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Paper Citation Record · LEDGER

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey

As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2511.02631.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.02631 v2

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measured 58 of 58 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation 20217e63-abe1-413d-a1a7-cfc08c995dbb · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey , " * write output.state after.block = add.period write newline

Reference 1

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Observation 5a486123-e0c5-4717-8771-734f93b56539 · outbound

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey write newline

Reference 2

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey - [1] #1 = = ^ ^ ^ .\!\!^ d .\!\!^ h .\!\!^ m .\!\!^ s .\!\!^ @mss

Reference 3

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

Reference 4

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Observation be7eb174-edeb-4841-ad52-57dff074e15d · outbound

This paper cites M., Lim , P.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey M., Lim , P

Reference 5

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Observation 60d55b91-0e88-4bb4-bc00-ff9d525ef478 · outbound

This paper cites C., Vincenzi, M., Scolnic, D., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey C., Vincenzi, M., Scolnic, D., et al

Reference 6

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This paper cites 2025, SNCosmo, v2.12.1, Zenodo, 10.5281/zenodo.15019859.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2025, SNCosmo, v2.12.1, Zenodo, 10.5281/zenodo.15019859

Reference 7

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Observation d8ce6ae0-1903-4b2c-a8bb-e4cb5572d31a · outbound

This paper cites G., Casertano, S., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey G., Casertano, S., et al

Reference 8

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This paper cites 2022, The Astrophysical Journal, 938, 110, 10.3847/1538-4357/ac8e04.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2022, The Astrophysical Journal, 938, 110, 10.3847/1538-4357/ac8e04

Reference 9

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Observation 571cf108-f0b3-4391-9916-b7697dd4ca51 · outbound

This paper cites M., Vincenzi, M., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey M., Vincenzi, M., et al

Reference 10

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Observation 0f2defec-edec-4990-a189-5823933a2452 · outbound

This paper cites 2022, , 511, 1830, 10.1093/mnras/stac151.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2022, , 511, 1830, 10.1093/mnras/stac151

Reference 11

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Observation 74088af8-3c21-4493-ad29-7202180bc5ae · outbound

This paper cites Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program

Reference 12

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

Reference 13

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This paper cites 2025, arXiv e-prints, arXiv:2507.04618, 10.48550/arXiv.2507.04618.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2025, arXiv e-prints, arXiv:2507.04618, 10.48550/arXiv.2507.04618

Reference 14

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Observation 0a4af42a-addd-4a3e-a8c9-aa9b3a2e0add · outbound

This paper cites The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

Reference 15

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Observation 7e1e68df-624e-44e2-892b-83e29836e859 · outbound

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

Reference 16

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

Reference 17

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Observation 4abaf48d-0847-4ea4-a1ea-ba5b233976e1 · outbound

This paper cites W., Lang , D., & Goodman , J.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey W., Lang , D., & Goodman , J

Reference 18

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Observation 97497e2d-1130-4c81-8580-bd1ead880296 · outbound

This paper cites ABC-SN: Attention Based Classifier for Supernova Spectra.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey ABC-SN: Attention Based Classifier for Supernova Spectra

Reference 19

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This paper cites 2025, Optimizing Supernova Classification with Interpretable Machine Learning Models.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2025, Optimizing Supernova Classification with Interpretable Machine Learning Models

Reference 20

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2010, , 709, 1420, 10.1088/0004-637X/709/2/1420

Reference 21

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2019, , 883, 203, 10.3847/1538-4357/ab391e

Reference 22

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Observation 732995b5-a7ad-4c82-bd61-11b7bb88e115 · outbound

This paper cites Future Cosmology: New Physics and Opportunity from the China Space Station Telescope (CSST).

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Future Cosmology: New Physics and Opportunity from the China Space Station Telescope (CSST)

Reference 23

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This paper cites 2012, The Astrophysical Journal, 752, 79, 10.1088/0004-637x/752/2/79.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2012, The Astrophysical Journal, 752, 79, 10.1088/0004-637x/752/2/79

Reference 24

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey M., Tyson , J

Reference 26

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey O., Scolnic , D

Reference 27

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey D., Jones , D

Reference 28

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2017, , 836, 56, 10.3847/1538-4357/836/1/56

Reference 29

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2023, The Astrophysical Journal Letters, 952, L8, 10.3847/2041-8213/ace34d

Reference 30

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This paper cites A., & Sen, A.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey A., & Sen, A

Reference 31

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Unresolved cited work

Reference 32

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Observation b636c60f-c92c-4a76-b560-989832cae44e · outbound

This paper cites Machine Learning in Stellar Astronomy: Progress up to 2024.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey Machine Learning in Stellar Astronomy: Progress up to 2024

Reference 33

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This paper cites 2023, Science China Physics, Mechanics, and Astronomy, 66, 229511, 10.1007/s11433-022-2018-0.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2023, Science China Physics, Mechanics, and Astronomy, 66, 229511, 10.1007/s11433-022-2018-0

Reference 34

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Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2024, Science China Physics, Mechanics & Astronomy, 67, 10.1007/s11433-024-2456-x

Reference 35

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This paper cites P., Kessler , R., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey P., Kessler , R., et al

Reference 36

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source=arxiv_source observed=2026-08-04T00:11:24.375179Z digest=sha256:ca5494cf85e15b7cc75728a1d01f953f4f294ede6a40503800b2a06204c19f37

Observation d7f8c44e-c73c-4e40-8c55-3d5ccd4c52be · outbound

This paper cites A., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey A., et al

Reference 37

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source=arxiv_source observed=2026-08-04T00:11:24.486077Z digest=sha256:0d2772e85a2cbc66fb1aab79b49460ca805507f724184529cf0ec3556101eaa3

Observation 095a95e2-3f1c-4cc3-ba87-5b3162c76ef7 · outbound

This paper cites 2020, , 491, 4277, 10.1093/mnras/stz3312.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2020, , 491, 4277, 10.1093/mnras/stz3312

Reference 38

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source=arxiv_source observed=2026-08-04T00:11:24.681841Z digest=sha256:d9436684513bb2961a0e9f36b39c3f7a1e7d03b272a9d14d487df0f6d5e8d947

Observation bb57fe6e-334b-43ea-a273-7a88d1a954c3 · outbound

This paper cites 2022, , 514, 5159, 10.1093/mnras/stac1691.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2022, , 514, 5159, 10.1093/mnras/stac1691

Reference 39

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source=arxiv_source observed=2026-08-04T00:11:24.826425Z digest=sha256:1ea6de920baeae97e0fe6ed1cea9bc47a3a08a1cf31dd4291662db40a1d9cc02

Observation 30b294ba-f31e-4752-9139-388ea2177f72 · outbound

This paper cites The Dark Energy Survey 5-year photometrically classified type Ia supernovae without host-galaxy redshifts.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey The Dark Energy Survey 5-year photometrically classified type Ia supernovae without host-galaxy redshifts

Reference 40

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source=arxiv_source observed=2026-08-04T00:11:24.999091Z digest=sha256:be576e743c94cb6859b5908761458d59a8ef885effcb5f599951e878655be649

Observation 3e17aea9-081e-400c-a182-dc20de0f8d8f · outbound

This paper cites 1999, The Astrophysical Journal, 517, 565–586, 10.1086/307221.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 1999, The Astrophysical Journal, 517, 565–586, 10.1086/307221

Reference 41

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no resolver link, observed 2026-08-04T00:11:25.174676Z

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source=arxiv_source observed=2026-08-04T00:11:25.174676Z digest=sha256:54ffa16ebd49eea32338780585ceabb97ec475ff1bc2d780268c328deadb4bb7

Observation 9aa1ac91-6ff9-48a4-84b8-5edd24f0f63b · outbound

This paper cites 2021, The Astronomical Journal, 162, 67, 10.3847/1538-3881/ac0824.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2021, The Astronomical Journal, 162, 67, 10.3847/1538-3881/ac0824

Reference 42

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no resolver link, observed 2026-08-04T00:11:25.303124Z

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source=arxiv_source observed=2026-08-04T00:11:25.303124Z digest=sha256:3ba1a7dc9b1e091d913a4720490791c7e19cffd2c15814fa37fe1b83c2c4988f

Observation 2fec732b-9f70-4a8e-89de-48bcbdf7c212 · outbound

This paper cites G., Casertano, S., Yuan, W., Macri, L.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey G., Casertano, S., Yuan, W., Macri, L

Reference 44

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source=arxiv_source observed=2026-08-04T00:11:25.546082Z digest=sha256:c143e9ee25f033129f8a5543da87c5967279ed66e18ef093a4280056b0d45c93

Observation 17fd8660-3f9c-407d-8b3f-63441cc5c000 · outbound

This paper cites G., Filippenko , A.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey G., Filippenko , A

Reference 45

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no resolver link, observed 2026-08-04T00:11:25.706678Z

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source=arxiv_source observed=2026-08-04T00:11:25.706678Z digest=sha256:6d507852ed7d751f66b0a9c8e36b134d52c7ffd41a0d7e522e9ee9d1c0d15082

Observation 3a702367-c16a-44bb-917d-4d4ade0a1619 · outbound

This paper cites G., Yuan , W., Macri , L.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey G., Yuan , W., Macri , L

Reference 46

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source=arxiv_source observed=2026-08-04T00:11:25.833917Z digest=sha256:197631069b2188b37bba3e7cb98e3b9c16f9d100deb7dc7575e6fe3a19e743e9

Observation 9f457ded-06b9-4dd8-96a8-09d375bed020 · outbound

This paper cites A., Riess , A.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey A., Riess , A

Reference 47

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source=arxiv_source observed=2026-08-04T00:11:26.014678Z digest=sha256:97bbda359996e2ac0e527f5c71b2204c4fde5e5fffd528b0bf61706db4abf578

Observation c27ef268-1270-4783-ba4d-cd3b6202ce53 · outbound

This paper cites A Reference Survey for Supernova Cosmology with the Nancy Grace Roman Space Telescope.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey A Reference Survey for Supernova Cosmology with the Nancy Grace Roman Space Telescope

Reference 48

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source=arxiv_source observed=2026-08-04T00:11:26.190329Z digest=sha256:666f8254ccd3b2fcf60823e0d3317774f1c7269b124b5f867e20f86604363529

Observation a076edfb-cff9-436f-8d51-d80c59abbe38 · outbound

This paper cites E., Hinton, G.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey E., Hinton, G

Reference 49

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source=arxiv_source observed=2026-08-04T00:11:26.317758Z digest=sha256:3973ed1c8940dacac6eb2326b20edecca586eaadcec5f955d200318dae2eb67e

Observation 870cedcb-edec-4a91-afa7-a40831fbc0f9 · outbound

This paper cites E., Barclay , T., Barnes , A., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey E., Barclay , T., Barnes , A., et al

Reference 50

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source=arxiv_source observed=2026-08-04T00:11:26.411988Z digest=sha256:5bd95ac3317446726d82f355aafb0ad7ba1c08c42f0dae32573fcdfe7f1a178e

Observation e5ea5377-ccca-4212-9958-2bdfe8afe6ea · outbound

This paper cites 2022, , 938, 113, 10.3847/1538-4357/ac8b7a.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2022, , 938, 113, 10.3847/1538-4357/ac8b7a

Reference 51

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source=arxiv_source observed=2026-08-04T00:11:26.528404Z digest=sha256:f05b627ef88199bc795d3f0752fd1d480aa427a4334af1742604f30867b21d04

Observation 04816d0f-8b48-4a87-a53f-853aad982278 · outbound

This paper cites A., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey A., et al

Reference 52

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source=arxiv_source observed=2026-08-04T00:11:26.628519Z digest=sha256:16b9e55666cb03453b4168f2a3bd34e8eefcc7a5caf5903c5bcb74ac853df9c4

Observation fb139967-929e-4023-8774-cc7fe31747be · outbound

This paper cites E., et al.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey E., et al

Reference 53

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source=arxiv_source observed=2026-08-04T00:11:26.780351Z digest=sha256:dcf2690acf96874aac914644e14473ff9fcaba354771877b886a985e7c4bb5ea

Observation 21429edc-62b7-4a5f-9241-10d40da40abb · outbound

This paper cites 2022, Monthly Notices of the Royal Astronomical Society, 518, 1106–1127, 10.1093/mnras/stac1404.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2022, Monthly Notices of the Royal Astronomical Society, 518, 1106–1127, 10.1093/mnras/stac1404

Reference 54

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no resolver link, observed 2026-08-04T00:11:26.944301Z

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source=arxiv_source observed=2026-08-04T00:11:26.944301Z digest=sha256:859831ac733d01d32f40a613f5d62144dc513649f64f68e8c7d81cd36a908eb6

Observation 90a2200b-f21f-4e54-9789-a311ef12942f · outbound

This paper cites The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

Reference 55

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no resolver link, observed 2026-08-04T00:11:27.072147Z

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source=arxiv_source observed=2026-08-04T00:11:27.072147Z digest=sha256:f1304e247ef193814fab49cae668a91e068e025f94954aa474fb85c569a6a5ff

Observation 4409115f-36f6-435f-8d30-f64af50960ce · outbound

This paper cites 2024, Monthly Notices of the Royal Astronomical Society, 530, 4288, 10.1093/mnras/stae1119.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2024, Monthly Notices of the Royal Astronomical Society, 530, 4288, 10.1093/mnras/stae1119

Reference 56

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verified exact
doi, observed 2026-08-04T00:14:08.849322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T00:11:27.249069Z digest=sha256:3cf1dc5d694c040d03f3301afca26ae64068c2bd748339cd5e94ecd9d02151c3

Observation d8fc6774-3659-44ec-89b1-8e550b2d662e · outbound

This paper cites 2011, SCIENTIA SINICA Physica, Mechanica & Astronomica, 41, 1441.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2011, SCIENTIA SINICA Physica, Mechanica & Astronomica, 41, 1441

Reference 57

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source=arxiv_source observed=2026-08-04T00:11:27.360602Z digest=sha256:c7fd6d4209420f1ea0d26a1871b3e64f282c9268de51b23066c95646a272bdf1

Observation b60d3811-cace-47ad-bcb4-95d8f686e904 · outbound

This paper cites 2021, Chinese Science Bulletin, 66, 1290, https://doi.org/10.1360/TB-2021-0016.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey 2021, Chinese Science Bulletin, 66, 1290, https://doi.org/10.1360/TB-2021-0016

Reference 58

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source=arxiv_source observed=2026-08-04T00:11:27.430004Z digest=sha256:4b8481767d1dcc94e8813064439749067159f13cd0549acf01bfeb1fcf043ca7

Observation 707270bb-b539-4014-853e-712aed9b5510 · outbound

This paper cites ?O' ]k!65fkMs z )5Vz'γ&4rnaAǧ˥溛y zM4\.

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey ?O' ]k!65fkMs z )5Vz'γ&4rnaAǧ˥溛y zM4\

Reference 59

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source=arxiv_source observed=2026-08-04T00:11:27.476576Z digest=sha256:e022e5b4d69c12b4b416c1d3ba4e4e1706a3ee447760e37e29a7d8cee9374458

Pith citing papers

No inbound Pith citation observations are available.