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1687 Publications visible to you, out of a total of 1687
Modeling and predicting spatio-temporal dynamics of PM_2.5 concentrations through time-evolving covariance models

Abstract

Not specified

Authors: Ghulam A. Qadir, Ying Sun

Date Published: 2025

Publication Type: Journal

Efficient large-scale nonstationary spatial covariance function estimation using convolutional neural networks

Abstract

Not specified

Authors: Pratik Nag, Yiping Hong, Sameh Abdulah, Ghulam A. Qadir, Marc G. Genton, Ying Sun

Date Published: 2025

Publication Type: Journal

Enhancing the statistical evaluation of earthquake forecasts—An application to Italy

Abstract

Not specified

Authors: Jonas R. Brehmer, Kristof Kraus, Tilmann Gneiting, Marcus Herrmann, Warner Marzocchi

Date Published: 2025

Publication Type: Journal

The robustness of inferred envelope and core rotation rates of red giant stars from asteroseismology

Abstract (Expand)

Context. Rotation is an important phenomenon influencing stellar structure and evolution, however, it has not been adequately modelled thus far. Therefore, accurate estimates of internal rotation rates … are valuable for constraining stellar evolution models. Aims. We aim to assess the accuracy of asteroseismic estimates of internal rotation rates and how they depend on the fundamental stellar parameters. Methods. We applied the recently developed extended-multiplicative optimally localised averages (eMOLA) inversion method, to infer localised estimates of internal rotation rates of synthetic observations of red giants. We searched for suitable reference stellar models, following a grid-based approach, and we assessed the robustness of the resulting inferences with respect to the choice of reference model. Results. We find that matching the mixed-mode pattern between the observation and the reference model is an important criterion for selecting suitable reference models. We propose (i) selecting a set of reference models based on the correlation between the observed rotational splittings and the mode-trapping parameter; (ii) computing the rotation rates for all these models; and (iii) using the average value obtained across the whole set as the estimate of the internal rotation rates. We find that the effect of a near surface perturbation in the synthetic observations on the rotation rates estimated based on the correlation between the observed rotational splittings and the mode-trapping parameter is negligible. Conclusions. We conclude that when using an ensemble of reference models that are selected by matching the mixed-mode pattern, the input rotation rates can be recovered across a range of fundamental stellar parameters such as mass, mixing-length parameter, and composition. Further, red giant rotation rates determined in this way are also independent of any near-surface perturbation of the stellar structure.

Authors: F. Ahlborn, E. P. Bellinger, S. Hekker, S. Basu, D. Mokrytska

Date Published: 2025

Publication Type: Journal

Gravitational-wave model for neutron star merger remnants with supervised learning

Abstract (Expand)

We present a time-domain model for the gravitational waves emitted by equal-mass binary neutron star merger remnants for a fixed equation of state. We construct a large set of numerical relativity … simulations for a single equation of state consistent with current constraints, totaling 157 equal-mass binary neutron star merger configurations. The gravitational-wave model is constructed using the supervised learning method of K-nearest neighbor regression. As a first step toward developing a general model with supervised learning methods that accounts for the dependencies on equation of state and the binary masses of the system, we explore the impact of the size of the dataset on the model. We assess the accuracy of the model for a varied dataset size and number density in total binary mass. Specifically, we consider five training sets of simulations uniformly distributed in total binary mass. We evaluate the resulting models in terms of faithfulness using a test set of 30 additional simulations that are not used during training and which are equidistantly spaced in total binary mass. The models achieve faithfulness with maximum values in the range of 0.980 to 0.995. We assess our models simulating signals observed by the three-detector network of Advanced LIGO-Virgo. We find that all models with training sets of size equal to or larger than 40 achieve an unbiased measurement of the main gravitational-wave frequency. We confirm that our results do not depend qualitatively on the choice of the (fixed) equation of state. We conclude that training sets, with a minimum size of 40 simulations, or a number density of approximately 11 simulations per 0.1⁢𝑀⊙ of total binary mass, suffice for the construction of faithful templates for the postmerger signal for a single equation of state and equal-mass binaries, and lead to mean faithfulness values of ℱ ≃0.95. Our model being based on only one fixed equation of state represents only a first step toward a method that is fully applicable for gravitational-wave parameter estimation. However, our findings are encouraging since we show that our supervised learning model built on a set of simulations for a fixed equation of state successfully recovers the main gravitational-wave features of a simulated signal obtained using another equation of state. This may indicate that the extension of this model to an arbitrary equation of state may actually be achieved with a manageable set of simulations.

Authors: Theodoros Soultanis, Kiril Maltsev, Andreas Bauswein, Katerina Chatziioannou, Friedrich K. Röpke, Nikolaos Stergioulas

Date Published: 2025

Publication Type: Journal

Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-rich Core-collapse Supernova Precursor Emission with the LSST

Abstract (Expand)

Abstract Enhanced emission in the months to years preceding explosion has been detected for several core-collapse supernovae (SNe). Though the physical mechanisms driving the emission remain hotly …hanisms driving the emission remain hotly debated, the light curves of detected events show long-lived (≥50 days), plateau-like behavior, suggesting hydrogen recombination may significantly contribute to the total energy budget. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will provide a decade-long photometric baseline to search for this emission, both in binned pre-explosion observations after an SN is detected and in single-visit observations prior to the SN explosion. In anticipation of these searches, we simulate a range of eruptive precursor models to core-collapse SNe and forecast the discovery rates of these phenomena in LSST data. We find a detection rate of ∼40–130 yr −1 for SN IIP/IIL precursors and ∼110 yr −1 for SN IIn precursors in single-epoch photometry. Considering the first three years of observations with the effects of rolling and observing triplets included, this number grows to a total of 150–400 in binned photometry, with the highest number recovered when binning in 100 day bins for 2020tlf-like precursors and in 20 day bins for other recombination-driven models from the literature. We quantify the impact of using templates contaminated by residual light (from either long-lived or separate precursor emission) on these detection rates, and explore strategies for estimating baseline flux to mitigate these issues. Spectroscopic follow-up of the eruptions preceding core-collapse SNe and detected with LSST will offer important clues to the underlying drivers of terminal-stage mass loss in massive stars.

Authors: A. Gagliano, E. Berger, V. A. Villar, D. Hiramatsu, R. Kessler, T. Matsumoto, A. Gilkis, E. Laplace

Date Published: 30th Dec 2024

Publication Type: Journal

Improving model chain approaches for probabilistic solar energy forecasting through post-processing and machine learning

Abstract

Not specified

Authors: Nina Horat, Sina Klerings, Sebastian Lerch

Date Published: 28th Dec 2024

Publication Type: Journal

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