activity
20182022
most citedWide-field Multi-object Spectroscopy to Enhance Dark Energy Science from LSST

5 citations · 9 across the 4 of their papers we have counts for

collaborators

6 papers

astro-ph.CO20222 cited

Snowmass2021 Cosmic Frontier White Paper: Enabling Flagship Dark Energy Experiments to Reach their Full Potential

Jonathan A. Blazek, Doug Clowe, Thomas E. Collett +25

A new generation of powerful dark energy experiments will open new vistas for cosmology in the next decade. However, these projects cannot reach their utmost potential without data…

astro-ph.IM2020

GPS Measurements of Precipitable Water Vapor Can Improve Survey Calibration: A Demonstration from KPNO and the Mayall z-band Legacy Survey

W. M. Wood-Vasey, Daniel Perrefort, Ashley Baker

We here show that dual-band GPS measurements of precipitable water vapor (PWV) at KPNO predict the overall per-image sensitivity of the Mayall z-band Legacy Survey (MzLS). The per-…

astro-ph.IM2020

A Template-Based Approach to the Photometric Classification of SN 1991bg-like Supernovae in the SDSS-II Supernova Survey

Daniel Perrefort, Yike Zhang, Lluís Galbany +2

The use of Type Ia Supernovae (SNe Ia) to measure cosmological parameters has grown significantly over the past two decades. However, there exists a significant diversity in the SN…

astro-ph.CO20192 cited

Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST

Renée A. Hložek, Thomas Collett, Lluís Galbany +9

Single-object imaging and spectroscopy on telescopes with apertures ranging from ~4 m to 40 m have the potential to greatly enhance the cosmological constraints that can be obtaine…

astro-ph.CO20195 cited

Wide-field Multi-object Spectroscopy to Enhance Dark Energy Science from LSST

Rachel Mandelbaum, Jonathan Blazek, Nora Elisa Chisari +12

LSST will open new vistas for cosmology in the next decade, but it cannot reach its full potential without data from other telescopes. Cosmological constraints can be greatly enhan…

astro-ph.IM2018

pwv_kpno: A Python Package for Modeling the Atmospheric Transmission Function due to Precipitable Water Vapor

Daniel Perrefort, W. M. Wood-Vasey, K. Azalee Bostroem +3

We present a Python package, pwv_kpno, that provides models for the atmospheric transmission due to precipitable water vapor (PWV) at user specified sites. Using the package, groun…