Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 astrometric data for the detection of dark companions

© 2024 The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Hidden within the Gaia satellite’s multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.Peer reviewe

Innovation Trends in NAFTA Countries: An Econometric Analysis of Patent Applications

This paper analyzes innovation trends in North America Free Trade Agreement (NAFTA) countries by means of the number of patent applications during the period 1965 to 2008. Making use of patent data released by the World Intellectual Property Organization (WIPO) and the Network for Science and Technology Indicators (Red Iberoamericana de Ciencia y Tecnología, RICYT), we search for presence of multiple structural changes in the patent applications series in Canada, Mexico, and the United States. Such changes may suggest that firms’ innovative activity has been modified in these countries (Mansfield, 1986). Accordingly, it would be expected that the new regulations implemented in these countries in the 1980s and 1990s have influenced their intellectual property regimes through the NAFTA and the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement. Consequently, the question conducting this research is how the new dispositions affecting intellectual regimes in NAFTA countries have affected innovation activities in these countries. The results achieved in this research confirm the existence of multiple structural changes in the series of patent applications resulting from the new legislation implemented in these countries

Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia

Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ~1.6 million ha, but this dropped to ~1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%–51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover

Leveraging Time Series Data in Similarity Based Healthcare Predictive Models: The Case of Early ICU Mortality Prediction

Patient time series classification faces challenges in high degrees of dimensionality and missingness. In light of patient similarity theory, this study explores effective temporal feature engineering and reduction, missing value imputation, and change point detection methods that can afford similarity-based classification models with desirable accuracy enhancement. We select a piecewise aggregation approximation method to extract fine-grain temporal features and propose a minimalist method to impute missing values in temporal features. For dimensionality reduction, we adopt a gradient descent search method for feature weight assignment. We propose new patient status and directional change definitions based on medical knowledge or clinical guidelines about the value ranges for different patient status levels, and develop a method to detect change points indicating positive or negative patient status changes. We evaluate the effectiveness of the proposed methods in the context of early Intensive Care Unit mortality prediction. The evaluation results show that the k-Nearest Neighbor algorithm that incorporates methods we select and propose significantly outperform the relevant benchmarks for early ICU mortality prediction. This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification. Keywords: time-series classification, similarity-based classification, mortality prediction, directional change poin

Gaussian process surrogates for failure detection: a Bayesian experimental design approach

An important task of uncertainty quantification is to identify {the probability of} undesired events, in particular, system failures, caused by various sources of uncertainties. In this work we consider the construction of Gaussian {process} surrogates for failure detection and failure probability estimation. In particular, we consider the situation that the underlying computer models are extremely expensive, and in this setting, determining the sampling points in the state space is of essential importance. We formulate the problem as an optimal experimental design for Bayesian inferences of the limit state (i.e., the failure boundary) and propose an efficient numerical scheme to solve the resulting optimization problem. In particular, the proposed limit-state inference method is capable of determining multiple sampling points at a time, and thus it is well suited for problems where multiple computer simulations can be performed in parallel. The accuracy and performance of the proposed method is demonstrated by both academic and practical examples

Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series

Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1 imagery of two wetlands with different ecological characteristics; a seasonal isolated wetland in southern Spain and a coastal wetland in the south of France. We test the S1-omnibus method against a commonly-used pairwise comparison of consecutive images to demonstrate its advantages. Additionally, we compare it with a pairwise change detection method using a subset of consecutive Landsat images for the same period of time. The results show how S1-omnibus is capable of capturing in space and time changes produced by water surface dynamics, as well as by agricultural practices, whether they are sudden changes, as well as gradual. S1-omnibus is capable of detecting a wider array of short-term changes than when using consecutive pairs of Sentinel-1 images. When compared to the Landsat-based change detection method, both show an overall good agreement, although certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes

Testing for Evidence of Nonlinear Structure in Daily and Weekly United Kingdom Stock and Property Market Indicies

In this paper we have tested for evidence of nonlinear structure in United Kingdom asset returns including those of real estate and investment trusts, stock market indices and returns for listed real estate companies. While some of our test procedures are designed to test for nonlinear deterministic (chaotic) structure against a random alternative, others have power against nonlinear stochastic structure. If nonlinear deterministic and random walk models are not appropriate to explain asset returns behaviour, then stochastic nonlinearity seems like a logical alternative. The results from our study lead us to that conclusion.