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We find that the parameter combination ay=1.76+0.12 and a,=0.3+0-1 is consistent with our inference for these parameters at 26. | We find that the parameter combination $\alpha_0=1.76\pm0.12$ and $\alpha_1=0.3\pm0.1$ is consistent with our inference for these parameters at $z\sim6$. |
Moreover. we find that our inferred scatter in à at z~6 agrees well with the Aa=0.76 found bv ?.. | Moreover, we find that our inferred scatter in $\alpha$ at $z\sim6$ agrees well with the $\Delta \alpha=0.76$ found by \citet[][]{telfer2002}. |
Thus. assuming our fiducial model of the ionizing background. there is no evidence for evolution in the EUV slope of quasars with redshift out to z6. | Thus, assuming our fiducial model of the ionizing background, there is no evidence for evolution in the EUV slope of quasars with redshift out to $z\sim6$. |
1n addition to those already. discussed. we have checked several other sources of uncertainty. | In addition to those already discussed, we have checked several other sources of uncertainty. |
Specifically. we have recalculated our constraints including the assumption of a fiducial model with a=0.5 rather than a=1.5. a model where the quasar continuum has been placed systematically hieh by on the data. and a model that includes an approximate treatment of Lyman limit svstems. | Specifically, we have recalculated our constraints including the assumption of a fiducial model with $\alpha=0.5$ rather than $\alpha=1.5$, a model where the quasar continuum has been placed systematically high by on the data, and a model that includes an approximate treatment of Lyman limit systems. |
Each of these ellects is found to have a relatively small ellect. on the derived parameters for the spectral index relative to the «quoted uncertainty. as detailed below. | Each of these effects is found to have a relatively small effect on the derived parameters for the spectral index relative to the quoted uncertainty, as detailed below. |
There is an uncertainty in the absolute temperature of the LGAL within the quasar near-zone. as well as scatter in the temperature from one near-zone to another. | There is an uncertainty in the absolute temperature of the IGM within the quasar near-zone, as well as scatter in the temperature from one near-zone to another. |
These uncertainties will impact on our constraints for the EUV spectral index. | These uncertainties will impact on our constraints for the EUV spectral index. |
Firstly. any scatter in temperature among different near-zones will contribute to scatter in the observed near-zone sizes. | Firstly, any scatter in temperature among different near-zones will contribute to scatter in the observed near-zone sizes. |
Llowever this scatter will be degenerate with scatter in the spectral index (Ae). | However this scatter will be degenerate with scatter in the spectral index $\Delta \alpha$ ). |
Thus the potential presence of scatter in temperature does not. introduce uncertainty into our estimated. mean value of ay or o4. | Thus the potential presence of scatter in temperature does not introduce uncertainty into our estimated mean value of $\alpha_0$ or $\alpha_1$. |
llowever. it does imply that our quoted za. should. be considered an upper Limit. | However, it does imply that our quoted $\Delta \alpha$ should be considered an upper limit. |
On the other hand. uncertainty in the absolute value of the temperature within the near-zones could. introduce a more serious error. | On the other hand, uncertainty in the absolute value of the temperature within the near-zones could introduce a more serious error. |
Ehe temperature of the IGM in the quasar near-zone has two independent contributions which acd. linearly. and hence the near-zone temperature has two components of uncertainty. | The temperature of the IGM in the quasar near-zone has two independent contributions which add linearly, and hence the near-zone temperature has two components of uncertainty. |
Firstly. the mean IGM has an uncertainty in temperature resulting from heating curing hyvelrogen reionization. | Firstly, the mean IGM has an uncertainty in temperature resulting from heating during hydrogen reionization. |
This component of uncertainty in the temperature due to hyelrogen reionization is. already included (2). in the caleulation of the ionizing background. | This component of uncertainty in the temperature due to hydrogen reionization is already included \citep{bolton2007} in the calculation of the ionizing background. |
llowever in addition the reionization of Hell by the quasar may heat the LGA in the near-zone to well above this mean IGM. temperature (2).. | However in addition the reionization of HeII by the quasar may heat the IGM in the near-zone to well above this mean IGM temperature \citep{boltonoh2009}. |
In. our modelling we have assumed a temperature (2). that is consistent. with observations of a single z6 quasar (SDSS JOSIS|1722). anc that the quasar is responsible for reionization of Hell within the near-zone. | In our modelling we have assumed a temperature \citep{bolton2010} that is consistent with observations of a single $z\sim6$ quasar (SDSS J0818+1722), and that the quasar is responsible for reionization of HeII within the near-zone. |
This latter assumption means the temperature increase from Hell reionization bv. the quasar is directly. related to the spectral hardness of the quasar and hence to o. which is the quantity we are trving to measure. | This latter assumption means the temperature increase from HeII reionization by the quasar is directly related to the spectral hardness of the quasar, and hence to $\alpha$, which is the quantity we are trying to measure. |
As a result.the assumption of an incorrect temperature. could bias the inferred. value of à in our analysis. | As a result,the assumption of an incorrect temperature could bias the inferred value of $\alpha$ in our analysis. |
A hardening of the spectral index. leads both to higher temperatures within the near-zone ancl to an enhanced photo-ionization rate. | A hardening of the spectral index leads both to higher temperatures within the near-zone and to an enhanced photo-ionization rate. |
By assuming a [fixed | By assuming a fixed |
1981:Tytler1982:Mo&Miralda-Escudé1996).. etal.2000:Gnedin&Fan2000). | \citep[e.g.,][]{weymann/etal:1981, tytler:1982,
mo/miralda-escude:1996}, \citep[e.g.,][]{miralda-escude/etal:2000,
gnedin/fan:2006}. |
. Αι: dN/dz=1.9[(1+2/4.7]7. 3.6<z«4.4. Aj=οΗ(ς)(dN/dz)~415 >=3.7. (e.g..Storrie-Lombardietal.1994).. AusX(I27°!) Songaila&Cowie(2010) z—6. Aap,x(1+2)°°, Agp,&34 z—5.7. Aap,- Bolton&Haehnelt(2007b) 20<Ag.50 z6. Direct simulation. of all. the processes involved in retonization 1s. not currently possible. | $\mfp$ \citet{prochaska/etal:2009}
$dN/dz =
1.9 [(1+z)/4.7]^{5.2}$ $3.6<z<4.4$ $\mfp=c/H(z)/(dN/dz)
\sim 415$ $z=3.7$ \citep[e.g.,][]{storrie-lombardi/etal:1994}, $\mfp\propto (1+z)^{-6.7}$ \citet{songaila/cowie:2010} $z\sim 6$ $\mfp\propto (1+z)^{-3.44}$ $\mfp\simeq 34$ $z\sim 5.7$ $\mfp$ \citet{bolton/haehnelt:2007a} $20 <\lambda_{\rm
abs}<50$ $z\sim 6$ $z<6$ Direct simulation of all the processes involved in reionization is not currently possible. |
Theoretical studies focus either on the small scale hydrodynamics and radiative transfer (e.g..Miralda-Escudéetal.2000;Gnedin 2011)... or on the large scale morphology of retonization (e.g..2008:Friedrichetal.201 1). | Theoretical studies focus either on the small scale hydrodynamics and radiative transfer \cite[e.g.,][]{miralda-escude/etal:2000, gnedin:2000,
haiman/etal:2001, shapiro/etal:2004, ciardi/etal:2006,
pawlik/etal:2009, raicevic/theuns:2011, mcquinn/etal:2011}, or on the large scale morphology of reionization \cite[e.g.,][]{2002MNRAS.330L..53A, furlanetto/etal:2004,
2006MNRAS.369.1625I, mcquinn/etal:2007, trac/cen:2007,
shin/etal:2008, friedrich/etal:2011}. |
. On the largest scales. it has been somewhat surprising how well simplified “semi-numerical™ approaches (e.g..Zahnetal.2007:Mesinger&Furlanettoetal.2011a) match much more computationally expensive radiative transfer techniques (e.g..Abeletal.1999:GnedinIlievetal.2006:McQuinn2007:Trac&Cen 2007). | On the largest scales, it has been somewhat surprising how well simplified “semi-numerical” approaches \citep[e.g.,][]{zahn/etal:2007, mesinger/furlanetto:2007,
thomas/etal:2009, choudhury/etal:2009, alvarez/etal:2009, santos/etal:2010,
zahn/etal:2011a, mesinger/etal:2011a}
match much more computationally expensive radiative transfer techniques \citep[e.g.,][]{1999ApJ...523...66A,2001NewA....6..437G,
sokasian/etal:2001, 2002MNRAS.330L..53A, 2006MNRAS.369.1625I,
mcquinn/etal:2007, trac/cen:2007}. |
. In order to efficiently survey the wide parameter space of Άμος and focus on the large scale morphology and overall progress of retonization. we have chosen the simplified semi-numerical approach. | In order to efficiently survey the wide parameter space of $\mfp$ and focus on the large scale morphology and overall progress of reionization, we have chosen the simplified semi-numerical approach. |
Our implementation is similar to that of Zahnetal.(2007).. with the the addition of a treatment of photon consumption in absorption systems. | Our implementation is similar to that of \citet{zahn/etal:2007}, with the the addition of a treatment of photon consumption in absorption systems. |
Our approach. in which we treat abundance of absorbers as an input parameter. is complementary to that taken by Crocianietal.(2011). where the semi-numerical approach was used to the large scale distribution of absorbers. rather than to model the effect of the absorbers on the progress of retonization. as we do here. | Our approach, in which we treat abundance of absorbers as an input parameter, is complementary to that taken by \citet{crociani/etal:2011}, where the semi-numerical approach was used to the large scale distribution of absorbers, rather than to model the effect of the absorbers on the progress of reionization, as we do here. |
Our results are parametrized by different values offor the minimum source halo mass.ionizingefficiency of collapsed matter. andthe absorption system mean free path. | Our results are parametrized by different values offor the minimum source halo mass,ionizingefficiency of collapsed matter, andthe absorption system mean free path. |
We describe our simulations in $22. followed by the main results in. $3. ending with a discussion in | We describe our simulations in 2, followed by the main results in 3, ending with a discussion in |
ave suggested (hat galaxy formation began in earnest at redshifts less than about 7. but a ninimum redshift at which it is highly likely that galaxies such as our will have formed is [ar ess Certain because no direct measurement of such a redshift has been possible. | have suggested that galaxy formation began in earnest at redshifts less than about 7, but a minimum redshift at which it is highly likely that galaxies such as our will have formed is far less certain because no direct measurement of such a redshift has been possible. |
Estimates in (he range of z©1—2 are not unreasonable. and in a cosmological constant-cdominatec universe (his could correspond to a cosmic age of 4-5 (ανν. | Estimates in the range of $z \approx 1-2$ are not unreasonable, and in a cosmological constant-dominated universe this could correspond to a cosmic age of 4-5 Gyr. |
By comparing WMADP observations with previous estimates of lobular cluster ages. one can derive provide important new handles to probe the likely formation of the milky wav galaxy. and in a broader sense the formation of large scale cosmic structures. | By comparing WMAP observations with previous estimates of globular cluster ages, one can derive provide important new handles to probe the likely formation of the milky way galaxy, and in a broader sense the formation of large scale cosmic structures. |
The two key WALAP observations in this regard are the estimate of cosmic age (13.72:0.2 Gyr). and the redshift of reionization. al z217 (Spergeletal (2003))). | The two key WMAP observations in this regard are the estimate of cosmic age $ 13.7 \pm 0.2$ Gyr), and the redshift of reionization, at $z \approx 17$ \cite{spergel}) ). |
A recent comprehensive Monte Carlo analvsis of the age of the oldest globula clusters that attempts to incorporate existing svstematic uncertainties vields a G8% lower confidence limit age of 11.2 Gyr (IxraussandChabover (2003))). | A recent comprehensive Monte Carlo analysis of the age of the oldest globular clusters that attempts to incorporate existing systematic uncertainties yields a $\%$ lower confidence limit age of 11.2 Gyr \cite{krausschab}) ). |
Comparing this with the 686 upper limit on the age of the Universe from WMAP of 13.9 Cyr suggests an 90% upper limit g2.7 Gvr as the time after the Big Bang that globular clusters in our galaxy first formed from the primordial halo of gas that ultimately. collapsed to form the Milkv Waa. | Comparing this with the $\%$ upper limit on the age of the Universe from WMAP of $13.9$ Gyr suggests an $90\%$ upper limit $\approx 2.7$ Gyr as the time after the Big Bang that globular clusters in our galaxy first formed from the primordial halo of gas that ultimately collapsed to form the Milky Way. |
At the 95% confidence level the limit becomes approximately 3 Gyr. | At the $ 95\%$ confidence level the limit becomes approximately 3 Gyr. |
This not only improves upon previous estimates. it is (he first direct constraint on this euantity. | This not only improves upon previous estimates, it is the first direct constraint on this quantity. |
Ol somewhat more interest is a determination of the most probable time after the Big Dang at which our globular clusters formed. | Of somewhat more interest is a determination of the most probable time after the Big Bang at which our globular clusters formed. |
Now that WMADP has determined a surprisinglv early. if somewhat broad range of redshifts near 2=17 where the Universe reionized. corresponding to an age of about 200 Myr after the Die Bane. itis interesting to know whether (his corresponds to an early period of star formation. and whether structures as large as globular clusters of stars also formed (his early. | Now that WMAP has determined a surprisingly early, if somewhat broad range of redshifts near $z=17$ where the Universe reionized, corresponding to an age of about 200 Myr after the Big Bang, it is interesting to know whether this corresponds to an early period of star formation, and whether structures as large as globular clusters of stars also formed this early. |
Note that Jimenez οἱ al (2003))) have recently assumed this to be the case. | Note that Jimenez et al \cite{jimenez}) ) have recently assumed this to be the case. |
analysis — albeit concentrating on the three-point function — though their error bar extends to low values. as=0.03.170 | analysis – albeit concentrating on the three-point function – though their error bar extends to low values, $\sigma_8=0.93^{+0.06}_{-0.2}$. |
The 2dFGRS clustering data we use are an updated version of the analysis of 2? (Norberg et al.. | The 2dFGRS clustering data we use are an updated version of the analysis of \citet{NOR01,NOR02a} (Norberg et al., |
in prep.) | in prep.). |
This is the same dataset as used by ?. in their study of the luminosity dependence of the galaxy pairwise velocity dispersion. | This is the same dataset as used by \citet{TIN07} in their study of the luminosity dependence of the galaxy pairwise velocity dispersion. |
We compare the sample with Mi,Slog,IS to corresponding catalogues from our models in Fig. 7.. | We compare the sample with $M_{b_J}-5\log_{10}h<-18$ to corresponding catalogues from our models in Fig. \ref{fig:rwgrid1_bj}. |
The grid of models used is the same as for Fig. 3.. | The grid of models used is the same as for Fig. \ref{fig:rwgrid1}, |
but we select samples using a 5; magnitude threshold so as to match the space density of the 2dF sample. | but we select samples using a $b_J$ magnitude threshold so as to match the space density of the 2dF sample. |
The threshold is chosen so that the space density. 0.002495Alpe7. is similar to that of our main SDSS sample. | The threshold is chosen so that the space density, $0.00249\ h^3\ \mathrm{Mpc}^{-3}$, is similar to that of our main SDSS sample. |
The model clustering appears to depend more weakly on cosmology than for the r-selected samples. but there is still a clear trend and so we would hope still to be able to use these data to estimate 7. | The model clustering appears to depend more weakly on cosmology than for the $r$ -selected samples, but there is still a clear trend and so we would hope still to be able to use these data to estimate $\sigma_8$. |
We calculate 47. between the data and the model using a principal component analysis. again ignoring the errors on the model correlation functions as we did for theSDSS. | We calculate $\chi^2$ between the data and the model using a principal component analysis, again ignoring the errors on the model correlation functions as we did for theSDSS. |
This analysis is performed on the dimensionless projected correlation function. wri. denoted S(a)/a by ?.. | This analysis is performed on the dimensionless projected correlation function, $w_\mathrm{p}(r_\mathrm{p})/r_\mathrm{p}$, denoted $\Xi(\sigma)/\sigma$ by \citet{NOR02a}. |
We use only the first six principal components. which account for over 99 per cent of the variance. | We use only the first six principal components, which account for over 99 per cent of the variance. |
Statistical errors in the estimate of the principal components dominate the contribution to X? of the less significant components. | Statistical errors in the estimate of the principal components dominate the contribution to $\chi^2$ of the less significant components. |
This illustrates the problems which would arise if we instead used the whole covariance matrix. as highlighted in Section ??.. | This illustrates the problems which would arise if we instead used the whole covariance matrix, as highlighted in Section \ref{subsubsec:lumdep}. |
The resulting constraints on os are given in Fig. 8.. | The resulting constraints on $\sigma_8$ are given in Fig. \ref{fig:bjpars}. |
The model numbering is the same as for Fig. 5.. | The model numbering is the same as for Fig. \ref{fig:bottomline}, |
and is given in Table 2.. | and is given in Table \ref{tab:modelkey}. |
Noting the change in axis scale from Fig. 5.. | Noting the change in axis scale from Fig. \ref{fig:bottomline}, |
we see that the statistical error on ox from the 2dF sample is comparable to that from the SDSS. | we see that the statistical error on $\sigma_8$ from the 2dF sample is comparable to that from the SDSS. |
While most of the grids of models yield ox estimates similar to those obtained from the SDSS (if perhaps a little lower). there are several model grids which give significantly lower values of e for the 5,;-selected samples than they did for the ;-selected ones. | While most of the grids of models yield $\sigma_8$ estimates similar to those obtained from the SDSS (if perhaps a little lower), there are several model grids which give significantly lower values of $\sigma_8$ for the $b_J$ -selected samples than they did for the $r$ -selected ones. |
In fact. these grids (numbers |. 4 and 10) all use the C2000hib model. | In fact, these grids (numbers 1, 4 and 10) all use the C2000hib model. |
Our results therefore suggest that in this model the blue galaxies are more clustered than in the others. and hence lower dark matter clustering is required to mateh the observational result. | Our results therefore suggest that in this model the blue galaxies are more clustered than in the others, and hence lower dark matter clustering is required to match the observational result. |
This may be because the feedback excessively reddens isolated galaxies. leaving a larger proportion of the bluer galaxies in more massive. more clustered haloes. | This may be because the feedback excessively reddens isolated galaxies, leaving a larger proportion of the bluer galaxies in more massive, more clustered haloes. |
In any case it supports the idea that the clustering of model galaxies selected in bluer wavebands may be more dependent on the semi-analytic prescription, | In any case it supports the idea that the clustering of model galaxies selected in bluer wavebands may be more dependent on the semi-analytic prescription. |
As we note in Section ??.. the constraints from the 12 different sets of catalogues in Fig. | As we note in Section \ref{subsec:const}, the constraints from the 12 different sets of catalogues in Fig. |
S are not independent. since the underlying ;/N-body simulations in each case were seeded with he same phases. | \ref{fig:bottomline} are not independent, since the underlying $N$ -body simulations in each case were seeded with the same phases. |
This does. though. mean that we can use the scatter between the catalogues to estimate the systematic error in he constraint arising from our choice of semi-analytic prescription. | This does, though, mean that we can use the scatter between the catalogues to estimate the systematic error in the constraint arising from our choice of semi-analytic prescription. |
While we only have three different models (along with variants in which we do not tweak the parameters to match the +-band uminosity function). we can see that they differ quite strongly in the luminosity dependence (Fig. 3)) | While we only have three different models (along with variants in which we do not tweak the parameters to match the $r$ -band luminosity function), we can see that they differ quite strongly in the luminosity dependence (Fig. \ref{fig:rwgrid1}) ) |
and colour dependence (Fig. 8)) | and colour dependence (Fig. \ref{fig:bjpars}) ) |
of their clustering. | of their clustering. |
They may. then. be representative of he scatter we can expect between models that fit the r-band luminosity function and the primary constraints listed in Section ??.. | They may, then, be representative of the scatter we can expect between models that fit the $r$ -band luminosity function and the primary constraints listed in Section \ref{subsec:sams}. |
From the range of ~0.07 in the value of the best- σς between sets of catalogues. we estimate a systematic error from this source of Ε.Ο. | From the range of $\sim 0.07$ in the value of the best-fitting $\sigma_8$ between sets of catalogues, we estimate a systematic error from this source of $\pm 0.04$. |
The average size of the statistical error bars among the catalogues for which the best-fitting ex was a good fit in a X7 sense suggests a statistical error of. again £0.04. | The average size of the statistical error bars among the catalogues for which the best-fitting $\sigma_8$ was a good fit in a $\chi^2$ sense suggests a statistical error of, again $\pm
0.04$. |
Adding these in errors in quadrature to the mean of the best-fitting values in these catalogues gives a final figure of o=0.97+0.06 for the r-band samples. | Adding these in errors in quadrature to the mean of the best-fitting values in these catalogues gives a final figure of $\sigma_8=0.97\pm 0.06$ for the $r$ -band samples. |
Within the scope of the parameter variations we investigated. if we assume that σε=0.5 then none of our models gives us a good fit to the SDSS clustering over the full range of scales. | Within the scope of the parameter variations we investigated, if we assume that $\sigma_8=0.8$ then none of our models gives us a good fit to the SDSS clustering over the full range of scales. |
This does not preclude the possibility that models that do not fit the luminosity function. or that include different physies to our particular analytic model. may achieve such a fit. | This does not preclude the possibility that models that do not fit the luminosity function, or that include different physics to our particular semi-analytic model, may achieve such a fit. |
Our value for ox is clearly at odds with the most striking recent measurement. from the three-year WMAP data. | Our value for $\sigma_8$ is clearly at odds with the most striking recent measurement, from the three-year WMAP data. |
Using those data alone. ? quote e=0.761nm for flat. power-law ACDM. and this value is not significantly increased (though the error bars tighten) when the data are analysed jointly with galaxy clustering or supernova data. | Using those data alone, \citet{SPE07} quote $\sigma_8=0.761^{+0.049}_{-0.048}$ for flat, power-law $\Lambda$ CDM, and this value is not significantly increased (though the error bars tighten) when the data are analysed jointly with galaxy clustering or supernova data. |
There is. though. some tension between the WMAP result and results from weak lensing surveys. which provide rather complementary parameter constraints (2).. | There is, though, some tension between the WMAP result and results from weak lensing surveys, which provide rather complementary parameter constraints \citep{TER05}. \citeauthor{SPE07}' |
2"s joint analysis of WMAP and the CFHTLS lensing survey (22). pulls their estimate up to ex=(N27(os. with the lensing data alone favouring even higher values. | 's joint analysis of WMAP and the CFHTLS lensing survey \citep{HOE06,SEM06} pulls their estimate up to $\sigma_8=0.827^{+0.026}_{-0.025}$, with the lensing data alone favouring even higher values. |
9? have combined data from the CFHTLS and other surveys to give σκ(ζλνι0.24)"=OSL+0.07. | \citet{BEN07} have combined data from the CFHTLS and other surveys to give $\sigma_8(\Omega_\mathrm{M}/0.24)^{0.59} = 0.84\pm
0.07$. |
a forest data can be used to constrain the power spectrum: ? quote σε=0.90+0.08 (reducing to 0.84 incorporating the new constraints on reionization from the three-year WMAP data). | $\alpha$ forest data can be used to constrain the power spectrum; \citet{SEL05a} quote $\sigma_8=0.90\pm 0.03$ (reducing to 0.84 incorporating the new constraints on reionization from the three-year WMAP data). |
Measurements of cluster abundance have frequently been used to constrain ay. but provide a very wide range of estimates because of the difficuly in relating the properties of an observed cluster to its mass (e.g.. 2).. | Measurements of cluster abundance have frequently been used to constrain $\sigma_8$, but provide a very wide range of estimates because of the difficuly in relating the properties of an observed cluster to its mass \citep[e.g.,][]{RAS05}. . |
Recent estimates are. though. consistent with the WMAP determination of oy (e.g.. 2).. | Recent estimates are, though, consistent with the WMAP determination of $\sigma_8$ \citep[e.g.,][]{PIE03}. . |
The overall picture of the value of σε from other methods is therefore a little confusing. but even the highest recent estimates are only marginally consistent with ours. | The overall picture of the value of $\sigma_8$ from other methods is therefore a little confusing, but even the highest recent estimates are only marginally consistent with ours. |
A possible source of tension between our constraints and those from WMAP isthat we have | A possible source of tension between our constraints and those from WMAP isthat we have |
contain a population of large dust erains to account for the shallow spectral slope with power law index <3 observed in this frequency range (Trilling et al. | contain a population of large dust grains to account for the shallow spectral slope with power law index $\lesssim3$ observed in this frequency range (Trilling et al. |
2001. Calvet et al. | 2001, Calvet et al. |
2002). | 2002). |
The eood fit of the new 89 GlIIz measurement wilh expectations gives confidence in the reliability of the ATCA svstem. and the accuraey of the absolute fIux scale. | The good fit of the new 89 GHz measurement with expectations gives confidence in the reliability of the ATCA system, and the accuracy of the absolute flux scale. |
Figuree 2 shows a series of imagese in four velocity bins for the J=10 line emission near LSR velocity ~3 kms +. | Figure \ref{fig:tw_4chan} shows a series of images in four velocity bins for the $^+$ J=1–0 line emission near LSR velocity $\sim3$ km $^{-1}$. |
A narrow line emission feature is clearly visible at the stellar position with a velocity and linewidth commensurate with previously reported. molecular line detections from the [ace-on disk. which have Vt4572.9 km | and AV&0.6 km 1 | A narrow line emission feature is clearly visible at the stellar position with a velocity and linewidth commensurate with previously reported molecular line detections from the face-on disk, which have $V_{\rm LSR}$ =2.9 km $^{-1}$ and $\Delta V\approx 0.6$ km $^{-1}$. |
Figure 3.» shows the spectrum at the continuum position. | Figure \ref{fig:tw_spec} shows the spectrum at the continuum position. |
The peak line Πας at 0.5 kin 1 resolution is 0.40 Jv. which corresponds to a brightness temperature of 4.3 IX in the 679x271 beam. | The peak line flux at 0.5 km $^{-1}$ resolution is 0.40 Jy, which corresponds to a brightness temperature of 4.3 K in the $6\farcs9\times2\farcs1$ beam. |
The line emission from the disk is clearly spatially resolved: a circular Gaussian fit (o the visibilities in à 0.5 kan J| bin gives a full-width at half maximum size of 372+(08, | The line emission from the disk is clearly spatially resolved; a circular Gaussian fit to the visibilities in a 0.5 km $^{-1}$ bin gives a full-width at half maximum size of $3\farcs2\pm0\farcs8$. |
Fieure 4 shows the 89 GIIz continuum emission detected [rom ID 100546. | Figure \ref{fig:hd_cont} shows the 89 GHz continuum emission detected from HD 100546. |
The peak position is coincident with the star to better than 1". | The peak position is coincident with the star to better than $1''$. |
A fit to the visibilities gives a point source flux of 36€3 mJy at the star position. | A fit to the visibilities gives a point source flux of $36\pm3$ mJy at the star position. |
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