Paper Citation Record · LEDGER

The classification of real and bogus transients using active learning and semi-supervised learning

As of 12 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.02409.

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

pith.paper-citation-record.v1
2412.02409 v2

Coverage vector

measured 42 of 42 reference resolution

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Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 468ad8e9-24bf-4853-bef7-9777370a550d · outbound

This paper cites LSST Science Book, Version 2.0.

The classification of real and bogus transients using active learning and semi-supervised learning LSST Science Book, Version 2.0

Reference 1

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Observation 46461aa9-ea0a-4083-b269-f4908cbb9777 · outbound

This paper cites What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction.

The classification of real and bogus transients using active learning and semi-supervised learning What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.275677Z

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Observation c8d908a1-ca4d-43c9-ab87-d1e91ea0bdba · outbound

This paper cites Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks.

The classification of real and bogus transients using active learning and semi-supervised learning Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.253633Z

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Observation e554caec-9185-45f5-a05e-1bcf0c859d2e · outbound

This paper cites How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging.

The classification of real and bogus transients using active learning and semi-supervised learning How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.230184Z

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Observation 47a83d4f-8a81-411c-a872-ec4b478c2ad6 · outbound

This paper cites The Zwicky Transient Facility: System Overview, Performance, and First Results.

The classification of real and bogus transients using active learning and semi-supervised learning The Zwicky Transient Facility: System Overview, Performance, and First Results

Reference 5

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Observation c5eb3bac-77b8-4801-90af-d225ca5cfe50 · outbound

This paper cites 1998, NeurIPS, 11 https://proceedings.neurips.cc/paper/1998/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html.

The classification of real and bogus transients using active learning and semi-supervised learning 1998, NeurIPS, 11 https://proceedings.neurips.cc/paper/1998/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html

Reference 6

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Observation fa94a61c-22a8-43a8-9cda-99b0f6066c3c · outbound

This paper cites 1996, ASTROPHYS J SUPPL S, 117, 393 https://aas.aanda.org/articles/aas/abs/1996/08/ds1060/ds1060.html.

The classification of real and bogus transients using active learning and semi-supervised learning 1996, ASTROPHYS J SUPPL S, 117, 393 https://aas.aanda.org/articles/aas/abs/1996/08/ds1060/ds1060.html

Reference 7

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Observation 777db82d-a22b-4638-9ad6-939c39efc6cd · outbound

This paper cites 2010, Astrophysics Source Code Library, ascl-1010.

The classification of real and bogus transients using active learning and semi-supervised learning 2010, Astrophysics Source Code Library, ascl-1010

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:34:23.426152Z

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Observation 06fb9944-ce21-4bc1-b8ab-8641128e83b2 · outbound

This paper cites Using Machine Learning for Discovery in Synoptic Survey Imaging.

The classification of real and bogus transients using active learning and semi-supervised learning Using Machine Learning for Discovery in Synoptic Survey Imaging

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.184488Z

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Observation 359d5c57-9fac-4034-895e-ed49a828fe1c · outbound

This paper cites Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection.

The classification of real and bogus transients using active learning and semi-supervised learning Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:22.490588Z

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Observation fd6a69ae-59f3-4515-b980-61a3a59ee9f4 · outbound

This paper cites The Pan-STARRS1 Surveys.

The classification of real and bogus transients using active learning and semi-supervised learning The Pan-STARRS1 Surveys

Reference 12

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Observation 31de1d74-fccf-401f-a161-a4af496fe55f · outbound

This paper cites 2009, IEEE trans.

The classification of real and bogus transients using active learning and semi-supervised learning 2009, IEEE trans

Reference 13

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Observation d070c858-fe7b-4a18-b345-30169f788ba7 · outbound

This paper cites The Dark Energy Survey.

The classification of real and bogus transients using active learning and semi-supervised learning The Dark Energy Survey

Reference 14

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Observation 5ddf5712-20e3-4fd5-a00d-2b173691263a · outbound

This paper cites A., Mahabal, A., Masci, F.

The classification of real and bogus transients using active learning and semi-supervised learning A., Mahabal, A., Masci, F

Reference 15

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Observation 63736c31-50c3-4f57-a651-fc9414cbb217 · outbound

This paper cites The High Cadence Transient Survey (HiTS) - I. Survey design and supernova shock breakout constraints.

The classification of real and bogus transients using active learning and semi-supervised learning The High Cadence Transient Survey (HiTS) - I. Survey design and supernova shock breakout constraints

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.027003Z

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Observation ee8542b4-f194-4c10-8e98-c81afafe17b4 · outbound

This paper cites Automated Transient Identification in the Dark Energy Survey.

The classification of real and bogus transients using active learning and semi-supervised learning Automated Transient Identification in the Dark Energy Survey

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:34:23.001276Z

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Observation 3fdc6704-23c4-4481-8ee7-6ecdfa506cdf · outbound

This paper cites Classifying Image Sequences of Astronomical Transients with Deep Neural Networks.

The classification of real and bogus transients using active learning and semi-supervised learning Classifying Image Sequences of Astronomical Transients with Deep Neural Networks

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:22.977360Z

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Observation e3ce89f6-23c6-4e8f-9414-bea0426ad9af · outbound

This paper cites 2022, MNRAS, 513, 1742 https://doi.org/10.1093/mnras/stac983.

The classification of real and bogus transients using active learning and semi-supervised learning 2022, MNRAS, 513, 1742 https://doi.org/10.1093/mnras/stac983

Reference 19

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Observation 77b97da4-9498-4e73-b5a7-23699cf287c8 · outbound

This paper cites Mask R-CNN.

The classification of real and bogus transients using active learning and semi-supervised learning Mask R-CNN

Reference 20

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Observation 060fbd98-154d-49b0-83f4-ed8b33aabb8e · outbound

This paper cites Deep Residual Learning for Image Recognition.

The classification of real and bogus transients using active learning and semi-supervised learning Deep Residual Learning for Image Recognition

Reference 21

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Observation e420bf8b-9190-43cc-b2c3-fe9dd785cbb0 · outbound

This paper cites MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning.

The classification of real and bogus transients using active learning and semi-supervised learning MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:22.910222Z

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Observation 0c632d80-a779-4d66-9dff-f916135247a4 · outbound

This paper cites Prospects of Searching for Type Ia Supernovae with 2.5-m Wide Field Survey Telescope.

The classification of real and bogus transients using active learning and semi-supervised learning Prospects of Searching for Type Ia Supernovae with 2.5-m Wide Field Survey Telescope

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:22.881040Z

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Observation 85ab3191-2770-4155-9747-b576833abb4d · outbound

This paper cites 1999, ICML, 99, 200 https://proceedings.neurips.cc/paper/1998/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html.

The classification of real and bogus transients using active learning and semi-supervised learning 1999, ICML, 99, 200 https://proceedings.neurips.cc/paper/1998/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html

Reference 24

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Observation 2f6ac7e1-c372-4d41-b283-00753890ceb8 · outbound

This paper cites Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream.

The classification of real and bogus transients using active learning and semi-supervised learning Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

Reference 25

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The classification of real and bogus transients using active learning and semi-supervised learning Unresolved cited work

Reference 26

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Observation 23718ae2-8688-4ef3-a77a-782103b4a56f · outbound

This paper cites O'TRAIN: a robust and flexible Real/Bogus classifier for the study of the optical transient sky.

The classification of real and bogus transients using active learning and semi-supervised learning O'TRAIN: a robust and flexible Real/Bogus classifier for the study of the optical transient sky

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:22.828507Z

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Observation 0e29c504-d0f6-4250-b9af-f577c1b9d4ba · outbound

This paper cites 2016, PASJ, psw096 https://doi.org/10.1093/pasj/psw096.

The classification of real and bogus transients using active learning and semi-supervised learning 2016, PASJ, psw096 https://doi.org/10.1093/pasj/psw096

Reference 28

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Observation 57e89cfb-e520-4836-a5d9-4a4b5e4da755 · outbound

This paper cites 2017, Automatic differentiation in pytorch https://openreview.net/forum?id=BJJsrmfCZ.

The classification of real and bogus transients using active learning and semi-supervised learning 2017, Automatic differentiation in pytorch https://openreview.net/forum?id=BJJsrmfCZ

Reference 29

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The classification of real and bogus transients using active learning and semi-supervised learning Unresolved cited work

Reference 30

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Observation 91cd818a-5a21-4768-8743-122d65ee22f2 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

The classification of real and bogus transients using active learning and semi-supervised learning Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T23:34:22.313652Z

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Observation 266b408a-3708-4790-8bc8-c9f78bf0438a · outbound

This paper cites Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection.

The classification of real and bogus transients using active learning and semi-supervised learning Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection

Reference 32

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T23:34:23.159000Z

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Observation 33eb0115-9478-4cb7-bc98-6579e8daf633 · outbound

This paper cites Multi-scale stamps for real-time classification of alert streams.

The classification of real and bogus transients using active learning and semi-supervised learning Multi-scale stamps for real-time classification of alert streams

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:34:22.602557Z

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Observation f9236fb2-8592-490f-b219-9c3029e4bfd4 · outbound

This paper cites Effective Image Differencing with ConvNets for Real-time Transient Hunting.

The classification of real and bogus transients using active learning and semi-supervised learning Effective Image Differencing with ConvNets for Real-time Transient Hunting

Reference 34

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Observation 6887e439-d460-4778-a2fc-8db157000d54 · outbound

This paper cites 2009, Active learning literature survey https://minds.wisconsin.edu/handle/1793/60660.

The classification of real and bogus transients using active learning and semi-supervised learning 2009, Active learning literature survey https://minds.wisconsin.edu/handle/1793/60660

Reference 35

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Observation 49709a2b-bdab-4785-9d93-416c86db1776 · outbound

This paper cites 2020, MNRAS, 497, 2641 https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.2641T/abstract.

The classification of real and bogus transients using active learning and semi-supervised learning 2020, MNRAS, 497, 2641 https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.2641T/abstract

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:34:23.360195Z

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Observation de3028ad-ea52-4dd7-a6db-9ead05d3bfd1 · outbound

This paper cites Sciences with the 2.5-meter Wide Field Survey Telescope (WFST).

The classification of real and bogus transients using active learning and semi-supervised learning Sciences with the 2.5-meter Wide Field Survey Telescope (WFST)

Reference 37

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Observation e782cca1-3f8a-4f59-a8b8-eb66b30f9e33 · outbound

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The classification of real and bogus transients using active learning and semi-supervised learning A transient search using combined human and machine classifications

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:34:22.538848Z

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Observation 0d9b3ec0-651c-40c2-b9b3-6b0077331ee6 · outbound

This paper cites Machine learning for transient discovery in Pan-STARRS1 difference imaging.

The classification of real and bogus transients using active learning and semi-supervised learning Machine learning for transient discovery in Pan-STARRS1 difference imaging

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:34:22.515973Z

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Observation 44ada92a-c6de-4fba-b597-e894eec79241 · outbound

This paper cites 2016, PMLR, 40 https://proceedings.mlr.press/v48/yanga16.html.

The classification of real and bogus transients using active learning and semi-supervised learning 2016, PMLR, 40 https://proceedings.mlr.press/v48/yanga16.html

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:34:23.343587Z

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Observation 24900bfd-c6f1-485a-9e34-2e0534f60c93 · outbound

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The classification of real and bogus transients using active learning and semi-supervised learning Unresolved cited work

Reference 41

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The classification of real and bogus transients using active learning and semi-supervised learning , " * write output.state after.block = add.period write newline

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T23:34:22.377229Z

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The classification of real and bogus transients using active learning and semi-supervised learning write newline

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T23:34:22.383713Z

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