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The accretion rate increases with source Iuninositv: this is consistent with the fiudiug iat the star formation rate in a cloud is roughly roportional to the bolometric Nuninosity according to ie expression AL.(AL.vr1je|10IFL. (Pluae ct al.
The accretion rate increases with source luminosity: this is consistent with the finding that the star formation rate in a cloud is roughly proportional to the bolometric luminosity according to the expression $\dot{M}_\ast(M_\odot{\rm yr^{-1}})\simeq4~10^{-10}\,L(L_\odot)$ (Plume et al.
1997).
1997).
Therefore. it is reasonable to expect that also Adj satisfies a simular relatiouship.
Therefore, it is reasonable to expect that also $\dot{M}_{\rm acc}$ satisfies a similar relationship.
We heuce fitted ie data in Fig.
We hence fitted the data in Fig.
7 assunuineg Mae—XLeip. thus obtainingbraiuimg οAMLyin—2410M.xrHL+.
\ref{fratlum} assuming $\dot{M}_{\rm acc}\propto L_{\rm FIR}$, thus obtaining $\dot{M}_{\rm acc}/L_{\rm FIR}=2.1~10^{-8}~M_\odot{\rm yr^{-1}}L_\odot^{-1}$.
The ratio AL{AlecLO is the star formation efficiency in the chluups.
The ratio $\dot{M}_\ast/\dot{M}_{\rm acc}\simeq1.9\%$ is the star formation efficiency in the clumps.
Ou the other haud. a lower init to the same value cau be obtained from the ratio between the mass of je earlv-tvpe star jonisus the embedded UC ireeion and the mass of the correspondiug clip: this is Happroximately 30AL,5000.AL.~0.6%. consistent with le previous estimate.
On the other hand, a lower limit to the same value can be obtained from the ratio between the mass of the early-type star ionising the embedded UC region and the mass of the corresponding clump: this is approximately $30~M_\odot/5000~M_\odot\simeq0.6\%$, consistent with the previous estimate.
It is also worth notius that the nass accretion rates are of the order o 10 2ALvrt: values such Ligh could support theories which predict that ielh-mass stars can form through accretion (Behrend Alaeder 2001).
It is also worth noting that the mass accretion rates are of the order of $\sim$ $^{-2}~M_\odot\,{\rm yr}^{-1}$: values such high could support theories which predict that high-mass stars can form through accretion (Behrend Maeder 2001).
Iun conclusion. although other explanations are possible. we cannot exclude the possibility. that the molecular chumps trace by the ttransitious are on the edee of eravitatioual collapse.
In conclusion, although other explanations are possible, we cannot exclude the possibility that the molecular clumps traced by the transitions are on the edge of gravitational collapse.
Iu support of this hypothesis we note that evidence for iufall in some of our clumps has been found by other authors ou the basis of hieh augular resolution observations iu various molecular tracers (IHlofuer et al.
In support of this hypothesis we note that evidence for infall in some of our clumps has been found by other authors on the basis of high angular resolution observations in various molecular tracers (Hofner et al.
1999: Maxia et al.
1999; Maxia et al.
2001).
2001).
In. Sect.
In Sect.
L.2.2 we will demoustrate that also the deusitv structure of the clumps favours the scenario preseuted above.
\ref{sdens} we will demonstrate that also the density structure of the clumps favours the scenario presented above.
these two filters (see Figure 1 Figure 5)), although the selection efficiency drops at the extremes of this range.
these two filters (see Figure \ref{fig:lbg_spectra} Figure \ref{fig:selection}) ), although the selection efficiency drops at the extremes of this range.
Searching for distant galaxies using only broadband photometry means that contamination is a potentially serious issue.
Searching for distant galaxies using only broadband photometry means that contamination is a potentially serious issue.
There are two main sources of contamination: objects whose intrinsic colours are similar to those of the target population; and faint objects with intrinsically different colours but whose observed colours scatter into our selection because of photometric noise.
There are two main sources of contamination: objects whose intrinsic colours are similar to those of the target population; and faint objects with intrinsically different colours but whose observed colours scatter into our selection because of photometric noise.
We note that the effect of transient phenomena is not significant for the selection of Y-drops, since the WFC3 Y, J H images were taken close in time.
We note that the effect of transient phenomena is not significant for the selection of $Y$ -drops, since the WFC3 $Y$, $J$ $H$ images were taken close in time.
This is unlike our selection of z'- (e.g. Wilkins et 22010a, 2010b; Bunker et 22010) where the ACS z'-band and WFC3 Y-band were separated by many years, so a transient such as a supernova or high-proper-motion object which entered the Y-band but was absent at that location in the ACS could be erroneously identified as à Lyman-break galaxy.
This is unlike our selection of $z'$ -drops (e.g. Wilkins et 2010a, 2010b; Bunker et 2010) where the ACS $z'$ -band and WFC3 $Y$ -band were separated by many years, so a transient such as a supernova or high-proper-motion object which entered the $Y$ -band but was absent at that location in the ACS could be erroneously identified as a Lyman-break galaxy.
Indeed, a probable supernova was identified in the WFC3 imaging of the HUDF (e.g., Bunker et 22010).
Indeed, a probable supernova was identified in the WFC3 imaging of the HUDF (e.g., Bunker et 2010).
There are two distinct types of objects whose apparent Yio5w/098m—J125w colours are similar to those of Lyman- galaxies at z~8: lower-redshift (2zz 2) galaxies have the Balmer/4000À break feature between the two filters used, Yiosw/o9sm and Ji25w, while some low mass dwarf stars, especially those of L and T spectral class, have low temperatures and broad absorption features that can mimic a spectral break.
There are two distinct types of objects whose apparent $Y_{105w/098m}-J_{125w}$ colours are similar to those of Lyman-break galaxies at $z\approx 8$: lower-redshift $z\approx 2$ ) galaxies have the $4000{\rm \AA}$ break feature between the two filters used, $Y_{105w/098m}$ and $J_{125w}$, while some low mass dwarf stars, especially those of L and T spectral class, have low temperatures and broad absorption features that can mimic a spectral break.
Examples of the spectral energy distributions (SEDs) of each of these types of object (a model 3.5 Gyr old single-aged stellar population at z—2.5 and a T4.5 dwarf star) are shown in Figure 1..
Examples of the spectral energy distributions (SEDs) of each of these types of object (a model $3.5$ Gyr old single-aged stellar population at $z=2.5$ and a T4.5 dwarf star) are shown in Figure \ref{fig:lbg_spectra}.
In the case of lower redshift galaxies the slope of the SED longward of the spectral break (i.e. longward of Yio5w/o9sm) is somewhat redder than that predicted for a high-z star forming galaxy.
In the case of lower redshift galaxies the slope of the SED longward of the spectral break (i.e. longward of $Y_{105w/098m}$ ) is somewhat redder than that predicted for a $z$ star forming galaxy.
The addition of a further filter at wavelengths redder than the Jios filter (Hieow in this case) can then be used to discriminate between high-z and lower redshift galaxies (Figure 2)).
The addition of a further filter at wavelengths redder than the $J_{125w}$ filter $H_{160w}$ in this case) can then be used to discriminate between $z$ and lower redshift galaxies (Figure \ref{fig:cc_1}) ).
L and T dwarfs contamination in the HUDF and P34 field is mostly ruled out by the Ύπρδυ—J125w colour selection we adopted.
L and T dwarfs contamination in the HUDF and P34 field is mostly ruled out by the $Y_{105w}-J_{125w}$ colour selection we adopted.
The addition of Higow photometry is stil important in excluding these objects in the ERS field (see Figure 3)), where the different Y-band filter used provides less good discrimination using Y—J colour alone.
The addition of $H_{160w}$ photometry is still important in excluding these objects in the ERS field (see Figure \ref{fig:cc_2}) ), where the different $Y$ -band filter used provides less good discrimination using $Y-J$ colour alone.
In Figures 2 and 3 the positions of both the interlopers and the tracks expected for high-redshift star forming galaxies are shown in the (Ji25w—Hieow) - —Ji25w) colour plane.
In Figures \ref{fig:cc_1} and \ref{fig:cc_2} the positions of both the interlopers and the tracks expected for high-redshift star forming galaxies are shown in the $(J_{125w} - H_{160w})$ - $(Y_{105w/098m} - J_{125w})$ colour plane.
With the exception of(Yi05w/098m the lowest temperature T dwarfs where the Yoosm filter is employed (the ERS field), these interlopers form a distinct locus separate from z8—9 star forming galaxies with UV spectral slope index B«0.0 (where f4=A? is used as a model of the UV properties of star forming galaxies).
With the exception of the lowest temperature T dwarfs where the $Y_{098m}$ filter is employed (the ERS field), these interlopers form a distinct locus separate from $z\approx 8-9$ star forming galaxies with UV spectral slope index $\beta<0.0$ (where $f_{\lambda}=\lambda^{\beta}$ is used as a model of the UV properties of star forming galaxies).
Using this analysis it is possible to design a window in (Yiosw/098m—J125w) - (Ji25w—Hicow) colour - colour space that selects mainly high-redshift star forming galaxies, while eliminating known contaminant populations.
Using this analysis it is possible to design a window in $(Y_{105w/098m} - J_{125w})$ - $(J_{125w} - H_{160w})$ colour - colour space that selects mainly high-redshift star forming galaxies, while eliminating known contaminant populations.
For the HUDF/P12/P34 fields (i.e. where we have Yio5w imaging) this YJH selection selection criteria is: The use of an alternative Y filter (Yoosm) in the ERS field necessitates the use of a slightly different criteria: We have designed our selection criteria to reject all known interlopers, while selecting most zο8—9 star-forming galaxies.
For the HUDF/P12/P34 fields (i.e. where we have $Y_{105w}$ imaging) this $YJH$ selection selection criteria is: The use of an alternative $Y$ filter $Y_{098m}$ ) in the ERS field necessitates the use of a slightly different criteria: We have designed our selection criteria to reject all known interlopers, while selecting most $z\approx 8-9$ star-forming galaxies.
Other groups have used similar colour:colour selection, but with slightly different colour cuts (e.g., Bouwens et 22010a).
Other groups have used similar colour:colour selection, but with slightly different colour cuts (e.g., Bouwens et 2010a).
Although this may affect the surface density of candidates (due to a slightly different redshift range and spectral range of spectral slopes probed for the LBGs, and a different contaminant fraction), the inferred luminosity functions should be similar as these selection effects are corrected for in the effective volume calculation.
Although this may affect the surface density of candidates (due to a slightly different redshift range and spectral range of spectral slopes probed for the LBGs, and a different contaminant fraction), the inferred luminosity functions should be similar as these selection effects are corrected for in the effective volume calculation.
The window we obtain with such criteria excludes a hypothetical population of z=8—9 galaxies with Jiosw—HieowZ1 colours.
The window we obtain with such criteria excludes a hypothetical population of $z\approx 8-9$ galaxies with $J_{125w}-H_{160w} \gtrsim 1$ colours.
Such a population would
Such a population would
hieh-mass (ail of the initial mass function. aud are therefore suppressed in volume limited saniples.
high-mass tail of the initial mass function, and are therefore suppressed in volume limited samples.
Cappellaroοἱal.(1997) measure their [raction to be ~2—554 of the CC population in the local universe.
\citet{Cappellaro_97} measure their fraction to be $\sim2-5\%$ of the CC population in the local universe.
Nonetheless. due to their brightness. in magnitude limited samples (heir fraction could be as high as ~15—20%.
Nonetheless, due to their brightness, in magnitude limited samples their fraction could be as high as $\sim15-20\%$.
For example. in LAU circulars between January 2005 and September 2006. the Nearby SN factory (Wood-Vasevetal.2004)... aud the SDSS IL SN survey. (Dildayetal.2005).. have together reported ~11 twpe Hn SNe out of ~55 CC-SNe.
For example, in IAU circulars between January 2005 and September 2006, the Nearby SN factory \citep{Wood-Vasey_04}, and the SDSS II SN survey \citep{DILDAY_05}, have together reported $\sim11$ type IIn SNe out of $\sim55$ CC-SNe.
SNe Hn appear to be an important contaminant of future SN la samples. and may cause difficulties [or cosmology oriented studies.
SNe IIn appear to be an important contaminant of future SN Ia samples, and may cause difficulties for cosmology oriented studies.
This may have already affected some current studies that relied to some degree on photometric classification. e.g.. Barris&Toury(2006).
This may have already affected some current studies that relied to some degree on photometric classification, e.g., \citet{BARRIS_SNR06}.
. such contamination could explain their measured SN rates. which seem inconsistent with other published results. as discussed by Neilletal.(2006).
Such contamination could explain their measured SN rates, which seem inconsistent with other published results, as discussed by \citet{Neill_06}.
. The possible contamination of SN-In samples by (vpe-IIn SNe has also been discussed by Germanyοἱal.(2004).
The possible contamination of SN-Ia samples by type-IIn SNe has also been discussed by \citet{Germany_04}.
.. Our algorithm correctly classifies most type Ibe SNe as CC-SNe. but with lower success rates than (vpe IL-P SNe.
Our algorithm correctly classifies most type Ibc SNe as CC-SNe, but with lower success rates than type II-P SNe.
Considering the [act that we are using H-P templates to recognize Ibe events. this should not be surprising.
Considering the fact that we are using II-P templates to recognize Ibc events, this should not be surprising.
As with SNe II-P. when SNe Ibe are near peak. their colors are degenerate with those of SNe Ia. A caveat however. is that the templates from which we have simulated the tvpe-Ibe SNe may not truly represent the high-: population. a possibility that must await future spectroscopy of such events.
As with SNe II-P, when SNe Ibc are near peak, their colors are degenerate with those of SNe Ia. A caveat however, is that the templates from which we have simulated the type-Ibc SNe may not truly represent the $z$ population, a possibility that must await future spectroscopy of such events.
As was the case for SNe la. we lind no trends in the SN-ABC results as a function of all the other simulated parameters. such as magnitudes in the different bands. photometric errors. ancl exGuction.
As was the case for SNe Ia, we find no trends in the SN-ABC results as a function of all the other simulated parameters, such as magnitudes in the different bands, photometric errors, and extinction.
The and two bottom panels of Figure 5. show that for CC-SNe. as was (he case for SNe Ia. the average Pec: values follow the success rate of the SN-ABC: when the success rale is low. the Pc values are. on (he average. also lower. indicating when the fit is poor and thus serving as measures of quality for the classification of each object.
The upper-right and two bottom panels of Figure \ref{f:MCavP} show that for CC-SNe, as was the case for SNe Ia, the average $P_{CC}$ values follow the success rate of the SN-ABC; when the success rate is low, the $P_{CC}$ values are, on the average, also lower, indicating when the fit is poor and thus serving as measures of quality for the classification of each object.
In order to simulate "rolling" survevs. where a field is imaged repeatedly. aud the SNe are found when they are voung. we have run simulations using only SNe vounger than (hree weeks past explosion.
In order to simulate "rolling" surveys, where a field is imaged repeatedly, and the SNe are found when they are young, we have run simulations using only SNe younger than three weeks past explosion.
Ássuming a voung SN age during elassilication. ie.. marginalizing (he likelihood only over the relevant extent in age. improves (he success lractions of the signilicantly. as expected. since il removes many of the voung vs. old degeneracies.
Assuming a young SN age during classification, i.e., marginalizing the likelihood only over the relevant extent in age, improves the success fractions of the SN-ABC significantly, as expected, since it removes many of the young vs. old degeneracies.
The improvement depends on the redshilt range and SN (vpe.
The improvement depends on the redshift range and SN type.
For example. for redshifts between 0.4 and 0.6. the success fraction for SNe In improves from to The fractional contamination bv false positives (e.g.. non-Ins among SNe classified as la) will depend on the intrinsic distribution among twpes in a given survey. which depends
For example, for redshifts between 0.4 and 0.6, the success fraction for SNe Ia improves from to The fractional contamination by false positives (e.g., non-Ia's among SNe classified as Ia) will depend on the intrinsic distribution among types in a given survey, which depends
Table 5)). which excludes the possibility that we overestimated the temperature in computing the LTE mass of the clump. from Eq.(3)).
Table \ref{SFassociations}) ), which excludes the possibility that we overestimated the temperature in computing the LTE mass of the clump, from \ref{LTEmass}) ).
A possible explanation for the small ratio ήνΜι. of clump co3 ts that the magnetic field might be playing an important role in stabilizing the clump.
A possible explanation for the small ratio $M_{\rm VIR}/M_{\rm LTE}$ of clump co5 is that the magnetic field might be playing an important role in stabilizing the clump.
It is also interesting to compare the continuum masses (column 10 of Table 4)) of all the continuum sources that have a CO counterpart with the LTE masses of the respective CO clumps (column 4 of Table 6)).
It is also interesting to compare the continuum masses (column 10 of Table \ref{param_cont}) ) of all the continuum sources that have a $^{13}$ CO counterpart with the LTE masses of the respective $^{13}$ CO clumps (column 4 of Table \ref{mass_nubi}) ).
All the continuum sources with a CO counterpart have Myyp=Meo. which is probably due to the fact that the CO clumps have larger sizes than the respective continuum counterparts.
All the continuum sources with a $^{13}$ CO counterpart have $M_{\rm LTE} \succsim M_{\rm cont}$, which is probably due to the fact that the $^{13}$ CO clumps have larger sizes than the respective continuum counterparts.
However. there are three exceptions: the continuum source CI. which 15 associated with CO clump col: the continuum source C7. which is associated with 'CO clump co7: and the continuum source C12. which is associated with CO clump col2.
However, there are three exceptions: the continuum source C1, which is associated with $^{13}$ CO clump co1; the continuum source C7, which is associated with $^{13}$ CO clump co7; and the continuum source C12, which is associated with $^{13}$ CO clump co12.
A possible explanation might be that we underestimated the temperature for these three sources. therefore we overestimated the continuum mass and we underestimated the LTE mass.
A possible explanation might be that we underestimated the temperature for these three sources, therefore we overestimated the continuum mass and we underestimated the LTE mass.
This explanation ts consistent with the association of the three sources with active star-formation tracers. in. particular with Spitzer emission at 3.6 jm. ὃ gam and 24 yan. Therefore we obtain the same
This explanation is consistent with the association of the three sources with active star-formation tracers, in particular with Spitzer emission at 3.6 $\mu$ m, 8 $\mu$ m and 24 $\mu$ m. Therefore we obtain the same
As a indication of chaotic behaviour we can use topological invariants. namely the correlation dimension and Lyapunov exponents.
As a indication of chaotic behaviour we can use topological invariants, namely the correlation dimension and Lyapunov exponents.
Algorithms for numerical estimates of
Algorithms for numerical estimates of
dynamical state of Westerlund I are discussed in Sect. 4..
dynamical state of Westerlund I are discussed in Sect. \ref{sec:disc}.
Finally we present our conclusions in Sect. 5..
Finally we present our conclusions in Sect. \ref{sec:conc}.
Observations were made using the MIKE spectrograph (?) located on the Magellan Clay telescope at the Las Campanas Observatory in June 2009, August 2009 and July 2010.
Observations were made using the MIKE spectrograph \citep{Ber03} located on the Magellan Clay telescope at the Las Campanas Observatory in June 2009, August 2009 and July 2010.
MIKE has a red and a blue arm, providing two echelle spectra with a combined wavelength coverage from 3200 to 9000A.
MIKE has a red and a blue arm, providing two echelle spectra with a combined wavelength coverage from 3200 to 9000.
. A slit of 0.7 arcseconds was used, corresponding to a resolution of 53.000 (6 km s! per resolution element).
A slit of 0.7 arcseconds was used, corresponding to a resolution of 53.000 $\sim 6$ km $^{-1}$ per resolution element).
Due to the large interstellar reddening towards Westerlund I (Αν= usable spectra are only obtained above 5000A.
Due to the large interstellar reddening towards Westerlund I \citep[A$_{\rm V}= usable spectra are only obtained above 5000.
. We observed 22 of the brightest spectroscopically confirmed members (J<14.7 mag) selected from ?..
We observed 22 of the brightest spectroscopically confirmed members $I < 14.7$ mag) selected from \citet{Cla05}.
These stars include two of the four red supergiants, all six yellow hypergiants, a sgB[e] star, a LBV in its cool phase and 12 OB-supergiants.
These stars include two of the four red supergiants, all six yellow hypergiants, a sgB[e] star, a LBV in its cool phase and 12 OB-supergiants.
All of these targets were observed for 2 or 3 epochs.
All of these targets were observed for 2 or 3 epochs.
The spectra were reduced with the MIKE Redux with some minor adjustments.
The spectra were reduced with the MIKE Redux with some minor adjustments.
All images were bias subtracted using the overscan regions.
All images were bias subtracted using the overscan regions.
Flat field images were derived from internal quartz lamp frames.
Flat field images were derived from internal quartz lamp frames.
The slit was in place during these measurements to ensure the variation of wavelengths with the location on the CCD is similar as in the observations.
The slit was in place during these measurements to ensure the variation of wavelengths with the location on the CCD is similar as in the observations.
To obtain the flat field in the dark area between the orders, a diffusing glass is positioned in the optical path just downstream of the slit illuminating the area between the orders.
To obtain the flat field in the dark area between the orders, a diffusing glass is positioned in the optical path just downstream of the slit illuminating the area between the orders.
After flat fielding we still find spurious low frequency spatial variations in the illumination of the detector above ~8200 for all spectra taken on August 2009 and July 2010, including the internal quartz lamp spectra.
After flat fielding we still find spurious low frequency spatial variations in the illumination of the detector above $\sim 8200$ for all spectra taken on August 2009 and July 2010, including the internal quartz lamp spectra.
These features are slightly weaker for the internal quartz lamp and are spread out by the diffusing glass, suggesting that these features are created in the optical path of the telescope.
These features are slightly weaker for the internal quartz lamp and are spread out by the diffusing glass, suggesting that these features are created in the optical path of the telescope.
To correct these variations we calculate a smoothed flat field, which no longer contains the high frequency pixel-to-pixel variations, but only these low frequency spatial features.
To correct these variations we calculate a smoothed flat field, which no longer contains the high frequency pixel-to-pixel variations, but only these low frequency spatial features.
Although these spurious features are weaker in the flat field than in the science images, we find that they can still be roughly corrected for by dividing the flat fielded image twice by this smoothed flat field.
Although these spurious features are weaker in the flat field than in the science images, we find that they can still be roughly corrected for by dividing the flat fielded image twice by this smoothed flat field.
The curvature of the orders along the CCD is fitted using traces along the edge of the orders obtained from internal quartz lamp images.
The curvature of the orders along the CCD is fitted using traces along the edge of the orders obtained from internal quartz lamp images.
The scattered light was fitted from the dark areas by a B-spline with 10 knots between the orders and subtracted.
The scattered light was fitted from the dark areas by a B-spline with 10 knots between the orders and subtracted.
The long slit of MIKE (5 arcseconds) allowed us to find the sky emission lines in the part of the order not illuminated by the star and subtract these emission lines.
The long slit of MIKE (5 arcseconds) allowed us to find the sky emission lines in the part of the order not illuminated by the star and subtract these emission lines.
Regularly during the nights ThAr images were taken for wavelength calibration.
Regularly during the nights ThAr images were taken for wavelength calibration.
Two ThAr lamp images were taken at different exposure times, to maximize the range of lamp line strengths, which could be used in the wavelength calibration.
Two ThAr lamp images were taken at different exposure times, to maximize the range of lamp line strengths, which could be used in the wavelength calibration.
Combined with the position of the orders on the CCD, these ThAr images were used to calculate a wavelength for every pixel on the image.
Combined with the position of the orders on the CCD, these ThAr images were used to calculate a wavelength for every pixel on the image.
The spectra are optimally extracted onto a common wavelength frame, allowing us to add the subsequent exposures of the same target.
The spectra are optimally extracted onto a common wavelength frame, allowing us to add the subsequent exposures of the same target.
The noise was estimated at every pixel as the sum of the Poisson error in the observed flux and the readout noise.
The noise was estimated at every pixel as the sum of the Poisson error in the observed flux and the readout noise.
This calculated noise is consistent with the variations between multiple subsequent observations of the same target and corresponds to a S/N of over 100 in the red part of the spectra.
This calculated noise is consistent with the variations between multiple subsequent observations of the same target and corresponds to a S/N of over 100 in the red part of the spectra.
The flux has been normalized using a cubic spline fit with two knots.
The flux has been normalized using a cubic spline fit with two knots.
To correct for possible off-center placement of the source star along the slit, we check for zero-point shifts of the wavelength solution in every observed spectrum.
To correct for possible off-center placement of the source star along the slit, we check for zero-point shifts of the wavelength solution in every observed spectrum.
To this end we measure the shift of the telluric absorption lines in our stellar target spectra compared to a National Solar Observatory (NSO) telluric spectrum convolved to the resolution of our spectra.
To this end we measure the shift of the telluric absorption lines in our stellar target spectra compared to a National Solar Observatory (NSO) telluric spectrum convolved to the resolution of our spectra.
We select sixteen wavelength ranges, where there are no or only very weak contamination from stellar spectral lines or diffuse interstellar bands and which contain strong telluric lines.
We select sixteen wavelength ranges, where there are no or only very weak contamination from stellar spectral lines or diffuse interstellar bands and which contain strong telluric lines.
Over these wavelength ranges we calculate the peak of the cross correlation between the NSO spectrum and the Observed spectra.
Over these wavelength ranges we calculate the peak of the cross correlation between the NSO spectrum and the observed spectra.
The shift of the zero-point of the wavelength for every spectrum is the average of the shifts calculated for the sixteen individual wavelength ranges.
The shift of the zero-point of the wavelength for every spectrum is the average of the shifts calculated for the sixteen individual wavelength ranges.
Figure 1 shows the difference between the individual measurements and the average offset.
Figure \ref{fig:tell} shows the difference between the individual measurements and the average offset.
The distribution of these variations is fairly narrow (σ=0.23 km s~!).
The distribution of these variations is fairly narrow $\sigma =0.23$ km $^{-1}$ ).
These offsets are smaller than the precision we obtain in measuring the radial velocity variations of our target stars, implying that we are not limited by the accuracy of the wavelength calibration.
These offsets are smaller than the precision we obtain in measuring the radial velocity variations of our target stars, implying that we are not limited by the accuracy of the wavelength calibration.
Radial velocity variations due to binary stars or activity in the stellar atmospheres can inflate our measurement of the velocity
Radial velocity variations due to binary stars or activity in the stellar atmospheres can inflate our measurement of the velocity
109). 7. cads to low-mass galaxies with shallow gravitational ootential wells selectively losing their metals (?77).
\nocite{lequeux79,tremonti04, geha09}) \cite{tremonti04} leads to low-mass galaxies with shallow gravitational potential wells selectively losing their metals \citep{maclow99,strickland04,brooks07}.
. Nonetheless. there are several other possible mechauisuis or explaining the correlation between low metal vields and ealaxy mass. including metal mixing iu extended eascous disks (7).. and a lower effective stellar upper nass lut to the initial mass function in dywiurf galaxies (HD.
Nonetheless, there are several other possible mechanisms for explaining the correlation between low metal yields and galaxy mass, including metal mixing in extended gaseous disks \citep{tassis08}, and a lower effective stellar upper mass limit to the initial mass function in dwarf galaxies \citep{koppen07, meurer09}.
The aget open question of whether the exteuded reutral eas disks of chwart galaxies are metal-euriched is quite relevant to this discussion. since cach proposed uodel makes a preciction for the metal distributions in galaxies.
The as-yet open question of whether the extended neutral gas disks of dwarf galaxies are metal-enriched is quite relevant to this discussion, since each proposed model makes a prediction for the metal distributions in galaxies.