The instruments developed for detecting gamma-rays accumulate many background events. The majority is rejected either by using an intelligent triggering system for the detectors, or in early stages of the data analysis. Unfortunately, there is still a dominant fraction of events passing the gamma-ray selection cuts, called the gamma-like background. In order to extract the gamma-ray signal, clever algorithms for modeling the gamma-like background are essential.
During the GSoC 2015 I will be working for the Gammapy project. Gammapy is an open source (BSD licensed) gamma-ray astronomy Python package. It is an in-development affiliated package of Astropy, a community effort to develop a single core package for Astronomy in Python. Gammapy builds on the core scientific Python stack to provide tools to simulate and analyze the gamma-ray sky for telescopes such as Fermi, H.E.S.S. and CTA.
Specifically I will implement the most successful background modeling methods largely in use by the gamma-ray community in the Astropy/Gammapy framework.
The background methods can be classified in two categories, according to the observation strategy:
- Background models from OFF observations, where the background is modeled from observations far away from any known sources. IACT experiments require dedicated OFF-source observations for modeling the background.
- Background models from ON observations, where the background is modeled using observation within or close-by to the region of interest (a.k.a. ON region).
In a first step I will implement tools to create background model templates from observations with no or only a few gamma-ray sources in the field of view. In a second step, I will develop algorithms to estimate the background in observations containing gamma-ray sources to detect them and measure their spatial shape and energy spectrum, in some cases using the model templates from the first step.