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Record W4387974995 · doi:10.3847/1538-4357/acf3e6

Magnetic Structures and Turbulence in SN 1006 Revealed with Imaging X-Ray Polarimetry

2023· article· en· W4387974995 on OpenAlexaff
Ping Zhou, Dmitry Prokhorov, Riccardo Ferrazzoli, Yi-Jung Yang, Patrick Slane, Jacco Vink, Stefano Silvestri, N. Bucciantini, E. M. Reynoso, D. A. Moffett, P. Soffitta, Doug Swartz, P. Kaaret, L. Baldini, E. Costa, C.‐Y. Ng, Dawoon E. Kim, Victor Doroshenko, Steven R. Ehlert, Jeremy Heyl, Frédéric Marin, Tsunefumi Mizuno, M. Pesce-Rollins, C. Sgró, Toru Tamagawa, Martin C. Weisskopf, Fei Xie, I. Agudo, L. A. Antonelli, Matteo Bachetti, W. H. Baumgartner, R. Bellazzini, S. Bianchi, Stephen D. Bongiorno, R. Bonino, A. Brez, Fiamma Capitanio, Simone Castellano, E. Cavazzuti, Chien‐Ting Chen, S. Ciprini, Alessandra De Rosa, E. Del Monte, Laura Di Gesu, Niccolò Di Lalla, Alessandro Di Marco, I. Donnarumma, Michal Dovčiak, Teruaki Enoto, Y. Evangelista, Sergio Fabiani, Javier A. García, Shuichi Gunji, Kiyoshi Hayashida, W. Iwakiri, Svetlana G. Jorstad, Fabian Kislat, V. Karas, Takao Kitaguchi, Jeffery J. Kolodziejczak, H. Krawczynski, Fabio La Monaca, L. Latronico, Ioannis Liodakis, S. Maldera, Alberto Manfreda, Andrea Marinucci, Alan P. Marscher, Herman L. Marshall, G. Matt, Ikuyuki Mitsuishi, Fabio Muleri, Michela Negro, Stephen L. O’Dell, N. Omodei, C. Oppedisano, Alessandro Papitto, George G. Pavlov, Abel L. Peirson, M. Perri, Pierre-Olivier Petrucci, M. Pilia, Andrea Possenti, Juri Poutanen, Simonetta Puccetti, Brian D. Ramsey, John Rankin, Ajay Ratheesh, O. J. Roberts, Roger W. Romani, G. Spandre, F. Tavecchio, Roberto Taverna, Yuzuru Tawara, Allyn F. Tennant, Nicholas E. Thomas, Francesco Tombesi, A. Trois, Sergey S. Tsygankov, Kinwah Wu, Silvia Zane

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of British Columbia
FundersSmithsonian Astrophysical ObservatoryIstituto Nazionale di Fisica NucleareIstituto Nazionale di AstrofisicaAgenzia Spaziale ItalianaGoddard Space Flight CenterSmithsonian InstitutionNederlandse Organisatie voor Wetenschappelijk OnderzoekHong Kong GovernmentNational Natural Science Foundation of ChinaMarshall Space Flight CenterNational Aeronautics and Space Administration
KeywordsPhysicsPolarization (electrochemistry)AstrophysicsMagnetic fieldSupernovaPolarimetryParticle accelerationSkyDegree of polarizationCosmic raySupernova remnantAstronomyOpticsScattering

Abstract

fetched live from OpenAlex

Abstract Young supernova remnants strongly modify the surrounding magnetic fields, which in turn play an essential role in accelerating cosmic rays (CRs). The X-ray polarization measurements probe magnetic field morphology and turbulence at the immediate acceleration site. We report the X-ray polarization distribution in the northeastern shell of SN 1006 from a 1 Ms observation with the Imaging X-ray Polarimetry Explorer. We found an average polarization degree of 22.4% ± 3.5% and an average polarization angle of −45.°4 ± 4.°5 (measured on the plane of the sky from north to east). The X-ray polarization angle distribution reveals that the magnetic fields immediately behind the shock in the northeastern shell of SN 1006 are nearly parallel to the shock normal or radially distributed, similar to that in the radio observations, and consistent with the quasi-parallel CR acceleration scenario. The X-ray emission is marginally more polarized than that in the radio band. The X-ray polarization degree of SN 1006 is much larger than that in Cas A and Tycho, together with the relatively tenuous and smooth ambient medium of the remnant, favoring that CR-induced instabilities set the magnetic turbulence in SN 1006, and CR acceleration is environment-dependent.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.207
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2023
Admission routes1
Has abstractyes

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