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Record W4210775356 · doi:10.1088/2515-7647/ac4ee4

Roadmap on topological photonics

2022· article· en· W4210775356 on OpenAlexaff
Hannah M. Price, Y. D. Chong, Alexander B. Khanikaev, Henning Schomerus, Lukas J. Maczewsky, Mark Kremer, Matthias Heinrich, Alexander Szameit, Oded Zilberberg, Yihao Yang, Andrea Alù, Ronny Thomale, Iacopo Carusotto, Philippe St-Jean, A. Amo, Avik Dutt, Luqi Yuan, Shanhui Fan, Xuefan Yin, Chao Peng, Tomoki Ozawa, Andrea Blanco‐Redondo

Bibliographic record

VenueJournal of Physics Photonics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversité de Montréal
FundersAir Force Office of Scientific ResearchCore Research for Evolutional Science and TechnologyOffice of Naval ResearchRIKENPrecursory Research for Embryonic Science and TechnologyEngineering and Physical Sciences Research CouncilJapan Society for the Promotion of ScienceHorizon 2020 Framework ProgrammeMultidisciplinary University Research InitiativeU.S. Air ForceNational Natural Science Foundation of ChinaAgence Nationale de la RechercheDeutsche ForschungsgemeinschaftNational Research FoundationProvincia Autonoma di TrentoMinistry of Education, IndiaU.S. Department of DefenseEuropean CommissionDivision of Materials ResearchAlfried Krupp von Bohlen und Halbach-StiftungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPhotonicsTopology (electrical circuits)EngineeringComputer scienceEngineering physicsNanotechnologyPhysicsElectrical engineeringMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Topological photonics seeks to control the behaviour of the light through the design of protected topological modes in photonic structures. While this approach originated from studying the behaviour of electrons in solid-state materials, it has since blossomed into a field that is at the very forefront of the search for new topological types of matter. This can have real implications for future technologies by harnessing the robustness of topological photonics for applications in photonics devices. This roadmap surveys some of the main emerging areas of research within topological photonics, with a special attention to questions in fundamental science, which photonics is in an ideal position to address. Each section provides an overview of the current and future challenges within a part of the field, highlighting the most exciting opportunities for future research and developments.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0060.010
Open science0.0020.005
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0330.007

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations154
Published2022
Admission routes1
Has abstractyes

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