Coder Social home page Coder Social logo

smsharma / phd-thesis Goto Github PK

View Code? Open in Web Editor NEW
6.0 4.0 1.0 103.88 MB

Princeton Ph.D. dissertation, defended August 2018.

License: Other

TeX 92.52% Shell 0.04% PostScript 7.44%
thesis dissertation phd princeton extragalactic dark matter annihilation cluster fermi

phd-thesis's Introduction

Extragalactic Searches for Dark Matter Annihilation

License: CC BY-NC-SA 4.0 arXiv

Ph.D. dissertation, Siddharth Mishra-Sharma. Defended on August 10, 2018.

Abstract

We are at the dawn of a data-driven era in astrophysics and cosmology. A large number of ongoing and forthcoming experiments combined with an increasingly open approach to data availability offer great potential in unlocking some of the deepest mysteries of the Universe. Among these is understanding the nature of dark matter (DM)---one of the major unsolved problems in particle physics. Characterizing DM through its astrophysical signatures will require a robust understanding of its distribution in the sky and the use of novel statistical methods.

The first part of this thesis describes the implementation of a novel statistical technique which leverages the “clumpiness” of photons originating from point sources (PSs) to derive the properties of PS populations hidden in astrophysical datasets. This is applied to data from the Fermi satellite at high latitudes (|b| > 30°) to characterize the contribution of PSs of extragalactic origin. We find that the majority of extragalactic gamma-ray emission can be ascribed to unresolved PSs having properties consistent with known sources such as active galactic nuclei. This leaves considerably less room for significant dark matter contribution.

The second part of this thesis poses the question: “what is the best way to look for annihilating dark matter in extragalactic sources?” and attempts to answer it by constructing a pipeline to robustly map out the distribution of dark matter outside the Milky Way using galaxy group catalogs. This framework is then applied to Fermi data and existing group catalogs to search for annihilating dark matter in extragalactic galaxies and clusters.

Mirrors

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Contents and Compilation

  • thesis.tex: Main LaTeX file. Uncommenting the lines after "Online mode" or "Print mode" at the top of the file enables compiling either the online or print copies, respectively.
  • arXiv: Folder containing the arXiv version of the thesis.

Contact

Siddharth Mishra-Sharma ([email protected])

phd-thesis's People

Contributors

smsharma avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar

Forkers

jackhcollins

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.