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

Abstract

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Authors: Ganna Gryn’ova, Tristan Bereau, Carolin Müller, Pascal Friederich, Rebecca C. Wade, Ariane Nunes-Alves, Thereza A. Soares, Kenneth Merz

Date Published: 12th Aug 2024

Publication Type: Journal

Abstract (Expand)

In the field of population genetics, the driving forces of evolution within species can be studied with trees. Along a genome, each tree describes the local ancestries of a small genomic region. Together, those trees form a tree sequence that describes the ancestry of a population at every site of the sequence. Inferring tree sequences for whole genomes with many haplotype samples is a computationally expensive task, however. The state-of-the-art tool to infer tree sequences is tsinfer, which infers ancestries for human chromosomes from 5000 samples within a few hours. The tool has the capability to parallelize the computation, but we identify a structure in the input data that limits its parallelizability. We propose a novel parallelization scheme aiming to improve scaling at high thread counts, independently of this structure. Furthermore, we propose several optimizations for the inference algorithm, improving cache efficiency and reducing the number of operations per iteration. We provide a proof-of-concept implementation, and compare the computation speed of our implementation and tsinfer. When inferring ancestries for the 1000 Genomes Project, our implementation is consistently faster by a factor of 1.9 to 2.4. Additionally, depending on the choice of parameters, our parallelization scheme scales better between 32 and 96 cores, improving its speed advantage, especially at higher core counts. In phases where our novel parallelization scheme does not apply, our optimizations still improve the runtime by a factor of 2.2. As available genomic data sets are growing rapidly in size, our contribution decreases the computation time and enables better parallelization, allowing the processing of larger data sets in reasonable time frames

Authors: Johannes Hengstler, Lukas Hübner, Alexandros Stamatakis

Date Published: 1st Aug 2024

Publication Type: Master's Thesis

Abstract

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Authors: Dandan Wei, Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace, Friedrich K. Röpke, Marco Vetter

Date Published: 1st Aug 2024

Publication Type: Journal

Abstract

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Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 1st Aug 2024

Publication Type: Journal

Abstract

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Authors: F. P. Callan, C. E. Collins, S. A. Sim, L. J. Shingles, R. Pakmor, S. Srivastav, J. M. Pollin, S. Gronow, F. K. Roepke, I. R. Seitenzahl

Date Published: 1st Aug 2024

Publication Type: Journal

Abstract

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Authors: Dandan Wei, Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace, Friedrich K. Röpke, Marco Vetter

Date Published: 1st Aug 2024

Publication Type: Journal

Abstract (Expand)

Aims. The KEYSTONE project aims to enhance our understanding of solar-like oscillators by delivering a catalogue of global asteroseismic parameters (Δv and v max) for 173 stars, comprising mainly dwarfs and subgiants, observed by the K2 mission in its short-cadence mode during campaigns 6–19. Methods. We derive atmospheric parameters and luminosities using spectroscopic data from TRES, astrometric data from Gaia, and the infrared flux method (IRFM) for a comprehensive stellar characterisation. Asteroseismic parameters are robustly extracted using three independent methods, complemented by an iterative refinement of the spectroscopic analyses using seismic log g values to enhance parameter accuracy. Results. Our analysis identifies new detections of solar-like oscillations in 159 stars, providing an important complement to already published results from previous campaigns. The catalogue provides homogeneously derived atmospheric parameters and luminosities for the majority of the sample. Comparison between spectroscopic Teff and those obtained from the IRFM demonstrates excellent agreement. The iterative approach to spectroscopic analysis significantly enhances the accuracy of the stellar properties derived.

Authors: Mikkel N. Lund, Sarbani Basu, Allyson Bieryla, Luca Casagrande, Daniel Huber, Saskia Hekker, Lucas Viani, Guy R. Davies, Tiago L. Campante, William J. Chaplin, Aldo M. Serenelli, J. M. Joel Ong, Warrick H. Ball, Amalie Stokholm, Earl P. Bellinger, Michaël Bazot, Dennis Stello, David W. Latham, Timothy R. White, Maryum Sayeed, Víctor Aguirre Børsen-Koch, Ashley Chontos

Date Published: 1st Aug 2024

Publication Type: Journal

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