NASSP Masters 5003F - Computational Astronomy Lecture 19 EPIC background Event lists and selection The RGA Calibration quantities Exposure calculations

NASSP Masters 5003F - Computational Astronomy Background Background from instrumental noise –Worse at low energies & higher chip temperatures. X-ray background –Cosmic –Fluorescence Si of course, but also Al and Cu from support structure. Particle background –Hard, penetrating – “cosmic rays”. Fairly constant in time; Fairly isotropic. –Soft protons (~100 eV). Flaring time behaviour. Funnelled by the mirrors. These weren’t suspected before launch! A major headache, because too strong a flare can damage the CCDs.

NASSP Masters 5003F - Computational Astronomy Background examples Note the background in the masked areas. Mostly from flares. Cu fluorescence.Instrumental noise at low energy. (Masking here is done via software.) MOS pn dec RA

NASSP Masters 5003F - Computational Astronomy Background – what to do with it Significance of background depends on what you want to do. –Spectra: obviously one needs to know the spectrum of the background as well as possible. –Images, in particular source detection and flux measurement: spatial properties of the background are important. Cosmic ray, x-ray and flares all have different spatial behaviour – so working out the proportions is important. –Time series: Soft proton flares dominate the problem.

NASSP Masters 5003F - Computational Astronomy Other mainly spatial problems with EPICs: Optical loading from a bright visible-light source (filters minimize this) Single-reflection arcs from far-field sources

NASSP Masters 5003F - Computational Astronomy Event lists In high-energy astronomy, we deal not with voltages or brightnesses (essentially floating-point quantities) but with lists of events – 1 event per photon. Each event comes with the following data: –Its pixel position on the CCD. –If necessary, the number of the CCD. –Its frame number. –Its energy. (XMM: the column is called PI.) Maybe also: a quality flag, event pattern, etc. In XMM output the events are stored in a table in a FITS file.

NASSP Masters 5003F - Computational Astronomy Event selection The aim is to separate ‘interesting’ events from ‘boring’ events – eg divide the events into those which probably come from a source and those which don’t. All events GoodBad r E t Define a selection volume Limits in defining volume shapes. Problems integrating over overlapping volumes. FITS format for storing selections: Data SubSpace (DSS)

NASSP Masters 5003F - Computational Astronomy Diagnostic plots: It’s helpful to plot 2 of the event coordinates – here energy vs time. PN Cu fluorescence line Al fluorescence line Time Photon energy ‘Soft proton’ bursts

NASSP Masters 5003F - Computational Astronomy Diagnostic plots: MOS 1 Al fluorescence line ‘Gatti’ events

NASSP Masters 5003F - Computational Astronomy V Gatti process – a kind of dithering. Histogram of events with voltage V. ADC levels are analog - thus not evenly spaced. Distorted digitized histogram. + V t ADC - Undistorted histogram. V t =

NASSP Masters 5003F - Computational Astronomy The Reflection Grating Spectrometer (RGA) Each MOS has one. They divert about ½ the x-rays. Diffraction grating  array of 9 CCDs. Pixel position in the dispersion direction is a function of x-ray energy. – But not a linear function (I think there is a cosine term in it). Energy resolution is much sharper than via amount of charge the photons generate. Spectral orders overlap – –but the 2 nd order has even finer resolution.

NASSP Masters 5003F - Computational Astronomy RGA – plot showing the event pixel locations:

NASSP Masters 5003F - Computational Astronomy The ‘banana plot’

NASSP Masters 5003F - Computational Astronomy An example RGS spectrum: Spectral resolution: about 2 eV

NASSP Masters 5003F - Computational Astronomy An example EPIC spectrum: Spectral resolution: about 100 eV

NASSP Masters 5003F - Computational Astronomy Charge redistribution Photons of a single, narrow energy give rise to broadened charge redistribution spectrum. –Partly because of Poisson (quantum) statistical variation; –Partly because of smearing out during the transfer of charges from row to row during readout. The relation between true spectrum S and measured spectrum S': R is called the redistribution matrix (RM). As the chips degrade with age (due mostly to particle impacts), the RM changes and has to be recalibrated. The philosophy with x-ray spectra is not to subtract background or deconvolve RM, but to begin with a model, and add background and RM-convolve this before comparing it with the measured spectrum. –See the program XSPEC.

NASSP Masters 5003F - Computational Astronomy MOS RM cross-section at 800 eV Energy of the x-rays

NASSP Masters 5003F - Computational Astronomy Evolution of the energy dispersion Black: pn Red and Green: the MOS chips MOS temperatures were lowered here. 1.5 keV 6.0 keV