The independent measurement of the Hubble constant with gravitational-wave standard sirens will potentially shed light on the tension between the local distance ladders and Planck experiments. Therefore, thorough understanding of the sources of systematic uncertainty for the standard siren method is crucial. In this Letter, we focus on two scenarios that will potentially dominate the systematic uncertainty of standard sirens. First, simulations of electromagnetic counterparts of binary neutron star mergers suggest aspherical emissions, so the binaries available for the standard siren method can be selected by their viewing angles. This selection effect can lead to ≳2% bias in Hubble constant measurement even with mild selection. Second, if the binary viewing angles are constrained by the electromagnetic counterpart observations but the bias of the constraints is not controlled under ∼10°, the resulting systematic uncertainty in the Hubble constant will be >3%. In addition, we find that both of the systematics cannot be properly removed by the viewing angle measurement from gravitational-wave observations. Comparing to the known dominant systematic uncertainty for standard sirens, the ≤2% gravitational-wave calibration uncertainty, the effects from the viewing angle appear to be more significant. Therefore, the systematic uncertainty from the viewing angle might be a major challenge before the standard sirens can resolve the tension in the Hubble constant, which is currently ∼9%.
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http://dx.doi.org/10.1103/PhysRevLett.125.201301 | DOI Listing |
Front Med (Lausanne)
January 2025
Eye Institute and Affiliated Xiamen Eye Center, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Background: Chronic kidney disease (CKD) is a significant global health issue, often linked to diabetes, hypertension, and glomerulonephritis. However, aggregated statistics can obscure heterogeneity across subtypes, age, gender, and regions. This study aimed to analyze global CKD trends from 1990 to 2021, focusing on age, gender, socio-demographic index (SDI), and regional variations.
View Article and Find Full Text PDFHeliyon
January 2025
Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, Iraq.
Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.
View Article and Find Full Text PDFRadiat Res
January 2025
Federal Office for Radiation Protection, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany.
Lifetime risk estimates play a key role in many areas of radiation research. Here, the focus is on the lifetime excess absolute risk (LEAR) for dying from lung cancer due to occupational radon exposure based on uranium miners cohort studies. The major components in estimating LEAR were systematically varied to investigate the variability and uncertainties of results.
View Article and Find Full Text PDFTrends Ecol Evol
January 2025
Department of Marine Sciences, University of the Aegean, Mytilene, 81100, Greece.
Systematic conservation planning (SCP) involves the cost-effective placement and application of management actions to achieve biodiversity conservation objectives. Given the political momentum for greater global nature protection, restoration, and improved management of natural resources articulated in the targets of the Global Biodiversity Framework, assessing the state-of-the-art of SCP is timely. Recent advances in SCP include faster and more exact algorithms and software, inclusion of ecosystem services and multiple facets of biodiversity (e.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, Helsinki, FI-00014, Finland; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China.
The reliability of land surface phenology (LSP) derived from satellite remote sensing is crucial for obtaining accurate estimates of the phenological response of vegetation to future climate change in urban ecosystems. Differences in phenological definition and extraction methodology using remote sensing can generate systemic errors in estimating the phenological temperature sensitivity to predict the biological response of vegetation. Here, we evaluated the start of the season (SOS), the end of the season (EOS), and the growing season length (GSL) between the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) and the Suomi National Polar-Orbiting Partnership NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics (VNP22Q2) over 1470 urban clusters worldwide.
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