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Pulsar Timing Data Analysis

This repository contains several exercises and explanatory Jupyter notebooks that detail basic frequentist and Bayesian statistical techniques, pulsar residual and noise modeling, and Gravitational Wave (GW) detection methods. The repository will be updated as new learning material is made available through student workshops and various busyweeks.


Materials

Here we host a collection of exercises, notes and, presentations from various schools and workshops dedicated to pulsar timing data analysis. Each page will contain a complete description of it's contents in the README.

  • Basic data analysis methods: Here we host materials used in several IPTA and NANOGrav schools. In this repository we cover basic frequentist and Bayesian data analysis techniques as well as some basic GW signal modeling theory.

  • Pulsar timing data analysis basics: In this repository we host a Jupyter notebook with an extensive explanaion of the noise modeling and Bayesian data analysis formulation that is used in nearly all modern pulsar timing analysis. This is an excellent place to start for new students that are already familiar with linear algebra and basic Bayesian data analysis concepts.

  • Pulsar timing noise modeling and GW detection exercises: In this repository we host material from an intensive school held at Caltech that begins with basic Markov Chain Monte-Carlo design and gradually builds a fully functional GW detection pipeline from scratch. If you are already familiar with Bayesian data analysis, are comfortable with the Python programming language, and want to get your hands dirty and learn a lot, then this is the place to go.

  • Pulsar timing code profiling: In this repository we host material from a NANOGrav hackathon held at NCSA at the University of Illinois at Urbana-Champaign in Fall 2016. The goal was to profile our likelihood and find out where the computational bottlenecks are. This profiling is done through a jupyter notebook, which calls various scripts and data, also within the folder.


Literature

Here we list several important papers in several different areas of pulsar timing data analysis. While this list is by no means exhaustive it gives a very good introduction to several areas of pulsar data analysis and modeling. The reading lists have been broken into broad topics:

0. Basic overview and introductory material

Basic pulsar timing overviews and historical papers are listed here for beginners in the field.

1. Ph.D theses on gravitational wave and pulsar data analysis

The Ph.D theses here are all focused on various aspects of pulsar timing and GW detection and modeling. While they are, of course, very long and detailed they provide a complete basis of nearly all modern pulsar timing and GW analysis techniques and theory.

2. Data analysis and sampling techniques

These papers form an excellent basis for nearly all of the data Bayesian data analysis and samping methods that are currently used in pulsar timing.

3. Pulsar noise modeling and analysis

Noise modeling in pulsar timing data analysis is extremely important and it permeates all other areas of analysis in pulsar timing. These papers are some of the most comprehensive covering physical and mathematical modeling of pulsar noise sources.

4. Non-linear Bayesian pulsar timing

Full Bayesian non-linear pulsar timing is a very new field and these two papers form the basis of that work to date.

5. Stochastic gravitational wave background analysis

Stochastic GWB analysis is the most active in the pulsar timing field. This list (and references therein) is a good base collection of a historical development of detection techniques for both anisotropic and isotropic backgrounds.

6. Continuous gravitational wave analysis

The papers here and references therein detail the modeling and detection techniques for continuous GW sources in both circular and eccentric orbits.

7. Gravitational wave burst analysis

Bursts are likely the least studied form of GWs in the pulsar timing frequency bands. This list forms a great basis for modeling and detection techniques for both un-modeled and bursts with memory (BWM).


Questions, Comments, Requests

If you have questions, comments or requests then submit an issue and one of us will try to answer it ASAP.


Contributions?

If you have material of your own that you think would be useful then fork this repository, add your material and submit a pull request.

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