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Artifact InSPECtor

Help us identify image artifacts in space telescope data!

अधिक जानकारी
संगठन की ओर से:NASA Zooniverse Projects
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यहां क्लिक करके आप वास्तविक रिसर्च कर सकते(ती) हैं!

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यह भी सुनिश्चित करें कि [Talk](/talk) की जाँच करें जहाँ आप अन्य समान विचारधारा वाले स्वयंसेवकों से चैट कर सकते हैं।

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    Artifact InSPECtor के आँकड़े

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    स्वयंसेवक / वालंटियर 0
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    Project launched 1 सितंबर 2026

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    हर क्लिक महत्वूर्ण है! Artifact InSPECtor कम्यूनिटी से इस परियोजना को पूरा करने के लिए जुड़ें और शोधकर्ताओं को महत्वपूर्ण शोध पूरा करने में सहायता करें

    91%
    प्रतिशत पूर्ण
    श्रेणियां0
    विषय0
    पूर्ण विषय0

    शोधकर्ता की ओर से संदेश

    Artifact InSPECtor avatar

    Your work to identify artifacts tells us which data to keep for scientific analysis. Improving our machine learning model to identify artifacts through your classifications enables us to hit the ground running with the new Roman Space Telescope!

    Artifact InSPECtor

    Artifact InSPECtor के बारे में

    Space telescope data contains both astronomical objects and artifacts. As you can imagine, we need to know what is an artifact so we know what data to ignore during analysis. This is especially important with data from new telescopes (like Euclid and Roman) since we aren’t familiar with the artifacts yet.

    We use a machine learning model to recognize these artifacts, but it isn’t perfect. Your job is to determine if the data is showing an artifact, and if our model recognized it correctly. In the future, we will use this model, improved with your classifications, to determine which data to keep and which to throw out.


    This work is funded by NASA. Learn more about NASA Citizen Science Projects here: science.nasa.gov/citizenscience.
    This project makes use of Q1 data from European Space Agency's Euclid mission; learn more here: cosmos.esa.int/web/euclid/euclid-q1-data-release

    ELSA: Euclid Legacy Science Advanced analysis tools (Grant Agreement no. 101135203) is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Innovate UK. Neither the European Union nor the granting authority can be held responsible for them. UK participation is funded through the UK Horizon guarantee scheme under Innovate UK grant 10093177.