A survey of published research, from the Essentia Memoria newsroom. It lists real papers, each looked up at its DOI registry; the game has not repeated their results.
Research survey
What AI research has found: planets and the sky
Happened: · Published:
8 published works on planets and the sky, each looked up at its DOI registry before it was listed here, with what each one reports in plain words.
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News from the Essentia Memoria game universe. What AI research has found: planets and the sky. 8 published works on planets and the sky, each looked up at its DOI registry before it was listed here, with what each one reports in plain words. This survey lists what each paper reports. The game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited. This report was written from the world record by a fixed template. The full story and its source records are on essentiamemoria.com.
The question
How do models find planets hidden in the light curves of distant stars?
Claims, and where each one comes from
- Where this research lives in the game (a place the world grammar builds)observatory → laboratory
Game simulation · Set in code
src/lib/world-grammar/place-grammar.ts · resolvePurpose('observatory') - Published works in this survey, each resolved at its DOI registry8
Real-world knowledge · Source [1] [2] [3] [4] [5] [6] [7] [8]
- A network searching Kepler telescope data found a new planet around Kepler-90, the first star known to hold eight.Shallue 2018
Real-world knowledge · Source [1]
- Rotation-invariant convolutional neural networks for galaxy morphology predictionDieleman 2015
Real-world knowledge · Source [2]
- Improving galaxy morphologies for SDSS with Deep LearningSánchez 2018
Real-world knowledge · Source [3]
- Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO dataGeorge 2017
Real-world knowledge · Source [4]
- DATA MINING AND MACHINE LEARNING IN ASTRONOMYBall 2010
Real-world knowledge · Source [5]
- Statistics, Data Mining, and Machine Learning in AstronomyIvezić 2014
Real-world knowledge · Source [6]
- Genetic Algorithms in Astronomy and AstrophysicsCharbonneau 1995
Real-world knowledge · Source [7]
- The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 updateCommunity 2022
Real-world knowledge · Source [8]
References, checked at their registry
[1] Shallue (2018). Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90. The Astronomical Journal. doi:10.3847/1538-3881/aa9e09
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90” (2018)
[2] Dieleman (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society. doi:10.1093/mnras/stv632
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Rotation-invariant convolutional neural networks for galaxy morphology prediction” (2015)
[3] Sánchez (2018). Improving galaxy morphologies for SDSS with Deep Learning. Monthly Notices of the Royal Astronomical Society. doi:10.1093/mnras/sty338
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Improving galaxy morphologies for SDSS with Deep Learning” (2018)
[4] George (2017). Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data. Physics Letters B. doi:10.1016/j.physletb.2017.12.053
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data” (2018)
[5] Ball (2010). DATA MINING AND MACHINE LEARNING IN ASTRONOMY. International Journal of Modern Physics D. doi:10.1142/s0218271810017160
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “DATA MINING AND MACHINE LEARNING IN ASTRONOMY” (2010)
[6] Ivezić (2014). Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press eBooks. doi:10.23943/princeton/9780691151687.001.0001
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data” (2014)
[7] Charbonneau (1995). Genetic Algorithms in Astronomy and Astrophysics. The Astrophysical Journal Supplement Series. doi:10.1086/192242
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “Genetic Algorithms in Astronomy and Astrophysics” (1995)
[8] Community (2022). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Research. doi:10.1093/nar/gkac247
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:40 PM UTC · the registry holds “The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update” (2022)
Review
Not peer reviewed. No human scientist has reviewed this survey. Its template and landmark summaries were written with an AI assistant (Claude); the papers it lists are real and each was checked at its registry, so anyone can follow the links and read them.
- Each listed DOI was resolved, and its title, year and first author matched the listing.
- Each work the search found is held by Crossref, a registry independent of the search.
- Each number in the headline and summary is accounted for by a listed claim.
- No candidate work was dropped: every one the desk found or named was confirmed by its registry.
Code version: fa127b6f988c
What the record does not say
This survey lists what each paper reports; the game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited.
How this was written
Landmark works named by the desk, then the most-cited works with a DOI that OpenAlex returns when titles and abstracts are searched for (exoplanet OR "light curve" OR galaxy OR galaxies OR "gravitational wave" OR astronomy OR astronomical) AND ("machine learning" OR "deep learning" OR "neural network" OR "neural networks" OR "artificial intelligence" OR convolutional OR "reinforcement learning" OR "language model" OR transformer); a hit is kept only if its own title names the field and its title or abstract names an AI method, as whole words, and the hits that name the most of these words in their title come first. Each candidate was looked up at Crossref (OpenAlex as second resolver) and its title, year and first author compared; a work the search found is shown only when Crossref holds it. Landmark summaries were written by the desk; the other works are listed by their own titles.
Source records: code:src/lib/world-grammar/place-grammar.ts#resolvePurpose · doi:10.3847/1538-3881/aa9e09 · doi:10.1093/mnras/stv632 · doi:10.1093/mnras/sty338 · doi:10.1016/j.physletb.2017.12.053 · doi:10.1142/s0218271810017160 · doi:10.23943/princeton/9780691151687.001.0001 · doi:10.1086/192242 · doi:10.1093/nar/gkac247