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1702 Publications visible to you, out of a total of 1702
Predicting hydrogen atom transfer energy barriers using Gaussian process regression

Abstract

Not specified

Authors: Evgeni Ulanov, Ghulam A. Qadir, Kai Riedmiller, Pascal Friederich, Frauke Gräter

Date Published: 10th Jan 2025

Publication Type: Journal

Comparative evaluation of standard machine learning models for liver fibrosis detection

Abstract

Not specified

Authors: Marcus Buchwald, Pascal Memmesheimer, Arash Dooghaie Moghadam, Ines Tuschner, Laura Santamaria Suarez, Timo Itzel, Christoph Antoni, Jimmy Daza, Catharina Gerhards, Michael Neumaier, Christop Brochhausen, Peter R. Galle, Matthias Ebert, Arndt Weinmann, Jürgen Hesser, Vincent Heuveline, Andreas Teufel

Date Published: 10th Jan 2025

Publication Type: Journal

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

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