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Paper Citation Record · LEDGER

Using deep learning to characterize single-exposure double-line spectroscopic binaries

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.

pith.paper-citation-record.v1
2507.12363 v1

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Outbound references

Observation e029c2b6-93eb-46f5-885d-12a0bf3de753 · outbound

This paper cites M., Lim, P.

Using deep learning to characterize single-exposure double-line spectroscopic binaries M., Lim, P

Reference 1

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This paper cites & Gies, D.

Using deep learning to characterize single-exposure double-line spectroscopic binaries & Gies, D

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Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work

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Observation 54920119-4a49-425f-8f4d-6a55731ce78b · outbound

This paper cites Machine Learning in Astronomy: a practical overview.

Using deep learning to characterize single-exposure double-line spectroscopic binaries Machine Learning in Astronomy: a practical overview

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Observation ee9ef6d7-b723-4f40-8a8c-33d4f3821eea · outbound

This paper cites 2020, A&A, 642, A146.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2020, A&A, 642, A146

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Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work

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This paper cites 2018, A&A, 616, A5.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2018, A&A, 616, A5

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This paper cites 2012, Res.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2012, Res

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Observation 784c8880-1ca4-4f86-945f-b5c047afff50 · outbound

This paper cites P., et al.

Using deep learning to characterize single-exposure double-line spectroscopic binaries P., et al

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Observation ca52f398-a485-4462-b2c1-d844841d9c74 · outbound

This paper cites 2016, A&A, 594, A68 de Jong, R.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2016, A&A, 594, A68 de Jong, R

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Observation 2c334dcd-a9d6-4718-91bf-b99ffe4012e6 · outbound

This paper cites 2009, in 2009 IEEE Conference on Com- puter Vision and Pattern Recognition, 248–255.

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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This paper cites 2022, AJ, 163, 237.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, AJ, 163, 237

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Observation a2e9da32-4c42-4b8b-a42f-85f0a9a29d51 · outbound

This paper cites 2022, MNRAS, 512, 5620.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, MNRAS, 512, 5620

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This paper cites 2018b, MNRAS, 476, 528 Gaia Collaboration, Prusti, T., de Bruijne, J.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2018b, MNRAS, 476, 528 Gaia Collaboration, Prusti, T., de Bruijne, J

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Observation 5adef484-3880-4042-9b7c-302978e29a99 · outbound

This paper cites 2022, Open As- tron., 31, 38.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, Open As- tron., 31, 38

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Observation 0358527f-6bd1-40e5-966f-0232f1a61e7a · outbound

This paper cites 2012, The Messenger, 147, 25.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2012, The Messenger, 147, 25

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This paper cites 2004, PAICz, 92, 15.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2004, PAICz, 92, 15

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This paper cites R., Millman, K.

Using deep learning to characterize single-exposure double-line spectroscopic binaries R., Millman, K

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This paper cites 1993, in Astronomical Society of the Pacific Conference Series, V ol.

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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This paper cites Half a Million Binary Stars from the low resolution spectra of LAMOST.

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

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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

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Using deep learning to characterize single-exposure double-line spectroscopic binaries & Straumit, I

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Using deep learning to characterize single-exposure double-line spectroscopic binaries 2024, MNRAS, 527, 521

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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

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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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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.305788Z digest=sha256:b0b7e3ee31e2b2127143f533f043bf5cdc9373d1119a660ab07818bf9fde0b77

Observation 83e8703c-fbf9-49a7-8113-da8dea5a2fa3 · outbound

This paper cites 2023, A&A, 674, A6.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2023, A&A, 674, A6

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T16:53:17.363564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:53:17.363564Z digest=sha256:bc67971d0dd6420fd054a5665ca723647431cca8ffcab8da06dcf74696f8b22f

Observation 812ac76f-559c-4645-9880-7181908a9edc · outbound

This paper cites 2020, Astrophysics Source Code Library, ascl:2007.022.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2020, Astrophysics Source Code Library, ascl:2007.022

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:19.393257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.408289Z digest=sha256:49d4b4a6f95785fab2cd99286039c7276b25caca761f30402bfa44f8f396509c

Observation 9afc4163-fa7f-4241-8286-1de098a67561 · outbound

This paper cites an unresolved cited work.

Using deep learning to characterize single-exposure double-line spectroscopic binaries Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-06T16:53:19.252398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.454026Z digest=sha256:612e17eb62dd245f0d11c61e2fc13b801bd9a9f4f82a779a754f7c9f2c08fc3f

Observation 5fb6c468-a33a-4d66-b734-31e4e2598383 · outbound

This paper cites 2020, A&A, 643, A122.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2020, A&A, 643, A122

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:19.111707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.502356Z digest=sha256:30ed881b15c09614514443abafce32df1ebfdd818205dc24981957b937841915

Observation b936efd0-2bf3-441a-9823-09143d15a225 · outbound

This paper cites E., et al.

Using deep learning to characterize single-exposure double-line spectroscopic binaries E., et al

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:18.896051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.583481Z digest=sha256:67c7c894d32bed70d78fb704214fd88358629a0ec80ecc75148710c0bf2ae0da

Observation 1cbdaa75-aa73-4e59-9e59-b01593c023b7 · outbound

This paper cites H., Nikolaou, N., Coronica, P., et al.

Using deep learning to characterize single-exposure double-line spectroscopic binaries H., Nikolaou, N., Coronica, P., et al

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:18.714757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.664560Z digest=sha256:e5bb827e99c0992a71b4a741a990ea50249b630c40dae49bc3d82e7b3622457e

Observation d47905f3-bbb4-4c39-b4f8-c3d8abb6461a · outbound

This paper cites 2022, ApJS, 258, 26.

Using deep learning to characterize single-exposure double-line spectroscopic binaries 2022, ApJS, 258, 26

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:18.542432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.742403Z digest=sha256:959c0591115cf44bcb9360bfbdd0302a3e290ee9447ed3ed089729ceb5e9ff2c

Observation 38e703b2-a231-467a-8788-623869f82a4e · outbound

This paper cites & Giryes, R.

Using deep learning to characterize single-exposure double-line spectroscopic binaries & Giryes, R

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:18.396171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.843619Z digest=sha256:07d2f0339dd60a3df4ffdc598436efd4f9da5f5fdf4a363366e91972481141b3

Observation 4cb77811-e4e1-47e0-a74e-4c99a63f0619 · outbound

This paper cites & Mazeh, T.

Using deep learning to characterize single-exposure double-line spectroscopic binaries & Mazeh, T

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:53:18.243648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T16:53:17.902891Z digest=sha256:36ed954dea5399a557dff41baa8901bf0eb538cc364cbf82bc690bdb59ab50bf

Pith citing papers

No inbound Pith citation observations are available.