1.4k citations · 1.6k across the 9 of their papers we have counts for
10 papers
Three-dimensional scene reconstruction using Roman slitless spectra
Tri L. Astraatmadja, Andrew S. Fruchter, Susana E. Deustua +10
The Nancy Grace Roman Space Telescope will carry out a wide-field imaging and slitless spectroscopic survey of Type Ia Supernovae to improve our understanding of dark energy. Cruci…
The Dark Energy Survey Supernova Program: Light curves and 5-Year data release
B. O. Sánchez, D. Brout, M. Vincenzi +82
We present photometric light curves for the full 5 years of the Dark Energy Survey Supernova program (DES-SN), obtained with both forced Point Spread Function (PSF) photomet…
The Dark Energy Survey Supernova Program: Cosmological Biases from Host Galaxy Mismatch of Type Ia Supernovae
H. Qu, M. Sako, M. Vincenzi +58
Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies us…
Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Helen Qu, Masao Sako
Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the…
The Dark Energy Survey Supernova Program: Corrections on photometry due to wavelength-dependent atmospheric effects
J. Lee, M. Acevedo, M. Sako +64
Wavelength-dependent atmospheric effects impact photometric supernova flux measurements for ground-based observations. We present corrections on supernova flux measurements from th…
A Convolutional Neural Network Approach to Supernova Time-Series Classification
Helen Qu, Masao Sako, Anais Moller +1
One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emissio…
What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction
Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler +3
We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" cla…
The Pantheon+ Analysis: Cosmological Constraints
Dillon Brout, Dan Scolnic, Brodie Popovic +43
We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from t…
The Pantheon+ Analysis: SuperCal-Fragilistic Cross Calibration, Retrained SALT2 Light Curve Model, and Calibration Systematic Uncertainty
Dillon Brout, Georgie Taylor, Dan Scolnic +9
We present here a re-calibration of the photometric systems used in the Pantheon+ sample of Type Ia supernovae (SNe Ia) including those used for the SH0ES distance-ladder measureme…
SCONE: Supernova Classification with a Convolutional Neural Network
Helen Qu, Masao Sako, Anais Möller +1
We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is train…