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Data Science Strategies for Multimessenger Astronomy.

Reinaldo R Rosa1

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Summary
This summary is machine-generated.

Data science strategies can enhance astronomy research, especially for multimessenger astronomy (MMA). This study proposes data workflow improvements and a prototype for managing big data in BRICS astronomy initiatives.

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Area of Science:

  • Astronomy
  • Data Science
  • Astrophysics

Background:

  • Scientific research in astronomy generates vast amounts of data.
  • Multimessenger astronomy (MMA) requires efficient data processing and analysis.
  • Big data networks present challenges for astronomical data management.

Purpose of the Study:

  • To identify and propose data science strategies for strengthening scientific research in astronomy.
  • To improve data workflow performance, particularly for multimessenger astronomy (MMA).
  • To address the treatment of raw data within big data networks for BRICS astronomy initiatives.

Main Methods:

  • Literature review of data science strategies applicable to astronomy.
  • Analysis of data workflow performance requirements for MMA.
  • Conceptual design of a data management prototype.

Main Results:

  • Identification of key data science strategies for enhancing astronomical research.
  • Demonstration of potential improvements in data workflow performance for MMA.
  • Presentation of a preliminary prototype design integrating MMA data cubes into a data lake.

Conclusions:

  • Data science offers crucial solutions for advancing astronomy research, especially in the context of MMA.
  • Effective data management strategies are essential for handling the increasing volume of astronomical data.
  • The proposed prototype design provides a foundation for future big data infrastructure in BRICS astronomy.