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  • 1
    In: The Astrophysical Journal Supplement Series, American Astronomical Society, Vol. 253, No. 1 ( 2021-03-01), p. 31-
    Abstract: We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N -body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin’s LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep area of approximately 300 deg 2 , as well as a deep drilling field of approximately 1 deg 2 . We simulate 5 yr of the planned 10 yr survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the data set to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic test bed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time domain cosmology.
    Type of Medium: Online Resource
    ISSN: 0067-0049 , 1538-4365
    RVK:
    Language: Unknown
    Publisher: American Astronomical Society
    Publication Date: 2021
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    SSG: 16,12
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 514, No. 4 ( 2022-07-13), p. 5905-5926
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 514, No. 4 ( 2022-07-13), p. 5905-5926
    Abstract: Upcoming deep imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will be confronted with challenges that come with increased depth. One of the leading systematic errors in deep surveys is the blending of objects due to higher surface density in the more crowded images; a considerable fraction of the galaxies which we hope to use for cosmology analyses will overlap each other on the observed sky. In order to investigate these challenges, we emulate blending in a mock catalogue consisting of galaxies at a depth equivalent to 1.3 yr of the full 10-yr Rubin Observatory that includes effects due to weak lensing, ground-based seeing, and the uncertainties due to extraction of catalogues from imaging data. The emulated catalogue indicates that approximately 12 per cent of the observed galaxies are ‘unrecognized’ blends that contain two or more objects but are detected as one. Using the positions and shears of half a billion distant galaxies, we compute shear–shear correlation functions after selecting tomographic samples in terms of both spectroscopic and photometric redshift bins. We examine the sensitivity of the cosmological parameter estimation to unrecognized blending employing both jackknife and analytical Gaussian covariance estimators. An ∼0.025 decrease in the derived structure growth parameter S8 = σ8(Ωm/0.3)0.5 is seen due to unrecognized blending in both tomographies with a slight additional bias for the photo-z-based tomography. This bias is greater than the 2σ statistical error in measuring S8.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 511, No. 1 ( 2022-02-04), p. 1029-1042
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 511, No. 1 ( 2022-02-04), p. 1029-1042
    Abstract: Systematic errors in the galaxy redshift distribution n(z) can propagate to systematic errors in the derived cosmology. We characterize how the degenerate effects in tomographic bin widths and galaxy bias impart systematic errors on cosmology inference using observational data from the Deep Lens Survey. For this we use a combination of galaxy clustering and galaxy–galaxy lensing. We present two end-to-end analyses from the catalogue level to parameter estimation. We produce an initial cosmological inference using fiducial tomographic redshift bins derived from photometric redshifts, then compare this with a result where the redshift bins are empirically corrected using a set of spectroscopic redshifts. We find that the derived parameter S8 ≡ σ8(Ωm/.3)1/2 decreases from $0.841^{+0.062}_{-0.061}$ to $0.781^{0.061}_{0.054}$ upon correcting the n(z) errors in the second method.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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  • 4
    In: The Astronomical Journal, American Astronomical Society, Vol. 160, No. 5 ( 2020-10-27), p. 226-
    Type of Medium: Online Resource
    ISSN: 1538-3881
    Language: Unknown
    Publisher: American Astronomical Society
    Publication Date: 2020
    detail.hit.zdb_id: 2207625-6
    detail.hit.zdb_id: 2003104-X
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  • 5
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Monthly Notices of the Royal Astronomical Society Vol. 499, No. 1 ( 2020-10-13), p. 1140-1153
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 499, No. 1 ( 2020-10-13), p. 1140-1153
    Abstract: The Legacy Survey of Space and Time (LSST) survey will provide unprecedented statistical power for measurements of dark energy. Consequently, controlling systematic uncertainties is becoming more important than ever. The LSST observing strategy will affect the statistical uncertainty and systematics control for many science cases; here, we focus on weak lensing (WL) systematics. The fact that the LSST observing strategy involves hundreds of visits to the same sky area provides new opportunities for systematics mitigation. We explore these opportunities by testing how different dithering strategies (pointing offsets and rotational angle of the camera in different exposures) affect additive WL shear systematics on a baseline operational simulation, using the ρ-statistics formalism. Some dithering strategies improve systematics control at the end of the survey by a factor of up to ∼3–4 better than others. We find that a random translational dithering strategy, applied with random rotational dithering at every filter change, is the most effective of those strategies tested in this work at averaging down systematics. Adopting this dithering algorithm, we explore the effect of varying the area of the survey footprint, exposure time, number of exposures in a visit, and exposure to the Galactic plane. We find that any change that increases the average number of exposures (in filters relevant to WL) reduces the additive shear systematics. Some ways to achieve this increase may not be favorable for the WL statistical constraining power or for other probes, and we explore the relative trade-offs between these options given constraints on the overall survey parameters.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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  • 6
    In: Physical Review D, American Physical Society (APS), Vol. 104, No. 1 ( 2021-7-30)
    Type of Medium: Online Resource
    ISSN: 2470-0010 , 2470-0029
    Language: English
    Publisher: American Physical Society (APS)
    Publication Date: 2021
    detail.hit.zdb_id: 2844732-3
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Monthly Notices of the Royal Astronomical Society Vol. 520, No. 2 ( 2023-02-07), p. 2484-2516
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 520, No. 2 ( 2023-02-07), p. 2484-2516
    Abstract: The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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  • 8
    Online Resource
    Online Resource
    American Astronomical Society ; 2023
    In:  The Astronomical Journal Vol. 166, No. 2 ( 2023-08-01), p. 59-
    In: The Astronomical Journal, American Astronomical Society, Vol. 166, No. 2 ( 2023-08-01), p. 59-
    Abstract: The apparent brightness of satellites is calculated as a function of satellite position as seen by a ground-based observer in darkness. Both direct illumination of the satellite by the Sun as well as indirect illumination due to reflection from the Earth are included. The reflecting properties of the satellite components and of the Earth must first be estimated (the Bidirectional Reflectance Distribution Function, or BRDF). The reflecting properties of the satellite components can be found directly using lab measurements or accurately inferred from multiple observations of a satellite at various solar angles. Integrating over all scattering surfaces leads to the angular pattern of flux from the satellite. Finally, the apparent brightness of the satellite as seen by an observer at a given location is calculated as a function of satellite position. We develop an improved model for reflection of light from Earth’s surface using aircraft data. We find that indirectly reflected light from Earth’s surface contributes significant increases in apparent satellite brightness. This effect is particularly strong during civil twilight. We validate our approach by comparing our calculations to multiple observations of selected Starlink satellites and show significant improvement on previous satellite brightness models. Similar methodology for predicting satellite brightness has already informed mitigation strategies for next-generation Starlink satellites. Measurements of satellite brightness over a variety of solar angles widens the effectiveness of our approach to virtually all satellites. We demonstrate that an empirical model in which reflecting functions of the chassis and the solar panels are fit to observed satellite data performs very well. This work finds application in satellite design and operations, and in planning observatory data acquisition and analysis.
    Type of Medium: Online Resource
    ISSN: 0004-6256 , 1538-3881
    RVK:
    Language: Unknown
    Publisher: American Astronomical Society
    Publication Date: 2023
    detail.hit.zdb_id: 2207625-6
    detail.hit.zdb_id: 2003104-X
    SSG: 16,12
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  • 9
    In: eBioMedicine, Elsevier BV, Vol. 96 ( 2023-10), p. 104799-
    Type of Medium: Online Resource
    ISSN: 2352-3964
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2799017-5
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  • 10
    In: JAMA Network Open, American Medical Association (AMA), Vol. 6, No. 7 ( 2023-07-13), p. e2323349-
    Abstract: Current data identifying COVID-19 risk factors lack standardized outcomes and insufficiently control for confounders. Objective To identify risk factors associated with COVID-19, severe COVID-19, and SARS-CoV-2 infection. Design, Setting, and Participants This secondary cross-protocol analysis included 4 multicenter, international, randomized, blinded, placebo-controlled, COVID-19 vaccine efficacy trials with harmonized protocols established by the COVID-19 Prevention Network. Individual-level data from participants randomized to receive placebo within each trial were combined and analyzed. Enrollment began July 2020 and the last data cutoff was in July 2021. Participants included adults in stable health, at risk for SARS-CoV-2, and assigned to the placebo group within each vaccine trial. Data were analyzed from April 2022 to February 2023. Exposures Comorbid conditions, demographic factors, and SARS-CoV-2 exposure risk at the time of enrollment. Main Outcomes and Measures Coprimary outcomes were COVID-19 and severe COVID-19. Multivariate Cox proportional regression models estimated adjusted hazard ratios (aHRs) and 95% CIs for baseline covariates, accounting for trial, region, and calendar time. Secondary outcomes included severe COVID-19 among people with COVID-19, subclinical SARS-CoV-2 infection, and SARS-CoV-2 infection. Results A total of 57 692 participants (median [range] age, 51 [18-95] years; 11 720 participants [20.3%] aged ≥65 years; 31 058 participants [53.8%] assigned male at birth) were included. The analysis population included 3270 American Indian or Alaska Native participants (5.7%), 7849 Black or African American participants (13.6%), 17 678 Hispanic or Latino participants (30.6%), and 40 745 White participants (70.6%). Annualized incidence was 13.9% (95% CI, 13.3%-14.4%) for COVID-19 and 2.0% (95% CI, 1.8%-2.2%) for severe COVID-19. Factors associated with increased rates of COVID-19 included workplace exposure (high vs low: aHR, 1.35 [95% CI, 1.16-1.58]; medium vs low: aHR, 1.41 [95% CI, 1.21-1.65] ; P   & amp;lt; .001) and living condition risk (very high vs low risk: aHR, 1.41 [95% CI, 1.21-1.66]; medium vs low risk: aHR, 1.19 [95% CI, 1.08-1.32] ; P   & amp;lt; .001). Factors associated with decreased rates of COVID-19 included previous SARS-CoV-2 infection (aHR, 0.13 [95% CI, 0.09-0.19]; P   & amp;lt; .001), age 65 years or older (aHR vs age & amp;lt;65 years, 0.57 [95% CI, 0.50-0.64]; P   & amp;lt; .001) and Black or African American race (aHR vs White race, 0.78 [95% CI, 0.67-0.91]; P  = .002). Factors associated with increased rates of severe COVID-19 included race (American Indian or Alaska Native vs White: aHR, 2.61 [95% CI, 1.85-3.69]; multiracial vs White: aHR, 2.19 [95% CI, 1.50-3.20] ; P   & amp;lt; .001), diabetes (aHR, 1.54 [95% CI, 1.14-2.08]; P  = .005) and at least 2 comorbidities (aHR vs none, 1.39 [95% CI, 1.09-1.76]; P  = .008). In analyses restricted to participants who contracted COVID-19, increased severe COVID-19 rates were associated with age 65 years or older (aHR vs & amp;lt;65 years, 1.75 [95% CI, 1.32-2.31]; P   & amp;lt; .001), race (American Indian or Alaska Native vs White: aHR, 1.98 [95% CI, 1.38-2.83]; Black or African American vs White: aHR, 1.49 [95% CI, 1.03-2.14] ; multiracial: aHR, 1.81 [95% CI, 1.21-2.69]; overall P  = .001), body mass index (aHR per 1-unit increase, 1.03 [95% CI, 1.01-1.04]; P  = .001), and diabetes (aHR, 1.85 [95% CI, 1.37-2.49]; P   & amp;lt; .001). Previous SARS-CoV-2 infection was associated with decreased severe COVID-19 rates (aHR, 0.04 [95% CI, 0.01-0.14]; P   & amp;lt; .001). Conclusions and Relevance In this secondary cross-protocol analysis of 4 randomized clinical trials, exposure and demographic factors had the strongest associations with outcomes; results could inform mitigation strategies for SARS-CoV-2 and viruses with comparable epidemiological characteristics.
    Type of Medium: Online Resource
    ISSN: 2574-3805
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
    detail.hit.zdb_id: 2931249-8
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