Abstract
X-ray sources with very few counts can be identified with low-noise X-ray detectors such as the Advanced CCD Imaging Spectrometer on board the Chandra X-ray Observatory. These sources are often too faint for parametric spectral modeling using well-established methods such as fitting with XSPEC. We discuss the estimation of apparent and intrinsic broadband X-ray fluxes and soft X-ray absorption from gas along the line of sight to these sources, using nonparametric methods. Apparent flux is estimated from the ratio of the source count rate to the instrumental effective area averaged over the chosen band. Absorption, intrinsic flux, and errors on these quantities are estimated from comparison of source photometric quantities with those of high signal-to-noise spectra that were simulated using spectral models characteristic of the class of astrophysical sources under study. The concept of this method is similar to the long-standing use of color-magnitude diagrams in optical and infrared astronomy, with X-ray median energy replacing color index and X-ray source counts replacing magnitude. Our nonparametric method is tested against the apparent spectra of 2000 faint sources in the Chandra observation of the rich young stellar cluster in the M 17 H II region. We show that the intrinsic X-ray properties can be determined with little bias and reasonable accuracy using these observable photometric quantities without employing often uncertain and time-consuming methods of nonlinear parametric spectral modeling. Our method is calibrated for thermal spectra characteristic of stars in young stellar clusters, but recalibration should be possible for some other classes of faint X-ray sources such as extragalactic active galactic nuclei.
Original language | English (US) |
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Pages (from-to) | 1760-1771 |
Number of pages | 12 |
Journal | Astrophysical Journal |
Volume | 708 |
Issue number | 2 |
DOIs | |
State | Published - 2010 |
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science