Identify exoplanet host
using AI
Below is a free classifier to identify exoplanet host. Just input your text, and our AI will predict if a star is likely to host an exoplanet - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("exoplanet-host", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/exoplanet-host/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/exoplanet-host/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict if a star is likely to host an exoplanet.
This pretrained text model uses a Nyckel-created dataset and has 2 labels, including Has Planets and No Planets.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if a star is likely to host an exoplanet).
Whether you're just curious or building exoplanet host detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify exoplanet host at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Astronomical Research Data Filtering: Researchers can utilize the True text classification function to efficiently filter large datasets of astronomical observations. By identifying which stars are likely exoplanet hosts, scientists can prioritize their studies, focusing on promising candidates without manually sifting through irrelevant data.
- Telescope Time Allocation: Observatory managers can leverage this function to streamline the allocation of telescope resources. By identifying exoplanet hosts in advance, they can optimize observation schedules, ensuring that valuable telescope time is spent on the most promising targets for further study.
- Citizen Science Platforms: Citizen science initiatives can use the classification tool to categorize data submitted by volunteers. By automating the identification of exoplanet hosts, these platforms can enhance participant engagement and contribute to more accurate scientific discoveries while maintaining manageable workloads for organizers.
- Exoplanet Discovery Alerts: Space agencies and astronomical institutions can implement the tool to set up automated alerts for newly identified exoplanet hosts. By notifying researchers as soon as potential exoplanets are detected, they can react swiftly to investigate further and gather valuable data.
- Teaching and Educational Tools: Educational institutions can integrate this classification function into astronomy curricula. It can serve as a practical teaching aid, helping students learn about exoplanet detection and classification while experimenting with real-world datasets, enhancing their understanding of astrophysics.
- Data Enrichment for Machine Learning Models: Data scientists can use the classification function to enrich datasets for machine learning projects related to exoplanet research. By identifying exoplanet hosts, they can create more robust models that improve predictions regarding exoplanet characteristics and distributions.
- Public Outreach and Engagement: Planetariums and science museums can employ the true text classification function to develop interactive exhibits. By showcasing how exoplanet hosts are identified, these institutions can engage visitors with the excitement of space exploration and the latest astronomical discoveries, fostering interest in sciences.