SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy
Abstract
The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ${\sim}10^6$ transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration (DESC) Data Challenge (DC2) simulation for the Wide-Fast-Deep (WFD) survey area. DC2 is the first large-scale (300 deg$^2$) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ${\sim}$15 deg$^2$ of DC2 over a 5-year time-span in which artificial point-sources from Type Ia Supernovae (SNIa) light curves have been overlaid onto the images. We measure the detection efficiency as a function of Signal-to-Noise Ratio (SNR) and find a $50%$ efficiency at $\rm{SNR}=5.8$. The magnitude limits for each filter are: $u=23.66$, $g=24.69$, $r=24.06$, $i=23.45$, $z=22.54$, $y=21.62$ $\rm{mag}$. The artifact contamination is $\sim90%$ of detections, corresponding to $\sim1000$ artifacts/deg$^2$ in $g$ band, and falling to 300 per deg$^2$ in $y$ band. The photometry has biases $<1%$ for magnitudes $19.5 < m <23$. Our DIA performance on simulated images is similar to that of the Dark Energy Survey pipeline applied to real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of SNR, redshift, and fitted light-curve properties. Applying a realistic SNIa-cosmology analysis for redshifts $z<1$, we recover the input cosmology parameters to within statistical uncertainties.
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Scholars@Duke
Daniel M. Scolnic
Use observational tools to measure the expansion history of the universe. Trying to answer big questions like 'what is dark energy?'.
Christopher Walter
I am a professor in the physics department studying particle physics and cosmology. I try to understand both the nature of the ghostly particles called neutrinos in giant detectors deep underground, and why the expansion of the universe is accelerating using telescopes on top of mountains. My background and training is originally in particle physics and I was part of the team that showed the sub-atomic particles called neutrinos have mass. The leader of our team, T. Kajita was co-awarded the 2015 Nobel Prize in Physics for this discovery which cited the work of our collaboration. I also began the effort in observational cosmology at Duke, joining the Vera C. Rubin Observatory, a giant telescope under construction in Chile designed to make a 10 year, three dimensional survey of the entire visible sky. Using the Rubin Observatory, we will focus on examining billions of galaxies, along with supernovae and other astronomical probes to try to determine the nature of the mysterious “Dark Energy” which is unaccountably causing the universe to pushed apart at a faster and faster rate.
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