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[EQUATION] [EQUATION] [EQUATION] [EQUATION] [EQUATION] where summations with the index are over hot ( [MATH] K) gas particles and the summation with the index is over all (baryon and dark matter) particles within [MATH] . Hot gas is assumed fully ionised. The quantities [MATH] [MATH] [MATH] and [MATH] |
are the mass, temperature, number density and mass density of gas particles, respectively. [MATH] is the bolometric cooling function in Sutherland & Dopita ( 1993 and [MATH] is the gas metallicity. Other quantities are the Boltzmann constant, [MATH] , the Thomson cross-section, [MATH] , the electron mass at rest, [MATH... |
[MATH] Scaling Relations In this paper we investigate the scalings of mass-weighted temperature, [MATH] , entropy, [MATH] , integrated Compton parameter, |
[MATH] and core excised X-ray bolometric luminosity, [MATH] , with mass, [MATH] . Taking into account Eq. ( ) these cluster scaling relations can be expressed as: |
[EQUATION] [EQUATION] [EQUATION] [EQUATION] where [MATH] and the powers of the [MATH] give the predicted evolution, extrapolated from the self-similar model, (Kaiser, 1986 , of the scalings in each case. The quantities, [MATH] [MATH] , and [MATH] , are the scalings normalisation at |
[MATH] ; the power on the independent variable; and the departures from the expected self similar evolution with redshift. These scalings can be expressed in a condensate form, |
[EQUATION] where [MATH] and [MATH] are cluster properties (e.g. [MATH] [MATH] ), [EQUATION] and [MATH] is some fixed power of the cosmological factor [MATH] To determine [MATH] [MATH] , and [MATH] for each scaling we use the method described in da Silva et al. ( 2004 ); Aghanim et al. ( 2008 . To summarize, the method ... |
[MATH] to determine the scaling normalisation factors [MATH] . This avoids unwanted correlations between [MATH] and [MATH] . The r.m.s. dispersion of the fit is also computed at each redshift according to the formula, |
[EQUATION] where [MATH] (see Eq. ( 15 )) and [MATH] are individual data points. Finally, we perform a linear fit of the normalisation factors with redshift in logarithmic scale, see Eq. ( 16 ), to determine the parameters [MATH] and [MATH] |
We note that above [MATH] the number of clusters in our catalogues decreases typically below 10, hence, we do not fit the scaling relations above this redshift value. |
Results 5.1 Scaling relations at [MATH] In this section we present cluster scaling relations obtained from simulations at redshift zero. We investigate the four scalings presented in Section for all models under investigation. |
Figure shows the [MATH] (top left panel), [MATH] , (top right panel), [MATH] (bottom left panel), and [MATH] (bottom right panel) scalings, with all quantities computed within [MATH] . In each case, the main plot shows the cluster distributions for the C (triangles), D4 (diamonds), D2 (filled circles) and D1 (crosses) ... |
versus mass relations which are tightly correlated with mass. An inspection of Fig. allows us to conclude that the cluster scalings laws studied here are sensitive to the underlaying dust model, and in particular to models where the dust cooling is stronger (model D1 and D2). The differences are more evident in the [MA... |
In fact a closer inspection of Fig. indicates that differences for the same cluster in different models (note that all simulations have the same initial conditions so a cluster-to-cluster comparison can be made), reflect the differences of intensity between cooling functions presented in Figure . For example, the diffe... |
A way of quantifying the effect of dust, is to look at the best fit slope, [MATH] , and normalisation, [MATH] , parameters of these scalings which are presented in Table for all cooling models considered in this paper. We find that fitting parameters are quite similar for models C, D5, and D4 whereas models with high d... |
5.2 Evolution of the scaling relations We now turn to the discussion of the evolution of the cluster scaling laws in our simulations. Here we apply the fit to a power law procedure described in Section to derive the logarithmic slope, [MATH] , of our fitting functions, Eqs. ( 11 )-( 14 ). As mentioned earlier, this qua... |
In Figs. , and we plot the redshift dependence of the power law slopes, [MATH] , (top panels), and normalisations, [MATH] , (middle panels) for our [MATH] |
[MATH] [MATH] , and [MATH] scalings, respectively. The bottom panels show straight lines best fits, up to z=1, to the data points in the middle panels of each Figure. The slopes of these lines are the |
[MATH] parameters in Eqs. ( 11 )-( 14 ). We decided not to include data points above z=1 in the computation of [MATH] because cluster numbers drop rapidly (below 20) which, in some cases, causes large oscillations in the computed normalizations. Moreover in the case of the [MATH] relation, the evolution of [MATH] with ... |
[MATH] . In the top and middle panels the coloured bands correspond to the [MATH] envelope of the best fit errors obtained at each redshift for [MATH] , and [MATH] . The shaded area in the bottom panels are r.m.s. fit dispersions of the normalisations, [MATH] computed for the cooling model using Eq. ( 17 ). |
Results from different simulation runs are coded in the following way: triangles and solid lines stand for the cooling model, diamonds and triple-dot-dashed lines represent the D4 model, squares and short-dashed lines are for D3 model, circles and dashed lines for the D2 models and crosses and dot-dashed lines are for ... |
The top panels of these Figures allow us to conclude that the [MATH] slopes of our scalings are fairly insensitive to dust cooling. These also show no evidence of systematic variations with redshift for all scalings, which is an important requirement when fitting the cluster distributions with power-laws of the form Eq... |
The main effect of cooling by dust is reflected in the changes it produces in the normalisations of the cluster scaling laws. Again, the the impact of dust is different depending on the scaling under consideration. For the [MATH] scaling in Fig. we see a sytematic variation with the dust model (ordered in the following... |
[MATH] , and [MATH] scalings (see Figs. , and ) we conclude that the inclusion of dust cooling causes significant departures from the standard radiative cooling model depending on the dust model parameters. For example, this is clear from the non-overlapping errors and fit dispersions of the normalisations for the D2 a... |
and [MATH] relations. We end this section by noting that we find positive evolution (relative to the expected self-similar evolution, i.e. for a given [MATH] |
in Eq ( 15 ) the property [MATH] is higher at higher redshifts) for the [MATH] and [MATH] (models D1 and D2 only) relations whereas the |
[MATH] , and [MATH] relations show negative evolutions relative to the self-similar model. This is in line with the findings from simulations with radiative cooling of similar size and cosmology, see eg (da Silva et al., 2004 ; Aghanim et al., 2008 |
discussion 6.1 Efficiency of the dust cooling In agreement with the cooling functions of (Montier & Giard, 2004 , the dust cooling is most effective in the cluster temperature regime. The relative importance of the dust cooling with respect to the gas radiative cooling is strongly dependent on the dust abundances and t... |
the [MATH] relation is almost unchanged when adding dust cooling to the radiative gas cooling (see Fig. ). Our results show that the (mass weighted) temperature–mass relation within [MATH] , is essentially driven by the gravitational heating of the gas (due to its infall on the cluster potential well), and that the phy... |
(Gnedin et al., 2004 , the cooling by dust may amplify this effect, and thus modify scaling relations like the [MATH] . In the case of observationally derived quantities, scaling laws will be drawn from overall quantities that will proceed from mixed-projected information over a wide range of radii. If a gradient exist... |
relation. On the other hand the other three scaling laws are deeply related to the clusters baryonic component. The clear effect on the |
[MATH] [MATH] , and [MATH] relations illustrates this fact (see Figs. , and ). We found that the slopes of these scalings remain fairly insensitive to dust, whereas normalisations show significant changes depending on the dust parameters. Relative changes in the normalisations at redshift zero and |
[MATH] can be as high as 25% for [MATH] and 10% for the [MATH] [MATH] relations for the D1 model. Models with lower dust abundances and MRN grain size distributions present smaller but systematic variations relative to the C model. As any other cooling process, the cooling due to dust tends to lower the normalisations ... |
Our simulations allow us to quantify the relative impact of the dust parameters on the investigated cluster scalings (see Figs. to and Table ). From one model to another one can identify two clear effects due to dust: (i) it shows the expected effect of the dust abundance, which from models D4 to D2 raise by a factor o... |
[MATH] [MATH] and [MATH] relations, respectively. (ii) Even more striking is the effect of the intrinsic dust grain physical properties. The variation of normalisations relative to the C model, change from a zero percent level for model D4 to about 25% ( [MATH] ) and 10% ( [MATH] , and [MATH] ) for the model D1 (ie the... |
From Figs. , and one finds that differences between normalisations become progressively important with decreasing redshift. This confirms expectations because metallicity was modelled in simulations as a linearly increasing function of time. Although our implementation of metallicity should only be regarded as a first ... |
6.2 Limitation of the dust implementation In order to implement the presence of dust in the numerical simulations, we chose a “zero order approach”: we directly correlated the presence of dust with the presence of metals under the assumption that there is no segregation in the nature of the material withdrawn from gala... |
Moreover, our implementation is also ad hoc . Indeed, beside the cooling function of dust, our implementation is not a physical implementation. We did not deal stricto senso with the physics of the dust creation and dust destruction processes. This would be a step further, and is yet beyond the scope of this paper as m... |
(Montier & Giard, 2004 , we have performed a fully self-consistent implementation of the effect of dust as a cooling vector of the ICM/IGM. Indeed, on the basis of the cooling function, the implementation encapsulates the major physical processes to which dust is subjected and acts as a non-gravitational process at the... |
As already mentioned, we directly correlated the abundance of dust with metallicity, thus to the metallicity evolution, which chosen evolution law is quite drastic: [MATH] . Indeed, if the metallicity at [MATH] is normalized to the value of [MATH] , it is lowered to [MATH] at [MATH] and [MATH] at [MATH] . However, othe... |
[MATH] , up to redshifts above 1.0 (Cora et al., 2008 ; Borgani et al., 2008 . This shows that, as for the stellar component which is already in place in galaxies when clusters form, the metal enrichment of the ICM/IGM has occured through the feedback of galaxy formation and evolution, and therefore it de facto |
strongly enriched the IGM/ICM bellow [MATH] . It also might give hints that the high metallicity of clusters could be correlated to the dust enrichment of the IGM/ICM. Indeed, the amount of gaseous iron in galaxies such as the Milky Way is [MATH] . An early enrichment of dust in the IGM and/or the ICM, which once sputt... |
[MATH] (Bianchi & Ferrara, 2005 . The latter work stressed that only big grains [MATH] m) can be transported on a few 100kpc physical scale, however leading to a very inhomogenous spatial enrichment in metals once the dust grains are sputtered. For all these reasons, by underestimating the metallicity at high redshifts... |
Conclusion In this work, we have presented the first simulations of structure formation investigating the effect of dust cooling on the properties of the intra-cluster medium. We have compared simulations with radiative [MATH] dust cooling with respect to a purely radiative cooling simulation. We have shown that: |
The cooling due to dust is effective at the cluster regime and has a significant effect on the “baryon driven” statistical properties of cluster such as [MATH] [MATH] [MATH] scaling relations. As an added non-gravitational cooling process dust changes the normalisation of these laws by a factor up to 27% for the [MATH]... |
relations. On the contrary, dust has almost no effect on a “dark matter driven” scaling relation such as the [MATH] relation. The inclusion of cooling by dust does not change significantly the slopes of the cluster scaling laws investigated in this paper. They compare with the results obtained in the radiative cooling ... |
Through the implementation of our different dust models, we have demonstrated that the dust cooling effect at the scale of clusters depends strongly on the dust abundance in the ICM, but also on a similar proportion on the size distribution of dust grains. Therefore the dust efficiency is strongly dependent on the natu... |
The setup of our simulations and the limitation of our dust implementation can be considered at a “zero order” test with which we demonstrated the active effect of dust on structure formation and especially at the cluster scale. In order to go one step further, a perspective of this work will be needed to couple the ra... |
Acknowledgments We are deeply indebted to Peter Thomas, Orrarujee Muanwong and collaborators for the their part in writing the original Sussex cluster extraction software used in this work, and to Nabila Aghanim for discussions and providing us access to the IAS (Orsay) computing facilities where simulations were run. ... |
# Source: arxiv 0808.2339 # Title: The Nature of a Cosmic-Ray Accelerator, CTB37 B, Observed with Suzaku and Chandra # Sections: all # Downloaded: 2026-03-02T07:58:48.907935+00:00 |
\Received [MATH] reception date [MATH] \Accepted [MATH] acception date [MATH] \Published [MATH] publication date [MATH] \SetRunningHead The SNR CTB37B with SuzakuR. Nakamura et al. |
and \KeyWords acceleration of particles — ISM: individual (CTB37B) — ISM: supernova remnants — X-rays: ISM The Nature of a Cosmic-ray Accelerator, CTB37B, Observed |
with Suzaku and Chandra Abstract We report on Suzaku and Chandra observations of the young supernova remnant CTB37B, from which TeV [MATH] -rays were detected by the H.E.S.S. Cherenkov telescope. The 80 ks Suzaku observation provided us with a clear image of diffuse emission and high-quality spectra. The spectra reveal... |
[MATH] -ray spectrum leads us to conclude that the TeV [MATH] -ray emission seems to be powered by either multi-zone Inverse Compton scattering or the decay of neutral pions. The point source resolved by Chandra near the shell is probably associated with CTB37B, because of the common hydrogen column density with the di... |
Introduction Supernova Remnants (SNRs) are one of the most promising acceleration sites of cosmic rays. In fact, ASCA detected synchrotron X-ray emission from the shell of SN 1006, which unambiguously indicates the acceleration of electrons up to [MATH] 100 TeV (Koyama et al., 1995 . Following this discovery, the synch... |
(Koyama et al., 1997 and RCW 86 (Bamba et al., 2000 . On the other hand, TeV [MATH] -rays have also been detected from some non-thermal shell-type SNRs. The radiation of TeV [MATH] -ray is explained by either (1) Inverse-Compton scattering (IC) of cosmic microwave background photons by the same high energy electron giv... |
[MATH] -ray and the X-ray provides the magnetic field intensity as long as one assumes that the TeV [MATH] -ray is produced through the IC mechanism. Utilizing this characteristic, |
Matsumoto et al. ( 2007 found that the TeV [MATH] -ray from HESS J1616 [MATH] 508 is likely the result of the proton acceleration, because the non-detection of X-ray using the Suzaku XIS provides much weaker magnetic field than the interstellar average. |
Although the evidence of particle acceleration has accumulated rapidly, our knowledge is still limited on what sort of conditions are necessary for SNRs to accelerate particles. A breakthrough may be brought about by searching SNRs from which the TeV [MATH] -ray emission is already detected for thermal emission systema... |
CTB37B locates at ( [MATH] [MATH] ) = ( [MATH] [MATH] with a distance of [MATH] kpc (Caswell et al., 1975 . This region is one of the most active regions in our galaxy where star burst activities, a number of shell structures probably associated with recent SNRs (Kassim et al., 1991 , and OH maser sources |
(Frail et al., 1996 are detected in radio band. TeV [MATH] -ray emission is also detected by the H.E.S.S. observation (Aharonian et al., 2007 . In spite of the evidence of the high activities in other wave bands, X-ray observations have been relatively poor. Only ASCA (Tanaka et al., 1994 has detected a part of CTB37B ... |
[MATH] 4.1, whereas fit of an optically thin thermal plasma model requires a high temperature of [MATH] 1.6 keV. These results strongly suggest that the X-ray spectrum is a mixture of a non-thermal power law and an optically thin thermal plasma emission. In addition, |
Aharonian et al. ( 2008 resolved a bright point source located near the shell of CTB37B from the diffuse emission by Chandra, although its spectral parameters are not constrained very well because of short exposure time. |
In order to take an image and high quality spectra of CTB37B, we have carried out an observation of CTB37B with Suzaku. We also refer to the Chandra archival data to include the spatial structure and to compare them to our Suzaku data. In § 2, we present the observation log and data reduction method. Image analysis is ... |
Observation and Data Reduction 2.1 Suzaku Observation CTB37B was observed with Suzaku (Mitsuda et al., 2007 during 2006 August 27–29. The nominal pointing position was (RA, Dec) = [MATH] [MATH] , J2000). Suzaku is equipped with two kinds of X-ray detectors; one is the Hard X-ray Detector (HXD; Takahashi et al. ( 2007 K... |
Koyama et al. ( 2007 ), which is an X-ray CCD camera mounted on the focal plane of the X-Ray Telescope (XRT; Serlemitsos et al. ( 2007 ). In total, there are four modules of the XIS, three of which are Front-Illuminated (FI) CCDs, which are hereafter referred to as XIS-0, 2, and 3, and the other one is a Back-Illuminat... |
The XIS was operated in the normal full-frame clocking mode with neither burst nor window options and SCI-off. The editing mode was 3 [MATH] in low and medium data rates and 5 [MATH] 5 in high and super-high data rates. In analysis, we employed the data processed with the revision 1.2 pipeline software, and used the HE... |
(Ishisaki et al., 2007 version 2007–09–22 under the assumption that the emissions are from point source. 2.2 Chandra Observation |
Chandra observation was performed on the 2 th February 2007 with the Advanced CCD Imaging Spectrometer (ACIS). Chips I0, I1, I2, I3 S2 and S3 were used. The angular resolution is [MATH] 0.5 arcsec which correspond to the CCD pixel size. The data reduction and analysis were made using the Chandra Interactive Analysis of... |
Image Analysis 3.1 Suzaku Images Fig. shows Suzaku XIS images in 0.3–3.0 keV and 3.0–10.0 keV. They are created by combining those from all the four XIS modules and smoothed with a Gaussian with [MATH] arcsec, which is close to the XRT core size and effective in highlighting the diffuse emission. The source that locate... |
[MATH] ) appears as the brightest source both in the soft and hard bands. Another source extending to the south of the brightest source, at ( [MATH] |
[MATH] [MATH] [MATH] [MATH] ) seems to be a diffuse source and manifests itself only in the band above 3 keV. In addition to these sources, a point source is detected at ( [MATH] [MATH] |
[MATH] [MATH] [MATH] ) in the band below 3 keV. The sky position is consistent with that of the point source 1RXS J171354.4 [MATH] 381740 listed in the ROSAT Bright Star Catalogue |
(Voges et al., 1999 . In order to investigate these sources separately, we defined the following photon-integration regions (see Fig. ) for the spectral analysis. Region 1 is the green circle with a radius of [MATH] centered at the intensity peak of the brightest source. Region 2 is the blue ellipse with a major and mi... |
[MATH] , respectively, which is centered at the second diffuse source. Region 3 is the circle colored in magenta with a radius of [MATH] . The other three regions with the same colors but with dashed lines define those collecting the background events. We set these background regions by taking into account the telescop... |
3.2 Chandra Images Fig. (c) shows the Chandra image in the 0.3–10.0 keV band corrected for the telescope vignetting and smoothed with a Gaussian with [MATH] arcsec to see the point sources clearly. In total, 18 sources are detected above [MATH] confidence level (Aharonian et al., 2008 . The panels (d) and (e) are image... |
3.3 Correlation with Other Energy Band Fig. shows brightness contours of radio at 1.4 GHz in blue and of TeV [MATH] -ray with H.E.S.S. in green, overlaid on the gray scale image of the Suzaku in the 0.3–10.0 keV band. Source A and B resolved by Chandra are represented by the filled red boxes. |
The radio image is taken from the NRAO VLA Sky Survey (NVSS) database (Condon et al., 1998 . The X-ray emission well conforms with the shell in radio. Particularly, the diffuse X-ray source detected by Suzaku in region 2 is associated with the southern radio sub-peak. On the other hand, the peak of TeV [MATH] -ray emis... |
[MATH] (Aharonian et al., 2008 Spectral Analysis In this section, we present results of spectral analysis of the three regions described above. We adopt the metal composition of |
Anders & Grevesse ( 1989 as the solar abundance. Spectral fits are carried out with XSPEC. We always adopt an ancillary response file (ARF) for a point source, since the sizes of region 1 and 2 are so small that the resultant spectral parameters including the flux will differ only by |
[MATH] 1% from the case if we take into account the spatial extent. The errors quoted are always at the 90% confidence level. 4.1 Region 1 |
The Chandra spectrum of source A is shown in Fig. (a). In extracting this spectrum, we took a circular integration region with a radius of [MATH] centered at the source. We made no background subtraction. Since there is no apparent emission lines, we attempted to fit a power-law model undergoing photoelectric absorptio... |
[MATH] ergs cm -2 -1 , respectively, which are consistent with those of Aharonian et al. ( 2008 Fig. (b) is the background-subtracted spectrum of Suzaku region 1. The black and red crosses represent the data points from the sum of the FI CCDs and those of the BI CCD, respectively. Although there is no sign of Fe K [MAT... |
emission line from He-like Si (2.18 keV). This means that the spectrum includes a thermal emission component. We thus tried to fit the Suzaku spectrum with a model composed of a power law representing source A and a non-equilibrium collisional ionization plasma emission model (“vnei” model in XSPEC; |
Borkowski et al. ( 2001 ); Hamilton et al. ( 1983 ); Borkowski et al. ( 1994 ); Liedahl et al. ( 1995 undergoing photoelectric absorption with a common [MATH] . In the fitting, we set abundances of Mg, Si, and S free to vary. The other abundances are fixed as solar abundance. The best-fit parameters are summarized in T... |
emission line was detected can be attributed that the non-thermal component dominates the spectrum in the energy band above 3 keV. The reduced [MATH] of 1.06 implies that the fit is acceptable at the 90% confidence level. The temperature and the ionization parameter of the “vnei” component are obtained to be |
[MATH] keV and [MATH] , respectively. On the other hand, the photon index of the power-law model is [MATH] and the intrinsic flux is [MATH] |
ergs cm -2 -1 . The photon index is consistent between Suzaku and Chandra, whereas the flux with Suzaku seems to be greater than that with Chandra by a factor of [MATH] 1.8. We remark that, since there is no sign of emission lines in the Chandra spectrum of source A, the thermal component should be extended. |
4.2 Region 2 The Suzaku spectra of region 2 together with best-fit model and residual are shown in Fig. The basic features such as the He-like Si K [MATH] emission line and no apparent sign of Fe emission line are similar to those in region 1. In addition to the thermal and non-thermal components, we need to take into ... |
[MATH] 1% of the source A flux, on the basis of the vignetting function (Serlemitsos et al., 2007 . As a result, the free parameters are the hydrogen column density, the normalization of the vnei component, and all parameters of power law (2). The result of the fit is shown in Fig. , and the best-fit parameters are sum... |
[MATH] 11 (4-20) % of the region 1 best-fit value. On the other hand, we simulated the contamination of the vnei component from region 1 using smoothed Chandra image, and found the contamination is |
[MATH] 7%. The thermal component apparent in region 2 spectra can therefore be entirely regarded as the contamination from region 1, and the upper limit of the [MATH] intrinsic to region 2 is 13% of that of region 1, or 2.7 [MATH] cm -3 . The photon index of power law (2) results in [MATH] with the reduced |
[MATH] of 0.36. It is remarkable that the X-ray photon index [MATH] is consistent with the standard radio energy index of non-thermal SNRs [MATH] |
We next replaced the power-law (2) component by an “srcut” model, which simulates a synchrotron spectrum from an exponentially cut off power-law distribution of electrons in a homogeneous magnetic field |
(Reynolds, 1998 ; Reynolds & Keohane, 1999 According to the Green’s catalogue , the radio spectral index ( [MATH] ) is 0.3 with a flux at 1 GHz of 26 Jy. This small index, however, is probably due to contamination of thermal emission. A similar situation has been reported for 30 Dor C |
(Smith & Wang, 2004 . We thus fixed [MATH] at 0.5, which is the typical value of the SNRs in the radio band, and set the flux at 1 GHz free to vary. As a result, the normalization of the srcut model is obtained to be 1.43 mJy at 1 GHz. This is much smaller than the radio flux 26 Jy at 1 GHz. Note, however, that this ra... |
[MATH] 1% of the total, or [MATH] mJy at 1 GHz. Even after this correction, simple extrapolation of the srcut model well fit to the X-ray spectra to the radio band is much smaller than the observed radio flux. We guess that the flux in the radio band is dominated by thermal emission. The resultant normalization (emissi... |
4.3 Region 3 Fig. shows the background-subtracted spectra of region 3. As indicated by the images in Fig. (a), X-ray flux is detected only below [MATH] 3 keV. Since the absorption is apparently weak and there is Fe-L hump in the 0.7-0.9 keV band, this source seems to be a foreground point source, probably an active sta... |
Mewe et al. ( 1985 1986 ); Liedahl et al. ( 1995 ); Kaastra et al. ( 1996 multiplied by photoelectric absorption, and fitted this model to the spectra in the 0.5–2.0 keV band. The result is shown in Fig. , and the best-fit parameters are listed in Table . Note that the fit residuals exhibit different behavior in the 0.... |
Timing Analysis In order to understand the nature of the point sources, we carried out timing analysis. 5.1 Source A We searched the Chandra data for a pulsation from source A. Unfortunately, source A locates close to the chip boundaries of the ACIS-I, and is affected by the instrumental dithering effects. In fact, the... |
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