iid2022: Statistical Methods for Event Data

Illuminating the Dynamic Universe

A Workshop and Winter School on Statistics hosted by the University of Alabama in Huntsville.

To be held at the Lake Guntersville State Park Lodge (AL) on Nov. 15-18, 2022

Slack channel added with the ID: C04AKM77WFP
- Can be used to discuss travel arrangements, etc.

Final Program now available here

Due to the overwhelming response to the request for student and early-career scientists' registration and travel grants, we have already reached the limit of support provided by the NSF

Publication of Refereed Proceedings (see Proceedings tab)
- The Submission webpage is now open, see tab for details

iid2022 is a Workshop and Winter School on Statistics aiming to further and disseminate the use of statistical methods for astronomy, the physical sciences, and related disciplines.

Event Data

The specific focus of this workshop is on statistical methods for event data. But what are event data?

A number of astronomical data begin with the collection of individual events: typically photons, but also neutrinos or other particles. Common statistical applications result from the study of these events as a function of location (images), as a function of time (such as light curves) or as a function of energy or wavelength (spectra). Events can be studied individually, or binned into predefined ranges, often resulting in the use of the Poisson distribution.

Events can also be more broadly defined as ensembles of quantities, such as gravitational wave events that are not per se detected through individual photons, or galaxy clusters detected through measurements of the CMB. Studies of the distribution of such ensembles would also fall under the purview of `event data'.

Perhaps, a common thread of event data is the enumeration (or list) of certain quantities of interest, be they individual events or aggregates that result from more complex measurements. In this respect, event data have a close relationship to the natural numbers.

As data become more complex and computers enable more sophisticated methods of analysis, it is useful to bring together data experts and mathematical statisticians to discuss how statistical methods are applicable to the data. The workshop will be organized in two morning/afternoon daily sessions, with time for discussion, collaboration and completion of statistical sample problems based on astronomical data. The workshop will also address advances in the software available for statistical data analysis.

Each Session Features

An introductory lecture of approximately 45 minutes, primarily aimed to explain the current state of the subject, with emphasis on mathematical statistics and sample applications.
Followed by shorter contributed talks and discussion of the methods, with emphasis on recent progress and applications to astronomy and other fields. This is the venue for astronomers to contribute their use of statistical methods for event data, while presenting new and original scientific results.
Hands-on collaborative analysis of sample problems with advanced software. Problems will be presented in advance of the workshop, and they can be completed in collaboration with other participants and the instructors present at the workshop. This School component of the Workshop is aimed primarily at students and early-career postdocs.

Motivation for the Workshop

To train and engage young scientists in proper statistical methods for the analysis and interpretation of data.
Provides a gathering of scientists (both astronomers and in related fields) to exchange recent advances in the statistics and analysis of event data. The idea is that contributed speakers focus their presentations on the statistical methods for the analysis of their event data, while also presenting original scientific results.

iid is a reference to independent and identically distributed random variables, a common assumption for many types of data.

Where: Lake Guntersville State Park Lodge, Guntersville, AL

When: November 15–18, 2022

Contact: Max Bonamente, UAH (SOC) bonamem@uah.edu, Samuel Johnson, UAH (LOC) sdj0014@uah.edu