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Record W4388683614 · doi:10.48550/arxiv.2311.01300

Waveform Modelling for the Laser Interferometer Space Antenna

2023· preprint· en· W4388683614 on OpenAlexaff
Niayesh Afshordi, Sarp Akçay, Pau Amaro‐Seoane, Josu C. Aurrekoetxea, Leor Barack, Enrico Barausse, Robert Benkel, Laura Bernard, Sebastiano Bernuzzi, Emanuele Berti, Matteo Bonetti, Béatrice Bonga, Gabriele Bozzola, Richard Brito, Alessandra Buonanno, Alejandro Cárdenas-Avendaño, Marc Casals, David Chernoff, Alvin J. K. Chua, Katy Clough, M. Colleoni, Mekhi Dhesi, Leanne Durkan, Guillaume Faye, D. L. Ferguson, Scott E. Field, W. Gabella, J. García-Bellido, Miguel Gracia-Linares, Davide Gerosa, Stephen Green, M. Haney, Mark Hannam, Anna Heffernan, Tanja Hinderer, Thomas Helfer, Scott A. Hughes, S. Husa, Soichiro Isoyama, Michael L. Katz, Chris Kavanagh, Gaurav Khanna, Larry Kidder, Valeriya Korol, Lorenzo Küchler, Pablo Laguna, François Larrouturou, Alexandre Le Tiec, Benjamin Leather, Eugene A. Lim, Hyun Lim, T. B. Littenberg, Oliver Long, Carlos O. Lousto, Geoffrey Lovelace, Georgios Lukes-Gerakopoulos, Philip Lynch, Rodrigo Panosso Macedo, C. Markakis, Elisa Maggio, Ilya Mandel, Andrea Maselli, Josh Mathews, Pierre Mourier, David Neilsen, David A. Nichols, Jan Novák, Maria Okounkova, R. O’Shaughnessy, Naritaka Oshita, Conor O’Toole, Zhen Pan, Paolo Pani, George Pappas, Vasileios Paschalidis, Harald Pfeiffer, Lorenzo Pompili, Adam Pound, G. Pratten, Hannes R. Rüter, Milton Ruiz, Zeyd Sam, Laura Sberna, Stuart L. Shapiro, Deirdre Shoemaker, Carlos F. Sopuerta, Andrew Spiers, Hari Sundar, Nicola Tamanini, Jonathan E. Thompson, Alexandre Toubiana, Antonios Tsokaros, Samuel D. Upton, Maarten van de Meent, Jeremy M. Wachter, Niels Warburton, Barry Wardell, Helvi Witek, Vojtěch Witzany, Huan Yang, Miguel Zilhão, Angelica Albertini, K. G. Arun, Miguel Bezares, Alexander Bonilla, Christian E. A. Chapman-Bird, Bradley Cownden, Kevin Cunningham, Chris Devitt, Sam R. Dolan, Francisco Duque, Conor Dyson, Chris L. Fryer, Jonathan Gair, Bruno Giacomazzo, Priti Gupta, Wen-Biao Han, Roland Haas, Eric Hirschmann, E. A. Huerta, Philippe Jetzer, Bernard Kelly, Mohammed Khalil, Jack Windsor Lewis, Nicole Lloyd-Ronning, S. Marsat, Germano Nardini, Jakob Neef, Adrian C. Ottewill, Christiana Pantelidou, Gabriel Andres Piovano, Jaime Redondo–Yuste, Laura Sagunski, Leo C. Stein, Viktor Skoupý, Ulrich Sperhake, Lorenzo Speri, Thomas F. M. Spieksma, Chris Stevens, David Trestini, Alex Vañó-Viñuales

Bibliographic record

VenueINFM-OAR (INFN Catania) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsGravitational waveWaveformPhysicsInterferometryGravitational-wave observatoryDetectorAstronomySpace (punctuation)OpticsComputer science

Abstract

fetched live from OpenAlex

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.360
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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