The Multi-Mission Maximum Likelihood framework (3ML)
Creators
- 1. Stanford University, CA (United States)
Description
The age of multi-wavelength and multi-messenger astronomy has arrived and with it, new tools are needed to analyze data from multiple instruments properly and with ease. The Multi-Mission Maximum Likelihood framework (3ML) provides this functionality via the novel use of instrument plugins which allow for every instrument's unique data to be treated independently with an appropriate likelihood. Under the 3ML framework, users can design plugins that handle instrument specific data routines transparently in the background. When multiple instruments are used together, their independent likelihoods are treated under a common minimization or Bayesian sampling framework. 3ML provides a multitude of minimization algorithm for maximum likelihood estimation (MLE) as well as several popular Bayesian posterior samplers. The entire framework is provided via a modern Python interface providing the user with a modern and easily transportable analysis framework well suited for modern astronomy. New models can be added easily. It is also possible to perform time-energy modeling. We present the framework and its main functionalities.
Availability note (English)
Available from https://www.osti.gov/servlets/purl/1565875; https://www.osti.gov/biblio/1565875; DOE Accepted Manuscript full text, or the publishers Best Available Version will be available free of charge after the embargo periodAdditional details
Identifiers
Publishing Information
- Journal Title
- Pos Proceedings of Science
- Journal Volume
- 312
- Journal Page Range
- vp.
- ISSN
- 1824-8039
Conference
- Title
- 7. International Fermi Symposium
- Dates
- 15-20 Oct 2017
- Place
- Garmisch-Partenkirchen (Germany)
INIS
- Country of Publication
- Italy
- Country of Input or Organization
- United States
- INIS RN
- 52110950
- Subject category
- S79: ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; S46: INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- ALGORITHMS; ASTRONOMY; ASTROPHYSICS; INTERFACES; MAXIMUM-LIKELIHOOD FIT; MINIMIZATION; PYTHON; SAMPLERS; SAMPLING; SIMULATION; WAVELENGTHS
- Descriptors DEC
- EQUIPMENT; MATHEMATICAL LOGIC; MATHEMATICAL SOLUTIONS; NUMERICAL SOLUTION; OPTIMIZATION; PHYSICS; PROGRAMMING LANGUAGES
Optional Information
- Funding organization
- USDOE Laboratory Directed Research and Development (LDRD) Program (United States)
- Secondary number(s)
- OSTIID--1565875