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The observation was performed norlally except for loss ofdata during October 1.57.1.53 (UT) because of a sudden cancelation of the Deep Space Network service.
The observation was performed normally except for loss of data during October 1.57–1.83 (UT) because of a sudden cancelation of the Deep Space Network service.
We have screened the data with the following criteria.
We have screened the data with the following criteria.
The data taken while the spacecraft passes the South Atlautic Anomaly are discarded.
The data taken while the spacecraft passes the South Atlantic Anomaly are discarded.
Iu order to avoid the Earthi-Inb effect. we have ouly chosen data when the Earth elevation anele of exceeds 57.
In order to avoid the Earth-limb effect, we have only chosen data when the Earth elevation angle of exceeds $^\circ$.
In addition to his. we have also discarded the SIS data while the clevation angle from the sunny Earth limb is less than 107.
In addition to this, we have also discarded the SIS data while the elevation angle from the sunny Earth limb is less than $^\circ$.
For the SIS. we have skipped dayv-uight transition periods of the spacecraft which occur during every satellite orbit.
For the SIS, we have skipped day-night transition periods of the spacecraft which occur during every satellite orbit.
With these selection criteria. some 77 ksec exposure time is retained for both he SIS aud the CIS.
With these selection criteria, some 77 ksec exposure time is retained for both the SIS and the GIS.
For the inteerationC» of the X-rav source photous. we have adopted an aperture of 3/77 and 100 in radius centered on1801 for the SIS aud Cds. respectively,
For the integration of the X-ray source photons, we have adopted an aperture of 7 and 0 in radius centered on for the SIS and GIS, respectively.
For the backerounc. the eutire CCD chip outside the aperture is used for the SIS (there are no other N-ray sources within the field of view). whereas an annular region Which has the same distance from the boresight of he NRT as the source-inteeration region is adopted for he CdS.
For the background, the entire CCD chip outside the aperture is used for the SIS (there are no other X-ray sources within the field of view), whereas an annular region which has the same distance from the boresight of the XRT as the source-integration region is adopted for the GIS.
Iu Fig. 1..
In Fig. \ref{LC},
we show the light curve of roni all the four detectors in the 0.510 keV band with 256 sec biuuimg after the data screening as described above.
we show the light curve of from all the four detectors in the 0.5–10 keV band with 256 sec binning after the data screening as described above.
Because is a low-Earth orbit satellite. he source is ustaly occulted by the Earth every 96 iiu. (
Because is a low-Earth orbit satellite, the source is usually occulted by the Earth every 96 min. (
satellite orbital period).
satellite orbital period).
As mentioned in 2. approxinatelv 6 hrs of data are lost iu the middle of the observation due to a failure of data retrieval on a erouud station.
As mentioned in 2, approximately 6 hrs of data are lost in the middle of the observation due to a failure of data retrieval on a ground station.
The average backerouud-subtracted counting rate is 0.052 c T with allthe four detectors (0.035 € 1 for the two SIS. aud 0.017 cs + for the two CIS).
The average background-subtracted counting rate is 0.052 c $^{-1}$ with allthe four detectors (0.035 c $^{-1}$ for the two SIS, and 0.017 c $^{-1}$ for the two GIS).
Fig.
Fig.
2 shows the folded Πο curves from all the four detectors in the 0.510 keV baud and in three separate energv bands.
\ref{FLC} shows the folded light curves from all the four detectors in the 0.5–10 keV band and in three separate energy bands.
Iu folding the light curves. we have adopted the epheuuuerris determined from ROSAT aud optical
In folding the light curves, we have adopted the ris determined from and optical
refappS.
.
. We also show how the slopes 71,4 and j;45 depend on the spatial resolution for all clouds in refallSG..
We also show how the slopes $\gamma_{\rm low}$ and $\gamma_{\rm high}$ depend on the spatial resolution for all clouds in \\ref{allSG}.
The gradients for all clouds. averaged and interpoated to ppe resolution. are summarised in refanatab..
The gradients for all clouds, averaged and interpolated to pc resolution, are summarised in \\ref{anatab}.
In general the 71, values are about twice as inegative as the qian values.
In general the $\gamma_{\rm low}$ values are about twice as negative as the $\gamma_{\rm high}$ values.
More importantly the scatter for the sopes in the high column density regions of the clouds is smaller han in the low 24 regions.
More importantly the scatter for the slopes in the high column density regions of the clouds is smaller than in the low $A_V$ regions.
For the averages and scatter for all clouds we find: £51,0.45+0.15 and “high0.20+0.06.
For the averages and scatter for all clouds we find: $\left< \gamma_{\rm low} \right> = -0.45 \pm 0.15$ and $\left< \gamma_{\rm high} \right> = -0.20 \pm 0.06$.
This indicates. that once gravity becomes important enough to influence the column density distribution (i.e. star formation starts). then we will tind far fewer differences between the various clouds.
This indicates, that once gravity becomes important enough to influence the column density distribution (i.e. star formation starts), then we will find far fewer differences between the various clouds.
While at low column densities. where external factors (proximity to Supernovae. etc.)
While at low column densities, where external factors (proximity to Supernovae, etc.)
determine the turbulent motions. much larger cloud to cloud differences are seen.
determine the turbulent motions, much larger cloud to cloud differences are seen.
As for the column density distribution we determine the slopes à of the log(AZ) vs ντ mass distribution for low and high column densities.
As for the column density distribution we determine the slopes $\delta$ of the $\log(M)$ vs $A_V$ mass distribution for low and high column densities.
We show as an example the mass distribution of the HI cloud in refaurlmass..
We show as an example the mass distribution of the 1 cloud in \\ref{aur1mass}.
Overplotted are the linear fits.
Overplotted are the linear fits.
The values for all slopes ὁ for different spatial resolutions are listed in refbintab..
The values for all slopes $\delta$ for different spatial resolutions are listed in \\ref{bintab}.
The slopes for all clouds averaged and interpolated to ppc id'e summarised in refanatab.. the mass distributions are shown in refappo..
The slopes for all clouds averaged and interpolated to pc are summarised in \\ref{anatab}, the mass distributions are shown in \\ref{app6}.
As for the values of . the scatter of the slopes differs for the low and high column density material.
As for the values of $\gamma$ , the scatter of the slopes differs for the low and high column density material.
For the averages of all clouds we find: os0.21x0.11 and (nian (0.025.
For the averages of all clouds we find: $\left< \delta_{\rm low} \right> = -0.21 \pm 0.11$ and $\left< \delta_{\rm high} \right> = -0.14 \pm 0.025$ .
Again. we find that the star forming (thigh column density) parts of the clouds are very similar. while the low ον. (turbulence dominated) regions show a larger scatter.
Again, we find that the star forming (high column density) parts of the clouds are very similar, while the low $A_V$ (turbulence dominated) regions show a larger scatter.
We show how the slopes 91, and Oyj. depend on the spatial resolution for all clouds in refallSG..
We show how the slopes $\delta_{\rm low}$ and $\delta_{\rm high}$ depend on the spatial resolution for all clouds in \\ref{allSG}.
We extrapolate the fit to the low 24 regions. which leads to the total mass of the cloud.
We extrapolate the fit to the low $A_V$ regions, which leads to the total mass of the cloud.
For the purpose ofthis paper. we calculate the mass in the cloud at a column density of above
For the purpose ofthis paper, we calculate the mass in the cloud at a column density of above
to confirm the presence of poor clusters around FR I radio galaxies. aud further explore the euvironiuents of radio galaxies which appear to reside iu very sparse regions of galaxies.
to confirm the presence of poor clusters around FR I radio galaxies, and further explore the environments of radio galaxies which appear to reside in very sparse regions of galaxies.
The velocities are used to evaluate velocity dispersions in the identified poor clusters aud estimate system άσσος under the assumption of virial equilibrium.
The velocities are used to evaluate velocity dispersions in the identified poor clusters and estimate system masses under the assumption of virial equilibrium.
In some cases. the obtained velocities also allow an investigation of potential substructure within the identified clusters aud the relationship ol the radio sources to such substructure.
In some cases, the obtained velocities also allow an investigation of potential substructure within the identified clusters and the relationship of the radio sources to such substructure.
The goals are to: 1) confir the existence of poor clusters identified through the preseuce of powerful radio galaxies. aud 2) better uuclerstaud the relationship between the characteristics of the radio galaxies and their local environments.
The goals are to: 1) confirm the existence of poor clusters identified through the presence of powerful radio galaxies, and 2) better understand the relationship between the characteristics of the radio galaxies and their local environments.
The observatious aud reductious will be discussed. briefly iu Section ??.. followed by the computation of quantities to characterize their environments (Section ??)).
The observations and reductions will be discussed briefly in Section \ref{sec:data}, followed by the computation of quantities to characterize their environments (Section \ref{sec:anal}) ).
A discussion of the results is preseuted in Section ??.. followed by a brief summary of the couclusious.
A discussion of the results is presented in Section \ref{sec:discuss}, followed by a brief summary of the conclusions.
Throughout this paper. we have adopted H,το km s| | and q,=0.1 in all calculations which require such factors.
Throughout this paper, we have adopted $H_o=75$ km $^{-1}$ $^{-1}$ and $q_o=0.1$ in all calculations which require such factors.
The complete sample is composed of 25 radio galaxies drawn from the 3CRR (Laing.Riley.& aud Wall&Peacock(1985) catalogs. plus 21 additional radio galaxies drawn frou. the B2 catalog (Collaetal.1970.1972).
The complete sample is composed of 25 radio galaxies drawn from the 3CRR \citep{lain1983} and \citet{wall1985} catalogs, plus 24 additional radio galaxies drawn from the B2 catalog \citep{coll1970,coll1972}.
. The B2 sources are generally of lower radio luminosity. but were added as the cleclination range of that survey inade them excellent targets for observation at Witt Peak.
The B2 sources are generally of lower radio luminosity, but were added as the declination range of that survey made them excellent targets for observation at Kitt Peak.
The radio galaxies were required to be nearby (z«0.06 for the 3CRR aud. Wall Peacock galaxies. 2«0.01 for the B2 galaxies). aud not members of Abell clusters.
The radio galaxies were required to be nearby $z<0.06$ for the 3CRR and Wall Peacock galaxies, $z<0.04$ for the B2 galaxies), and not members of Abell clusters.
We report οι a subset of 25 of these radio galaxies iu this paper.
We report on a subset of 25 of these radio galaxies in this paper.
The remainder were unobservable due to declinatiou limits of the telescope (6 sources) aud observing time lost due to weather.
The remainder were unobservable due to declination limits of the telescope (6 sources) and observing time lost due to weather.
The full sample of radio galaxies aud comments ou their properties may be found in Table 1..
The full sample of radio galaxies and comments on their properties may be found in Table \ref{tbl:sample}.
Spectra were obtained using the MX Spectrometer ou the Steward Observatory 2.3 meter Bok telescope.
Spectra were obtained using the MX Spectrometer on the Steward Observatory 2.3 meter Bok telescope.
MX utilizes fibers on mechanical probes to obtain spectra of up to 32 targets at once. plus sky spectra from 30 fibers "piggy-backed" to target spectra probes.
MX utilizes fibers on mechanical probes to obtain spectra of up to 32 targets at once, plus sky spectra from 30 fibers “piggy-backed” to target spectra probes.
The Ποιά of view of the telescope with MX is15%. which matches nicely with the angular sizes of the poor clusters being studied.
The field of view of the telescope with MX is, which matches nicely with the angular sizes of the poor clusters being studied.
Iu 15 of the 25 systems being studied this corresponds to a linear size of 1 Mpc or greater.
In 18 of the 25 systems being studied this corresponds to a linear size of 1 Mpc or greater,
where A, (As) and wy; (ws) are the mean longitude and longitude of pericentre of Jupiter (and Saturn).
where $\lambda_J$ $\lambda_S$ ) and $\omega_J$ $\omega_S$ ) are the mean longitude and longitude of pericentre of Jupiter (and Saturn).
Capture in the 3:2 resonance causes the eccentricities of both planets to grow to ej0.03 and es~0.1, as seen in the third panel in Fig. 1..
Capture in the 3:2 resonance causes the eccentricities of both planets to grow to $e_J\sim 0.03$ and $e_S\sim 0.1$, as seen in the third panel in Fig. \ref{orbits}.
In agreement with previous studies (Masset Snellgrove 2001, Morbidelli Crida 2007, Pierens Nelson 2008), the long-term outcome for model I1 after capture in the 3:2 resonance is outward migration of the Jupiter-Saturn system with the two planets maintaining the 3:2 resonance and sharing a common gap.
In agreement with previous studies (Masset Snellgrove 2001, Morbidelli Crida 2007, Pierens Nelson 2008), the long-term outcome for model I1 after capture in the 3:2 resonance is outward migration of the Jupiter-Saturn system with the two planets maintaining the 3:2 resonance and sharing a common gap.
Here, the migration reversal occurs at t~2500 orbits, when m;~0.6 M; and ms~0.3 Mj.
Here, the migration reversal occurs at $t\sim 2500$ orbits, when $m_J\sim 0.6$ $M_J$ and $m_S \sim 0.3$ $M_J$.
These values are in reasonable agreement with Masset Snellgrove (2001) who estimated a critical mass ratio of m;/m,x0.62 for the positive torque exerted by the inner disk on Jupiter to be larger than the negative torque exerted by the outer disk on Saturn.
These values are in reasonable agreement with Masset Snellgrove (2001) who estimated a critical mass ratio of $m_s/m_J \lesssim 0.62$ for the positive torque exerted by the inner disk on Jupiter to be larger than the negative torque exerted by the outer disk on Saturn.
Fig.
Fig.
shows a snapshot of the disk at a point in time where Jupiter and Saturn are fully-formed, locked in 3:2 MMR and migrate outward.
\ref{fig:disk2d_i1} shows a snapshot of the disk at a point in time where Jupiter and Saturn are fully-formed, locked in 3:2 MMR and migrate outward.
For this model, we observe a trend for the amplitude of the resonant angles to slightly increase with time to such an extent that $ switches from libration to circulation at 1.3x10* orbits (see upper panel of Fig. 3)).
For this model, we observe a trend for the amplitude of the resonant angles to slightly increase with time to such an extent that $\phi$ switches from libration to circulation at $t\sim 1.3\times 10^4$ orbits (see upper panel of Fig. \ref{fig:angles}) ).
This causes not only the outward migration rate to subsequently slow down (second panel) but also the eccentricities to slightly decrease to values such that e;~0.02 and es~0.04 at the end of the simulation (third panel).
This causes not only the outward migration rate to subsequently slow down (second panel) but also the eccentricities to slightly decrease to values such that $e_J\sim 0.02$ and $e_S\sim 0.04$ at the end of the simulation (third panel).
It is interesting to note that some
It is interesting to note that some
ofAdet is only slightly off set from that ofCTAobs.
of is only slightly off set from that of.
In the afterglow case the condition Taaay<Too is not required.
In the afterglow case the condition $T_{\rm delay}<T_{90}$ is not required.
Although shortening Taclay to less than 100 sec does not have much impact on the improvement of the detection rate, we should note that shorter delay time is necessary for detecting the prompt emission.
Although shortening $\tau_{\rm delay}$ to less than 100 sec does not have much impact on the improvement of the detection rate, we should note that shorter delay time is necessary for detecting the prompt emission.
Because the minimum cutoff of Tactay is fixed to be 20 sec in our simulation, even when larger Taclay is assumed, shorter Ίαειαν plays a significant role as mentioned above in the explanation of Odelay=0.
Because the minimum cutoff of $T_{\rm delay}$ is fixed to be 20 sec in our simulation, even when larger $\tau_{\rm delay}$ is assumed, shorter $T_{\rm delay}$ plays a significant role as mentioned above in the explanation of $\sigma_{\rm delay} = 0$.
Actually, in the bottom panel of Figure 7,, for Tdelay=100 sec, we can see that Taciay is distributed at less than ~100 sec in most cases where the prompt emission is detectable.
Actually, in the bottom panel of Figure \ref{fig:delay-dist}, , for $\tau_{\rm delay}=100$ sec, we can see that $T_{\rm delay}$ is distributed at less than $\sim 100$ sec in most cases where the prompt emission is detectable.
Therefore it is crucial to keep the delay time as short as possible if the duration of the bursts in the CTA band and in the GBM band are similar to each other.
Therefore it is crucial to keep the delay time as short as possible if the duration of the bursts in the CTA band and in the GBM band are similar to each other.
In order to reduce Taaay, fast alerts with good localization is equally important to a rapid slewing of LSTs.
In order to reduce $T_{\rm delay}$, fast alerts with good localization is equally important to a rapid slewing of LSTs.
As an example, we simulate the case in which Tuclay can not be shorter than 100 sec with the other parameters fixed to the fiducial values, and find that the detection rate of the prompt emission decreases by a factor of 2 in this case.
As an example, we simulate the case in which $T_{\rm delay}$ can not be shorter than 100 sec with the other parameters fixed to the fiducial values, and find that the detection rate of the prompt emission decreases by a factor of 2 in this case.
One of the criteria for LSTs to start follow-up observations concerns oth, whose fiducial value is set to 3.5? (see Section 3.1)).
One of the criteria for LSTs to start follow-up observations concerns $\sigma_{\rm th}$, whose fiducial value is set to $3.5^\circ$ (see Section \ref{subsec:GBMlocalization}) ).
Most of the GBM alerts have the error radius larger than this fiducial value.
Most of the GBM alerts have the error radius larger than this fiducial value.
The larger σαι we use, the more chances of the follow-ups we have, while the efficiency of the detection decreases.
The larger $\sigma_{\rm th}$ we use, the more chances of the follow-ups we have, while the efficiency of the detection decreases.
This is because the bursts with larger error radii are less probable to lie in the FOV of the LSTs.
This is because the bursts with larger error radii are less probable to lie in the FOV of the LSTs.
In Figure 8,, we show the dependence of the detection rate on oth.
In Figure \ref{fig:Errth-vs-PAdet}, we show the dependence of the detection rate on $\sigma_{\rm th}$.
The results are shown as the ratio to the fiducial case.
The results are shown as the ratio to the fiducial case.
The red solid line (Pdet) andthe blue dot-dashed line (Adet) show the increase in the detection rate by a factor of 1.2-1.3 for σιι=5? compared to 3.5?.
The red solid line ) andthe blue dot-dashed line ) show the increase in the detection rate by a factor of 1.2–1.3 for $\sigma_{\rm th} = 5^\circ$ compared to $3.5^\circ$.
On the other hand, one should keep in mind that the more rapid increase ofCTAobs (black dotted line) shows the decline in the detection efficiency(i.e.,
On the other hand, one should keep in mind that the more rapid increase of (black dotted line) shows the decline in the detection efficiency(i.e.,
the ratio of orAdet to CTAobs) by a factor of 0.6-0.7.
the ratio of or to ) by a factor of 0.6–0.7.
On the contrary, if we take σι to be less than 3.2?, most of the GBM alerts do not satisfy this criterion, so that the detection rate decreases very rapidly.
On the contrary, if we take $\sigma_{\rm th}$ to be less than $3.2^\circ$, most of the GBM alerts do not satisfy this criterion, so that the detection rate decreases very rapidly.
It can be seen from Figure 8 that the detection rates of andAdet saturate around σι=5°, so that it seems better to set otn to near this value aslong as we take a simple strategy of the follow-up observations, i.e. all 4 LSTs point toward the centroid of the GBM error circle.
It can be seen from Figure \ref{fig:Errth-vs-PAdet} that the detection rates of and saturate around $\sigma_{\rm th} = 5^\circ$, so that it seems better to set $\sigma_{\rm th}$ to near this value aslong as we take a simple strategy of the follow-up observations, i.e. all 4 LSTs point toward the centroid of the GBM error circle.
Table 2 summarizes how the detection rate changes when one of our model parameters takes different values from that in our fiducial parameter set (with the other parameters fixed).
Table \ref{table:GRBrate_theory2} summarizes how the detection rate changes when one of our model parameters takes different values from that in our fiducial parameter set (with the other parameters fixed).
Each column describes the result for different cases: column) The fiducial case. 1)
Each column describes the result for different cases: ) The fiducial case. )
The typical delay time of the follow-up Taelay is set to 60 sec. 2)
The typical delay time of the follow-up $\tau_{\rm delay}$ is set to 60 sec. )
The case including 8>—2 according to the BATSE observation. 3)
The case including $\beta>-2$ according to the BATSE observation. )
The extra spectral component of Rextra=0.1 is assumed for all the samples. 4)
The extra spectral component of $R_{\rm extra}=0.1$ is assumed for all the samples. )
The EBL model by Kneiskeetal.(2004) is used.
The EBL model by \cite{Kneiske2004} is used.
and 6) The temporal index of the afterglowp; is taken as —1.3 and —1.8, respectively.
and ) The temporal index of the afterglow$p_t$ is taken as $-1.3$ and $-1.8$ , respectively.
7 and8) The spectral energy index of the afterglow pz is taken as —0.5 and —1.5, respectively.
and) The spectral energy index of the afterglow $p_E$ is taken as $-0.5$ and $-1.5$ , respectively.
on the point of least radius of curvature on the bisector. or alternatively. the point where the rate of change of the velocity fields is largest.
on the point of least radius of curvature on the bisector, or alternatively, the point where the rate of change of the velocity fields is largest.
The CBS measure too correlates very well with logg and Των.
The CBS measure too correlates very well with $\log g$ and $T_\mathrm{eff}$.
Although these correlations are not linear across the full range. we have shown that the shape of the CCF bisector changes in an understandable and predictable way across the HR diagram.
Although these correlations are not linear across the full range, we have shown that the shape of the CCF bisector changes in an understandable and predictable way across the HR diagram.
We have shown that current state-of-the-art 3D. stellar atmosphere models are able to reproduce the behavior of the CCF quite well for the Sun. which is very encouraging.
We have shown that current state-of-the-art 3D stellar atmosphere models are able to reproduce the behavior of the CCF quite well for the Sun, which is very encouraging.
Further modeling will be needed in order to extend this understanding to other late type stars across the HR diagram.
Further modeling will be needed in order to extend this understanding to other late type stars across the HR diagram.
The measures presented here have obviously predictive power: based on the CCF bisector. the fundamental atmospheric parameters of the star can be estimated.
The measures presented here have obviously predictive power; based on the CCF bisector, the fundamental atmospheric parameters of the star can be estimated.
This may be relevant for studies of fainter stars where the SNR of individual spectral lines is not high enough for analysis.
This may be relevant for studies of fainter stars where the SNR of individual spectral lines is not high enough for analysis.
Such cases are still quite rare. partly because of the bias towards the study of brighter stars with HARPS.
Such cases are still quite rare, partly because of the bias towards the study of brighter stars with HARPS.