Datasets:

Allanatrix
/

Astro

	---
	title: Stellar Classification Dataset - SDSS17
	emoji: 🌟
	tags:
	- astronomy
	- stellar-classification
	- spectral-data
	- machine-learning
	- sdss
	---

	# Stellar Classification Dataset - SDSS17

	## Welcome to the Dataset!

	Get ready to explore the cosmos with the Stellar Classification Dataset from the Sloan Digital Sky Survey (SDSS) Data Release 17! This dataset contains 100,000 observations of celestial objects—stars, galaxies, and quasars—captured through their spectral characteristics. Whether you're an astronomer studying the universe, a data scientist building classification models, or a student curious about the night sky, this dataset offers a fantastic opportunity to dive into the world of stellar classification.

	## Context

	In astronomy, stellar classification is the process of categorizing stars, galaxies, and quasars based on their spectral characteristics—the unique "fingerprints" of light they emit. This classification is a cornerstone of astronomy, helping us understand how stars are distributed in our Milky Way galaxy and beyond. The discovery that the Andromeda Galaxy was separate from our own sparked a wave of galaxy surveys, made possible by increasingly powerful telescopes. This dataset builds on that legacy, providing spectral data to classify celestial objects and uncover insights about the universe.

	## Dataset Description

	### Content

	The dataset includes 100,000 observations from the SDSS, each described by 17 feature columns and 1 class column. Here's what each column represents:

	- obj_ID: Unique identifier for the object in the SDSS image catalog (CAS).
	- alpha: Right Ascension angle (J2000 epoch, in degrees).
	- delta: Declination angle (J2000 epoch, in degrees).
	- u: Ultraviolet filter magnitude in the photometric system.
	- g: Green filter magnitude in the photometric system.
	- r: Red filter magnitude in the photometric system.
	- i: Near-infrared filter magnitude in the photometric system.
	- z: Infrared filter magnitude in the photometric system.
	- run_ID: Run number identifying the specific scan.
	- rerun_ID: Rerun number specifying how the image was processed.
	- cam_col: Camera column identifying the scanline within the run.
	- field_ID: Field number identifying each field.
	- spec_obj_ID: Unique ID for optical spectroscopic objects (objects with the same `spec_obj_ID` share the same class).
	- class: Object class, labeled as `STAR`, `GALAXY`, or `QUASAR`.
	- redshift: Redshift value, indicating the increase in wavelength due to the object’s motion or cosmic expansion.
	- plate: Plate ID, identifying each SDSS plate.
	- MJD: Modified Julian Date, indicating when the data was collected.
	- fiber_ID: Fiber ID, identifying the fiber that directed light to the focal plane.

	### Format

	- File: Likely stored as a CSV file (e.g., `stellar_classification.csv`) in the `data/` directory.
	- Size: 100,000 rows, 18 columns (17 features + 1 class).

	### Source

	The data is sourced from the Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17), a publicly available collection of astronomical observations. The dataset was curated by fedesoriano and hosted on Kaggle.

	## Use Cases

	This dataset is a treasure trove for various applications:

	- Astronomy Research: Study the distribution and properties of stars, galaxies, and quasars in the universe.
	- Machine Learning: Train classification models to predict whether an object is a star, galaxy, or quasar based on spectral features.
	- Data Visualization: Create stunning visualizations of celestial objects’ spectral characteristics or spatial distributions.
	- Education: Use in astronomy or data science courses to teach spectral classification and ML techniques.
	- Exploratory Analysis: Investigate relationships between redshift, photometric filters, and object classes.

	## Similar Datasets

	If you’re interested in related datasets, check out these:

	- CERN Proton Collision Dataset: Particle collision data for high-energy physics research. [Link](#)
	- Airfoil Self-Noise Dataset: Acoustic data for aerodynamic studies. [Link](#)
	- CERN Electron Collision Data: Electron collision events from CERN experiments. [Link](#)
	- Wind Speed Prediction Dataset: Meteorological data for wind forecasting. [Link](#)
	- Spanish Wine Quality Dataset: Chemical properties for wine quality classification. [Link](#)

	Note: Links are placeholders as specific URLs were not provided. Replace with actual links if available.

	## Citation

	To give credit to the dataset creator, please cite:

	> fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved (January 2022) from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17.

	For the SDSS data source, cite:

	> Abdurro’uf et al., The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data (Abdurro’uf et al., submitted to ApJS) [arXiv:2112.02026].

	## Acknowledgements

	The data is released under the public domain by the Sloan Digital Sky Survey (SDSS) as part of Data Release 17 (DR17). For more details on the SDSS license, visit: http://www.sdss.org/science/image-gallery/.

	We thank the SDSS team for making this data publicly available and fedesoriano for curating and sharing the dataset on Kaggle.

	## License

	Public Domain (see SDSS license for details: http://www.sdss.org/science/image-gallery/).

	---

	Have questions or ideas? Open a GitHub issue or join the discussion on Hugging Face. Clear skies and happy exploring!

	---
	title: Stellar Classification Dataset - SDSS17
	emoji: 🌟
	tags:
	- astronomy
	- stellar-classification
	- spectral-data
	- machine-learning
	- sdss
	---

	# Stellar Classification Dataset - SDSS17

	## Welcome to the Dataset!

	Get ready to explore the cosmos with the Stellar Classification Dataset from the Sloan Digital Sky Survey (SDSS) Data Release 17! This dataset contains 100,000 observations of celestial objects—stars, galaxies, and quasars—captured through their spectral characteristics. Whether you're an astronomer studying the universe, a data scientist building classification models, or a student curious about the night sky, this dataset offers a fantastic opportunity to dive into the world of stellar classification.

	## Context

	In astronomy, stellar classification is the process of categorizing stars, galaxies, and quasars based on their spectral characteristics—the unique "fingerprints" of light they emit. This classification is a cornerstone of astronomy, helping us understand how stars are distributed in our Milky Way galaxy and beyond. The discovery that the Andromeda Galaxy was separate from our own sparked a wave of galaxy surveys, made possible by increasingly powerful telescopes. This dataset builds on that legacy, providing spectral data to classify celestial objects and uncover insights about the universe.

	## Dataset Description

	### Content

	The dataset includes 100,000 observations from the SDSS, each described by 17 feature columns and 1 class column. Here's what each column represents:

	- obj_ID: Unique identifier for the object in the SDSS image catalog (CAS).
	- alpha: Right Ascension angle (J2000 epoch, in degrees).
	- delta: Declination angle (J2000 epoch, in degrees).
	- u: Ultraviolet filter magnitude in the photometric system.
	- g: Green filter magnitude in the photometric system.
	- r: Red filter magnitude in the photometric system.
	- i: Near-infrared filter magnitude in the photometric system.
	- z: Infrared filter magnitude in the photometric system.
	- run_ID: Run number identifying the specific scan.
	- rerun_ID: Rerun number specifying how the image was processed.
	- cam_col: Camera column identifying the scanline within the run.
	- field_ID: Field number identifying each field.
	- spec_obj_ID: Unique ID for optical spectroscopic objects (objects with the same `spec_obj_ID` share the same class).
	- class: Object class, labeled as `STAR`, `GALAXY`, or `QUASAR`.
	- redshift: Redshift value, indicating the increase in wavelength due to the object’s motion or cosmic expansion.
	- plate: Plate ID, identifying each SDSS plate.
	- MJD: Modified Julian Date, indicating when the data was collected.
	- fiber_ID: Fiber ID, identifying the fiber that directed light to the focal plane.

	### Format

	- File: Likely stored as a CSV file (e.g., `stellar_classification.csv`) in the `data/` directory.
	- Size: 100,000 rows, 18 columns (17 features + 1 class).

	### Source

	The data is sourced from the Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17), a publicly available collection of astronomical observations. The dataset was curated by fedesoriano and hosted on Kaggle.

	## Use Cases

	This dataset is a treasure trove for various applications:

	- Astronomy Research: Study the distribution and properties of stars, galaxies, and quasars in the universe.
	- Machine Learning: Train classification models to predict whether an object is a star, galaxy, or quasar based on spectral features.
	- Data Visualization: Create stunning visualizations of celestial objects’ spectral characteristics or spatial distributions.
	- Education: Use in astronomy or data science courses to teach spectral classification and ML techniques.
	- Exploratory Analysis: Investigate relationships between redshift, photometric filters, and object classes.

	## Similar Datasets

	If you’re interested in related datasets, check out these:

	- CERN Proton Collision Dataset: Particle collision data for high-energy physics research. [Link](#)
	- Airfoil Self-Noise Dataset: Acoustic data for aerodynamic studies. [Link](#)
	- CERN Electron Collision Data: Electron collision events from CERN experiments. [Link](#)
	- Wind Speed Prediction Dataset: Meteorological data for wind forecasting. [Link](#)
	- Spanish Wine Quality Dataset: Chemical properties for wine quality classification. [Link](#)

	Note: Links are placeholders as specific URLs were not provided. Replace with actual links if available.

	## Citation

	To give credit to the dataset creator, please cite:

	> fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved (January 2022) from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17.

	For the SDSS data source, cite:

	> Abdurro’uf et al., The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data (Abdurro’uf et al., submitted to ApJS) [arXiv:2112.02026].

	## Acknowledgements

	The data is released under the public domain by the Sloan Digital Sky Survey (SDSS) as part of Data Release 17 (DR17). For more details on the SDSS license, visit: http://www.sdss.org/science/image-gallery/.

	We thank the SDSS team for making this data publicly available and fedesoriano for curating and sharing the dataset on Kaggle.

	## License

	Public Domain (see SDSS license for details: http://www.sdss.org/science/image-gallery/).

	---

	Have questions or ideas? Open a GitHub issue or join the discussion on Hugging Face. Clear skies and happy exploring!