|Title||XMM-RGS Spectroscopy of the Magellanic Cloud Supernova Remnant Sample.|
|Author||Dr Albert Brinkman|
|Description||GT- We propose to observe a sample of the supernova remnants in the Magellanic Clouds. The sample is comprised of E0102-72.3, N49, N103B, PSR0540-69.3 and 0519-69.0. These SNRs form a biased sample of the SNR population (they are drawn from the brighter and smaller ends of the distribution), but the sample spans the spectrum of origin, age and dynamical conditions: Two are type Ia, two are type II (one with a pulsar) and the other is evolved and containing a known soft gamma repeater. This set, studied using the RGS, promises unambiguous spectra and line diagnostics that provide strong clues that address the composition and physical state of the interstellar medium within the MCs, as well as that of the ejected stellar material.|
|Publication||No observations found associated with the current proposal|
|Instrument||EMOS1, EMOS2, EPN, OM, RGS1, RGS2|
|Mission Description||The European Space Agency's (ESA) X-ray Multi-Mirror Mission (XMM-Newton) was launched by an Ariane 504 on December 10th 1999. XMM-Newton is ESA's second cornerstone of the Horizon 2000 Science Programme. It carries 3 high throughput X-ray telescopes with an unprecedented effective area, and an optical monitor, the first flown on a X-ray observatory. The large collecting area and ability to make long uninterrupted exposures provide highly sensitive observations.
Since Earth's atmosphere blocks out all X-rays, only a telescope in space can detect and study celestial X-ray sources. The XMM-Newton mission is helping scientists to solve a number of cosmic mysteries, ranging from the enigmatic black holes to the origins of the Universe itself. Observing time on XMM-Newton is being made available to the scientific community, applying for observational periods on a competitive basis.
|Publisher And Registrant||European Space Agency|
|Credit Guidelines||European Space Agency, 2003-01-04T00:00:00Z, 011300, 17.56_20190403_1200. https://doi.org/10.5270/esa-7to9arn|