English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern X-ray telescopes have detected hundreds of thousands of X-ray sourcesin the universe. However, current methods to classify these sources using theX-ray data themselves suffer problems - detailed X-ray spectroscopy ofindividual sources is too time-consuming, while hardness ratios often lackaccuracy, and can be difficult to use effectively. These methods fail to usethe power of X-ray CCD detectors to identify X-ray emission lines anddistinguish line-dominated spectra (from chromospherically active stars,supernova remnants, etc.) from continuum-dominated ones (e.g., compact objectsor active galactic nuclei [AGN]). In this paper, we probe the use of artificialneural networks (ANN) in differentiating Chandra spectra of young stars in theChandra Orion Ultradeep Project (COUP) survey from AGN in the Chandra DeepField South (CDFS) survey. We use these surveys to generate 100,000 artificialspectra of stars and AGN and train our ANN models to separate the two kinds ofspectra. We find that our methods reach an accuracy of approx. 92% inclassifying simulated spectra of moderate-brightness objects in typicalexposures, but their performance slightly decreases on the observed COUP andCDFS spectra (approx. 91%), due in large part to the relatively high backgroundof these long-exposure datasets. We also investigate the performance of ourmethods with changing properties of the spectra such as the net source counts,the relative contribution of background, the absorption column of the sources,etc. We conclude that these methods have substantial promise for application tolarge X-ray surveys.

Distinguishing X-ray Stars vs. AGN through ML