Explore the vast range of titles available.

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will image billions of astronomical objects in the wide–fast–deep primary survey and in a set of minisurveys including intensive observations of a group of deep drilling fields (DDFs). The DDFs are a critical piece of three key aspects of the LSST Dark Energy Science Collaboration (DESC) cosmological measurements: they provide a required calibration for photometric redshifts (photo-z) and weak gravitational lensing (WL) measurements and they directly contribute to cosmological constraints from the most distant Type Ia supernovae (SNe Ia). We present a set of cohesive DDF strategies fulfilling science requirements relevant to DESC and following the guidelines of the Survey Cadence Optimization Committee. We propose a method to estimate the observing strategy parameters and we perform simulations of the corresponding surveys. We define a set of metrics for each science case to assess the performance of the proposed observing strategies. We show that the most promising results are achieved with deep rolling surveys characterized by two sets of fields: ultradeep fields (z ≲ 1.1) observed at a high cadence with a large number of visits over a limited number of seasons, and deep fields (z ≲ 0.7), observed with a cadence of ∼3 nights for 10 yr. These encouraging results should be confirmed with realistic simulations using the LSST scheduler. A DDF budget of ∼8.5% is required to design observing strategies satisfying all the cosmological requirements. A lower DDF budget leads to surveys that either do not fulfill the photo-z/WL requirements or are not optimal for SN Ia cosmology.

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, however, requires accurately measuring the two components of the Hubble diagram, distance modulus and redshift. Distance is estimated from SN Ia parameters extracted from light-curve fits, where the average quality of light curves is primarily driven by survey parameters. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis to assess the impact of the deep drilling (DD) strategy parameters on three critical aspects of the survey: redshift completeness, the number of well-measured SNe Ia, and cosmological measurements. We demonstrate that the current DD survey plans (internal LSST simulations) are characterized by a low completeness (z ∼ 0.55–0.65), and irregular and low cadences (several days), which dramatically decrease the size of the well-measured SN Ia sample. We propose a method providing the number of visits required to reach higher redshifts. We use the results to design a set of optimized DD surveys for SN Ia cosmology taking full advantage of spectroscopic resources for host galaxy redshift measurements. The most accurate cosmological measurements are achieved with deep rolling surveys characterized by a high cadence (1 day), a rolling strategy (at least two seasons of observation per field), and ultradeep (z ≳ 0.8) and deep (z ≳ 0.6) fields. A deterministic scheduler including a gap recovery mechanism is critical to achieving a high-quality DD survey.

The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade for both static and time-domain science. Maximizing the LSST’s scientific output requires a nontrivial survey strategy (i.e., the sequence of observations in space, time, and passband). For time-domain science, the most promising strategy to date is a rolling survey strategy, whereby alternating subsets of the full LSST area are observed at a higher-than-nominal rate. Focusing on static science (galaxy clustering and weak lensing), we study how time-domain-optimized rolling strategies affect the depth uniformity at intermediate survey years and present new metrics directly connecting depth uniformity with science return. We characterize the amount of survey area at high risk of being lost in static-science analyses of a rolling LSST data set due to insufficient survey uniformity. At intermediate data releases, nearly half of the survey could be lost for static science, decreasing the dark energy figure of merit by 40%. We describe additional metrics focused on key analysis tasks, such as photometric redshifts and galaxy clustering. Finally, we propose a new strategy that returns the survey to uniformity at key release years, enabling use of the full area and restoring our metrics to the values they would have in a nonrolling cadence—without losing time domain data relative to a rolling survey with the same number of rolling cycles. These new “uniform rolling” strategies have been incorporated into the LSST baseline strategy.

The generation-defining Vera C. Rubin Observatory will make state-of-the-art measurements of both the static and transient universe through its Legacy Survey for Space and Time (LSST). With such capabilities, it is immensely challenging to optimize the LSST observing strategy across the survey’s wide range of science drivers. Many aspects of the LSST observing strategy relevant to the LSST Dark Energy Science Collaboration, such as survey footprint definition, single-visit exposure time, and the cadence of repeat visits in different filters, are yet to be finalized. Here, we present metrics used to assess the impact of observing strategy on the cosmological probes considered most sensitive to survey design; these are large-scale structure, weak lensing, type Ia supernovae, kilonovae, and strong lens systems (as well as photometric redshifts, which enable many of these probes). We evaluate these metrics for over 100 different simulated potential survey designs. Our results show that multiple observing strategy decisions can profoundly impact cosmological constraints with LSST; these include adjusting the survey footprint, ensuring repeat nightly visits are taken in different filters, and enforcing regular cadence. We provide public code for our metrics, which makes them readily available for evaluating further modifications to the survey design. We conclude with a set of recommendations and highlight observing strategy factors that require further research.

Dark energy is a leading theory to explain cosmic acceleration, and forthcoming astronomical surveys have been specifically designed to probe this mysterious energy component of our universe. This thesis addresses aspects of using large galaxy surveys to study dark energy, which requires an unprecedented understanding and mitigation of systematics—a challenge that can be addressed on two fronts: quantification of the impacts of systematics, and new tools to mitigate them. Here, we specifically study the impacts of three key systematics: those induced by 1) the telescope observing strategy, 2) the Milky Way dust, and 3) uncertain photometric redshifts. Focusing on the Legacy Survey of Space and Time (LSST) carried out by the Vera C. Rubin Observatory, we quantify the impacts of LSST observing strategy on large-scale structure studies, which is a probe of dark energy. We demonstrate the effectiveness of large translational dithers—telescope-pointing offsets—in increasing LSST survey uniformity and reducing systematic uncertainties (Awan et al., 2016; LSST Science Collaboration et al., 2017)—a result that has now been adopted for the baseline LSST observing strategy. We also study the impacts of Milky Way dust on dark energy science and demonstrate that ~25% of the default LSST survey area would not be useful for extragalactic static science given the Milky Way dust extinction, motivating the reconfiguration of the LSST survey footprint to avoid high-extinction regions of the sky (Lochner et al., 2018; Olsen et al., 2018). And finally, we present a new formalism that provides a novel way to correct for redshift contamination arising from photometric redshift estimation (Awan & Gawiser, 2020). Specifically, we first introduce a general formalism to correct for sample contamination for photometric galaxy samples when measuring two-point angular correlation functions, and then a new weighted estimator that assigns each galaxy a weight in each redshift bin based on its probability of being in that bin, thereby fully utilizing the probabilistic distance information available for photometric galaxies. While these techniques are motivated by preparations for LSST, they are applicable to other large galaxy surveys like Dark Energy Survey (DES), Dark Energy Spectroscopic Instrument (DESI), Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), Euclid, and Wide-Field Infrared Survey Telescope (WFIRST).

With the advent of surveys containing millions to billions of galaxies, it is imperative to develop analysis techniques that utilize the available statistical power. In galaxy clustering, even small sample contamination arising from distance uncertainties can lead to large artifacts, which the standard estimator does not account for. We first introduce a formalism, termed decontamination, that corrects for sample contamination by utilizing the observed cross-correlations in the contaminated samples; this corrects any correlation function estimator for contamination. Using this formalism, we present a new estimator that uses the standard estimator to measure correlation functions in the contaminated samples but then corrects for contamination. We also introduce a weighted estimator that assigns each galaxy a weight in each redshift bin based on its probability of being in that bin. We demonstrate that these estimators effectively recover the true correlation functions and their covariance matrices. Our estimators can correct for sample contamination caused by misclassification between object types as well as photometric redshifts; they should be particularly helpful for studies of galaxy evolution and baryonic acoustic oscillations, where forward-modeling the clustering signal using the contaminated redshift distribution is undesirable.

Large near-future galaxy surveys offer sufficient statistical power to make our cosmology analyses data-driven, limited primarily by systematic errors. Understanding the impact of systematics is therefore critical. We perform an end-to-end analysis to investigate the impact of some of the systematics that affect large-scale structure studies by doing an inference analysis using simulated density maps with various systematics; these include systematics caused by photometric redshifts (photo-\\(z\\)s), Galactic dust, structure induced by the telescope observing strategy and observing conditions, and incomplete covariance matrices. Specifically, we consider the impacts of incorrect photo-\\(z\\) distributions (photometric biases, scatter, outliers; spectroscopic calibration biases), dust map resolution, incorrect dust law, selecting none or only some contaminant templates for deprojection, and using a diagonal covariance matrix instead of a full one. We quantify the biases induced by these systematics on cosmological parameter estimation using tomographic galaxy angular power spectra, with a focus on identifying whether the maximum plausible level of each systematic has an adverse impact on the estimation of key cosmological parameters from a galaxy clustering analysis with Rubin Observatory Legacy Survey of Space and Time (LSST). We find photo-\\(z\\) systematics to be the most pressing out of the systematics investigated, with spectroscopic calibration biases leading to the greatest adverse impact while helpfully being flagged by a high \\(^2\\) value for the best fit model. Larger-than-expected photo-\\(z\\) scatter, on the other hand, has a significant impact without necessarily indicating a poor fit. In contrast, in the analysis framework used in this work, biases from observational systematics and incomplete covariance matrices are comfortably subdominant.

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will image billions of astronomical objects in the wide-fast-deep primary survey and in a set of minisurveys including intensive observations of a group of deep drilling fields (DDFs). The DDFs are a critical piece of three key aspects of the LSST Dark Energy Science Collaboration (DESC) cosmological measurements: they provide a required calibration for photometric redshifts and weak gravitational lensing measurements and they directly contribute to cosmological constraints from the most distant type Ia supernovae. We present a set of cohesive DDF strategies fulfilling science requirements relevant to DESC and following the guidelines of the Survey Cadence Optimization Committee. We propose a method to estimate the observing strategy parameters and we perform simulations of the corresponding surveys. We define a set of metrics for each of the science case to assess the performance of the proposed observing strategies. We show that the most promising results are achieved with deep rolling surveys characterized by two sets of fields: ultradeep fields (z<1.1) observed at a high cadence with a large number of visits over a limited number of seasons; deep fields (z<0.7), observed with a cadence of ~3 nights for ten years. These encouraging results should be confirmed with realistic simulations using the LSST scheduler. A DDF budget of ~8.5% is required to design observing strategies satisfying all the cosmological requirements. A lower DDF budget lead to surveys that either do not fulfill photo-z/WL requirements or are not optimal for SNe Ia cosmology.

Large near-future galaxy surveys offer sufficient statistical power to make our cosmology analyses data-driven, limited primarily by systematic errors. Understanding the impact of systematics is therefore critical. We perform an end-to-end analysis to investigate the impact of some of the systematics that affect large-scale structure studies by doing an inference analysis using simulated density maps with various systematics; these include systematics caused by photometric redshifts (photo-\\(z\\)s), Galactic dust, structure induced by the telescope observing strategy and observing conditions, and incomplete covariance matrices. Specifically, we consider the impacts of incorrect photo-\\(z\\) distributions (photometric biases, scatter, outliers; spectroscopic calibration biases), dust map resolution, incorrect dust law, selecting none or only some contaminant templates for deprojection, and using a diagonal covariance matrix instead of a full one. We quantify the biases induced by these systematics on cosmological parameter estimation using tomographic galaxy angular power spectra, with a focus on identifying whether the maximum plausible level of each systematic has an adverse impact on the estimation of key cosmological parameters from a galaxy clustering analysis with Rubin Observatory Legacy Survey of Space and Time (LSST). We find photo-\\(z\\) systematics to be the most pressing out of the systematics investigated, with spectroscopic calibration biases leading to the greatest adverse impact while helpfully being flagged by a high \\(^2\\) value for the best fit model. Larger-than-expected photo-\\(z\\) scatter, on the other hand, has a significant impact without necessarily indicating a poor fit. In contrast, in the analysis framework used in this work, biases from observational systematics and incomplete covariance matrices are comfortably subdominant.

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~\\) 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 0.8\\)) and deep (\\(z 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.