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Title: The Multi-Mission Maximum Likelihood framework (3ML)

Abstract

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.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Stanford Univ., CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1565875
Report Number(s):
LA-UR-17-26137
Journal ID: ISSN 1824-8039
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
PoS Proceedings of Science
Additional Journal Information:
Journal Volume: 312; Conference: 7th International Fermi Symposium, Garmisch-Partenkirchen (Germany), 15-20 Oct 2017; Journal ID: ISSN 1824-8039
Publisher:
SISSA
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; Astronomy and Astrophysics

Citation Formats

Vianello, Giacomo, Lauer, R. J., Burgess, J. M., Ayala, H., Fleischhack, H., Harding, P., Hui, M., Marinelli, S., Savchenko, Volodymyr, and Zhou, H. The Multi-Mission Maximum Likelihood framework (3ML). United States: N. p., 2017. Web. doi:10.22323/1.312.0130.
Vianello, Giacomo, Lauer, R. J., Burgess, J. M., Ayala, H., Fleischhack, H., Harding, P., Hui, M., Marinelli, S., Savchenko, Volodymyr, & Zhou, H. The Multi-Mission Maximum Likelihood framework (3ML). United States. doi:10.22323/1.312.0130.
Vianello, Giacomo, Lauer, R. J., Burgess, J. M., Ayala, H., Fleischhack, H., Harding, P., Hui, M., Marinelli, S., Savchenko, Volodymyr, and Zhou, H. Tue . "The Multi-Mission Maximum Likelihood framework (3ML)". United States. doi:10.22323/1.312.0130. https://www.osti.gov/servlets/purl/1565875.
@article{osti_1565875,
title = {The Multi-Mission Maximum Likelihood framework (3ML)},
author = {Vianello, Giacomo and Lauer, R. J. and Burgess, J. M. and Ayala, H. and Fleischhack, H. and Harding, P. and Hui, M. and Marinelli, S. and Savchenko, Volodymyr and Zhou, H.},
abstractNote = {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.},
doi = {10.22323/1.312.0130},
journal = {PoS Proceedings of Science},
number = ,
volume = 312,
place = {United States},
year = {2017},
month = {12}
}

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