Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:53:17.902891Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.12363.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:53:17.902891Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
46 of 46 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Using deep learning to characterize single-exposure double-line spectroscopic binaries M., Lim, P
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Machine Learning in Astronomy: a practical overview
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2018, A&A, 616, A5
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2012, Res
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Using deep learning to characterize single-exposure double-line spectroscopic binaries P., et al
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2016, A&A, 594, A68 de Jong, R
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2009, in 2009 IEEE Conference on Com- puter Vision and Pattern Recognition, 248–255
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, AJ, 163, 237
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, MNRAS, 512, 5620
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2018b, MNRAS, 476, 528 Gaia Collaboration, Prusti, T., de Bruijne, J
Reference 14
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, Open As- tron., 31, 38
Reference 15
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2012, The Messenger, 147, 25
Reference 16
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2004, PAICz, 92, 15
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Using deep learning to characterize single-exposure double-line spectroscopic binaries R., Millman, K
Reference 18
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 1993, in Astronomical Society of the Pacific Conference Series, V ol
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work
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Using deep learning to characterize single-exposure double-line spectroscopic binaries O., Wende-von Berg, S., Dreizler, S., et al
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Half a Million Binary Stars from the low resolution spectra of LAMOST
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2019, Open Astron., 28, 68
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2010, in EAS Publications Series, V ol
Reference 24
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2023, A&A, 674, A5
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Adam: A Method for Stochastic Optimization
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Using deep learning to characterize single-exposure double-line spectroscopic binaries R., Stassun, K
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, MNRAS, 517, 356
Reference 28
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Using deep learning to characterize single-exposure double-line spectroscopic binaries & Straumit, I
Reference 29
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2024, MNRAS, 527, 521
Reference 30
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work
Reference 31
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Using deep learning to characterize single-exposure double-line spectroscopic binaries LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan
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Using deep learning to characterize single-exposure double-line spectroscopic binaries N., Omukai, K., Matsumoto, T., & Inutsuka, S.-I
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Using deep learning to characterize single-exposure double-line spectroscopic binaries R., Schiavon, R
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2013, The Messenger, 154, 47
Reference 35
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Using deep learning to characterize single-exposure double-line spectroscopic binaries A., et al
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Using deep learning to characterize single-exposure double-line spectroscopic binaries P., Järvinen, S., & Weber, M
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2023, A&A, 674, A6
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2020, Astrophysics Source Code Library, ascl:2007.022
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Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work
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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2020, A&A, 643, A122
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Using deep learning to characterize single-exposure double-line spectroscopic binaries E., et al
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Using deep learning to characterize single-exposure double-line spectroscopic binaries & Mazeh, T
Reference 46
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