darshanbeniwal / Astro_data_analysis_w_Python_GHRCE_IUCAA_2023

This repository contains files related to the Python programming workshop conducted for astrophysics and cosmology work at Nagpur. The repository includes sample codes, datasets, and presentations used during the workshop. These materials can be useful for beginners who want to learn Python programming for astrophysics and cosmology work.

Two Days Workshop on Python Programming in Astronomy, Astrophysics & Cosmology

Organized by Department of Applied Science, G H Raisoni College of Engineering, GHRCE, Nagpur

Funded by Inter-University Centre for Astronomy and Astrophysics (IUCAA)

I was among the invited speakers, along with Prof S. N. Hasan and Prof Priya Hasan, and in this repository, I have included all the files, including the data file and Python notebook, that were covered in two lecture sessions and four hands-on sessions by me.

Day-1. April 07th 2023; 🔗

• Lecture-1

  • Introduction to Statistics
  • Maximum Likelihood Estimator (MLE)
  • Minimum Chi-square Test

• Hands-on Session-1

  • Linear Model Fitting with Mock Data Using Basic Code: A Tutorial
  • Linear Model Fitting with Mock Data Using lmfit

• Hands-on Session-2

  • Flat $\Lambda CDM$ Model fitting with Hubble Parameter Measurements using lmfit

• Exercise-1

  • Estimate the value of $\pi$ using Monte Carlo simulation, by considering a sphere inscribed inside a cube.

Day-2. April 08th 2023; 🔗

• Lecture-2

  • Bayesian Statistics
  • Monte Carlo
  • Markov Chain Monte Carlo
  • Metropolis-Hastings Algorithm

• Hands-on Session-3

  • $\pi$ Value Estimation using Monte Carlo
  • Integration Evaluation using Monte Carlo

• Hands-on Session-4

  • Flat $\Lambda CDM$ Model fitting with Hubble Parameter Measurements using Metropolis-Hastings Algorithm
  • Non-Flat $\Lambda CDM$ Model fitting with Hubble Parameter Measurements using Metropolis-Hastings Algorithm

• Exercise-2

  • Fit $\omega CDM$ Model with Hubble Parameter Measurements and compare your results with arXiv:2209.05782

References for Further Reading

• Basics of Statistics

  • Introduction To Error Analysis by J. R. Taylor 🔗
  • Data analysis: a Bayesian tutorial by Devinderjit Sivia and John Skilling 🔗
  • Data Reduction and Error Analysis for the Physical Sciences by P. R. Bevington and D. K. Robinson 🔗

• Statistical Cosmology

  • Statistical methods in cosmology by L. Verde 🔗
  • Statistical methods for cosmological parameter selection and estimation by A. R. Liddle 🔗
  • Bayesian Methods in Cosmology by R. Trotta 🔗

Contact Email: [e-mail]

Copyright

© Darshan Kumar Beniwal, University of Delhi, 2023, Presented at Department of Applied Science, G H Raisoni College of Engineering, Nagpur.