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A Binary Classification of SFGs and AGNs Employing Different Clustering Techniques: BAWG2023 Hackathon

Type de record:

paper
Créateur:
Hussein, Eslam
Éditeur:
Zenodo
Hôte:avatar

The BRICS Astronomy Working Group (BAWG) Hackathon took place in Cape Town, South Africa, on October 18-19, 2023, subsequent to two days of BAWG science conference meetings. This two-day hackathon offered participants the opportunity to apply machine learning techniques to tackle a data-intensive astronomical challenge.

The primary task was to develop an optimal unsupervised learning pipeline for binary clustering between Active Galactic Nuclei (AGN) and Star-Forming Galaxies (SFGs). Participants, mainly postgraduate astronomy students from BRICS countries with varying levels of machine learning experience, engaged in this challenge.

Pre-hackathon preparation included Jupyter notebooks provided a week in advance, showcasing an unsupervised learning approach. This preparatory material aimed to equip participants with the necessary insights to undertake the challenge effectively.

The hackathon’s goal was to equip postgrad students with the necessary data science skills to derive new insights from a provided dataset within the two-day event timeframe, fostering a hands-on experience in applying data science to real-world astronomical data.

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doi.org

Tags

AstronomyMachine learningHackathonsClustering

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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