Astronomy

What does less than one count from an x-ray detector mean? (Swift BAT detector)

What does less than one count from an x-ray detector mean? (Swift BAT detector)



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I'm reading a paper about a recent x-ray burst from a suspected magnetar (A. Dai et al 2016) where they show a light curve of a burst that lasted about 10ms. (Their figure 1). The value of count rate for the peak of the burst from the graph is $6 ext{ counts/s}$ over a timespan of less than $0.01s$. This corresponds to about $6*0.01=0.06 ext{ counts}$ for that time period. What does this mean? Is it not counts of photons? What is 0.06 photons? Is there a step in the data analysis I'm missing?

(Fig. 1 Swift/BAT mask-weighted light curves in different energy bands)


I think there is a missing piece of information. The BAT is a coded mask telescope. The imaging is done by photons passing through a mask and falling onto an array of 32768 detectors. http://swift.gsfc.nasa.gov/about_swift/bat_desc.html

The "mask-weighted" light curve is produced after a complex ray tracing exercise using an estimate of the position of the source. Looking at some of the software specifications (eg https://heasarc.gsfc.nasa.gov/ftools/caldb/help/batmasktaglc.html ) it seems that the count rate is divided by the number of active detectors.

So in this case it looks like the detection rate was more like 200,000 counts/s in the peak 2ms of the event. There would be 400 detected counts, assuming there are no other corrections for sensitivity or vignetting. However, I think there probably are - the mask must take out about 50% of these counts and there are gaps between the detectors. A background rate has also been subtracted. So overall I guess that the first point in the burst is due to around 100 detected photons, and so on.


What does less than one count from an x-ray detector mean? (Swift BAT detector) - Astronomy

The task batsurvey performs basic analysis of BAT "survey" data, also known as detector plane histograms or DPHs. As opposed to event data, BAT survey data is accumulated in histograms on-board the spacecraft, with typical integration times of 300 seconds. An 80-channel binned spectrum is recorded for each of the 32768 detectors and saved in the DPH files.

The batsurvey task reduces a set of "raw" observed DPHs. Most importantly, it performs data screening that the BAT team has found vital for obtaining good quality results. It produces sky images and source fluxes for each independent "snapshot," corresponding to a single pointed visit by Swift. Users may choose a set of independent energy bins, and the batsurvey will record the images and fluxes in each of those bands separately. A practical number of energy bands is eight to twelve.


Frequently Asked Questions

The following answer is from our Education and Public Outreach pages:

What's in a name? Many people have asked, "What does Swift stand for?". In truth it does not stand for anything. The "Swift" name is not an acronym. The observatory is actually named after a small, nimble bird, the same bird that appears in the Swift logo. The Swift observatory is built to be agile, quickly turning to point its instruments at gamma-ray bursts and relaying the burst locations to the ground within seconds, much like the Swift bird might grab up insects as it flies through the sky. Thus the Swift bird serves as a metaphor for the Swift observatory.

How does Swift turn so quickly toward a newly detected Gamma Ray Burst? What kind of fuel is used? Where are these devices located on the spacecraft?

The Swift observatory can slew 50 degrees in less than 75 seconds to quickly observe the afterglow from a Gamma Ray Burst (GRB). Swift is able to rapidly maneuver and point using six on-board "momentum wheels." Momentum wheels, also known as reaction wheels, are used by many satellites to keep the spacecraft pointed, as well as move the spacecraft to a new pointing location (this is called "slewing"). Swift doesn't use fuel to slew from object to object. Instead, the momentum wheels are set spinning, and this exerts torque forces on Swift that control its pointing direction.

The quote below, from the free, on-line encyclopedia, Wikipedia, describes how momentum wheels work. Following that is a more detailed technical description of how Swift maneuvers.

"Typically a spacecraft will have several momentum wheels oriented along orthogonal axes, and when it wishes to change its rotation along those axes it will increase or decrease the spin of the momentum wheels in the opposite direction. When the spacecraft achieves its desired orientation, it can then halt its rotation by braking the momentum wheels by the same amount.

Since the momentum wheel is a small fraction of the spacecraft's total mass, easily-measurable changes in its speed provide very precise changes in angle. It therefore permits very precise changes in a spacecraft's attitude. For this reason, momentum wheels are often used to aim spacecraft with cameras or telescopes." (-- Wikipedia, Momentum wheel entry)

Normal Swift operations involve pointing to preselected regions of the sky, while the Burst Alert Telescope (BAT) searches a 2 steradian area for GRBs. When a burst is detected, the BAT collects data as it determines the coordinates of the burst. These coordinates are then transferred to the spacecraft's attitude control system, which immediately begins the reorientation maneuver to point the narrow field of view instruments (XRT and UVOT) at the burst location. Upon notification of the GRB, the on-board attitude control system autonomously engages Maneuver Mode, initiating a rapid slew to the indicated target. While autonomously slewing toward the GRB, the spacecraft must obey four constraints: avoid pointing toward the Sun, Moon, Earth, and in the direction of satellite motition (satellite ram). An onboard algorithm called the Predict Ahead Planner Algorithm (PAPA) determines the quickest slew maneuver path to obey all pointing constraints.

Swift's rapid maneuvering is implemented with a zero-momentum biased system that uses six reaction wheels. The six high-torque reaction wheels are arranged in a hexagon about the roll axis and raised 15 degrees above the pitch/yaw plane. This arrangement is capable of accelerating the spacecraft angularly and achieving a maximum rate of 2.4 degress to 3.3 degress per sec, depending on the target location relative to the combined wheel torque distribution. Wheel momentum is unloaded by three orthogonal magnetic torquers, using a 3-axis magnetometer for field sensing. Attitude determination is performed onboard using star trackers for fine pointing and a high quality ASC ring laser gyroscope (RLG) to sense rates.

Is the Swift satellite part of the SWIFT Corporation?

No. Swift is a multiwavelength observatory in space, run by the U.S. government's National Aeronautics and Space Administration (NASA). If you are looking for the Society for Worldwide Interbank Financial Telecommunication (SWIFT), please try their Web site: http://www.swift.com/.

Software

    I found a software bug. Is it known?

We maintain a list of known bugs and pipeline problems that affect the data. If you encounter a bug, consult the Bugs List to see if your bug is known. If so, you will get pointers and learn the status of the bug fix. If you have encountered a new bug, contact us via our Feedback form.

What should I be careful of when using BAT software tools?

The BAT Digest contains a comprehensive listing of caveats and known bugs for BAT software tools.

Will I be able to use existing software to analyze data from BAT, XRT and UVOT spectra?

Yes. The Swift analysis software for each instrument produces spectra and response matrices that are understood by XSPEC. In addition, UVOT spectra can be analyzed using IRAF.

Will I be able to use standard time series analysis tools for Swift data?

Yes. The Swift analysis software for each instrument includes tasks that operate on event files and generate standard lightcurve files that can then be read into XRONOS timing analysis tools.

Is there a way to use xselect in a script rather than interactively?

Yes. First, create an ASCII file containing the input you would normally supply to xselect. (See example, below). Give the file name an ".xco" extension. You may then run the file by typing:

Here is an example of what might be in "file.xco":

    read event
    ./GRB05124
    sw00103647001xlrb1po_ufre.evt
    yes
    filter time file ./GRB05124/sw00103647001xlrb1po_clgti.fits
    filter grade "0-5"
    extract events
    yes
    save events ./GRB05124/xselect12073evt.flt
    yes
    quit
    no

For more information on xselect, see the Xselect User's Guide.

Data and Observations

    What should I be careful of when using XRT data?

Two general pointers when using XRT data from this early stage of the mission:

  • Data from a burst can end up in the wrong directory. This occurs because XRT is in "manual mode" while performing calibration observations. If an AT sequence begins while XRT is in the midst of a calibration observation, then the earliest data from the afterglow will appear in the same directory as the calibration observation in progress. A fix for this problem is being worked by the SDC.
  • The XRT is operating at a warmer temperature than planned. The thermoelectric cooler that was to keep XRT at a constant and low operating temperature failed early in the mission. The XRT detector will actually operate at a temperature close to the ASCA value. While the XRt will still achieve its primary science goals at the new warmer temperature, the instrument will need to be calibrated at a variety of operating temperatures. This calibration is in progress.

What should I be careful of when using UVOT data?

UVOT data from before velocity-aiding was disabled on the spacecraft suffered from a residual drift, which is actually due to the apparent shift in stars' positions due to velocity aberration of the spacecraft about the Earth. Pipeline software has been rewritten to correct this problem on the ground. When UVOT data is reprocessed the drift will be removed. A second issue is the slight misalignment (on the order of 5 arcsec) between one snapshot on a position and the next. Current software does not correct for this slight mispointing, so co-added images will have blurred or multiple stars in them instead of a single point source per star. Software will be updated to handle this misalignment.

Where can I obtain Swift data?

How can I find a specific file for a specific GRB in the Swift archive?

The names and locations of standard files produced for GRB observations are listed in sections 3 and 4 of the Overview of the Swift Data Archive (PDF file, 196 Kb).

To retrieve data for a specific burst, go to the special Swift interface of Browse. (Browse can also be found under the "HEASARC Quick Links" menu on any Swift Web page. Once you're on the Browse page, select the Swift interface.)

Select the GRB that you are interested in. Upon selection, the target id box will be filled with the target-id. Next, click on Start Search. The results page you will see is from Browse, which may be familiar to users of other missions served by the HEASARC.

To actually retrieve the data, click the box on the right for the observation you are interested in, and use one of the data retrieval options listed at the bottom of the page. The results page gives an 11 digit number for each observation, known as its observation id. (Note: most GRBs have many separate observations, and therefore many observation IDs.) For more information on observation IDs and data organization in the Swift Archive, see the Overview of the Swift Data Archive (PDF file, 196 Kb).

If you wish to use FTP instead of Browse to retrieve the data, you can FTP to legacy.gsfc.nasa.gov as user "anonymous" and cd to swift/data. From there, follow the directory structure outlined in the Overview of the Swift Data Archive guide, mentioned above, to retrieve the file(s) you are interested in.

The sky positions of Swift GRBs will be distributed by the Gamma-Ray Burst Coordinates Nework (GCN). The GCN sends out both Notices and Circulars to subscribe, you will need to register with the GCN: http://gcn.gsfc.nasa.gov/invitation.html. The sky positions that are received from the satellite will be distributed in the GCN Notices. Refinements to the sky position of a burst will be distributed in either the GCN Notices or GCN Circulars, as appropriate. We recommend subscribing to both.

Data Analysis

    Where can I get pointers on UVOT data analysis?

See the "UVOT" section of our Data Analysis page, which has links to the "UVOT User's Guide," "UVOT Data Analysis Threads," and the "UVOT Digest."

Where can I get pointers on XRT data analysis?

See the "XRT" section of our Data Analysis page, which has links to the "XRT User's Guide" and the "XRT Digest," among other resources.

Where can I get pointers on BAT data analysis?

See the "BAT" section of our Data Analysis page, which has links to the "BAT User's Guide," "BAT Data Analysis Threads," and the "BAT Digest."

Where can I find the BAT enable-disable map for a given observation?

BAT enable-disable maps are presently organized by *time*, not observation number. They are stored in the bat/hk directory of an observation, and follow the format: swt[obsid]bdecb.fits. The "swt" at the beginning of the file name indicates that the value in the file name is the mission elapsed time of the first map in the file.

It is quite possible for an enable map generated during one observation to be included in the subdirectory of a different observation. The maps are produced on-board as needed, not according to any ground-defined schedule. The ground processing pipeline is meant to find the nearest map in time.

A reprocessing of existing Swift data is planned soon, which will, among other things, merge any files into a single one, and rename each one according to the observation number. Until then it is safe to take the first file. This is usually appropriate for GRBs since the GRB occurs at the beginning of the first observation snapshot.

What is the confidence of the absolute timing of BAT data, and how is the absolute timing on Swift verified?

For an introduction to the time systems and keywords used in Swift FITS files, see our Timing Guide. In order to achieve an accurate absolute time for Swift data, you will first want to apply the UT Correction Factor (UTCF). The value of UTCF at the start time of the observations appears in the FITS files in the keyword UTCFINIT and is accurate to +/- few tenths ms. You can get a more accurate correction than that by using the tool "swifttime" which is part of the Swift software package, or using the Web tool X-time which now also converts Swift times between several time systems.

There is a description of the testing of the XRT absolute timing accuracy in SPIE.

This of course relies on the absolute accuracy of the spacecraft clock. The requirement was that the clock (Mission Elapsed Time plus UTCF) be kept within 200 microseconds of UTC, and it has been comfortably doing so throughout the mission, sometimes much better.

The MOC measures this clock offset routinely, and this data is stored in a calibration file, the clock offset log. This file is part of the Swift caldb and is used for fine the time corrections. See the Swift CALDB for links and documentation. The clock offset log is documented under "Swift Common Calibration Products".

The BAT timetags photons with a relative precision of 100 microseconds.

Once the UTCF correction is applied to BAT data, you should be confident to 0.2 msec. If you want times better than that, use the "swifttime" tool or X-time Web tool (mentioned above), both of which incorporate the clock offset log.

How do I combine different XRT data sets for spectral analysis

XRT data from different data sets can be combined using the following procedure.

Use XSelect to sum the individual event files. Read all the cleaned event files into XSelect and extract the total event file. The "copyall=yes" option must be used when extracting the total event file. For example:

> extract events copyall=yes

Sum the individual exposure maps. The total exposure map can be used for spectral analysis of the total spectrum. You can sum the individual exposure maps using, for example, the command "sum_ima" in the XImage package. (See The XImage User's Guide for details). The following XImage commands may be useful:

XIMAGE>read sw00036093001xpcw2po_ex.img XIMAGE>read sw00036093002xpcw2po_ex.img XIMAGE>sum_ima XIMAGE>save_ima XIMAGE>read sw00036093003xpcw2po_ex.img XIMAGE>sum_ima XIMAGE>save_ima XIMAGE>write_ima/file="sum_01_02_03_ex.img" template=all XIMAGE>exit

At this point one can extract the spectrum (with XSelect) and generate its ARF (with "xrtmkarf" using the option "expofile=sum_01_02_03_ex.img".

Xrtpipeline sometimes returns errors that are similar to the example below.

This error (cannot retrieve file from CALDB) usually indicates that there is a problem with the way that CalDB is set up on your system. The first thing to check is if you have the most recent Swift/XRT and Swift/mis (common calibration products) CalDB files The most recent calibration data can be obtained from http://swift.gsfc.nasa.gov/docs/heasarc/caldb/swift/. You must install both the XRT and the common calibration products CalDBs in order to run xrtpipeline. Second, check that your CALDB environment variable is pointing to the correct location.

You can test your local installation of CalDB by temporarily setting your CALDB environment variable to

and rerunning xrtpipeline. You will need an active internet connection to do this. If xrtpipeline works using the online version of CalDB then check your local CalDB installation for errors.

Guest Investigator Program

    Does Swift have a Guest Investigator program? What are the guidelines? How can I apply?

Swift has a Guest Investigator program. See our Proposals & Tools page for further details.

When will I get the money from my Swift grant?

The Swift Guest Investigator program awards grants to once a year to support Swift-related projects. If you were a successful proposer, you may check the status of your grant from the GSFC Grant Status page. For more information on the Swift Guest Investigator program, see our Proposals and Tools Web page.

What are the Swift Key Projects?

The Swift Key Projects are research programs that the Swift Science Team has committed to perform with data from the BAT, XRT and UVOT. NASA will not accept Guest Investigator proposals that duplicate the Key Projects.

Citations & References

    Are there published references for the Swift instruments?

Yes. See our Swift Instrument Description Papers Web page you can also find the page under the Swift Results button on the Swift homepage.

Is there a listing of published Swift results that I can consult?

Yes. See the Swift Results in the center of the lower menu bar on the Swift homepage.

If you have a question about Swift, please contact us via the Feedback form.


Introduction

Most currently operating astronomical X-ray instruments, e.g. Chandra-ACIS, XMM-Newton-EPIC, Swift-XRT, and Suzaku-XIS, employ variants of CCD technology as their focal plane detector. CCDs have performed successfully in this application for more than two decades and the technology is in a high state of maturity. Next-generation X-ray telescope mission concepts, such as Athena [1], are expected to have effective areas more than an order of magnitude greater than XMM-Newton, the largest currently orbiting X-ray telescope. Given similar plate scale (field of view per pixel), such an increase in effective area will require a proportional increase in detector read speed to ensure that X-ray photons do not saturate the detector. The basic design of the CCD prevents it from achieving frame rates that are fast enough to avoid saturation. X-ray optimized Hybrid CMOS detectors (HCDs) are a specific type of active pixel sensor that have shown significant promise for meeting the focal plane array requirements set by proposed next-generation X-ray telescopes.

Through a joint collaboration with Teledyne Imaging Sensors (TIS), the Pennsylvania State University (PSU) X-ray Instrumentation Group is currently developing X-ray optimized Hybrid Visible Silicon Imager (HyViSI) HCDs [2], [3]. The cut-away schematic of the HCD shown in Fig. 1 displays the hybrid structure of these detectors. In this architecture, the front-side absorber layer and the backside readout electronics layer can be individually optimized because they are separate substrates before bump bonding. This enables the assembled HCD to be both deeply depleted and retain the benefits of being an active pixel sensor.

These devices exhibit better noise performance at high pixel rates (1 MHz) than CCDs [4], very low power consumption ( < 300 mW per channel at a pixel rate of 100 kHz, per TIS documentation), and are radiation hard and inherently resistant to micrometeoroid damage. Radiation damage in X-ray imagers manifests as degraded charge transfer efficiency due to radiation-induced charge carrier traps caused by lattice displacement. HCDs exhibit greater radiation hardness than CCDs because in the active pixel sensor design, photon-generated charge is transferred to the in-pixel readout node over microscopic distances through the pixel rather than over macroscopic distances across the width of the detector as with a CCD. When charge is transferred through a greater length of silicon, it will encounter proportionally more traps and lose more charge carriers along the way. Furthermore, HCDs are expected to be more resistant to micrometeoroid damage than front illuminated CCDs because their gate structures are located underneath the absorber layer, where they are shielded from impacts. Their faster read speeds will mitigate saturation effects when used with large effective area optics and also translate into reduced demand for focal plane cooling due to decreased dark current per readout. The low power consumption of HCDs translates into decreased self-heating, which reduces cooling system power demand and reduces instrument mass and cost. The inherent radiation hardness and micrometeroid damage resistant architecture of HCDs will lead to increased on-flight reliability and mission longevity, when compared to current-generation instruments. The read noise, soft X-ray energy resolution, and dark current of four such HCDs have already been measured at PSU [5].

In this paper, we describe the modeling and measurement of the X-ray quantum efficiency (QE) of one TIS device, serial number H1RG-167. The QE of a detector is the fraction of incident radiation that registers a response in the detector and is a function of photon energy. In general, improvements in detector QE yield direct improvements in overall sensitivity and lead to more favorable trade-offs between signal to noise, integration time, and a telescope׳s primary optic size/mass. For both the model and measurement, we report a line intensity weighted average of the QE at the 5.89 and 6.49 keV photon energies emitted by a 55 Fe X-ray source.


Acknowledgements

GRB research at Caltech is supported in part by funds from NSF and NASA. We are, as always, indebted to S. Barthelmy and the GCN. The VLA is operated by the National Radio Astronomy Observatory, a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. A.M.S. and S.B.C. are supported by NASA Graduate Research Fellowships. E.B. and A.G.-Y. acknowledge support by NASA through a Hubble Fellowship grant. D.N.B. and J.A.N. acknowledge support by NASA.


Author information

Affiliations

Department of Astrophysical Sciences, Princeton University, Ivy Lane, Princeton, New Jersey 08544, USA

A. M. Soderberg, E. Berger & A. Burrows

Carnegie Observatories, 813 Santa Barbara Street, Pasadena, California 91101, USA

Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK

K. L. Page, A. P. Beardmore & P. T. O’Brien

Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK

P. Schady, M. J. Page & M. de Pasquale

Physics and Astronomy Department, Dartmouth College, Hanover, New Hampshire 03755, USA

Astronomy Department, University of Wisconsin, 475 North Charter Street, Madison, Wisconsin 53706, USA

Department of Astronomy, Nanjing University, Nanjing 210093, China

Department of Astronomy, 105-24, California Institute of Technology, Pasadena, California 91125, USA

E. O. Ofek, A. Rau, J. D. Simon, M. Kasliwal & S. R. Kulkarni

Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, Pennsylvania 16802, USA

A. Cucchiara, P. Brown, D. N. Burrows, D. B. Fox, P. Mészáros & J. Racusin

Faculty of Physics, Weizmann Institute of Science, Rehovot 76100, Israel

Radio Astronomy Laboratory, University of California, Berkeley, California 94720, USA

Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, Arizona 85721, USA

Department of Astronomy, University of Texas at Austin, Austin, Texas 78712, USA

NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

Department of Physics and Astronomy, York University, Toronto, Ontario M3J 1P3, Canada

Hartebeestehoek Radio Observatory, PO Box 443, Krugersdorp, 1740, South Africa

Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA

Space Radiation Laboratory, 220-47, California Institute of Technology, Pasadena, California 91125, USA

Department of Astronomy, University of Virginia, PO Box 400325, Charlottesville, Virginia 22904, USA

CRESST and NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

J. R. Cummings, N. Gehrels, S. Immler & H. A. Krimm

Institute of Astronomy and Department of Physics, National Tsing Hua University, Hsinchu, Taiwan

Universities Space Research Association, 10211 Wincopin Circle, #500, Columbia, Maryland 21044, USA

School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK

Theoretical Astrophysics, 130-33, California Institute of Technology, Pasadena, California 91125, USA

Max-Planck-Institut für Astrophysik, D-85748 Garching, Germany

University of Amsterdam, Astronomical Institute ‘Anton Pannekoek’, Kruislaan 403, 1098SJ, Amsterdam, The Netherlands

Department of Astronomy and Astrophysics, University of Chicago, 5640 S. Ellis Avenue, Chicago, Illinois 60637, USA

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Geant4 simulations of soft proton scattering in X-ray optics

Low energy protons (< 300 keV) can enter the field of view of X-ray telescopes, scatter on their mirror surfaces at small incident angles, and deposit energy on the detector. This phenomenon can cause intense background flares at the focal plane decreasing the mission observing time (e.g. the XMM-Newton mission) or in the most extreme cases, damaging the X-ray detector. A correct modelization of the physics process responsible for the grazing angle scattering processes is mandatory to evaluate the impact of such events on the performance (e.g. observation time, sensitivity) of future X-ray telescopes as the ESA ATHENA mission. The Remizovich model describes particles reflected by solids at glancing angles in terms of the Boltzmann transport equation using the diffuse approximation and the model of continuous slowing down in energy. For the first time this solution, in the approximation of no energy losses, is implemented, verified, and qualitatively validated on top of the Geant4 release 10.2, with the possibility to add a constant energy loss to each interaction. This implementation is verified by comparing the simulated proton distribution to both the theoretical probability distribution and with independent ray-tracing simulations. Both the new scattering physics and the Coulomb scattering already built in the official Geant4 distribution are used to reproduce the latest experimental results on grazing angle proton scattering. At 250 keV multiple scattering delivers large proton angles and it is not consistent with the observation. Among the tested models, the single scattering seems to better reproduce the scattering efficiency at the three energies but energy loss obtained at small scattering angles is significantly lower than the experimental values. In general, the energy losses obtained in the experiment are higher than what obtained by the simulation. The experimental data are not completely representative of the soft proton scattering experienced by current X-ray telescopes because of the lack of measurements at low energies (< 200 keV) and small reflection angles, so we are not able to address any of the tested models as the one that can certainly reproduce the scattering behavior of low energy protons expected for the ATHENA mission. We can, however, discard multiple scattering as the model able to reproduce soft proton funnelling, and affirm that Coulomb single scattering can represent, until further measurements at lower energies are available, the best approximation of the proton scattered angular distribution at the exit of X-ray optics.

This is a preview of subscription content, access via your institution.


Abstract

The Hard X-ray Detector (HXD) on board Suzaku covers a wide energy range from 10 keV to 600 keV by the combination of silicon PIN diodes and GSO scintillators. The HXD is designed to achieve an extremely low in-orbit background based on a combination of new techniques, including the concept of a well-type active shield counter. With an effective area of |$142 ,mathrm^<2>$| at 20 keV and |$273 ,mathrm^<2>$| at 150 keV, the background level at sea level reached |$sim 1 imes 10^ <-5>,mathrm ,mathrm^ <-1>,mathrm^ <-2>,mathrm^<-1>$| at 30 keV for the PIN diodes, and |$sim 2 imes 10^ <-5>,mathrm ,mathrm^ <-1>,mathrm^ <-2>,mathrm^<-1>$| at 100 keV, and |$sim 7 imes 10^ <-6>,mathrm ,mathrm^ <-1>,mathrm^ <-2>,mathrm^<-1>$| at 200 keV for the phoswich counter. Tight active shielding of the HXD results in a large array of guard counters surrounding the main detector parts. These anti-coincidence counters, made of |$sim 4 ,mathrm$| thick BGO crystals, have a large effective area for sub-MeV to MeV |$gamma$| -rays. They work as an excellent |$gamma$| -ray burst monitor with limited angular resolution ( ⁠|$sim 5^$|⁠ ). The on-board signal-processing system and the data transmitted to the ground are also described.


Fig. 2

Criteria for the consistency check between EPI values from both sides (red lines). Color maps show 2-D histograms of the EPIs of HXI2 obtained from the ground calibrations irradiated by x-rays from Am 241 .

After finishing the event reconstruction processes in one layer, hits in five layers are reconstructed as a photon event. In this process, single-hit events detected in a single layer and double-hit events at the combination of one CdTe-DSD and one DSSD with an energy of DSSD consistent with a fluorescence line of Cd or Te are accepted. Otherwise, no values are assigned to PI, hit positions in the final event list and hence discarded in the following processes. In terms of physical processes, this algorithm accepts photoabsorption events and fluorescence escape events, where K-shell fluorescence photons of Cd or Te escaped from CdTe-DSDs are photoabsorbed in DSSDs. Compton scattered events are ignored in current implementation because a fraction of such events composed of Si–Si double hits or Si-CdTe nonfluorescence double hits in total events are less than ∼ 1 % in the ground data obtained with Am 241 and Ba 133 radioisotopes.

The event reconstruction algorithm described above must be tested with in-flight data because it was determined based on the ground data analysis. For the purpose of investigating whether this algorithm properly rejects the background data without excluding much of the real x-ray signals, fractions of accepted events (black) and discarded events (red and blue) of the ground calibration data (trigger rate ≃ 630 Hz ), Crab data (trigger rate ≃ 610 Hz ), and the NXB data (trigger rate ≃ 35 Hz ) are shown in Fig. 3. In this figure, the denominator of the fractions is the number of events, in which at least one signal exceeds the analysis thresholds. The nonsignal events account for 5.341 % ± 0.007 % of ground calibration data, 25.27 % ± 0.02 % of Crab data, and 97.680 % ± 0.008 % of the NXB data. These nonsignal events are thought to originate from noise triggers and soft photons below the analysis thresholds because trigger thresholds are set to be as low as possible within a range where the dead-time fraction due to noise triggers does not affect the scientific observations. The reason it is very high in the NXB data is simply because its trigger rate by the external photon and particle background is much lower than those by the instrumental noise (typically a few Hz).


Change log

  • 2021 April 15 - Made a small change to the image generation, so that images larger than 1000×1000 pixels can be created. Also output the combined event lists (with WCS reprojected for images >1000×1000). Please read the warning before using these event lists.
  • 2020 November 27 - Made a small change to source detection, which improves pile-up handling when fitting all bands: the pile-up parameters of the PSF from the total band are used without refitting for the sub-bands.
  • 2020 November 12 - Added source detection as a new product.
  • 2020 July 20 - made several revisions:
    • Provided a new grade selection option for spectra and light curves: &lsquoRestricted&rsquo.
    • Provided a facility to register your email address, which will become mandatory in September.
    • Moved the &lsquoemail&rsquo box higher up the form, and enabled the ability to remember your email address, to support ease of use when provision of this becomes mandatory.
    • Added various new options to the form, as described above in the appropriate sections above.
    • Added new products: colour images and astrometric positions.
    • Made several changes to the light curves (see the light curve documentation for more information)
      • Systematic errors are now added to WT-mode data where available.
      • Where the WT-mode centroid cannot be constrained, the points are hidden by default.
      • The list of background sources is constructed once, on a deep image, or is taken from 1SXPS.
      • Pile-up handling has been made more efficient.
      • The Verner et al. cross-sections are now used for absorption models.
      • A custom RMF is now created for each spectrum. This reflects the fact that the appropriate RMF to use is time-dependent. The custom RMF is created by identifying the appropriate matrix in the CALDB for each observation, and then creating a weighted mean of these, weighting by the number of source counts in each observation.
      • The list of background sources is constructed once, on a deep image, or is taken from 1SXPS.
      • Pile-up handling has been made more efficient.

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