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Recently. many such galaxies have been selected photometrically € Stanway et al..
Recently, many such galaxies have been selected photometrically ( Stanway et al.,
2003. Bouwens et al..
2003, Bouwens et al.,
2003. Iwata et al..
2003, Iwata et al.,
2003) and some have been confirmed to be at high redshift by spectroscopy (Lehnert Bremer. 2003: Bunker et al..
2003) and some have been confirmed to be at high redshift by spectroscopy (Lehnert Bremer, 2003; Bunker et al.,
2003: Cuby et al..
2003; Cuby et al.,
2003) In a recent paper. we used perhaps the simplest technique to select and confirm redshifts for a high redshift galaxy sample drawn from a single 40 aremin? field imaged by the VLT (Lehnert Bremer 2003).
2003) In a recent paper, we used perhaps the simplest technique to select and confirm redshifts for a high redshift galaxy sample drawn from a single 40 $^2$ field imaged by the VLT (Lehnert Bremer 2003).
The opacity of neutral hydrogen along the line-of-sight to the galaxies means that the light shortward of 1216 in their rest-frame is strongly absorbed. leading to a sharp drop in their spectra at the corresponding redshifted wavelength.
The opacity of neutral hydrogen along the line-of-sight to the galaxies means that the light shortward of 1216 in their rest-frame is strongly absorbed, leading to a sharp drop in their spectra at the corresponding redshifted wavelength.
By selecting objects detected in the £ band to /1g.=26.3. but undetected at [Hygc27.8 Gc. R-band "dropouts"). we identified 13 candidate >4.8 galaxies.
By selecting objects detected in the $I-$ band to $I_{AB}=26.3$, but undetected at $R_{AB}>27.8$ $i.e.$ R-band “dropouts”), we identified 13 candidate $z>4.8$ galaxies.
Spectroscopy of 12 of these confirmed that 6 were at 4S<z« 5.S.as they possessed a Lyman à emission line at the same wavelength as a break in their spectra.
Spectroscopy of 12 of these confirmed that 6 were at $4.8<z<5.8$, as they possessed a Lyman $\alpha$ emission line at the same wavelength as a break in their spectra.
The spectra were consistent with the objects being strongly starforming galaxies.
The spectra were consistent with the objects being strongly starforming galaxies.
Similar techniques have been used by other groups to identify other unobscured star forming galaxies at even higher redshifts (Stanway et al.
Similar techniques have been used by other groups to identify other unobscured star forming galaxies at even higher redshifts (Stanway et al.
2003. Bouwens et al..
2003, Bouwens et al.,
2003).
2003).
From the study of galaxies at 2755. Lehnert Bremer (2003) concluded that relatively luminous galaxies CAie(1700.1) 21) have insufficient numbers and ionizing luminosity to keep the universe at z>S ionized and. since no broad line objects were detected. that QSOs contribute very little to the overall ionization.
From the study of galaxies at $>$ 5, Lehnert Bremer (2003) concluded that relatively luminous galaxies $M_{AB}(1700\AA)>-21$ ) have insufficient numbers and ionizing luminosity to keep the universe at $>$ 5 ionized and, since no broad line objects were detected, that QSOs contribute very little to the overall ionization.
This last conclusion is supported by Barger et al. (
This last conclusion is supported by Barger et al. (
2003) who found that the number of luminous X-ray-selected QSOs in the Chandra Deep Field North is insufficient to contribute greatly to the overall ionization of the IGM at zz:5.5.
2003) who found that the number of luminous X-ray-selected QSOs in the Chandra Deep Field North is insufficient to contribute greatly to the overall ionization of the IGM at $\approx$ 5.5.
However. our original study was limited by the size of the field imaged by the VLT (40 aremin?). the ground-based seeing (0.8-0.9 aresec) of the imaging and spectroscopy. and the lack of multi-wavelength data for this field.
However, our original study was limited by the size of the field imaged by the VLT (40 $^2$ ), the ground-based seeing (0.8-0.9 arcsec) of the imaging and spectroscopy, and the lack of multi-wavelength data for this field.
Of particular concern was the AGN fraction in faint galaxies at these redshifts and the possible contribution of "hidden AGN” to the overall ionization budget of the high redshift universe.
Of particular concern was the AGN fraction in faint galaxies at these redshifts and the possible contribution of “hidden AGN” to the overall ionization budget of the high redshift universe.
Classes of AGN exist that have strong X-ray emission with the spectral characteristics of AGN but with only subtle or no signs of AGN activity in the rest-frame UV or optical (e.g.. Giacconi et al.
Classes of AGN exist that have strong X-ray emission with the spectral characteristics of AGN but with only subtle or no signs of AGN activity in the rest-frame UV or optical (e.g., Giacconi et al.
2001).
2001).
However. by performing a comparable selection on a larger field imaged by HST and other telescopes over a range in wavelengths. we can determine scale
However, by performing a comparable selection on a larger field imaged by HST and other telescopes over a range in wavelengths, we can determine scale
simulationbox!®.
simulation.
. With the probability 1—f, the object is associated with one of the selected subhalos, and with the probability f it is put in the center of one of the halos.
With the probability $1-\fcen$, the object is associated with one of the selected subhalos, and with the probability $\fcen$ it is put in the center of one of the halos.
This procedure is repeated multiple times randomly selecting one of the box axes as the line of sight.
This procedure is repeated multiple times randomly selecting one of the box axes as the line of sight.
Using these simulated we derive a model of €,(rp) and &(a) for each objects,combination of Vinin and f.
Using these simulated objects, we derive a model of $\xir$ and $\xip$ for each combination of $\Vmin$ and $\fcen$.
The correlation functions £j(rj) and &,(7) were derived from the simulation outputs on a grid of parameters within the range Vinin€[200;370] and f€[0;1].
The correlation functions $\xir$ and $\xip$ were derived from the simulation outputs on a grid of parameters within the range $\Vmin\in[200;370]\,$ and $\fcen\in[0;1]$.
Examples are shown in Fig.
Examples are shown in Fig.
7 and 8..
\ref{fig:plot_f} and \ref{fig:vel}. .
Comparison of the two panels in Fig.
Comparison of the two panels in Fig.
7 illustrates the effect of f on the correlation functions.
\ref{fig:plot_f} illustrates the effect of $\fcen$ on the correlation functions.
For smaller f (more located in satellite left panel), the projected objectscorrelation function €,(rp)galaxies, shows a stronger component at d< in excess of a power law extrapolation from larger 1/'!Mpcseparations.
For smaller $\fcen$ (more objects located in satellite galaxies, left panel), the projected correlation function $\xir$ shows a stronger component at $d<1\,h^{-1}\,$ Mpc in excess of a power law extrapolation from larger separations.
This excess is attributed to the “1-halo” term in the analytic halo model of the correlation function.
This excess is attributed to the “1-halo” term in the analytic halo model of the correlation function.
At the same time, é£j(z) shows a of the correlation amplitude at d<1h! largerMpc with suppressionrespect to &,(rp) at the same separations, and a enhancement over &,(rp) at d=2—10h! Mpc for small f. stronge
At the same time, $\xip$ shows a larger suppression of the correlation amplitude at $d<1\,h^{-1}\,$ Mpc with respect to $\xir$ at the same separations, and a stronger enhancement over $\xir$ at $d=2-10\,h^{-1}\,$ Mpc for small $\fcen$.
rThis is the consequence of a stronger "finger of God" effect in the case when more objects are located in the satellite galaxies.
This is the consequence of a stronger “finger of God” effect in the case when more objects are located in the satellite galaxies.
Unfortunately, the statistical uncertainties in the real data do not allow a detailed modeling of the observed £j(ry) at small separations.
Unfortunately, the statistical uncertainties in the real data do not allow a detailed modeling of the observed $\xir$ at small separations.
Modeling of the &,(7) at small separations is further complicated by the effect of uncertainties in the redshift measurements 3.2)).
Modeling of the $\xip$ at small separations is further complicated by the effect of uncertainties in the redshift measurements \ref{ssec:fun:results}) ).
At large separations, z0.1 of the simulation box size, the correlation functions derived from the simulations are not reliable(???).
At large separations, $\gtrsim 0.1$ of the simulation box size, the correlation functions derived from the simulations are not reliable.
. Taking all these considerations into account, we will match the observed and model correlation functions in the intermediate range of radii, α-1--12/1 Μρο.
Taking all these considerations into account, we will match the observed and model correlation functions in the intermediate range of radii, $d=1-12\,h^{-1}\,$ Mpc.
First, we compute the correlation length, ro, for each combination (Vin,f).
First, we compute the correlation length, $r_{0}$, for each combination $(\Vmin,\fcen)$.
This is done by fitting a power law function, to in the r,=1—12ho} Mpc.
This is done by fitting a power law function, $(r_{p}/r_{0})^{-\gamma}$, to $\xir$ in the range $r_{p}=1-12\,h^{-1}\,$ Mpc.
We then compute (rj/ro)7,the ratio £j(ry)&,(7)/€,(rp) range(an example is shown in Fig. 9))
We then compute the ratio $\xip/\xir$ (an example is shown in Fig. \ref{fig:ratio:a}) )
and fit it in the same range of separations with a modified log-normal function, The index g is fixed at the mean best-fit value for all (Vmin,f) combinations, g=1.2.
and fit it in the same range of separations with a modified log-normal function, The index $g$ is fixed at the mean best-fit value for all $(\Vmin,\fcen)$ combinations, $g=1.2$.
The ratio derived from the simulations shows substantial &(x)/€,(rp)variations related to cosmic variance (this can be estimated by comparing theresults for three viewing angles, see errorbars in Fig. 9)).
The $\xip/\xir$ ratio derived from the simulations shows substantial variations related to cosmic variance (this can be estimated by comparing theresults for three viewing angles, see errorbars in Fig. \ref{fig:ratio:a}) ).
Therefore, we need to smooth the results of the fit by eq. 17..
Therefore, we need to smooth the results of the fit by eq. \ref{frm:gauss}. .
This is achieved by fitting low-order polynomials to the parameters D, do, and A as a function of Vinin and f.
This is achieved by fitting low-order polynomials to the parameters $D$, $d_{0}$, and $A$ as a function of $\Vmin$ and $\fcen$.
We found that an adequate description is achieved if the fit values of D and do are approximated as a linear function of f, and A(Vmin,f) is fit a second-order
We found that an adequate description is achieved if the best-fit values of $D$ and $d_{0}$ are approximated as a linear function of $\fcen$, and $A(\Vmin,\fcen)$ is fit by a second-order polynomial.
An example of the fitting functionby derived from this smooth polynomial.map is shown in Fig. 9..
An example of the fitting function derived from this smooth map is shown in Fig. \ref{fig:ratio:a}.
Due to the size of the simulation box, the uncertainties in the smoothed model are still finite, but we verified that they are negligible compared to those in the data.
Due to the size of the simulation box, the uncertainties in the smoothed model are still finite, but we verified that they are negligible compared to those in the data.
In applying the correlation function model to the data we avoid including any sensitivity to the of the correlation function.
In applying the correlation function model to the data we avoid including any sensitivity to the of the correlation function.
The primary motivation is that our method assigning the AGN locations to the dark matter halos may be overly simplistic to correctly predict the details of the correlation function shape.
The primary motivation is that our method assigning the AGN locations to the dark matter halos may be overly simplistic to correctly predict the details of the correlation function .
. Also, the cosmological parameters used in the simulation are slightly different from the currently accepted values, resulting in a systematic difference in the shape of the perturbation power spectrum in the simulated and real universes.
Also, the cosmological parameters used in the simulation are slightly different from the currently accepted values, resulting in a systematic difference in the shape of the perturbation power spectrum in the simulated and real universes.
This said, the models derived from simulations do a fit to the £,(ry) data (see discussion in providegoodrefsec:res:vmin-f below).
This said, the models derived from simulations do provide a good fit to the $\xir$ data (see discussion in \\ref{sec:res:vmin-f} below).
Based on these considerations, our Xy? includes two components.
Based on these considerations, our $\chi^{2}$ includes two components.
First, we use the value of the correlation length derived from fitting the €,(r)) function 3.2)).
First, we use the value of the correlation length derived from fitting the $\xir$ function \ref{ssec:fun:results}) ).
Second, we use the ratio x=&,(7)/€p(rp) in the range of separations 1—1247! Mpc (the data at π«1Η Mpc are not used because they are likely affected by the redshift measurement uncertainties, see refssec:fun:results)).
Second, we use the ratio $x=\xip/\xir$ in the range of separations $1-12\,h^{-1}\,$ Mpc (the data at $\pi<1\,h^{-1}\,$ Mpc are not used because they are likely affected by the redshift measurement uncertainties, see \\ref{ssec:fun:results}) ).
For halos with circular velocities Vmax~300km (as indicated by the amplitude of the AGN correlation function, see below), the "fingers of God" extend toVmax/H~3h7! Mpc, just in the middle of this range of separations.
For halos with circular velocities $\vmax\sim 300\,$ (as indicated by the amplitude of the AGN correlation function, see below), the “fingers of God” extend to$\vmax/H\sim 3\,h^{-1}\,$ Mpc, just in the middle of this range of separations.
Formally, the constraints on the parameters of the AGN population model, Vinin and f, are derived using a y?function computed as where the summation in the second term is over the data points in the 1—10/1 Mpc separation range, and the model functions are those describedin 4.3..
Formally, the constraints on the parameters of the AGN population model, $\Vmin$ and $\fcen$ , are derived using a $\chi^{2}$function computed as where the summation in the second term is over the data points in the $1-10\,h^{-1}\,$ Mpc separation range, and the model functions are those describedin \ref{sec:model:corr:functions}. .
narrow bands on these diagrams.
narrow bands on these diagrams.
This means that the luminosities. raciii anc temperatures of these stars depend on their masses.
This means that the luminosities, radii and temperatures of these stars depend on their masses.
These statistical relations can be described by the following formulas: We assume that the bigger scatter. of the mass-temperature diagram is due mainly to the weakly established calibration {ο4) for the late low-mass stars.
These statistical relations can be described by the following formulas: We assume that the bigger scatter of the mass-temperature diagram is due mainly to the weakly established calibration $T$ $(V-I)$ for the late low-mass stars.
Moreover. some star temperatures probably have been. determined without taking into account the reddening.
Moreover, some star temperatures probably have been determined without taking into account the reddening.
The manifestations of stellar activity as Lla emission. spots. Lares. etc..
The manifestations of stellar activity as $\alpha$ emission, spots, flares, etc.,
are consequences of magnetic fields.
are consequences of magnetic fields.
It is assumed that the fully-convective late stars have strong. long-lasting. magnetic field.
It is assumed that the fully-convective late stars have strong, long-lasting, magnetic field.
According to Mullan&MacDonald.(2001) the larger radii and lower temperatures of dM stars can be explained by the presence of strong magnetic fields and their activity is at the saturation limit.
According to \citet{mullan01} the larger radii and lower temperatures of dM stars can be explained by the presence of strong magnetic fields and their activity is at the saturation limit.
Perhaps the significant spot coverage clecreases the photospheric temperature which the star compensates by increasing its racius to conserve the total racliative Hux.
Perhaps the significant spot coverage decreases the photospheric temperature which the star compensates by increasing its radius to conserve the total radiative flux.
The photospheric activity of the late stars is demonstrated mainlv by OConnell effect and distorted Light curves.
The photospheric activity of the late stars is demonstrated mainly by O'Connell effect and distorted light curves.
They can be reproduced. by surface temperature inhomogeneities (spots).
They can be reproduced by surface temperature inhomogeneities (spots).
Lt is reasonable to assume existence of cool spots by analogy with our Sun.
It is reasonable to assume existence of cool spots by analogy with our Sun.
Usually they are put on the primary star although the same effect can be reached by spots on the
Usually they are put on the primary star although the same effect can be reached by spots on the
instead of along with the partial pressure of CRs normalized to dnp and to the shock ram pressure Using these variables. denoting g=P?/p.. assuming a steady state and 1.. eqs.(4..9)) can be rewritten as (Bell1973:Drury.etal. 19963) llere Qu/Ox and the wave intensity is now treated as a [uncion of p rather (han f according to the resonance relation Ap=const.
instead of along with the partial pressure of CRs normalized to p and to the shock ram pressure Using these variables, denoting g=P/p, assuming a steady state and 1, \ref{dc1}, \ref{wke}) ) can be rewritten as \citealt{bell78a, dru96}) ) Here x and the wave intensity is now treated as a funcion of p rather than k according to the resonance relation kp=const.
. The CR. diffusion coellicient can be expressed (hrough the wave intensity by where &p(p) is the Dohm diffusion coefficient.
The CR diffusion coefficient can be expressed through the wave intensity by where (p) is the Bohm diffusion coefficient.
The difference between these equations and those used by. Dell(1978):Drury.etal.(1996) is due to the terms with 0 and the 5/--term on the r.l.s.
The difference between these equations and those used by, \cite{bell78a, dru96} is due to the terms with 0 and the St -term on the r.h.s.
of eq.(14)).
of \ref{wke2}) ).
Far away Irom (he sub-shock where 0.. and where the wave collision term is also small due to the low particle pressure P.. one simply obtains Note. that this shows the limitation of the linear approach in the case of strong shocks 1..
Far away from the sub-shock where 0, and where the wave collision term is also small due to the low particle pressure P, one simply obtains Note, that this shows the limitation of the linear approach in the case of strong shocks 1.
The most important change to the acceleration process comes from the
The most important change to the acceleration process comes from the
dramatic decrease is à consequence of the accelerated. global chemical enrichment produced by the flatter IME: as these isotopic ratios decrease rapidly with increasing metallicity (recall the earlier cliscussion surrounding Figures 3. and 4)). as opposed to any mass-depencdeney in the vields.
dramatic decrease is a consequence of the accelerated global chemical enrichment produced by the flatter IMF, as these isotopic ratios decrease rapidly with increasing metallicity (recall the earlier discussion surrounding Figures \ref{fig:ww95_metallicity} and \ref{fig:kl07_metallicity}) ), as opposed to any mass-dependency in the yields.
While an IME slope of 0.9 appears to be a viable solution to the low ratios seen at z=0.89. the predicted global metallicity would be ~4 solar: such an extreme value seems unlikely for a fairly average looking late-tvpe spiral at We note that neither scenario appears consistent with the low CSC seen in the local halo stars within the Milky Wav (Le. the t=1 Gar datum in Fig. 7)).
While an IMF slope of 0.9 appears to be a viable solution to the low ratios seen at z=0.89, the predicted global metallicity would be $\sim$ $\times$ solar; such an extreme value seems unlikely for a fairly average looking late-type spiral at We note that neither scenario appears consistent with the low $^{12}$ $^{13}$ C seen in the local halo stars within the Milky Way (i.e., the t=1 Gyr datum in Fig. \ref{fig:imf}) ).
To try and reach such low values. we examined a range of halo infall timescales and. disk infall “delays”. for exiunple. having a 5 Car delay. between the first and. second infall phase.
To try and reach such low values, we examined a range of halo infall timescales and disk infall “delays”, for example, having a 5 Gyr delay between the first and second infall phase.
ALL of these attempts to reach the low values seen in halo stars were to no avail. however. and the models could not be made to reproduce the observational data.
All of these attempts to reach the low values seen in halo stars were to no avail, however, and the models could not be made to reproduce the observational data.
This is not completely surprising. as none of the vields used in this work have ratios as low as 20. as shown in bie. 1..
This is not completely surprising, as none of the yields used in this work have ratios as low as 20, as shown in Fig. \ref{fig:c_s.isotopes},
other than the 5AZ.. value of IXLOT.
other than the $M_{\odot}$ value of KL07.
Therefore. there is no combination of parameters in this work that would. lead. to such low values. ancl the answer must lic in additional physics.
Therefore, there is no combination of parameters in this work that would lead to such low values, and the answer must lie in additional physics.
One such solution. ancl perhaps the most likely. is the idea of rotationallv-induced mixing at low-metallicity. as demonstrated by Chiappini οἱ al (2008). to which we refer the reacler.
One such solution, and perhaps the most likely, is the idea of rotationally-induced mixing at low-metallicity, as demonstrated by Chiappini et al (2008), to which we refer the reader.
besides the MSHO05 masses and the evolutionary masses presented in this work.
besides the MSH05 masses and the evolutionary masses presented in this work.
The Tables 4. and 5 show the determined initial masses as well as the mean. minimal. and maximal present-day masses according to the rotating (300 km/s) and non-rotating stellar evolution models. and the MSHOS5 O star spectral type definition.
The Tables \ref{tab:Orot} and \ref{tab:Onorot} show the determined initial masses as well as the mean, minimal, and maximal present-day masses according to the rotating (300 km/s) and non-rotating stellar evolution models, and the MSH05 O star spectral type definition.
Tables 6 and 7. show the same for LMC and SMC metallicities. respectively.
Tables \ref{tab:Oz08} and \ref{tab:Oz04} show the same for LMC and SMC metallicities, respectively.
The errors shown for the mass determination are the lowest mass and maximum mass models that pass through the spectral type.
The errors shown for the mass determination are the lowest mass and maximum mass models that pass through the spectral type.
They also include an error margin of + 1000 K for the MSHOS spectral type definitions.
They also include an error margin of $\pm$ 1000 K for the MSH05 spectral type definitions.
The differences in the supergiant mass errors from one subtype to the next have two main reasons.
The differences in the supergiant mass errors from one subtype to the next have two main reasons.
Spectral types later than O 6.5 | are reached during stellar evolution from the hot end by more massive stars and the cold end by less massive stars.
Spectral types later than O 6.5 I are reached during stellar evolution from the hot end by more massive stars and the cold end by less massive stars.
This results in a larger range of possible masses.
This results in a larger range of possible masses.
Also. the subtypes are not of the same area in the {Τε -space (see Fig. 1)).
Also, the subtypes are not of the same area in the $L$ -space (see Fig. \ref{fig:sptypes}) ).
Therefore. some subtypes simply have a higher probability to be encountered by the model tracks.
Therefore, some subtypes simply have a higher probability to be encountered by the model tracks.
It would be possible to reduce these errors by introducing more Juminosity classes. like IL. Ia and Ib.
It would be possible to reduce these errors by introducing more luminosity classes, like II, Ia and Ib.
But no MSHO05 definitions for these classes presently exist.
But no MSH05 definitions for these classes presently exist.
Furthermore. the table shows the mean time the models spend in each spectral type box.
Furthermore, the table shows the mean time the models spend in each spectral type box.
Again. the errors are defined by the lowest and most-massive model passing through the
Again, the errors are defined by the lowest and most-massive model passing through the
has always been difficult to understand because il is very hard to explain how nitrogen abundances could differ substantially between otherwise verv similar svstems.
has always been difficult to understand because it is very hard to explain how nitrogen abundances could differ substantially between otherwise very similar systems.
Li&Burstein(200:WN) proposed a scenario where GCs were formed from zero-metallicity material. pre-enriched by Lypernovae explosions in the center of ~LO?AL. gas clouds.
\cite{lb03} proposed a scenario where GCs were formed from zero-metallicity material, pre-enriched by hypernovae explosions in the center of $\sim 10^6 M_\odot$ gas clouds.