SNIa Cosmology Analysis Results from Simulated LSST Images: From Difference Imaging to Constraints on Dark Energy

B. O. Sánchez, R. Kessler, D. Scolnic, R. Armstrong, R. Biswas, J. Bogart, J. Chiang, J. Cohen-Tanugi, D. Fouchez, Ph Gris, K. Heitmann, R. Hložek, S. Jha, H. Kelly, S. Liu, G. Narayan, B. Racine, E. Rykoff, M. Sullivan, C. W. WalterW. M. Wood-Vasey

Research output: Contribution to journalArticlepeer-review


The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼106 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 Data Challenge (DC2) simulation for the Wide-Fast-Deep survey area. DC2 is the first large-scale (300 deg2) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ∼15 deg2 of DC2 over a 5 yr time span in which artificial point sources from Type Ia supernova (SNIa) light curves have been overlaid onto the images. The magnitude limits per filter are u = 23.66 mag, g = 24.69 mag, r = 24.06 mag, i = 23.45 mag, z = 22.54 mag, and y = 21.62 mag. The artifact contamination levels are ∼90% of all detections, corresponding to ∼1000 artifacts deg-2 in g band, and falling to 300 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 difference-imaging pipeline on 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 signal-to-noise ratio, 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.

Original languageEnglish (US)
Article number96
JournalAstrophysical Journal
Issue number2
StatePublished - Aug 1 2022

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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