Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00368593" target="_blank" >RIV/68407700:21240/23:00368593 - isvavai.cz</a>

Výsledek na webu

<a href="https://proceedings.mlr.press/v220/yip22a.html" target="_blank" >https://proceedings.mlr.press/v220/yip22a.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes

Popis výsledku v původním jazyce

Exo-atmospheric studies, i.e. the study of exoplanetary atmospheres, is an emerging frontier in Planetary Science. To understand the physical properties of hundreds of exoplanets, astronomers have traditionally relied on sampling-based methods. However, with the growing number of exoplanet detections (i.e. increased data quantity) and advancements in technology from telescopes such as JWST and Ariel (i.e. improved data quality), there is a need for more scalable data analysis techniques. The Ariel Data Challenge 2022 aims to find interdisciplinary solutions from the NeurIPS community. Results from the challenge indicate that machine learning (ML) models have the potential to provide quick insights for thousands of planets and millions of atmospheric models. However, the machine learning models are not immune to data drifts, and future research should investigate ways to quantify and mitigate their negative impact.

Název v anglickém jazyce

Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes

Popis výsledku anglicky

Exo-atmospheric studies, i.e. the study of exoplanetary atmospheres, is an emerging frontier in Planetary Science. To understand the physical properties of hundreds of exoplanets, astronomers have traditionally relied on sampling-based methods. However, with the growing number of exoplanet detections (i.e. increased data quantity) and advancements in technology from telescopes such as JWST and Ariel (i.e. improved data quality), there is a need for more scalable data analysis techniques. The Ariel Data Challenge 2022 aims to find interdisciplinary solutions from the NeurIPS community. Results from the challenge indicate that machine learning (ML) models have the potential to provide quick insights for thousands of planets and millions of atmospheric models. However, the machine learning models are not immune to data drifts, and future research should investigate ways to quantify and mitigate their negative impact.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the NeurIPS 2022 Competitions Track

ISBN

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ISSN

2640-3498

e-ISSN

2640-3498

Počet stran výsledku

17

Strana od-do

1-17

Název nakladatele

Proceedings of Machine Learning Research

Místo vydání

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Místo konání akce

New Orleans

Datum konání akce

28. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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