Planning the following VO session at the upcoming Jan AAS meeting :

"Flexible Multi‐dimensional Modeling of Complex Data in Astronomy" session on Wednesday, January 4 from 9:30 am ‐ 11:30 am in Grapevine 4 at the 229th AAS meeting.

In this session, we will explore methods for building and analyzing multi-wavelength, N-dimensional datasets with Sherpa, a Python-based general modeling and fitting application, and Iris, a tool built on top of Sherpa for analyzing spectral energy distributions. See the full abstract appended below.

We will use IPython Notebooks to guide the participants through Sherpa-Python sessions and present a demonstration showing Iris connectivity to data archives and examples of SED modeling. Participants will have free time to explore any of the several science worksheets provided at the end of the demonstrations, or work out their own analysis; the session leaders will be available for help and answering any questions participants have on Sherpa and Iris during this free time.

The following web page has been set up to review the material planned for the workshop, sign up if you know that you’d like to attend, and download the software prior to the workshop to save time later.

https://hea-www.harvard.edu/AstroStat/aas229_2017/modeling_ws/

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Flexible Multi‐dimensional Modeling of Complex Data in Astronomy abstract:

Recent improvements in instrumentation and the data collection process across the entire electromagnetic spectrum have resulted in an increasing amount of high quality multi-wavelength observations. The analysis of these modern data sets present several statistical challenges that require new methods and techniques to support the scientific inference. Our session will focus on the discussion of applied methodology that can be used to tackle some of these challenges. We will present tutorials based on the Sherpa-Python and IRIS tools developed by the Chandra X-ray Observatory.

Sherpa is a Python based general modeling and fitting application that provides an environment for modeling multi-dimensional data with a set of optimization methods, including MCMC simulations for sampling posterior distributions. Sherpa provides flexible mechanisms for modeling Poisson (sparse) and Gaussian (rich) data with appropriate likelihoods, including both pre-defined models and an interface to incorporate user defined models (Python functions or external code). Sherpa can be used for modeling 1D, 2D, or 3D data, i.e., spectra, time-series, or images, and can be extended to spectral-timing and spatial-timing domains. An upcoming 'Sherpa to Astropy' Python package will allow users to use Sherpa's optimizers and error estimators seamlessly within the Astropy's modeling framework. Iris has been built on top of Sherpa for fitting SEDs to multi-wavelength data. Iris also provides a front-end to Virtual Observatory archival catalogs that can supply the appropriate data for the modeling session.

We will use IPython Notebooks to guide the participants through Sherpa-Python sessions and present a tutorial demonstration showing Iris connectivity to the archives and examples of SED modeling.