, Associate Dean of the Graduate School, University of Charleston, Charleston, SC 29424, USA, Lars Hernquist999Harvard University, Cambridge, MA 02138, USA, Daniela Huppenkothen222DIRAC Institute, Department of Astronomy, University of Washington, Seattle, WA 98195, USA333Department of Astronomy, University of Washington, Seattle, WA 98195, USA, David J. James666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Casey Law444Department of Astronomy, University of California, Berkeley, CA 94720 USA, Joseph Lazio202020Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA, Thomas Lee212121University of California Davis, CA 95616, USA, Mercedes López-Morales666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Ashish A. Mahabal222222TAPIR Group, Division of Physics, Mathematics, & Astronomy, California Institute of Technology, Pasadena, CA 91125, USA, Kaisey Mandel232323University of Cambridge, Cambridge, CB3 0HA, UK, Xiao-Li Meng999Harvard University, Cambridge, MA 02138, USA, John Moustakas242424Department of Physics & Astronomy, Siena College, Loudonville, NY 12211, USA, Demitri Muna252525Center for Cosmology and AstroParticle Physics, The Ohio State University, Columbus, OH 43210, USA, J. E. G. Peek262626Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA272727Space Telescope Science Institute, Baltimore, MD 21218, USA, Gordon Richards282828Drexel University, Department of Physics, Philadelphia, PA 19104, Stephen K.N. Portillo222DIRAC Institute, Department of Astronomy, University of Washington, Seattle, WA 98195, USA333Department of Astronomy, University of Washington, Seattle, WA 98195, USA, Jeff Scargle292929Space Science Division, NASA Ames Research Center, Moffett Field, CA 94035-0001, Rafael S. de Souza303030Department of Physics & Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA, Joshua S. Speagle999Harvard University, Cambridge, MA 02138, USA, Keivan G. Stassun313131Vanderbilt School of Engineering, Vanderbilt University, Nashville, TN 37235, USA, David C. Stenning151515Department of Mathematics, Imperial College London, SW7 2AZ, UK, Stephen R. Taylor222222TAPIR Group, Division of Physics, Mathematics, & Astronomy, California Institute of Technology, Pasadena, CA 91125, USA, Grant R. Tremblay666Center for Astrophysics Harvard & Smithsonian, Cambridge, MA 02138, USA, Virginia Trimble323232University of California, Irvine, CA 92697, USA, Padma A. Yanamandra-Fisher333333Space Science Institute, Boulder, CO 80301, USA, C. Alex Young343434NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA.

Abstract: Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. Research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics; (2) dedicating space and time at conferences for interdisciplinary research and promotion; (3) developing sustainable funding for long-term astrostatisics appointments; and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing.

1. What is the role of astrostatistics and astroinformatics research?

To develop modern methods for extracting scientific information from astronomical data.

Astrostatistics forms the foundation for robust algorithms and principled methods that are applied to a variety of problems in astronomy. Astroinformatics involves the systematic and disciplined development of code, data management and dissemination techniques, high-performance computing, and machine learning based inference. Both astrostatistics and astroinformatics (i.e., astro data science) have been rapidly emerging fields of research rigorously pursued at the intersection of observational astronomy, statistics, algorithm development, and data science Borne (2010); Loredo (2012); Feigelson & Babu (2013); van Dyk et al. (2015); STScI Big Data (2016). The number of articles with keyword ‘Methods: Statistical’ increased by a factor of 2.5 in the past decade; those with ‘machine learning’ increased by 4 times over five years; and those with ‘deep learning’ have more than tripled every year since 2015. Thus, the challenges of astronomical sciences reveal a deep and broad demand for advanced methodology and techniques. Astronomical problems impossible to approach with traditional methods are now forefront research efforts because of advancements in astrostatistics and astroinformatics.

In the next decade, astronomy data will present new challenges, and will make astrostatistics and astroinformatics research a necessity for nontrivial scientific inference in an increasing range of critical research areas. Astronomy ‘big data’ described by the four V’s — volume, velocity, variety, and veracity — demand new methodologies. It is vitally important that the quality and sophistication of the techniques match the quality and sophistication of the data. The specific application of any new method requires research involving data, statistics, algorithm development and computations, and, thus, the combined knowledge and experience of astronomers, statisticians, and computational experts. Cross-disciplinary collaboration and communication at a very high level are critical to such research; conceptual and jargon barriers between disciplines must be overcome.

Several white papers on astrostatistics and astroinformatics research, endorsed by dozens of leaders in the fields, were submitted to the Astro2010 Decadal Survey Loredo et al. (2009); Borne et al. (2009a, b); Ferguson et al. (2009). Since then, some recommendations have been implemented, such as the formation of the Working Group on Astroinformatics & Astrostatistics within the American Astronomical Society, and the Astrostatistics Interest Group within the American Statistical Association. What remains underdeveloped, however, is the formal recognition of and financial commitment to the efforts needed to make necessary progress in astrostatistics and astroinformatics.

Astrostatistics and astroinformatics research impacts all areas of astronomy and needs to be recognized as a science area within astronomy. explorations. Our recommendation is to: (1) create supportive environments for long-term research in astrostatistics and astroinformatics; (2) promote research in this field with specific national level programs, fellowships, professional development, and consulting; and (3) provide sustained funding for long-term research programs.

2. How do modern astrostatistics and astroinformatics methods impact astronomy?

They overcome challenges with data and improve scientific inference.

Astrostatistics and astroinformatics research does not fit traditional thematic boundaries, as it includes both technological development and scientific research in statistical and information sciences. However, these disciplines are now a necessity for modern astronomical research. Tables 1 and 2 highlight recent advances and expected challenges, and indicate the impact of emerging methods in thematic areas of astronomy.

3. How can the state-of-the-art methods be best applied in astronomy?

Through astronomy involvement in active methodology research

Existing statistical and machine learning methods need to be further developed to be applicable in astronomy. For example, adaptation of recent machine learning advancements to address building explanatory models rather than task-specific predictive models requires astronomy involvement in two active research areas of machine learning:

Scalable probabilistic machine learning (including deep learning): Most ML algorithms seek to make one set of predictions or point estimates, optimal to one specific end task. In astronomy, methods need to quantify uncertainty and provide results (e.g., probabilistic catalogs) that enable uncertainty propagation. Probabilistic methods are well suited to this, but are computationally expensive and not easily scalable to large datasets. Scalable approaches using ML are being investigated. Collaboration with statisticians and computer scientists is needed to develop such methods tailored to astronomers’ needs. Astronomy needs to become a driver of this research.

Interpretable machine learning (especially deep learning): Complex machine learning methods are coming to astronomy (e.g., deep learning methods involving convolutional and adversarial nets for analyzing image data Reiman & Göhre (2018); Fussell & Moews (2018); Pasquet-Itam & Pasquet (2018); Ntampaka et al. (2018), and recurrent neural nets for time series data Narayan et al. (2019)). Unfortunately, such methods often are used as ‘black box’ predictors, while generalizable understanding of a phenomenon requires an interpretable model. The emerging field of interpretable machine learning involves explanatory goals, not just predictive goals. Astronomy needs to actively participate in this research.

4. Recommendations

The compilations in Tables 1 and 2 highlight two important facts: (1) common methodology is repeatedly used with small alterations across different wave-bands to address diverse problems that span many thematic areas; and (2) duplicated development efforts slow the pace of advance. To facilitate the faster development and dissemination of advanced methods we recommend:

Funding: Astrostatistics and astroinformatics must be recognized as a subfield of astronomical research that affects all of its thematic areas. Proposals in this field must be evaluated by appropriately cross-disciplinary panels.

Communication: Astronomy conferences must make room for methodological discussion, both to disseminate new advances and to raise the awareness for non-experts. Funding for tutorials and other means of communication should be encouraged.

Sustainability: There must be sustained funding through grants and fellowships, to support graduate students and post-docs for several years. Astronomy departments should be encouraged to have more tenure-track positions focused on data science research.

Infrastructure: There must be support for both maintaining data archives and training data sets, for publicly available and supported software, and efficient computing.

Table 1: Science, Methodology, and Issues


to 1.05X[1,1]X[1,1]X[1,1]X[1,1] Science Measurements & Traditional Methods & Limitations & Challenges & Emergent Methodologies
Distance Measurements
e.g., to stars, dust, and quasars & inverting parallaxes, sample truncation, astrometry-based luminosity, template fitting, stellar variability, photo-z & biases, need bias corrections, uncertainties ignored & Bayesian Inference for distances from parallaxes and for proper motions from astrometric data Luri et al (2018), machine learning methods, photometric redshfits Cavuoti et al. (2015); Elliott et al. (2015); Almosallam et al. (2016); Beck et al. (2017); de Jong et al. (2017); Salvato et al. (2018)
Mass Estimates
e.g., of the Milky Way, dwarf galaxies, supermassive black holes, galaxy groups and clusters & kinematic tracers, timing argument, hyper velocity stars, reverberation, mass- relation, power-law scaling relations & data incompleteness, large uncertainties, scatter, extrapolation to larger distances, over-simplified models, biases & Bayesian hierarchical models, Approximate Bayesian Computation (ABC), carma models Kelly et al. (2009, 2011, 2013); machine learning, Bayesian model averaging (McMillan , 2011; Eadie et al , 2017; Patel et al , 2017)
Stellar Properties & Evolution
e.g., stellar type, temperature, composition, metallicity, coronal composition, density, stellar evolution; & forward fitting physics-based models (with chemical networks, MHD and planets for protoplanetary disks); stellar evolution models, spectral lines fitting, isochrone fitting, catalog matching and membership classification & degenerate models and parameters, difficulty in model selection, uncertainty quantification, correlated measurement uncertainties; inefficient sampling methods (e.g., MCMC) and need simplifications to the forward fitting models & Gaussian Processes Czekala et al. (2015); Foreman-Mackey et al. (2017); Foreman-Mackey (2018), machine learning methods Ting et al. (2018), Bayesian inference; model independent data-driven approaches for rotation curves, matched-filter for line searches
Population Studies
e.g. source detection, structures of diffuse regions, classifying galaxies and stars; identifying moving groups, stellar populations, globular cluster populations & spectral line fitting, photometry, colour-magnitude diagrams; two-point correlation function Peebles (1980) & overlapping sources, faint structures Stein et al. (2015); McKeough et al. (2016), non-Gaussian uncertainties, unknown populations, complex morphology Cartwright & Whitworth (2004); Grasha et al. (2019) & probabilistic catalogues Brewer et al. (2013); Jones et al. (2015); Portillo et al. (2017, 2019); machine learning for open clusters Cantat-Gaudin et al. (2018); Regier et al. (2018); identifying members of stellar groups Gagné et al. (2018); Buckner et al. (2019), spatial point patterns Baddeley et al. (2015); account for uncertainties Xu et al. (2014); wavelet-based clustering methods

Table 2: Astroinformatics and Astrostatistics Research in Thematic Areas


to 1.05X[1,l]X[1,l]X[1,l] Advances (issues under consideration) & (Data/Analysis) Challenges & Future (Emergent Methods/Methodology)

Stars and Stellar Evolution: Magnetic Activity, Populations Evolution, Environment

Stellar cluster catalog matching and membership classification Broos et al. (2011); Budavári et al. (2017); structure in diffuse X-ray background Albacete Colombo et al. (2019); solar feature classification and properties Hurlburt et al. (2010); Stenning et al. (2013); flare modeling, energy release and evolution Aschwanden et al. (2016); thermal segmentation of the corona Stein et al. (2016); stellar coronal thermal and density structure via Emission Measure distributions Kashyap & Drake (1998); Wood et al. (2018); sources of coronal heating (e.g., nanoflares) Cargill (1994); Cranmer et al. (2007); effect of stellar activity on exoplanets. Cuntz & Shkolnik (2002); Shkolnik et al. (2003) & Solar and stellar flare onsets and distributions Kashyap et al. (2002); characterization of stellar activity to reveal hidden signals of exoplanets Rajpaul et al. (2015); Nelson et al. (2014); determining the nature (magnetic or tidal) and magnitude of the Star-Planet Interaction effect Cuntz, Saar, & Musielak (2000); Poppenhaeger, Schmitt, & Wolk (2013); isochrone fitting to determine ages, metallicity, and star formation history of star clusters Bitsakis et al. (2017); Carnall et al. (2018); completeness and limitations of the Heliophysics Event Knowledgebase McCauley et al. (2015); Aggarwal et al. (2018) & Solar dispersed image spectral decomposition (also applicable to SNRs) Winebarger et al. (2018); solar and stellar DEMs that incorporate atomic data uncertainties Yu et al. (2018); disambiguating photons from overlapping close binaries in confused fields to facilitate spectral and timing analysis Jones et al. (2015); loop recognition in solar coronal images Aschwanden (2010); Stenning et al. (2013); morphological analysis to recognize diffuse structure Picquenot et al. (2019); Fan et al. (2019)

Formation and Evolution of Compact Objects

X-ray spectral-timing analysis Stevens & Uttley (2016); Bayesian Inference and evolutionary algorithms for neutron star equation of state Özel & Psaltis (2015); Özel et al. (2016); Steiner et al. (2018); merging systemsRoulet & Zaldarriaga (2019); transient detectionsCabrera-Vives et al. (2017); accretion states Uttley et al. (2011). & Use of spectral, spatial, time and polarimetry domain Rosa et al. (2019); Ray et al. (2019); periodicity detection Vaughan et al. (2016); ”needle in a haystack” searches Law et al. (2018, 2018); state transitions Uttley et al. (2014); Heil et al. (2015); Uttley et al. (2017); localization Corley et al. (2019) & New models, computational power; Gaussian processes in time domain for Poisson (Cox process) X-ray and gamma-ray Kelly et al. (2011, 2013, 2014); Sobolewska et al. (2014); use of higher order Fourier product and non-linear signal processing Huppenkothen & Bachetti (2018); machine learning methods Huppenkothen et al. (2017); Mahabal et al. (2017); Sedaghat & Mahabal (2018); Mahabal et al. (2019)

Galactic Astronomy and Galaxy Evolution

Gaia dataGaia Collaboration et al. (2018); Hogg (2018); Machine Learning methods and Bayesian inference & Account for uncertainties, incompleteness and biases & Machine learning to discover new stellar open clusters Cantat-Gaudin et al. (2018); photometric redshifts Cavuoti et al. (2015); Elliott et al. (2015); Almosallam et al. (2016); Beck et al. (2017); de Jong et al. (2017); Salvato et al. (2018); Bayesian Inference for distances from parallaxes and for proper motions from astrometric data Hogg et al. (2018); Luri et al (2018); the mass of the Milky Way McMillan (2011); Eadie et al (2017); Patel et al (2017); Callingham et al. (2019), identifying members of stellar groups Gagné et al. (2018)

Multi-Messenger Astronomy and Astrophysics

Detecting transients, multi-band identifications & Localization, nanohertz GW detection Law et al. (2018, 2018), GW-EM coincidence Blackburn et al. (2015)& Bayesian hierarchical models with efficient samplers, Gaussian mixture models Del Pozzo et al. (2018)

Table 2: Astroinformatics and Astrostatistics Research in Thematic Areas - continued


to 1.02X[1,l]X[1,l]X[1,l] Advances (issues under consideration) & (Data/Analysis) Challenges & Future (Emergent Methods/Methodology)


Planetary Systems

100,000 target stars identified in the Kepler 4-year mission Jenkins et al. (2017); Hsu et al. (2018); characterization of planetary systems Thompson et al. (2018); analysis of transit timing variations to characterize the exoplanet mass-radius relationship & Inadequate statistical estimators used in early analysis Foreman-Mackey et al. (2014); Hsu et al. (2018); Bayesian hierarchical models to combine large measurement uncertainties and intrinsic astrophysical variability Wolfgang et al. (2016); Ning et al. (2018); The combination of TESS and ground-based Doppler surveys is poised to significantly expand the sample of planets for such analyses, providing new constraints for physical modeling of exoplanets & Advanced methods applicable to new generation of instruments; for characterizing stellar variability Davis et al. (2017); machine learning and data-driven models for analyzing high resolution spectroscopic time series Jones et al. (2017); Bedell et al. (2019); quantifying the evidence of low-mass planets, even with simple RV time series Dumusque et al. (2017); Nelson et al. (2018); Gaussian Process for RV time series Czekala et al. (2017)

Stars and Planet Formation

Extract information from the high resolution optical and infrared spectra; ALMA ALMA Partnership et al. (2015) images; use molecular lines to probe high-dimensional space with dynamic and chemical information multiwavelegth studies of spatial and kinematic structures in clustersKuhn et al. (2015) & Gaussian processes to deal with correlated residuals from model systematics, and to construct physics-based forward-models Czekala et al. (2015); Narayan et al. (2018); data-driven approaches for accurate spectral models on a pixel-by-pixel basis; only over-simplified models are used Czekala et al. (2015) as complex models with chemical networksHogg et al. (2016), non-ideal MHD, effects of planet clearing, are computationally inefficient for Bayesian inference Lyra et al. (2016); Bai (2017); inadequate statistics for understanding the highly non-homogeneous distributions of young stellar objects & Bayesian inference for sophisticated models in a computationally tractable implementation; data-driven approaches to model disk rotation curves and search for the influence of planets Yen et al. (2016); Teague et al. (2018); Fourier-based matched filters to enable sensitive and efficient molecular line searches (e.g., detection of faint molecular emission from methanol Loomis et al. (2018)); new statistics for inhomogeneous point processes, modifications of the two-point correlation function Peebles (1980); Baddeley et al. (2015); advanced cluster analysis methods including mixture models and density clustering algorithms Kuhn & Feigelson (2017); Joncour et al. (2018); Bovy et al. (2011)

Cosmology and Fundamental Physics

Harness subtle signals to reduce scatter Ntampaka et al. (2015, 2018); Ho et al. (2019), quickly generate mock data Rodríguez et al. (2018); He et al. (2018) discriminate models to quantify statistical and systematic uncertainties de Souza, R. et al. (2019); de Souza et al. (2019); to perform likelihood-free cosmological inference Ishida et al. (2015); Hahn et al. (2017); Alsing et al. (2018); Kacprzak et al. (2018); Leclercq (2018) and classify objects Ishida & de Souza (2013); Varughese et al. (2015); Charnock & Moss (2017); Dai et al. (2018); Lanusse et al. (2018); Ishida et al. (2019) & Apply new advancements in ML interpretability, including saliency maps Simonyan et al. (2013) and the deep k-nearest neighbors approach Papernot & McDaniel (2018) & Development of a brand-new Bayesian machine learning framework suitable to handle the many layers of statistical error structures presented in Astronomy (selection bias, errors-in-measurements, systematics, etc.)


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