Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae

Abstract

The Vera C. Rubin Observatory's Legacy Survey of Space and Time is forecast to collect a large sample of Type Ia supernovae (SNe Ia) that could be instrumental in unveiling the nature of Dark Energy. The feat, however, requires measuring the two components of the Hubble diagram - distance modulus and redshift - with a high degree of accuracy. Distance is estimated from SNe Ia parameters extracted from light curve fits, where the average quality of light curves is primarily driven by survey parameters such as the cadence and the number of visits per band. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis aiming at quantifying the impact of the Deep Drilling (DD) strategy parameters on three critical aspects of the survey: the redshift completeness (originating from the Malmquist cosmological bias), the number of well-measured SNe Ia, and the cosmological measurements. Analyzing the current LSST survey simulations, we demonstrate that the current DD survey plans are characterized by a low completeness ($z\sim$ 0.55-0.65), and irregular and low cadences (few days) that dramatically decrease the size of the well-measured SNe Ia sample. We then propose a modus operandi that provides the number of visits (per band) required to reach higher redshifts. The results of this approach are used to design a set of optimized DD surveys for SNe Ia cosmology. We show that most accurate cosmological measurements are achieved with Deep Rolling surveys characterized by a high cadence (one day), a rolling strategy (each field observed at least two seasons), and two sets of fields: ultra-deep ($z\gtrsim 0.8$) and deep ($z\gtrsim 0.6$) fields. We also demonstrate that a deterministic scheduler including a gap recovery mechanism is critical to achieve a high quality DD survey required for SNe Ia cosmology.

arXiv comment26 pages, 16 figures

References in corpus (40)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The NumPy array: a structure for efficient numerical computation
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
• 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
• Exploring the Expansion History of the Universe
• Accelerating Universes with Scaling Dark Matter
• Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond LambdaCDM
• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• The Pantheon+ Analysis: Cosmological Constraints
• Cosmology and fundamental physics with the Euclid satellite
• SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators
• The Supernova Legacy Survey 3-year sample: Type Ia Supernovae photometric distances and cosmological constraints
• Photometric Calibration of the First 1.5 Years of the Pan-STARRS1 Survey
• Pan-STARRS Photometric and Astrometric Calibration
• 4MOST: Project overview and information for the First Call for Proposals
• 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
• It's Dust: Solving the Mysteries of the Intrinsic Scatter and Host-Galaxy Dependence of Standardized Type Ia Supernova Brightnesses
• SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances
• Prime Focus Spectrograph (PFS) for the Subaru Telescope: Overview, recent progress, and future perspectives
• Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: a pioneering process of community-focused experimental design
• Photometric Calibration of the Supernova Legacy Survey Fields
• First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation
• Measuring Type Ia Supernova Populations of Stretch and Color and Predicting Distance Biases
• Determination of the Hubble Constant Using a Two-Parameter Luminosity Correction for Type Ia Supernovae
• Forward Global Photometric Calibration of the Dark Energy Survey
• Testing Models of Intrinsic Brightness Variations in Type Ia Supernovae, and their Impact on Measuring Cosmological Parameters
• Evolution in the Volumetric Type Ia Supernova Rate from the Supernova Legacy Survey
• Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
• Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies
• A Framework for Telescope Schedulers: With Applications to the Large Synoptic Survey Telescope
• 4MOST Consortium Survey 10: The Time-Domain Extragalactic Survey (TiDES)
• The Impact of Observing Strategy on Cosmological Constraints with LSST
• Measuring Cosmological Parameters with Type Ia Supernovae in redMaGiC galaxies
• Testing LSST Dither Strategies for Survey Uniformity and Large-Scale Structure Systematics
• Sky Variability in the y Band at the LSST Site
• Eddington-Malmquist bias in a cosmological context
• An Improved Photometric Calibration of the Sloan Digital Sky Survey Imaging Data

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