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This suggests that the lme-emitting gas is overabundant in heavy elements by a factor of at leasta lew. | This suggests that the line-emitting gas is overabundant in heavy elements by a factor of at leasta few. |
Reeves et al. ( | Reeves et al. ( |
2002) estimated an abundance of ten times solar | 2002) estimated an abundance of ten times solar |
third night was usable for about half of the night. the others were good. | third night was usable for about half of the night, the others were good. |
On the three nights 20th. 23rd and 24th. the target was observed for 7.5 hours continuously. on the 21st for 4.5 hours. and on the 22nd for 2 hours. | On the three nights 20th, 23rd and 24th, the target was observed for 7.5 hours continuously, on the 21st for 4.5 hours, and on the 22nd for 2 hours. |
The total time on target during these five nights was 29 hours of which the 2 hours on the 22nd were of poor quality and discarded. | The total time on target during these five nights was 29 hours of which the 2 hours on the 22nd were of poor quality and discarded. |
A total of 542 v data points were acquired. | A total of 542 $y$ data points were acquired. |
Only Strómmgren y was employed. | Only Strömmgren $y$ was employed. |
These data were calibrated to the standard mmagnitudes. | These data were calibrated to the standard magnitudes. |
The external rms of the time-series 18 1.2 mmag in 3. | The external rms of the time-series is $\approx1.2$ mmag in $y$. |
We have been conducting ultra-stable radial velocity monitoring of EK Ert with HARPS (22). from September 2004 to April 2007. mostly with rather uneven spacing between data points. | We have been conducting ultra-stable radial velocity monitoring of EK Eri with HARPS \citep{harps2003,harps2004} from September 2004 to April 2007, mostly with rather uneven spacing between data points. |
Typical exposure times were 480—600 s resulting in à typical peak S/N = 300 for individual exposures. which translates to an intrinsic RV precision of better than | pper exposure. | Typical exposure times were 480–600 s resulting in a typical peak S/N $\approx$ 300 for individual exposures, which translates to an intrinsic RV precision of better than 1 per exposure. |
The HARPS resolution is R=100.000. | The HARPS resolution is $R=100,000$. |
Accurate radial velocities are derived by the HARPS pipeline using cross-correlation with line templates (masks). | Accurate radial velocities are derived by the HARPS pipeline using cross-correlation with line templates (masks). |
The high-precision comes from the use of a simultaneous Th-Ar calibration spectrum for accurate wavelength calibration and from the intrinsic high stability of the spectrograph. | The high-precision comes from the use of a simultaneous Th-Ar calibration spectrum for accurate wavelength calibration and from the intrinsic high stability of the spectrograph. |
The resulting Cross-Correlation Function (CCF) is fitted with a Gaussian to obtain the RV. | The resulting Cross-Correlation Function (CCF) is fitted with a Gaussian to obtain the RV. |
In March 2007 we had three consecutive half-nights of continuous monitoring to search for oscillations and probe the asteroseismic parameters of the star. | In March 2007 we had three consecutive half-nights of continuous monitoring to search for oscillations and probe the asteroseismic parameters of the star. |
We used exposure times of 360—480 s which resulted in peak S/N = 250-300 for individual exposures at a cadence of about 8 min. | We used exposure times of 360–480 s which resulted in peak S/N $\approx$ 250–300 for individual exposures at a cadence of about 8 min. |
We observed for about three hours per night. obtaining a total of 70 spectra. | We observed for about three hours per night, obtaining a total of 70 spectra. |
We have also retrieved archive data of EK Eri taken with the CES high-resolution spectrograph at ESO/La Silla Observatory. | We have also retrieved archive data of EK Eri taken with the CES high-resolution spectrograph at ESO/La Silla Observatory. |
One 900 s spectrum at a central wavelength of 6155 wwas obtained on UT date 2004-09-03 and one 600 s spectrum at 6463 wwas obtained on UT date 2005-08-11. | One 900 s spectrum at a central wavelength of 6155 was obtained on UT date 2004-09-03 and one 600 s spectrum at 6463 was obtained on UT date 2005-08-11. |
The spectra cover 39 παπα 43Α.. | The spectra cover 39 and 43, respectively. |
These data were reduced using the CES. | These data were reduced using the CES. |
. The respectively. resolutionis R =220.000 with S/N=500 in both spectra. | The resolution is R $\approx 220,000$ with S/N $\approx500$ in both spectra. |
In the following we will investigate the high-resolution spectroscopy and long-term photometric time-series data. | In the following we will investigate the high-resolution spectroscopy and long-term photometric time-series data. |
The numerical results are summarized in Table 1.. | The numerical results are summarized in Table \ref{tab:results}. |
? determined the period of the long-term photometric variation of EK Eri based on the data obtained to that date. | \citet{strassmeier+1999} determined the period of the long-term photometric variation of EK Eri based on the data obtained to that date. |
They also found that the period may have changed slightly when | They also found that the period may have changed slightly when |
data offers all sky coverage we have the opportunity to study farther reaches of the clusters. | data offers all sky coverage we have the opportunity to study farther reaches of the clusters. |
The centers of the clusters are determined using a program which, given an eye estimated center and radius, counts the number of stars and calculates the average X and Y of the stars within the radius. | The centers of the clusters are determined using a program which, given an eye estimated center and radius, counts the number of stars and calculates the average $\bar{X}$ and $\bar{Y}$ of the stars within the radius. |
If the difference in the position (X.Y) from the eye estimated center is smaller than a given tolerance value (a pixel), then the eye estimated center is taken as the center. | If the difference in the position $\bar{X}$ $\bar{Y}$ ) from the eye estimated center is smaller than a given tolerance value (a pixel), then the eye estimated center is taken as the center. |
If larger. then (X,Y) is taken as the new approximate center. | If larger, then $\bar{X}$ $\bar{Y}$ ) is taken as the new approximate center. |
The same procedure is repeated iteratively until the difference in the position (.\,)’) and the center lies within the tolerance value (?).. | The same procedure is repeated iteratively until the difference in the position $\bar{X}$ $\bar{Y}$ ) and the center lies within the tolerance value \citep{sag98}. |
An error of a few arc seconds is expected in locating the center. | An error of a few arc seconds is expected in locating the center. |
For the determination of the radial surface density of stars p(r) in a cluster, a number of concentric circles with respect to the estimated center are made in such a way that each annular region contains statistically significant number of stars. | For the determination of the radial surface density of stars $\rho(r)$ in a cluster, a number of concentric circles with respect to the estimated center are made in such a way that each annular region contains statistically significant number of stars. |
The number density of stars, p; in the /’”i region is calculated as p;=Nj/-\). where .\; is the number of stars in the /"i region of area .1;. | The number density of stars, $\rho_{i}$ in the $i^{th}$ region is calculated as $\rho_{i}=N_{i}/A_{i}$, where $N_{i}$ is the number of stars in the $i^{th}$ region of area $A_{i}$. |
The RDPs for the clusters are shown in. the Fig.. 2.. | The RDPs for the clusters are shown in the Fig. \ref{radall}. |
The 47 minimization. technique. was used to fit" the RDPs to the function (?) to determine r, and other constants. | The $\chi ^{2}$ minimization technique was used to fit the RDPs to the function \citep{king62} to determine $r_{c}$ and other constants. |
The cluster's core radius r. is the radial distance at which the value of p(r) becomes half of the central density, fu. | The cluster's core radius $r_{c}$ is the radial distance at which the value of $\rho(r)$ becomes half of the central density, $\rho_{0}$. |
Probable members are selected from all the stars in the cluster area which satisfy the photometric criterion ? described in the next section. | Probable members are selected from all the stars in the cluster area which satisfy the photometric criterion \cite{walker65} described in the next section. |
The best fits | The best fits |
In the present work. we have computed tve dynamical evolution of model clusters whose stricture conform. with the oserved elolyal properties of R136 — the central massive cluster ii the 30 Dor complex of the LMC. uxiug the clirect N-body integration method. | In the present work, we have computed the dynamical evolution of model clusters whose structure conform with the observed global properties of R136 — the central massive cluster in the 30 Dor complex of the LMC, using the direct N-body integration method. |
The evolution of the iucdividual stars. clisen Initialy from the cauorical IMF with the staudard 15044. upper cutoll (Weiuer&Ixroupa2001).. heis also beer incorporated and as well the evolution of the individua bin:‘ies. | The evolution of the individual stars, chosen initially from the canonical IMF with the standard $150\Ms$ upper cutoff \citep{wk2004}, has also been incorporated and as well the evolution of the individual binaries. |
We focus ou the ejecjon of massive sta5 from our model clusters which is a process that depds crucially ou the properties of tie. primordial binaries in the cluster. | We focus on the ejection of massive stars from our model clusters which is a process that depends crucially on the properties of the primordial binaries in the cluster. |
For computationa simplicity. we have taken ouly the stars withi initial masses 114>SAL. to be in binaries (see Sec. ??) | For computational simplicity, we have taken only the stars with initial masses $m_s > 5\Ms$ to be in binaries (see Sec. \ref{initcond}) ) |
which are the only ones that are efficiett in ejecting the massive stars. | which are the only ones that are efficient in ejecting the massive stars. |
Hence. the properties of he massive rulawavs are nol expected o be allected siguilicantly by the aence of lower mass jnarles. | Hence, the properties of the massive runaways are not expected to be affected significantly by the absence of lower mass binaries. |
Recent observatious of the R136 αιd the κ)+)30 Dor regionil have raised [uudamental questious 'eeardiug massive sar formation iuechliauisims. | Recent observations of the R136 and the 30 Dor region have raised fundamental questions regarding massive star formation mechanisms. |
Iu particular. he apparently isolated aud relatively slow-moving single VMS VETS 682 has raised suspicion tlat it nmught be au instance of isolated uasslve star formation (Bestenlehuereta..2011:: see Sec. ?? ). | In particular, the apparently isolated and relatively slow-moving single VMS VFTS 682 has raised suspicion that it might be an instance of isolated massive star formation \citealt{blh2011}; see Sec. \ref{intro}) ). |
Additionally. t1e presence of single VMSsS in R136 witl inferred. initial masses upto cAv320AM.. las questione the canonical L5OAL. ipper limit of the IMF (Crowtheretal.2010). | Additionally, the presence of single VMSs in R136 with inferred initial masses upto $\approx 320\Ms$ has questioned the canonical $150\Ms$ upper limit of the IMF \citep{crw2010}. |
. The inost impolant outcome of our computations is the couirmation tha a “slow runaway. with a 3-dimeusion:il velocity similar to that of VETS 682. is it [act the nost probable type of ejected’ VALS froin :| R136-like οἱinter Fig. 3:: | The most important outcome of our computations is the confirmation that a “slow runaway”, with a 3-dimensional velocity similar to that of VFTS 682, is in fact the most probable type of ejected VMS from a R136-like cluster Fig. \ref{fig:mvplot}; |
Sec. ?2)). | Sec. \ref{vfts682}) ). |
In fact. all of [9]r computed models vield one or iore runawayss with kinematic properties agreeing [airly with those of VETS 682 Table 1)). | In fact, all of our computed models yield one or more runaways with kinematic properties agreeing fairly with those of VFTS 682 Table \ref{tab:tab1}) ). |
Given such a likeliness of à VETS 682-type runaway Iroin R136. his apparently isolated star clearly does imply isolated massive star formation and 1 is very ikely a former inember of IR136. | Given such a likeliness of a VFTS 682-type runaway from R136, this apparently isolated star clearly does imply isolated massive star formation and it is very likely a former member of R136. |
Furthermore. as explained in Sec. ??.. | Furthermore, as explained in Sec. \ref{res}, |
massive. close binaries are necessary to dyuauically eject VMSs from star clusters. which. iu turn. are susceptible to merge due to their hardening and/or eccentricitv-punipiug. by the frequent close encounters that they receive. | massive, close binaries are necessary to dynamically eject VMSs from star clusters, which, in turn, are susceptible to merge due to their hardening and/or eccentricity-pumping, by the frequent close encounters that they receive. |
As our computalonis sliow (see Sec. 223). | As our computations show (see Sec. \ref{res}) ), |
such massive binary 1lereers ean easily produce single stars. within a few lvr. with masses well exceeclit& the 120A. upper limit. even if the cluster beeins with the canoical upper limit. | such massive binary mergers can easily produce single stars, within a few Myr, with masses well exceeding the $150\Ms$ upper limit, even if the cluster begins with the canonical upper limit. |
Our L compilatious have produced upto z2504. members aud mereer procucts upto cAvBOQAL. are possible if the most 1lassive biuaries merge. | Our 4 computations have produced upto $\approx 250\Ms$ members and merger products upto $\approx 300\Ms$ are possible if the most massive binaries merge. |
Therefore. it cloesut seem to be a surprise that R136 has ipto zzB20AL. siugle-star members. given the large uucertainties in the stellar evolution mocels used to infer the passes (see Crowtheretal.2010. and references the'eln) aud the presence of these siper-canonical VMSs (or the preseuce ofa super-saturated mass Duuc1011) In | Therefore, it doesn't seem to be a surprise that R136 has upto $\approx 320\Ms$ single-star members, given the large uncertainties in the stellar evolution models used to infer the masses (see \citealt{crw2010} and references therein) and the presence of these super-canonical VMSs (or the presence of a super-saturated mass function) is |
When comparing the intrinsic colour distributions, we find that the rest-frame B—/ colour distribution is clearly bimodal both in the VVDS-Deep and in the model, but the agreement is only qualitative. | When comparing the intrinsic colour distributions, we find that the rest-frame $B-I$ colour distribution is clearly bimodal both in the VVDS-Deep and in the model, but the agreement is only qualitative. |
As shown in Fig. 7,, | As shown in Fig. \ref{bimod}, |
the SAM does not reproduce quantitatively the observed intrinsic colour distributions: there are many fewer very blue galaxies (i.e. with B—I« 0.3) and much more “green valley" galaxies (i.e. with B—J« 1) in the model than in the observations, at all probed redshifts. | the SAM does not reproduce quantitatively the observed intrinsic colour distributions: there are many fewer very blue galaxies (i.e. with $B-I\simeq0.3$ ) and much more “green valley” galaxies (i.e. with $B-I\simeq1$ ) in the model than in the observations, at all probed redshifts. |
In addition, the model predicts an excess of red galaxies at low redshift. | In addition, the model predicts an excess of red galaxies at low redshift. |
It could be argued that part of the discrepancies between the SAM and observed colours could be related to uncertainties in the modelling of dust extinction (e.g.??).. | It could be argued that part of the discrepancies between the SAM and observed colours could be related to uncertainties in the modelling of dust extinction \citep[e.g.][]{kitzbichler07,fontanot09a}. |
The dashed line in Fig. | The dashed line in Fig. |
7 shows the rest-frame B—I colour distribution in the SAM without including dust extinction in the model galaxies. | \ref{bimod} shows the rest-frame $B-I$ colour distribution in the SAM without including dust extinction in the model galaxies. |
In that case, when comparing to VVDS-Deep colour distributions, one finds that while in the highest-redshift intervals the predicted and observed colour distributions are quite similar, the lack of blue galaxies in the model is remarkable at z«1.1. | In that case, when comparing to VVDS-Deep colour distributions, one finds that while in the highest-redshift intervals the predicted and observed colour distributions are quite similar, the lack of blue galaxies in the model is remarkable at $z<1.1$. |
? show that the fixed apparent magnitude selection of the VVDS-Deep sample can in principle introduce a mild bias in the intrinsic colours, partially displacing rest-frame U—V colours towards the blue at z>1.2. | \citet{franzetti07} show that the fixed apparent magnitude selection of the VVDS-Deep sample can in principle introduce a mild bias in the intrinsic colours, partially displacing rest-frame $U-V$ colours towards the blue at $z>1.2$. |
However, they find that this effect is marginal and cannot be invoked to explain the discrepancies found in the SAM at all probed redshift. | However, they find that this effect is marginal and cannot be invoked to explain the discrepancies found in the SAM at all probed redshift. |
Intrinsically, the SAM may not form enough very blue galaxies. | Intrinsically, the SAM may not form enough very blue galaxies. |
Because of the discrepancies between the predicted and observed colour distributions, it is difficult and possibly meaningless to define “blue” and “red” galaxies using the same colour cut. | Because of the discrepancies between the predicted and observed colour distributions, it is difficult and possibly meaningless to define “blue” and “red” galaxies using the same colour cut. |
We therefore opted to use a different colour cut for the SAM and the VVDS-Deep sample, with the aim of separating blue and red populations on the basis of the colour bimodality. | We therefore opted to use a different colour cut for the SAM and the VVDS-Deep sample, with the aim of separating blue and red populations on the basis of the colour bimodality. |
In the VVDS-Deep we use a cut at (B—I)“=0.95. | In the VVDS-Deep we use a cut at $(B-I)^{cut}=0.95$. |
? show that galaxies selected above and below this value largely overlap with those classified as early- and late-type galaxies using a more refined method based on spectral energy distribution fitting. | \citet{zucca06} show that galaxies selected above and below this value largely overlap with those classified as early- and late-type galaxies using a more refined method based on spectral energy distribution fitting. |
We adopt a larger value of (Β--I)"=1.3 to separate red and blue populations in the SAM. | We adopt a larger value of $(B-I)^{cut}=1.3$ to separate red and blue populations in the SAM. |
We compare in Fig. | We compare in Fig. |
8 the total number and the fraction of red and blue galaxies at different redshifts. | \ref{fraction} the total number and the fraction of red and blue galaxies at different redshifts. |
In additio ito having immensely eubanucecd memories. we now also have immensely euliauced perception. | In addition to having immensely enhanced memories, we now also have immensely enhanced perception. |
The new data intensive science (Caray 2007) rests not siuiply iu the mechanical extension of our perception. as begun by Galileo aud. ναι Leeuwenuhoek. |out on the auomated. perception aud analysis ο| huge data sets. | The new data intensive science (Gray 2007) rests not simply in the mechanical extension of our perception, as begun by Galileo and van Leeuwenhoek, but on the automated perception and analysis of huge data sets. |
The ATLAS experiment at CER«das a raw ¢ala rate of GOTB/s (Ixlous 2010). abou fifty. trilion times the iuforuation handling capacity of luinaus (Fitts 1951). | The ATLAS experiment at CERN has a raw data rate of 60TB/s (Klous 2010), about fifty trillion times the information handling capacity of humans (Fitts 1954). |
The unde‘vine technolges are being deveoped to satisfy tre needs of hige systems. like the LHC experiments. )ut ever arger systeus of sesor networks are beiig created to take advantage ol tle new capabióties. | The underlying technologies are being developed to satisfy the needs of huge systems, like the LHC experiments, but ever larger systems of sensor networks are being created to take advantage of the new capabilities. |
For these sysems to combiue their inpact. they need to be able to COLLulicate with eac1 otler: they need to sire a lauguage (lxu‘tg 1989: 1992). | For these systems to combine their impact, they need to be able to communicate with each other; they need to share a language (Kurtz 1989; 1992). |
The shared lauguage need not be native tO ally paricular system or experiment (Hanisch 2001). but needs to be universally uncerstooc. ike meclievei| Latin. or Euglisli tocav. | The shared language need not be native to any particular system or experiment (Hanisch 2001), but needs to be universally understood, like medieval Latin, or English today. |
v us staucdareization is takine place across all scientific and technical ields. examples are the Inter1ational Virtual Observatory Alliaice's Simple Application Messaging Protocol (SAMP: Tayor. el al 2009) οἱ the Open Access Iuitiative's Object Rease and Exchange (OAI-ORE) protocol (Vau ce Sompel. e al 2009). | This standardization is taking place across all scientific and technical fields, examples are the International Virtual Observatory Alliance's Simple Application Messaging Protocol (SAMP; Taylor, et al 2009) or the Open Access Initiative's Object Re-use and Exchange (OAI-ORE) protocol (Van de Sompel, et al 2009). |
Automated memory and automated perception are combinit eto orn automated ideas. | Automated memory and automated perception are combining to form automated ideas. |
Staudardized cata descriptors aloug with semalic taMD00ine of text (Accomazzi 2000 produce a new environment. in which inference engines. similar to the stochastic/svtlactic oocedures used to analyze bubble Chamber tracts (Fu aud Bhareava 1973) are able to ¢IscOver new. üunportant assoclations aud yatterus. | Standardized data descriptors along with semantic tagging of text (Accomazzi 2009) produce a new environment, in which inference engines, similar to the stochastic/syntactic procedures used to analyze bubble chamber tracts (Fu and Bhargava 1973) are able to discover new, important associations and patterns. |
These systems wi| be able to model aud. prectict he belavior of humans (Barabasi 2010). incleecl Ossojo (LOGT. LOTT) spent a good fraction of his career using his methods to model the yehavior of specific Lumars. | These systems will be able to model and predict the behavior of humans (Barabasi 2010), indeed Ossorio (1967, 1977) spent a good fraction of his career using his methods to model the behavior of specific humans. |
The role of these systems will 1ot be to model individual huinau )ehaviors. but will be to ac tas the functional core for ow collective iiitelligence. | The role of these systems will not be to model individual human behaviors, but will be to act as the functional core for our collective intelligence. |
This colection of macüues will not provide the totality o ‘the brain for the emereent wlich is human society: the higher fuuctions. the coglilive E:wer. will be provided by the stun of all people. | This collection of machines will not provide the totality of the brain for the emergent super-organism which is human society; the higher functions, the cognitive layer, will be provided by the sum of all people. |
The maclines will provide the core fuuctionality. the lizard brain. | The machines will provide the core functionality, the lizard brain. |
Analogous with our own evolution there will be innate cayabilities. like our instant reac‘ion to heat. aud anomalies. ike our fear of snakes. | Analogous with our own evolution there will be innate capabilities, like our instant reaction to heat, and anomalies, like our fear of snakes. |
Iiciividual scholarly discidlines have oug functioued as seii-autononiots super-organisims. | Individual scholarly disciplines have long functioned as semi-autonomous super-organisms. |
The nelxy function (on paper) being jour.al articles in uuiversity libra‘ies. | The memory function (on paper) being journal articles in university libraries. |
With the advent of the ποet scholars are rapicM7 rausformin& their work habits to take avantage of the possibilities (Bore[n]nan 2007). | With the advent of the internet scholars are rapidly transforming their work habits to take advantage of the possibilities (Borgman 2007). |
New {ουologies ellectiug the speed of informatiou tanser (Ciuspare 1991). and he ability to clirectly aud οἱectively access |we datasets (Szalay aixl C"ay 2001) have already yee created. tuore are coii& daily. | New technologies effecting the speed of information transfer (Ginsparg 1994), and the ability to directly and effectively access huge datasets (Szalay and Gray 2001) have already been created, more are coming daily. |
The schoarly literature has bee1 fullv digital for more thau a decade. | The scholarly literature has been fully digital for more than a decade. |
The idea that a «aiscipliue. such as astronomy. already. furctiors as a COhereut with electronic meuory aud perception systenis is not at al far fetchect. | The idea that a discipline, such as astronomy, already functions as a coherent super-organism, with electronic memory and perception systems is not at all far fetched. |
cosmological parameters for the mDGP analysis and 8 for the growth parameterisation analysis. | cosmological parameters for the mDGP analysis and 8 for the growth parameterisation analysis. |
We vary Qm, h, σε, a, b, ο which are common to both models, in addition to α for mDGP and wo and s for the growth. | We vary $\Omega_{m}$ , $h$ , $\sigma_{8}$ $a$ , $b$, $c$ which are common to both models, in addition to $\alpha$ for mDGP and $w_{0}$ and $\gamma$ for the growth. |
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