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 $\mathrm{SNR}=5.8$. The magnitude limits for each filter are: $u=23.66$, $g=24.69$, $r=24.06$, $i=23.45$, $z=22.54$, $y=21.62$ $\mathrm{mag}$. The artifact contamination is $\sim 90\%$ of detections, corresponding to $\sim 1000$ 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.

Submitting to Astronomy & Astrophysics

References in corpus (36)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• Planck 2015 results. XIII. Cosmological parameters
• Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• The Supernova Legacy Survey 3-year sample: Type Ia Supernovae photometric distances and cosmological constraints
• Cosmological Constraints from Measurements of Type Ia Supernovae discovered during the first 1.5 years of the Pan-STARRS1 Survey
• First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters
• Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
• The Data Release of the Sloan Digital Sky Survey-II Supernova Survey
• Real-bogus classification for the Zwicky Transient Facility using deep learning
• The Difference Imaging Pipeline for the Transient Search in the Dark Energy Survey
• Automated Transient Identification in the Dark Energy Survey
• Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
• Machine Learning for the Zwicky Transient Facility
• First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation
• A Measurement of the Rate of type-Ia Supernovae at Redshift $z\approx$ 0.1 from the First Season of the SDSS-II Supernova Survey
• CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST
• Using Machine Learning for Discovery in Synoptic Survey Imaging
• The Outer Rim Simulation: A Path to Many-Core Supercomputers
• The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope
• First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases
• The LSST DESC DC2 Simulated Sky Survey
• How Many Kilonovae Can Be Found in Past, Present, and Future Survey Datasets?
• Extending Supernova Spectral Templates for Next-Generation Space Telescope Observations
• The Impact of Observing Strategy on Cosmological Constraints with LSST
• Binning is Sinning (Supernova Version): The Impact of Self-Calibration in Cosmological Analyses with Type Ia Supernovae
• Real-Time Recovery Efficiencies and Performance of the Palomar Transient Factory's Transient Discovery Pipeline
• The Variable Sky of Deep Synoptic Surveys
• The LSST DESC Data Challenge 1: Generation and Analysis of Synthetic Images for Next Generation Surveys
• Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey

Cited by in corpus (20)

• The Pantheon+ Analysis: Cosmological Constraints
• Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure
• The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset
• Prospects for constraining interacting dark energy models from gravitational wave and gamma ray burst joint observation
• Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernova in the LSST Era
• The observed number counts in luminosity distance space
• Interacting Models of Dark Energy and Dark Matter in Einstein scalar Gauss Bonnet Gravity
• Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
• Forecast for growth-rate measurement using peculiar velocities from LSST supernovae
• SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data
• Recovered SN Ia rate from simulated LSST images
• Exploring Alternative Cosmologies with the LSST: Simulated Forecasts and Current Observational Constraints
• A Synthetic Roman Space Telescope High-Latitude Time-Domain Survey: Supernovae in the Deep Field
• Magnification and evolution bias of transient sources: GWs and SNIa
• Revisiting model-independent constraints on spatial curvature and cosmic ladders calibration: updated and forecast analyses
• Gravitational waves and galaxies cross-correlations: a forecast on GW biases for future detectors
• Improving the Determination of Supernova Cosmological Redshifts by Using Galaxy Groups
• The DEHVILS in the Details: Type Ia Supernova Hubble Residual Comparisons and Mass Step Analysis in the Near-Infrared
• CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
• FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology