Astrophysical Jets Analysis

Project Overview

The Astrophysical Jets Analysis project aims to explore the relationship between black hole mass and jet length for different astrophysical sources. The goal is to create a dataset of black holes and their corresponding jets, perform visualizations, and eventually model the relationships between their properties. This study focuses on active galactic nuclei (AGN), quasars, and other black holes that have been documented for their powerful jet activity.

Project Goals:

1. Data Collection: Gather data about black holes, specifically their mass and jet length.
2. Data Visualization: Plot black hole mass against jet length to observe trends or correlations.
3. Data Expansion: Use tools like Astroquery and NASA ADS to find more data and enrich the dataset.
4. Modeling: Lay the groundwork for potential modeling of relationships between black hole properties.

Getting Started

To get started with this project on your local machine, follow the instructions below.

Prerequisites

• Python 3.8+: The codebase is built using Python 3.
• Git: For cloning the repository and tracking changes.
• Virtual Environment: Recommended for dependency isolation.
• API Token for NASA ADS: Required for accessing scientific papers through ADS. You can generate one here.

System Requirements

• OS: Tested on Linux Ubuntu.
• Python Packages: The following packages are required and will be installed via requirements.txt.
  • pandas
  • matplotlib
  • astroquery
  • ads

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/yourusername/astrophysical_jets_analysis.git
   cd astrophysical_jets_analysis

2. Create a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Set Up NASA ADS Token: Set up the ADS API token as an environment variable to interact with ADS:

   export ADS_DEV_KEY='your_ads_api_key_here'

Project Structure

• expanded_black_hole_data.csv: The main dataset containing black hole names, coordinates, mass, and jet length.
• collect_ads_data.py: Script for querying NASA ADS to gather scientific literature on black holes.
• collect_jet_data.py: Script for gathering initial data about black holes using Astroquery.
• visualize_black_hole_data.py: Script for creating visualizations, such as scatter plots, of black hole mass vs. jet length.
• requirements.txt: Lists all required Python packages for the project.
• README.md: Contains information about the project, setup, and goals (this file).

Using the Scripts

Collecting Data from NASA ADS

To gather data using NASA ADS, run:

python collect_ads_data.py

This will generate a CSV file called ads_black_hole_papers.csv that lists relevant papers, which you can use to manually extract further black hole information.

Expanding the Dataset

After reviewing papers collected from NASA ADS or other sources, add relevant black hole masses and jet lengths to the expanded_black_hole_data.csv.

Visualizing the Data

To visualize the current dataset:

python visualize_black_hole_data.py

This script will generate a scatter plot showing black hole mass vs. jet length and save it as black_hole_mass_vs_jet_length.png.

Technical Details

• Programming Language: Python 3
• Data Sources:
  • Simbad and NED via Astroquery for initial black hole data.
  • NASA ADS for scientific papers.
• Tools Used:
  • Astroquery: For accessing astronomical databases.
  • NASA ADS API: For searching scientific literature.
  • Matplotlib: For plotting and visualizing data.

Development Workflow

Here is an outline of our workflow during this project:

1. Data Collection: Initially used Astroquery to gather basic information about black holes and their jets.
2. Data Enrichment: Utilized NASA ADS to find literature for additional data.
3. Data Visualization: Used Matplotlib to plot black hole mass vs. jet length.
4. Manual Review and Input: Enriched data by manually reviewing papers and adding black hole properties to the dataset.

Future Work

• Data Automation: Further automate the process of data collection using NLP tools to parse scientific abstracts.
• Modeling: Use machine learning to explore potential relationships between black hole mass and jet properties.
• Interactive Visualization: Improve visualizations to be more interactive, possibly using Plotly or Bokeh.

Contributors

• Kym Derriman - Project Lead & Developer
• OpenAI ChatGPT - Assistance in generating ideas and code guidance

License

This project is licensed under the MIT License.