Kara A. Ponder

I am currently a postdoctoral researcher working at the Berkeley Center for Cosmological Physics as a Computational Data Science Fellow after graduating from the University of Pittsburgh with a PhD in Physics in 2017. I work with Saul Perlmutter's group at UC Berkeley and LBNL on The Nearby Supernova Factory pipeline.
My research goals are understanding host galaxy correlations with supernovae for cosmology. I have explored ways to improve parameter estimation with the likelihood function motivated by this correlation. I am also exploring these correlations with NIR data obtained in the SweetSpot survey, which I was the lead graduate student for from 2014 to 2017. I have taken IFU spectra of host galaxies and am leading that data reduction. I will be analyzing the resulting correlations and incorporating them into likelihood functions for cosmology to be used for large scale surveys such as the Large Synoptic Survey Telescope (LSST) and the Wide Field Infrared Survey Telescope (WFIRST).


  • Berkeley Center for Cosmological Sciences Computational Data Scientist
  • PhD and Master of Science in Physics: University of Pittsburgh
  • Bachelor of Science in Physics and Astronomy: University of Georgia
  • Coding: Python, C++, PyRAF/IRAF, emcee, AstroPy, Django
  • Bayesian/Hierarchical Bayesian analyses
  • Dark Energy Parameter estimation
  • NIR Photometric Light curves of SNeIa for the SweetSpot Survey
  • Optical Spectroscopy of SN host galaxies


Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC)

PLAsTiCC was a machine learning challenge hosted at Kaggle with the aim to classify millions of lightcurves with a small, unrepresentative training set. I helped validate the simulations so that this competition could be leak free!

Allegheny Observatory Public Lectures

"Exploring Dark Energy with the Large Synoptic Survey Telescope"
January 20, 2017

Incorporating Astrophysical Systematics into a Generalized Likelihood for Cosmology with Type Ia Supernovae

The Astrophysical Journal, Volume 825, Issue 1, article id. 35, 13 pp. (2016)

This plot, referred to by the authors as a "Butterfly" plot, illustrates a toy model looking at the distributions of supernovae. We built a framework to model systematics with a non-Gaussian likelihood that can remove bias with minimal precision losses.

Member of the Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC)

Member of Supernova working group
2015-2017: member of Collaboration Council


WIYN 3.5 m on Kitt Peak using WIYN High-resolution InfraRed Camera (WHIRC) for SweetSpot.

WIYN 3.5 m on Kitt Peak using HexPak, a hexagonal shaped integral field unit mounted on the WIYN bench spectrograph for SweetSpot.

Bok 2.3 m on Kitt Peak for SDSS III Reverberation Mapping project.

Magellan Telescopes using Low Dispersion Survey Spectrograph 3 (LDSS-3): Slit spectrograph in Optical and Folded-port InfraRed Echellette (FIRE): Echelle mode NIR spectrograph. With LDSS-3, I helped observe the highest spectroscopically confirmed redshifted SLSNe.

CV and Links