Katherine Whitaker

Associate Professor of Astronomy University of Massachusetts Amherst

  • Amherst MA

Kate Whitaker is an observational extragalactic astronomer who studies galaxy formation and evolution at the very edges of the universe.

Contact

University of Massachusetts Amherst

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Expertise

Detection of Dead Galaxies
Galaxy formation and evolution
Astronomy

Biography

Kate Whitaker is an observational extragalactic astronomer who studies galaxy formation and evolution over the past twelve billion years of cosmic time.

Working with the Cosmic Dawn Center in Copenhagen, Denmark, Whitaker and her team are working towards pushing our detection of quiescent “red and dead” galaxies even earlier in time (within a billion years of the Big Bang itself!) with a goal understand the detailed physics of the structures and underlying stellar populations of these early massive galaxies.

In 2019, Whitaker gained international attention for her work on a team that discovered a new monster galaxy hiding behind a cloud of stardust.

Social Media

Video

Education

Yale University

Ph.D.

Astronomy

Yale University

M.Phil.

Astronomy

Yale University

M.Sc.

Astronomy

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Select Recent Media Coverage

The James Webb Space Telescope prompts a rethink of how galaxies form

PNAS  

2023-08-02

Katherine Whitaker talks about the first pictures from the ultra-powerful James Webb Space Telescope. “We would zoom in and be like, ‘Oh wow,’ and ‘What the heck is that?’ It was joy—pure joy.” JWST’s initial results may suggest that stars and galaxies were forming far faster than anyone expected.

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New image from Webb Telescope, processed by UMass astronomers, reveals the deepest parts of space in Pandora’s Cluster

The Boston Globe  

2023-02-16

“These galaxies are some of the very first galaxies in the universe,” Katherine Whitaker, an assistant professor of astronomy at UMass Amherst, said in a phone interview. “Webb is like a time machine.”

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UMass Amherst astronomers help uncover new details deep in space

WWLP  online

2023-02-15

“With these pictures, we’re looking back in time, 97% of the way to the Big Bang,” says Kate Whitaker, professor of astronomy at UMass Amherst. “The James Webb Space Telescope is fundamentally changing our understanding of our cosmic origins.”

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Select Publications

The JWST FRESCO survey: legacy NIRCam/grism spectroscopy and imaging in the two GOODS fields

Monthly Notices of the Royal Astronomical Society

2023

We present the JWST cycle 1 53.8 h medium program FRESCO, short for ‘First Reionization Epoch Spectroscopically Complete Observations’. FRESCO covers 62 arcmin2 in each of the two GOODS/CANDELS fields for a total area of 124 arcmin2 exploiting JWST’s powerful new grism spectroscopic capabilities at near-infrared wavelengths. By obtaining ∼2 h deep NIRCam/grism observations with the F444W filter, FRESCO yields unprecedented spectra at R ∼ 1600 covering 3.8–5.0 µm for most galaxies in the NIRCam field of view.

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Dust attenuation, dust content, and geometry of star-forming galaxies

Monthly Notices of the Royal Astronomical Society

2023

We analyse the joint distribution of dust attenuation and projected axis ratios, together with galaxy size and surface brightness profile information, to infer lessons on the dust content and star/dust geometry within star-forming galaxies at 0 < z < 2.5. To do so, we make use of large observational data sets from KiDS + VIKING + HSC-SSP and extend the analysis out to redshift z = 2.5 using the HST surveys CANDELS and 3D-DASH. We construct suites of SKIRT radiative transfer models for idealized galaxies observed under random viewing angles with the aim of reproducing the aforementioned distributions, including the level and inclination dependence of dust attenuation.

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As Simple as Possible but No Simpler: Optimizing the Performance of Neural Net Emulators for Galaxy SED Fitting

The Astrophysical Journal

2023

IOP Publishing
Description
Artificial neural network emulators have been demonstrated to be a very computationally efficient method to rapidly generate galaxy spectral energy distributions, for parameter inference or otherwise. Using a highly flexible and fast mathematical structure, they can learn the nontrivial relationship between input galaxy parameters and output observables. However, they do so imperfectly, and small errors in flux prediction can yield large differences in recovered parameters. In this work, we investigate the relationship between an emulator's execution time, uncertainties, correlated errors, and ability to recover accurate posteriors.

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