Chrome Extension
WeChat Mini Program
Use on ChatGLM

Testing Source Confusion and Identification Capability in Cherenkov Telescope Array Data

Enrique Mestre, Diego F. Torres,Emma de Ona Wilhelmi, Josep Marti

Monthly Notices of the Royal Astronomical Society(2022)

Cited 3|Views1
No score
Abstract
The Cherenkov Telescope Array will provide the deepest survey of the Galactic Plane performed at very-high-energy gamma-rays. Consequently, this survey will unavoidably face the challenge of source confusion, i.e., the non-unique attribution of signal to a source due to multiple overlapping sources. Among the known populations of Galactic gamma-ray sources and given their extension and number, pulsar wind nebulae (PWNe, and PWN TeV halos) will be the most affected. We aim to probe source confusion of TeV PWNe in forthcoming CTA data. For this purpose, we performed and analyzed simulations of artificially confused PWNe with CTA. As a basis for our simulations, we applied our study to TeV data collected from the H.E.S.S. Galactic Plane Survey for ten extended and two point-like firmly identified PWNe, probing various configurations of source confusion involving different projected separations, relative orientations, flux levels, and extensions among sources. Source confusion, defined here to appear when the sum of the Gaussian width of two sources is larger than the separation between their centroids, occurred in ∼30% of the simulations. For this sample and 0.5○ of average separation between sources, we found that CTA can likely resolve up to 60% of those confused sources above 500 GeV. Finally, we also considered simulations of isolated extended sources to see how well they could be matched to a library of morphological templates. The outcome of the simulations indicates a remarkable capability (more than 95% of the cases studied) to match a simulation with the correct input template in its proper orientation.
More
Translated text
Key words
instrumentation: detectors,ISM: supernova remnants
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined