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Nuclear Theory·nucl-th

Monday·October 31, 2022

7 papers—4 primary·3 cross-listed

All7Primary4Cross-lists3
  1. 05

    Bootstrapped Block Lanczos for large-dimension eigenvalue problems

    Ryan M. Zbikowski · Calvin W. Johnson

    The Lanczos algorithm has proven itself to be a valuable matrix eigensolver for problems with large dimensions, up to hundreds of millions or even tens of billions. The computational cost of using any Lanczos algorithm is dominated by the number of sparse matrix-vector multiplications until suitable convergence is reached. Block Lanczos replaces sparse matrix-vector multiplication with sparse matrix-matrix multiplication, which is more efficient, but for a randomly chosen starting block (or pivot), more multiplications are required to reach convergence. We find that a bootstrapped pivot block, that is, an initial block constructed from approximate eigenvectors computed in a truncated space, leads to a dramatically reduced number of multiplications, significantly outperforming both standard vector Lanczos and block Lanczos with a random pivot. A key condition for speed-up is that the pivot block have a non-trivial overlap with the final converged vectors. We implement this approach in a configuration-interaction code for nuclear structure, and find a reduction in time-to-solution by a factor of two or more, up to a factor of ten.

    ↳ physics.comp-phcs.NAmath.NAnucl-thComput.Phys.Commun.(2023)·0 citations
  2. 06

    Gamow-Teller strengths from unstable O via the reaction in inverse kinematics

    S. Giraud · J. C. Zamora · R. Zegers · D. Bazin · Y. Ayyad · S. Bacca · S. Beceiro-Novo · B. A. Brown · A. Carls · J. Chen · M. Cortesi · M. DeNudt and 15 other authors

    For the first time, the reaction was successfully used in inverse kinematics to extract the Gamow-Teller transition strength in the direction from an unstable nucleus. The nucleus studied was O, and the Gamow-Teller transition strength to N was extracted up to an excitation energy of 22 MeV. The measurement of the reaction in inverse kinematics was made possible by the combination of an active target time projection chamber and a magnetic spectrometer. The data were used to test shell-model and state-of-the-art coupled cluster calculations. Shell-model calculations reproduce the measured Gamow-Teller strength distribution up to about 15 MeV reasonably well, after the application of a phenomenological quenching factor. Coupled-cluster calculation reproduces the full strength distribution well without such quenching, owing to the large model space, the inclusion of strong correlations, and the coupling of the weak interaction to two nucleons through two-body currents. This indicates that such calculations provide a very promising path for answering long-standing questions about the observed quenching of Gamow-Teller strengths in nuclei.

    ↳ nucl-exnucl-thPRL(2023)·15 citations
  3. 07

    Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers

    Richard Qiu🇺🇸 · Plamen Krastev🇺🇸 · Kiranjyot Gill🇺🇸 · Edo Berger🇺🇸

    The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo Interferometer Collaborations have now detected all three classes of compact binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron star-black hole (NSBH). For coalescences involving neutron stars, the simultaneous observation of gravitational and electromagnetic radiation produced by an event, has broader potential to enhance our understanding of these events, and also to probe the equation of state (EOS) of dense matter. However, electromagnetic follow-up to gravitational wave (GW) events requires rapid real-time detection and classification of GW signals, and conventional detection approaches are computationally prohibitive for the anticipated rate of detection of next-generation GW detectors. In this work, we present the first deep learning based results of classification of GW signals from NSBH mergers in \textit{real} LIGO data. We show for the first time that a deep neural network can successfully distinguish all three classes of compact binary mergers and separate them from detector noise. Specifically, we train a convolutional neural network (CNN) on data samples of real LIGO noise with injected BBH, BNS, and NSBH GW signals, and we show that our network has high sensitivity and accuracy. Most importantly, we successfully recover the two confirmed NSBH events to-date (GW200105 and GW200115) and the two confirmed BNS mergers to-date (GW170817 and GW190425), together with of all BBH candidate events from the third Gravitational Wave Transient Catalog, GWTC-3. These results are an important step towards low-latency real-time GW detection, enabling multi-messenger astronomy.

    ↳ astro-ph.IMastro-ph.HEgr-qcnucl-thPLB(2023)·43 citations

Affiliations

first authorsco-authorsvia INSPIRE