From stellar light to astrophysical insight: automating variable star research with machine learning | Paper Citation Record

Observation 6f60b0cc-e166-4d87-82a8-62ddfa4c9e8a · outbound

This paper cites write newline.

From stellar light to astrophysical insight: automating variable star research with machine learning write newline

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Observation 15575ea7-b73f-4694-b941-7264893043c5 · outbound

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

From stellar light to astrophysical insight: automating variable star research with machine learning , " * write output.state after.block = add.period write newline

Reference 2

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Observation de0ff3ae-02a0-4ba9-bd75-3fcef776aaa5 · outbound

This paper cites write newline.

From stellar light to astrophysical insight: automating variable star research with machine learning write newline

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.199784Z

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Observation 914beafd-b44e-4ae2-8f94-a75ed23293d7 · outbound

This paper cites Probing the interior physics of stars through asteroseismology.

From stellar light to astrophysical insight: automating variable star research with machine learning Probing the interior physics of stars through asteroseismology

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.249904Z

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Observation 6f4659ea-18cd-4efd-8efc-f1ef4fd674fc · outbound

This paper cites Asteroseismic Modelling of Fast Rotators and its Opportunities for Astrophysics.

From stellar light to astrophysical insight: automating variable star research with machine learning Asteroseismic Modelling of Fast Rotators and its Opportunities for Astrophysics

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.320709Z

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Observation ba041df2-effb-407f-9f49-afcc431ca4a8 · outbound

This paper cites Springer Dordrecht , doi:10.1007/978-1-4020-5803-5.

From stellar light to astrophysical insight: automating variable star research with machine learning Springer Dordrecht , doi:10.1007/978-1-4020-5803-5

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.400504Z

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Observation dcb47b8c-35cf-483a-ae21-c45b66d13d01 · outbound

This paper cites Angular Momentum Transport in Stellar Interiors.

From stellar light to astrophysical insight: automating variable star research with machine learning Angular Momentum Transport in Stellar Interiors

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.480839Z

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Observation cfdb863a-78dc-4854-8f39-130e1e7197c9 · outbound

This paper cites Applications of Deep Learning to physics workflows.

From stellar light to astrophysical insight: automating variable star research with machine learning Applications of Deep Learning to physics workflows

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:22:42.955918Z

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Observation 68a40b64-f425-45fc-b844-31bab4e9fa15 · outbound

This paper cites K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4.

From stellar light to astrophysical insight: automating variable star research with machine learning K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.645678Z

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Observation b955cba7-e06b-49fd-ac88-3090b0d1d45a · outbound

This paper cites Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators.

From stellar light to astrophysical insight: automating variable star research with machine learning Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.701242Z

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Observation 89f08b05-7030-43f3-ae6e-2ee0b2bdc8d2 · outbound

This paper cites TESS Data for Asteroseismology (T'DA) Stellar Variability Classification Pipeline: Set-Up and Application to the Kepler Q9 Data.

From stellar light to astrophysical insight: automating variable star research with machine learning TESS Data for Asteroseismology (T'DA) Stellar Variability Classification Pipeline: Set-Up and Application to the Kepler Q9 Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.777269Z

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Observation 1713c88a-4845-489d-9eab-04294ff1fec1 · outbound

This paper cites In: 1st ICML Workshop on Foundation Models for Structured Data, ://openreview.net/forum?id=803t3Qi7S2.

From stellar light to astrophysical insight: automating variable star research with machine learning In: 1st ICML Workshop on Foundation Models for Structured Data, ://openreview.net/forum?id=803t3Qi7S2

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.871644Z

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Observation dcdf0b3f-88d2-448d-b5c4-8e27257c8995 · outbound

This paper cites Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning.

From stellar light to astrophysical insight: automating variable star research with machine learning Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:25.969036Z

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Observation 84916e30-01fa-4fbf-963d-1e2e7c050107 · outbound

This paper cites Scalable End-to-end Recurrent Neural Network for Variable star classification.

From stellar light to astrophysical insight: automating variable star research with machine learning Scalable End-to-end Recurrent Neural Network for Variable star classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.049951Z

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Observation 63877920-b6cc-4d3b-b4c5-cfa9d929d691 · outbound

This paper cites OmniJet-$\alpha$: The first cross-task foundation model for particle physics.

From stellar light to astrophysical insight: automating variable star research with machine learning OmniJet-$\alpha$: The first cross-task foundation model for particle physics

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.135845Z

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Observation 63c5b956-bdec-4d18-a7d5-97b64c41b387 · outbound

This paper cites Automated classification of variable stars in the asteroseismology program of the Kepler space mission.

From stellar light to astrophysical insight: automating variable star research with machine learning Automated classification of variable stars in the asteroseismology program of the Kepler space mission

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.224942Z

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Observation 9359ca80-a681-4e6b-928c-d993ac71a722 · outbound

This paper cites 418(1):96--106.

From stellar light to astrophysical insight: automating variable star research with machine learning 418(1):96--106

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.305624Z

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Observation 879f4839-72cc-4835-8d95-7e6d982a1fa3 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

From stellar light to astrophysical insight: automating variable star research with machine learning On the Opportunities and Risks of Foundation Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.432798Z

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Observation 8f035024-4396-49a3-a986-ba9ed0e7a6ec · outbound

This paper cites Science 327(5968):977.

From stellar light to astrophysical insight: automating variable star research with machine learning Science 327(5968):977

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.531360Z

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Observation a130b73e-b224-47c4-a4d6-7632140a2b27 · outbound

This paper cites Frontiers in Astronomy and Space Sciences 7:70.

From stellar light to astrophysical insight: automating variable star research with machine learning Frontiers in Astronomy and Space Sciences 7:70

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.624320Z

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Observation 334b6ebb-70fb-409c-aaf0-020158800123 · outbound

This paper cites Making waves in massive star asteroseismology.

From stellar light to astrophysical insight: automating variable star research with machine learning Making waves in massive star asteroseismology

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.695923Z

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Observation 6dad8ceb-268c-4078-bda7-a8f71d251016 · outbound

This paper cites Characterising the observational properties of {\delta} Sct stars in the era of space photometry from the Kepler mission.

From stellar light to astrophysical insight: automating variable star research with machine learning Characterising the observational properties of {\delta} Sct stars in the era of space photometry from the Kepler mission

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.763169Z

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Observation be72745a-5ed6-447f-abac-07f2aae1854a · outbound

This paper cites Amplitude modulation in $\delta$ Sct stars: statistics from an ensemble study of Kepler targets.

From stellar light to astrophysical insight: automating variable star research with machine learning Amplitude modulation in $\delta$ Sct stars: statistics from an ensemble study of Kepler targets

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.857677Z

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Observation 7c92bbd0-9ee8-492b-8039-bdf47ba6fd22 · outbound

This paper cites The CubeSpec space mission. I. Asteroseismology of massive stars from time series optical spectroscopy: science requirements and target list prioritisation.

From stellar light to astrophysical insight: automating variable star research with machine learning The CubeSpec space mission. I. Asteroseismology of massive stars from time series optical spectroscopy: science requirements and target list prioritisation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:26.957963Z

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Observation cdfe5b7c-ca8a-4f3f-ba28-af507fe6af84 · outbound

This paper cites Machine Learning 45(1):5--32.

From stellar light to astrophysical insight: automating variable star research with machine learning Machine Learning 45(1):5--32

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.056254Z

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Observation ba1ec999-edf4-42da-a5e1-3d4c3e8c7fb8 · outbound

This paper cites ROOSTER: a machine-learning analysis tool for Kepler stellar rotation periods.

From stellar light to astrophysical insight: automating variable star research with machine learning ROOSTER: a machine-learning analysis tool for Kepler stellar rotation periods

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.149299Z

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Observation 8ae77991-b7e6-4a19-82c3-c5b3b81f8bf7 · outbound

This paper cites Monthly Notices of the Royal Astronomical Society 353(2):369--376.

From stellar light to astrophysical insight: automating variable star research with machine learning Monthly Notices of the Royal Astronomical Society 353(2):369--376

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.261944Z

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Observation 805ec0db-e1dc-43bc-ade1-9747fe5b7a51 · outbound

This paper cites The GALAH+ Survey: Third Data Release.

From stellar light to astrophysical insight: automating variable star research with machine learning The GALAH+ Survey: Third Data Release

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.495772Z

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Observation a312d343-5a76-413e-9131-bf1fafe053b8 · outbound

This paper cites FliPer: A global measure of power density to estimate surface gravities of main-sequence Solar-like stars and red giants.

From stellar light to astrophysical insight: automating variable star research with machine learning FliPer: A global measure of power density to estimate surface gravities of main-sequence Solar-like stars and red giants

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.565344Z

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Observation de63fa6d-c65f-4114-9d9b-bdc58c2e5e70 · outbound

This paper cites A calibration point for stellar evolution from massive star asteroseismology.

From stellar light to astrophysical insight: automating variable star research with machine learning A calibration point for stellar evolution from massive star asteroseismology

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.638862Z

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Observation 60ea5f57-f0bc-4bb9-b231-21e6ffb91302 · outbound

This paper cites ATAT: Astronomical Transformer for time series And Tabular data.

From stellar light to astrophysical insight: automating variable star research with machine learning ATAT: Astronomical Transformer for time series And Tabular data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.718087Z

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Observation f0ad3421-052c-42e0-972e-2f107bf3cc58 · outbound

This paper cites TESS Science Processing Operations Center FFI Target List Products.

From stellar light to astrophysical insight: automating variable star research with machine learning TESS Science Processing Operations Center FFI Target List Products

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.789590Z

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Observation 2456a350-5a38-4e3d-b7ee-6630a9e342f9 · outbound

This paper cites In: Pei J, Tseng VS, Cao L, et al (eds) Advances in Knowledge Discovery and Data Mining.

From stellar light to astrophysical insight: automating variable star research with machine learning In: Pei J, Tseng VS, Cao L, et al (eds) Advances in Knowledge Discovery and Data Mining

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.861945Z

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Observation de54575b-729c-439b-bf4c-c5bf686e268a · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

From stellar light to astrophysical insight: automating variable star research with machine learning A Simple Framework for Contrastive Learning of Visual Representations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.929338Z

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Observation c685c70d-e766-4119-81d6-3e1c7dd7b92f · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

From stellar light to astrophysical insight: automating variable star research with machine learning Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:27.999510Z

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Observation 90c7a4c4-d30d-4fb9-a66e-43aa1d9cad66 · outbound

This paper cites DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection.

From stellar light to astrophysical insight: automating variable star research with machine learning DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.094441Z

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Observation 82418cad-f8e6-45ba-b4ac-f44099d5828b · outbound

This paper cites Recovery of TESS Stellar Rotation Periods Using Deep Learning.

From stellar light to astrophysical insight: automating variable star research with machine learning Recovery of TESS Stellar Rotation Periods Using Deep Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.171114Z

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Observation 466e11e1-9d0c-4787-9366-88010ec46b99 · outbound

This paper cites Methods for the detection of stellar rotation periods in individual TESS sectors and results from the Prime mission.

From stellar light to astrophysical insight: automating variable star research with machine learning Methods for the detection of stellar rotation periods in individual TESS sectors and results from the Prime mission

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.265800Z

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Observation 205ef4ad-f9b2-43f7-a766-bda6d2347102 · outbound

This paper cites 89(6):068102.

From stellar light to astrophysical insight: automating variable star research with machine learning 89(6):068102

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.353704Z

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Observation a12c5f5d-e5ea-4f63-9dc6-aae8645633c9 · outbound

This paper cites 71(2):021906.

From stellar light to astrophysical insight: automating variable star research with machine learning 71(2):021906

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.433573Z

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Observation cddfdc2f-7e7e-4433-afb6-db7a6583e3eb · outbound

This paper cites Linking Anomalous Behaviour with Stellar Properties: An Unsupervised Exploration of TESS Light Curves.

From stellar light to astrophysical insight: automating variable star research with machine learning Linking Anomalous Behaviour with Stellar Properties: An Unsupervised Exploration of TESS Light Curves

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.497144Z

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Observation 65c21231-6dab-4e1d-bedd-7dca2a945597 · outbound

This paper cites Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection.

From stellar light to astrophysical insight: automating variable star research with machine learning Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.570320Z

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Observation 2fc02959-1b80-4d15-9461-d33c1220fafd · outbound

This paper cites Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework.

From stellar light to astrophysical insight: automating variable star research with machine learning Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.632204Z

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Observation c91b0835-82f0-43f0-9485-6cf4043c145c · outbound

This paper cites Automated supervised classification of variable stars I. Methodology.

From stellar light to astrophysical insight: automating variable star research with machine learning Automated supervised classification of variable stars I. Methodology

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.687575Z

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Observation 135dc7b7-e117-4938-ad24-8f8073444cba · outbound

This paper cites Method and application to the first four exoplanet fields.

From stellar light to astrophysical insight: automating variable star research with machine learning Method and application to the first four exoplanet fields

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Resolution: verified exact; doi, observed 2026-08-06T20:22:42.629897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a73adf23-d934-405e-967f-f168e049b104 · outbound

This paper cites Global stellar variability study in the field-of-view of the Kepler satellite.

From stellar light to astrophysical insight: automating variable star research with machine learning Global stellar variability study in the field-of-view of the Kepler satellite

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.814720Z

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Observation 9caabaab-ff17-4ad3-bbe4-0e711abf4eaf · outbound

This paper cites ASTROMER: A transformer-based embedding for the representation of light curves.

From stellar light to astrophysical insight: automating variable star research with machine learning ASTROMER: A transformer-based embedding for the representation of light curves

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.876682Z

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Observation bc38fb52-5e6e-4510-9311-29410c792889 · outbound

This paper cites arXiv e-prints arXiv:2502.02717.

From stellar light to astrophysical insight: automating variable star research with machine learning arXiv e-prints arXiv:2502.02717

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:28.951420Z

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Observation b69b2961-1b89-455e-a76f-c3cc6ce80a52 · outbound

This paper cites 414:L17--L20.

From stellar light to astrophysical insight: automating variable star research with machine learning 414:L17--L20

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.011251Z

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Observation 56171f06-460d-4530-a3fe-9ef8e7e6d9a3 · outbound

This paper cites an unresolved cited work.

From stellar light to astrophysical insight: automating variable star research with machine learning Unresolved cited work

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.124681Z

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Observation 0ad063c5-f27f-4d72-aaf5-a3660e9ea64e · outbound

This paper cites Planet Hunters TESS II: Findings from the first two years of TESS.

From stellar light to astrophysical insight: automating variable star research with machine learning Planet Hunters TESS II: Findings from the first two years of TESS

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.191263Z

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Observation 56942953-549b-47ff-a1b3-3e5f108b4613 · outbound

This paper cites Planet Hunters TESS V: a planetary system around a binary star, including a mini-Neptune in the habitable zone.

From stellar light to astrophysical insight: automating variable star research with machine learning Planet Hunters TESS V: a planetary system around a binary star, including a mini-Neptune in the habitable zone

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.277809Z

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Observation 67ef835b-6b96-401d-8749-cc481597127e · outbound

This paper cites Viewing the PLATO LOPS2 Field Through the Lenses of TESS.

From stellar light to astrophysical insight: automating variable star research with machine learning Viewing the PLATO LOPS2 Field Through the Lenses of TESS

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.347681Z

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Observation 214f3c7b-dd5c-4de2-85fa-5596725cac35 · outbound

This paper cites Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model.

From stellar light to astrophysical insight: automating variable star research with machine learning Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.438377Z

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Observation fa174950-f10e-4fe9-91ee-c39be1316a8b · outbound

This paper cites Variable stars across the observational HR diagram.

From stellar light to astrophysical insight: automating variable star research with machine learning Variable stars across the observational HR diagram

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.530688Z

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Observation f821efb7-fa7b-4814-bc2f-26ac897e547f · outbound

This paper cites Gaia Data Release 3. Summary of the variability processing and analysis.

From stellar light to astrophysical insight: automating variable star research with machine learning Gaia Data Release 3. Summary of the variability processing and analysis

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.605478Z

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Observation df742b7a-2b18-4828-b242-84a593ff8377 · outbound

This paper cites eleanor: An open-source tool for extracting light curves from the TESS Full-Frame Images.

From stellar light to astrophysical insight: automating variable star research with machine learning eleanor: An open-source tool for extracting light curves from the TESS Full-Frame Images

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.705499Z

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Observation e799e06c-e54d-458d-a3f2-ef484868da5d · outbound

This paper cites Variability Catalog of Stars Observed During the TESS Prime Mission.

From stellar light to astrophysical insight: automating variable star research with machine learning Variability Catalog of Stars Observed During the TESS Prime Mission

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.774765Z

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Unavailable: canonical work link unavailable.

Observation d1a611a9-ff8b-4118-9202-6b704217a9a8 · outbound

This paper cites Annals of statistics pp 1189--1232.

From stellar light to astrophysical insight: automating variable star research with machine learning Annals of statistics pp 1189--1232

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.846255Z

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Observation 64ca2930-3d3e-4e47-9b36-eb419d85d86d · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning The Gaia mission

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:29.938954Z

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Unavailable: canonical work link unavailable.

Observation 930f768e-7351-4d6a-b5d2-21675337b466 · outbound

This paper cites Gaia Data Release 2: Variable stars in the colour-absolute magnitude diagram.

From stellar light to astrophysical insight: automating variable star research with machine learning Gaia Data Release 2: Variable stars in the colour-absolute magnitude diagram

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.010305Z

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Unavailable: canonical work link unavailable.

Observation b79ac198-34ef-42f6-a766-37aaaaeaddab · outbound

This paper cites Gaia Data Release 3: Pulsations in main sequence OBAF-type stars.

From stellar light to astrophysical insight: automating variable star research with machine learning Gaia Data Release 3: Pulsations in main sequence OBAF-type stars

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25fb5d96-2f1e-418c-bedc-ad5e145af57a · outbound

This paper cites Gaia Data Release 3: Summary of the content and survey properties.

From stellar light to astrophysical insight: automating variable star research with machine learning Gaia Data Release 3: Summary of the content and survey properties

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.173810Z

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Unavailable: canonical work link unavailable.

Observation 067b24fd-8826-46e3-8d71-7d2c9822d002 · outbound

This paper cites A homogeneous spectroscopic analysis of a Kepler legacy sample of dwarfs for gravity-mode asteroseismology.

From stellar light to astrophysical insight: automating variable star research with machine learning A homogeneous spectroscopic analysis of a Kepler legacy sample of dwarfs for gravity-mode asteroseismology

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.235569Z

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Unavailable: canonical work link unavailable.

Observation 156b0d81-9741-4e01-acf0-66a2a3c684e5 · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning MIT Press, http://www.deeplearningbook.org

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.311643Z

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Unavailable: canonical work link unavailable.

Observation f924c578-028d-4096-a9ea-8fe601acf3bf · outbound

This paper cites The Zwicky Transient Facility: Science Objectives.

From stellar light to astrophysical insight: automating variable star research with machine learning The Zwicky Transient Facility: Science Objectives

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Unavailable: canonical work link unavailable.

Observation e7b71223-b651-4d2e-abdf-35cb2e24a25b · outbound

This paper cites Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST.

From stellar light to astrophysical insight: automating variable star research with machine learning Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.528589Z

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Unavailable: canonical work link unavailable.

Observation b349b2a8-694c-4cb9-aa67-88ec19778b3a · outbound

This paper cites Universal New Physics Latent Space.

From stellar light to astrophysical insight: automating variable star research with machine learning Universal New Physics Latent Space

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Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:22:42.405605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c45463e0-5cf9-4a47-9346-eaf2e0089d24 · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning 165(2):71

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.682029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb74ca0c-6883-4464-9a93-1b1b0646b764 · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning TESS Data for Asteroseismology: Photometry

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.751645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d79d02df-d881-4bbd-8a61-7b0a6b0233c7 · outbound

This paper cites A Catalogue of Solar-Like Oscillators Observed by TESS in 120-second and 20-second Cadence.

From stellar light to astrophysical insight: automating variable star research with machine learning A Catalogue of Solar-Like Oscillators Observed by TESS in 120-second and 20-second Cadence

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.825679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa97ea75-5c31-4001-8e7d-43397ae7bc2f · outbound

This paper cites The unpopular Package: a Data-driven Approach to De-trend TESS Full Frame Image Light Curves.

From stellar light to astrophysical insight: automating variable star research with machine learning The unpopular Package: a Data-driven Approach to De-trend TESS Full Frame Image Light Curves

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.893818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b67fcc0-96f5-446f-bdc5-c65283962e28 · outbound

This paper cites Giant star seismology.

From stellar light to astrophysical insight: automating variable star research with machine learning Giant star seismology

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:30.965832Z

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Unavailable: canonical work link unavailable.

Observation 599307b9-2b45-4204-8039-a02805e3b22d · outbound

This paper cites Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes.

From stellar light to astrophysical insight: automating variable star research with machine learning Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.053958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31d797df-7992-49d9-b207-a5b7cc2b7a9a · outbound

This paper cites Confronting sparse Gaia DR3 photometry with TESS for a sample of around 60,000 OBAF-type pulsators.

From stellar light to astrophysical insight: automating variable star research with machine learning Confronting sparse Gaia DR3 photometry with TESS for a sample of around 60,000 OBAF-type pulsators

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.116722Z

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Unavailable: canonical work link unavailable.

Observation f7102bb1-ca9a-4961-b14a-c85441dfa8b5 · outbound

This paper cites Localizing Sources of Variability in Crowded TESS Photometry.

From stellar light to astrophysical insight: automating variable star research with machine learning Localizing Sources of Variability in Crowded TESS Photometry

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.189630Z

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Unavailable: canonical work link unavailable.

Observation cf9ace93-027c-4064-b0c5-e03bef0bcd8c · outbound

This paper cites Denoising Diffusion Probabilistic Models.

From stellar light to astrophysical insight: automating variable star research with machine learning Denoising Diffusion Probabilistic Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.243909Z

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Unavailable: canonical work link unavailable.

Observation 5d3b138a-f747-41cf-8c76-1bea236709ef · outbound

This paper cites Neural Computation 9(8):1735--1780.

From stellar light to astrophysical insight: automating variable star research with machine learning Neural Computation 9(8):1735--1780

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.302719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122bb905-c692-4ea6-8436-5036cf7de631 · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning OpenReview.net, p arXiv:2405.18095, doi:10.48550/arXiv.2405.18095, ://openreview.net/forum?id=rU8o0QQCy0, 2405.18095

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.379819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 723acaa6-7674-4a54-8e00-fb08aded8e16 · outbound

This paper cites Deep Learning Classification in Asteroseismology Using an Improved Neural Network: Results on 15000 Kepler Red Giants and Applications to K2 and TESS Data.

From stellar light to astrophysical insight: automating variable star research with machine learning Deep Learning Classification in Asteroseismology Using an Improved Neural Network: Results on 15000 Kepler Red Giants and Applications to K2 and TESS Data

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.480915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b027da41-008c-441f-8c54-7b8b2a85148e · outbound

This paper cites Detecting Solar-like Oscillations in Red Giants with Deep Learning.

From stellar light to astrophysical insight: automating variable star research with machine learning Detecting Solar-like Oscillations in Red Giants with Deep Learning

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.560722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7d0cd0-d073-4060-9bfd-bd9b61e55dc6 · outbound

This paper cites A Search for Red Giant Solar-like Oscillations in All Kepler Data.

From stellar light to astrophysical insight: automating variable star research with machine learning A Search for Red Giant Solar-like Oscillations in All Kepler Data

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.632757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca285a28-5f61-4120-bfa7-7aa334221ace · outbound

This paper cites A 'Quick Look' at All-Sky Galactic Archeology with TESS: 158,000 Oscillating Red Giants from the MIT Quick-Look Pipeline.

From stellar light to astrophysical insight: automating variable star research with machine learning A 'Quick Look' at All-Sky Galactic Archeology with TESS: 158,000 Oscillating Red Giants from the MIT Quick-Look Pipeline

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.682638Z

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Unavailable: canonical work link unavailable.

Observation df97e8db-c26b-48a7-99d3-29b0ee8fcd59 · outbound

This paper cites Flow-Based Generative Emulation of Grids of Stellar Evolutionary Models.

From stellar light to astrophysical insight: automating variable star research with machine learning Flow-Based Generative Emulation of Grids of Stellar Evolutionary Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.726491Z

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Unavailable: canonical work link unavailable.

Observation de21262f-0305-4ba9-8490-8620c1302606 · outbound

This paper cites A Disintegrating Rocky Planet with Prominent Comet-like Tails Around a Bright Star.

From stellar light to astrophysical insight: automating variable star research with machine learning A Disintegrating Rocky Planet with Prominent Comet-like Tails Around a Bright Star

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.784963Z

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Unavailable: canonical work link unavailable.

Observation f330eb3b-e512-4010-89a2-85dda551a780 · outbound

This paper cites $Lux$: A generative, multi-output, latent-variable model for astronomical data with noisy labels.

From stellar light to astrophysical insight: automating variable star research with machine learning $Lux$: A generative, multi-output, latent-variable model for astronomical data with noisy labels

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.846529Z

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Unavailable: canonical work link unavailable.

Observation d41e8bd8-41e2-4610-9f53-fdadb5043c1c · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning The K2 Mission: Characterization and Early results

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:31.912910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ba15259-c39c-459d-b501-8391250e73b2 · outbound

This paper cites Photometry of 10 Million Stars from the First Two Years of TESS Full Frame Images.

From stellar light to astrophysical insight: automating variable star research with machine learning Photometry of 10 Million Stars from the First Two Years of TESS Full Frame Images

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.076434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5a3dbb4-a8b2-4588-8ebf-66d0e7518731 · outbound

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From stellar light to astrophysical insight: automating variable star research with machine learning arXiv e-prints arXiv:2505.16320

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Resolution: verified exact; doi, observed 2026-08-06T20:22:42.125533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a7554f8-ac4f-43c8-8e99-deb445328d90 · outbound

This paper cites An all-sky sample of intermediate- to high-mass OBA-type eclipsing binaries observed by TESS.

From stellar light to astrophysical insight: automating variable star research with machine learning An all-sky sample of intermediate- to high-mass OBA-type eclipsing binaries observed by TESS

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.177351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 056ec858-15bc-44e2-9b1f-0b02a66a132b · outbound

This paper cites Statistical view of orbital circularisation with 14 000 characterised TESS eclipsing binaries.

From stellar light to astrophysical insight: automating variable star research with machine learning Statistical view of orbital circularisation with 14 000 characterised TESS eclipsing binaries

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.231111Z

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Unavailable: canonical work link unavailable.

Observation 12cff254-8bd0-413a-b5a9-eed3f6c34c21 · outbound

This paper cites Automated eccentricity measurement from raw eclipsing binary light curves with intrinsic variability.

From stellar light to astrophysical insight: automating variable star research with machine learning Automated eccentricity measurement from raw eclipsing binary light curves with intrinsic variability

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.293073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87fe518d-2579-4e13-b89f-82114858b610 · outbound

This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

From stellar light to astrophysical insight: automating variable star research with machine learning LSST: from Science Drivers to Reference Design and Anticipated Data Products

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.337484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e13d987-1b5c-40df-b357-509e92753bd5 · outbound

This paper cites On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars.

From stellar light to astrophysical insight: automating variable star research with machine learning On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.400927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 307c9185-0a62-4c48-aba0-2d2b180588a3 · outbound

This paper cites The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey.

From stellar light to astrophysical insight: automating variable star research with machine learning The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.457010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd6c27d0-f292-4b15-b63d-66bfb7d908cf · outbound

This paper cites The ASAS-SN Catalog of Variable Stars II: Uniform Classification of 412,000 Known Variables.

From stellar light to astrophysical insight: automating variable star research with machine learning The ASAS-SN Catalog of Variable Stars II: Uniform Classification of 412,000 Known Variables

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.501832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ab4cbf-0313-4a1c-b2e2-39d97c3841ae · outbound

This paper cites In: Chiozzi G, Guzman JC (eds) Software and Cyberinfrastructure for Astronomy IV, p 99133E, doi:10.1117/12.2233418.

From stellar light to astrophysical insight: automating variable star research with machine learning In: Chiozzi G, Guzman JC (eds) Software and Cyberinfrastructure for Astronomy IV, p 99133E, doi:10.1117/12.2233418

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.560552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8925385f-a670-478c-a706-d07437b16380 · outbound

This paper cites A Package for the Automated Classification of Periodic Variable Stars.

From stellar light to astrophysical insight: automating variable star research with machine learning A Package for the Automated Classification of Periodic Variable Stars

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.625756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cc01eb3-9524-43bb-8042-418c3fc23ef2 · outbound

This paper cites Deep Transfer Learning for Classification of Variable Sources.

From stellar light to astrophysical insight: automating variable star research with machine learning Deep Transfer Learning for Classification of Variable Sources

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.672955Z

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Unavailable: canonical work link unavailable.

Observation 513bda4c-ac0e-4f88-8ccc-fd0c5b6b5313 · outbound

This paper cites SpectraFM: Tuning into Stellar Foundation Models.

From stellar light to astrophysical insight: automating variable star research with machine learning SpectraFM: Tuning into Stellar Foundation Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:32.731159Z

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