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Record W4409017247 · doi:10.3204/pubdb-2025-01203

A Linear Collider Vision for the Future of Particle Physics

2025· preprint· en· W4409017247 on OpenAlexaff
E. Adli, A. Aryshev, S. Asai, D. Attié, J. L. Avila-Jimenez, Y. Bai, Csaba Balázs, T. Barklow, A. Bellerive, Y. Benhammou, M. Besançon, S. Bilanishvili, Victoria Bjelland, C. Blanch, Johannes Braathen, Cedric Breuning, Bartłomiej Brudnowski, P. N. Burrows, Jun Chen, T. Chikamatsu, Vera Cilento, P. Colas, I. Chaikovska, F. Corriveau, R. D'Arcy, A. Das, Fernando Cornet, J. de Blas, Ankur Dhar, S. Dittmaier, A. T. Doyle, P. Drobniak, T. A. du Pree, G. Eckerlin, Ulrich Einhaus, Lyn Evans, M. Fernandez, Gauthier Durieux, Manuel Formela, M. C. Fouz, C. D. Fu, J. Fujimoto, Arianna Formenti, B. Foster, C. D. Fu, J. Fujimoto, Xavier González-Argenté, S. Gori, P. D. Grannis, Howard E. Haber, Niclas Hamann, Ioannis D. Gialamas, C. Hensel, J. Fuster, Mark Hogan, L. Gray, G. Grenier, R. Hosokawa, S. Huang, M. Idzik, Syuhei Iguro, K. Ikematsu, A. Irles Quiles, R. Jaramillo, D. Jeans, W. Kaabi, J. Kalinowski, Daniel Kalvik, Αλέξανδρος Καράμ, Sameen Ahmed Khan, Chunguang Jing, Sabine Kraml, K. Kubo, J. Kamiński, K. Kannike, Y. Kato, I. B. Laktineh, A. Latina, F. LeDiberder, B. List, Xueying Lu, B. Madison, J. Maeda, Pablo Martín-Luna, V. J. Martin, A. Levy, S. Matsumoto, Kentaro Mawatari, A. Lopez-Virto, Ken Mimasu, Laura Monaco, G. Moortgat-Pick, M. Moreno Llácer, Nathan Majernik, Sarah Morton, D. Moya, Enrico Nardi, Mihoko M. Nojiri, D. Melini, O. M. Ogreid, Yasuhiro Okada, Maja Olvegård, Y. Onel, H. Ono, C. Orero, K. Österberg, Sung Chul Park, E. Musumeci, L. K. Pedraza-Motavita, D. Protopopescu, Alexander Ody, John P. Ralston, R. Rimmer, M. Q. Ruan, S. Rudrabhatla, T. Saeki, L. K. Pedraza-Motavita, Y. Seiya, A. Şenol, Kyrre Sjobak, I. Smiljanic, Simon Spannagel, M. Spira, S. Stapnes, Amir Subba, T. Takahashi, F. Richard, T. Tauchi, Jinshou Tian, J. Timmermans, Tony W. Tong, A. Tricoli, M. Tytgat, A. Ukleja, C. Vallée, R. Van Kooten, J. van Tilborg, L. Verra, Mário Pinto, I. Vidaković, Emma Viklund, M. Vos, H. Sert, Katinka Wandall-Christensen, T. Shidara, Z. Wąs, N. K. Watson, Håkan Wennlöf, S. Westhoff, James W. Wetzel, Peter Williams, M. Wing, Alasdair Winter, M. Winter, Tomasz Wojtoń, X. M. Xia, Y. Yamamoto, S. Yamashita, M. Yamauchi, M. Yoshioka, A. F. Żarnecki, Kamil Zembaczyński, M. Zielinski, Douglas Tuckler

Bibliographic record

VenueDesy Publications Database (Deutsches Elektronen-Synchrotron DESY) · 2025
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsMcGill University
Fundersnot available
KeywordsColliderPhysicsParticle physicsParticle (ecology)Nuclear physicsTheoretical physics

Abstract

fetched live from OpenAlex

In this paper we review the physics opportunities at linear $e^+e^-$ colliders with a special focus on high centre-of-mass energies and beam polarisation, take a fresh look at the various accelerator technologies available or under development and, for the first time, discuss how a facility first equipped with a technology mature today could be upgraded with technologies of tomorrow to reach much higher energies and/or luminosities. In addition, we will discuss detectors and alternative collider modes, as well as opportunities for beyond-collider experiments and R&D facilities as part of a linear collider facility (LCF). The material of this paper will support all plans for $e^+e^-$ linear colliders and additional opportunities they offer, independently of technology choice or proposed site, as well as R&D for advanced accelerator technologies. This joint perspective on the physics goals, early technologies and upgrade strategies has been developed by the LCVision team based on an initial discussion at LCWS2024 in Tokyo and a follow-up at the LCVision Community Event at CERN in January 2025. It heavily builds on decades of achievements of the global linear collider community, in particular in the context of CLIC and ILC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.312
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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