Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey

Lochner, Michelle; Scolnic, D.; Awan, Humna; Regnault, Nicolas; Gris, Philippe Luc Yves; Mandelbaum, Rachel; Gawiser, Eric; Almoubayyed, Husni; Setzer, Christian N.; Huber, Simon; Graham, Melissa; Hložek, Renée; Biswas, R.; Eifler, T. F.; Rothchild, Daniel; Allam, Tarek; Blazek, Jonathan; Chang, C.; Collett, Thomas S; Goobar, A.; Hook, I. M.; Jarvis, Mike; Kim, Alex; Marshall, Phil; McEwen, Jason D.; Moniez, M.; Newman, Jeffrey A.; Peiris, Hiranya V.; Petrushevska, T.; Rhodes, Jason; Sevilla-Noarbe, I.; Slosar, Anže; Suyu, S. H.; Yoachim, Peter

doi:10.48550/arxiv.1812.00515

Cited by 28 publications

(47 citation statements)

References 13 publications

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“…The DC2 baseline cadence is sub-optimal with respect to the recent developments in LSST cadence studies Lochner et al 2018Lochner et al , 2021. Repeating this DC2 image simulation and analysis on alternative cadences is impractical from both a computational and human-effort perspective.…”

Section: Discussionmentioning

confidence: 99%

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

Sánchez,

Kessler,

Scolnic

et al. 2021

Preprint

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The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼10 6 transient detections per night. To use these transients for precision measurements of cosmological parameters and rates studies, 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 ∼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 SNR = 5.8 averaged over all bands. The corresponding magnitude limits for each filter are: u = 23.66, g = 24.69, r = 24.06, i = 23.45, z = 22.54, y = 21.62 mag. The artifact contamination levels is ∼ 90% of all detections, corresponding to ∼ 1000 artifacts/deg 2 n g band, and falling to 300/deg 2 n y band. The recovered photometry has biases < 1% for magnitudes 19.5 < m < 23. We show that our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging 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. Finally, we discuss further applications of this dataset and analysis, and we suggest pipeline improvements before LSST operations begins.

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Section: Discussionmentioning

confidence: 99%

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

Sánchez,

Kessler,

Scolnic

et al. 2021

Preprint

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“…This code has been used through the direct use of API in the study of serendipitous discoveries of Kilonovae using the LSST (Setzer et al 2018) which also formed part of a LSST DESC survey strategy white paper for Wide Fast Deep Fields in LSST (Lochner et al 2018). SNANA observation library files (Biswas et al 2017) generated through previous versions of OpSimSummary (and distributed publicly with the SNANA code) have been used in the study of serendipitous detection of Kilonovae (Scolnic et al 2018a) and the LSST DESC Science Requirement Document (The LSST Dark Energy Science Collaboration et al 2018).…”

Section: Summary and Discussionmentioning

confidence: 99%

Enabling Catalog Simulations of Transient and Variable Sources Based on LSST Cadence Strategies

Biswas

Daniel

Hložek

et al. 2020

ApJS

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The Large Synoptic Survey Telescope (LSST) project will conduct a ten year multi-band survey starting in 2022. Observing strategies for this survey are being actively investigated, and the science capabilities can be best forecasted on the basis of simulated strategies from the LSST Operations Simulator (OpSim). OpSim simulates a stochastic realization of the sequence of LSST pointings over the survey duration, and is based on a model of the observatory (including telescope) and historical data of observational conditions. OpSim outputs contain a record of each simulated pointing of the survey along with a complete characterization of the pointing in terms of observing conditions, and some useful quantities derived from the characteristics of the pointing. Thus, each record can be efficiently used to derive the properties of observations of all astrophysical sources found in that pointing. However, in order to obtain the time series of observations (light curves) of a set of sources, it is often more convenient to compute all observations of an astrophysical source, and iterate over sources. In this document, we describe the open source python package OpSimSummary which allows for a convenient reordering. The objectives of this package are to provide users with an Application Programming Interface (API) for accessing all such observations and summarizing this information in the form intermediate data products usable by third party software such as SNANA, thereby also bridging the gap between official LSST products and pre-existing simulation codes.

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“…Surveys with the characteristics of the low-𝑧 sample require high cadences ( 6 days). Future surveys, such as the LSST, will produce high-quality data with good S/N and cadence (e.g., Lochner et al 2018;Scolnic et al 2018a), overcoming some of the limitations found in this work, thus allowing PISCOLA to produce reliable fits.…”

Section: Effect Of Observational Uncertaintiesmentioning

confidence: 96%

PISCOLA: a data-driven transient light-curve fitter

Müller-Bravo,

Sullivan,

Smith

et al. 2021

Preprint

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Forthcoming time-domain surveys, such as the Rubin Observatory Legacy Survey of Space and Time, will vastly increase samples of supernovae (SNe) and other optical transients, requiring new data-driven techniques to analyse their photometric light curves. Here, we present the 'Python for Intelligent Supernova-COsmology Light-curve Analysis' (PISCOLA), an open source data-driven light-curve fitter using Gaussian Processes that can estimate rest-frame light curves of transients without the need for an underlying light-curve template. We test PISCOLA on large-scale simulations of type Ia SNe (SNe Ia) to validate its performance, and show it successfully retrieves rest-frame peak magnitudes for average survey cadences of up to 7 days. We also compare to the existing SN Ia light-curve fitter SALT2 on real data, and find only small (but significant) disagreements for different light-curve parameters. As a proof-of-concept of an application of PISCOLA, we decomposed and analysed the PISCOLA rest-frame light-curves of SNe Ia from the Pantheon SN Ia sample with Non-Negative Matrix Factorization. Our new parametrization provides a similar performance to existing light-curve fitters such as SALT2. We further derived a SN Ia colour law from PISCOLA fits over ∼3500 to 7000Å, and find agreement with the SALT2 colour law and with reddening laws with total-to-selective extinction ratio 𝑅 𝑉 3.1.

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Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey

Cited by 28 publications

References 13 publications

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

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

Enabling Catalog Simulations of Transient and Variable Sources Based on LSST Cadence Strategies

PISCOLA: a data-driven transient light-curve fitter

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