Data Science

Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, Arya Farahi, Doug Finkbeiner, Shy Genel, Alyssa Goodman, and alia. 2019. “The Role of Machine Learning in the Next Decade of Cosmology.” Bulletin of the American Astronomical Society, 51, 14. Publisher's VersionAbstract
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
Milky Way

Astronomy Magazine Talks to the Team Who Discovered the Radcliffe Wave

December 1, 2020


Astronomy Magazine talks to the team from Harvard’s Radcliffe Institute for Advanced Study and Harvard-Smithsonian Center for Astrophysics (CfA) about their serendipitous discovery of the Radcliffe Wave, a massive interconnected stream of stellar nurseries, molecular clouds, and supernovae that snakes through the Milky Way galaxy - and how history, art and science came together to enable this paradigm-changing discovery.  Read the full article ...

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glue-ing Together the Universe, at Microsoft New England Research Division, Cambridge, MA , Friday, March 6, 2020:
wids_msr_glue_universe_2020.pdf

Astronomers have a long history of visualization. Going back only as far as Galileo, discoveries were made using sketches of celestial objects moving over time. Today, Astronomy inquiries can, and often do, make use of petabytes of data at once. Huge surveys are analyzed statistically to understand tiny fluctuations that hint at the fundamental nature of the Universe, and myriad data sets, from telescopes across the globe and in space are brought together to solve problems ranging from the nature of black holes to the structure of the Milky Way to the origins of planets like Earth. In...

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Pecan Pie Logo for "PRISEd Conversation 2020" with photo of Dr. Goodman

Blog Feature: Dr. Alyssa Goodman talks with the The Harvard College Program in Science and Engineering (PRISE)

September 23, 2020

Dr. Alyssa Goodman talks with Felicia Ho, PRISE, Harvard College '23 about Jacques Cousteau, data visualization, climate change, prediction science, and the wide arc of influences that have shaped her multifaceted career as the Robert Wheeler Wilson Professor of Applied Astronomy at Harvard. 

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Aneta Siemiginowska, Gwendolyn Eadie, Ian Czekala, Eric Feigelson, Eric B. Ford, Vinay Kashyap, Michael Kuhn, and et al. 5/2019. “The Next Decade of Astroinformatics and Astrostatistics.” Bulletin of the American Astronomical Society, 51, Pp. 355. Publisher's VersionAbstract

Over the past century, major advances in astronomy and astrophysics have been driven by improvements in instrumentation. With the amassing of high quality data from new telescopes it is becoming clear that research in astrostatistics and astroinformatics will be necessary to develop new methodology needed in astronomy.

Catherine Zucker, Joshua S. Speagle, Edward F. Schlafly, Gregory M. Green, Douglas P. Finkbeiner, Alyssa Goodman, and João Alves. 1/2020. “A Compendium of Distances to Molecular Clouds in the Star Formation Handbook.” Astronomy and Astrophysics, 633, Pp. A51.Abstract
Accurate distances to local molecular clouds are critical for understanding the star and planet formation process, yet distance measurements are often obtained inhomogeneously on a cloud-by-cloud basis. We have recently developed a method that combines stellar photometric data with Gaia DR2 parallax measurements in a Bayesian framework to infer the distances of nearby dust clouds to a typical accuracy of ∼5%. After refining the technique to target lower latitudes and incorporating deep optical data from DECam in the southern Galactic plane, we have derived a catalog of distances to molecular clouds in Reipurth (2008, Star Formation Handbook, Vols. I and II) which contains a large fraction of the molecular material in the solar neighborhood. Comparison with distances derived from maser parallax measurements towards the same clouds shows our method produces consistent distances with ≲10% scatter for clouds across our entire distance spectrum (150 pc-2.5 kpc). We hope this catalog of homogeneous distances will serve as a baseline for future work. Table A.1 is also available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz- bin/cat/J/A+A/633/A51. It is also available on the Harvard Dataverse at http://https://doi.org/1 0.7910/DVN/07L7YZ An interactive 3D version of Fig. 2 is available at http://https://www.aanda.org
Che-Yu Chen, Erica A. Behrens, Jasmin E. Washington, Laura M. Fissel, Rachel K. Friesen, Zhi-Yun Li, Jaime E. Pineda, and alia. 2020. “Relative Alignment between Dense Molecular Cores and Ambient Magnetic Field: The Synergy of Numerical Models and Observations.” arXiv E-Prints.Abstract

The role played by magnetic field during star formation is an important topic in astrophysics. We investigate the correlation between the orientation of star-forming cores (as defined by the core major axes) and ambient magnetic field directions in 1) a 3D MHD simulation, 2) synthetic observations generated from the simulation at different viewing angles, and 3) observations of nearby molecular clouds. We find that the results on relative alignment between cores and background magnetic field in synthetic observations slightly disagree with those measured in fully 3D simulation data, which is partly because cores identified in projected 2D maps tend to coexist within filamentary structures, while 3D cores are generally more rounded. In addition, we examine the progression of magnetic field from pc- to core-scale in the simulation, which is consistent with the anisotropic core formation model that gas preferably flow along the magnetic field toward dense cores. When comparing the observed cores identified from the GBT Ammonia Survey (GAS) and Planck polarization-inferred magnetic field orientations, we find that the relative core-field alignment has a regional dependence among different clouds. More specifically, we find that dense cores in the Taurus molecular cloud tend to align perpendicular to the background magnetic field, while those in Perseus and Ophiuchus tend to have random (Perseus) or slightly parallel (Ophiuchus) orientations with respect to the field. We argue that this feature of relative core-field orientation could be used to probe the relative significance of the magnetic field within the cloud.

Photo of Milky Way

Professors Offer Insights From Their Fields Amid COVID

July 10, 2020

In this time of profound uncertainty, society can be sure of one thing: more uncertainty. The seemingly opaque path forward for us, individually and collectively, was the Gazette’s topic with three Harvard professors, including Robert Wheeler Willson Professor of Applied Astronomy, Dr. Alyssa Goodman, who shared insights into how uncertainty is viewed in their fields, and the surprising ways in which it’s not necessarily a bad thing. ...

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Photo of Astronomer João Alves

Radcliffe Fellow Dr. João Alves On Discovering the "Radcliffe Wave"

June 30, 2020

Astronomer João Alves came to the Radcliffe Institute for Advanced Study to create a 3D map of the sky, but what he discovered overturned the common conception of how stars are born and compelled scientists to rethink the framework of the galaxy.

A professor of stellar astrophysics at the University of Vienna, Alves focuses on understanding how natural processes change large interplanetary clouds of gas into stars and planets, and ultimately form life. He chose to pursue his research at Radcliffe because of its creative, multidisciplinary approach to collaboration. ...

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