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1703 Publications visible to you, out of a total of 1703
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

Temperature-Annealed Boltzmann Generators

Abstract

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Authors: Henrik Schopmans, Pascal Friederich

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

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Authors: Nina Horat, Sina Klerings, Sebastian Lerch

Date Published: 28th Dec 2024

Publication Type: Journal

Structural bioinformatics approach to exploring transient binding pockets in proteins

Abstract

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Editor:

Date Published: 18th Dec 2024

Publication Type: Bachelor's Thesis

Impact of an irreversible β-galactosylceramidase inhibitor on the lipid profile of zebrafish embryos

Abstract

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Authors: Jessica Guerra, Mirella Belleri, Giulia Paiardi, Chiara Tobia, Davide Capoferri, Marzia Corli, Elisa Scalvini, Marco Ghirimoldi, Marcello Manfredi, Rebecca C. Wade, Marco Presta, Luca Mignani

Date Published: 1st Dec 2024

Publication Type: Journal

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD

Abstract (Expand)

Abstract The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction …tions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

Authors: Giulia D’Arrigo, Daria B. Kokh, Ariane Nunes-Alves, Rebecca C. Wade

Date Published: 1st Dec 2024

Publication Type: Journal

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