Datasets:
ReadingTimeMachine
/
rtm-sgt-ocr-v1

Dataset card Data Studio Files Community
1
source
stringlengths
1
2.05k
⌀
target
stringlengths
1
11.7k
The stellar. magnitudes. predicted: by the photo-ionization models are in good. agreement. with observed. values.
The stellar magnitudes predicted by the photo-ionization models are in good agreement with observed values.
We thank PPottasch. WWaters and vvan der Hulst. for helpful. comments.
We thank Pottasch, Waters and van der Hulst for helpful comments.
OOucdmaijer. T. de Jong. C.J. Ferland and the anonymous referee are thanked. for critically reading the manuscript.
Oudmaijer, T. de Jong, G.J. Ferland and the anonymous referee are thanked for critically reading the manuscript.
Drs.
Drs.
RBC. llenrv and J. IXópppen are thanked. for re-anuning their data.
R.B.C. Henry and J. Köpppen are thanked for re-examining their data.
The authors acknowledge support rom the Netherlands Foundation for Research in Astronomy (ASTRON) through grant 7782372033 and 782372135.
The authors acknowledge support from the Netherlands Foundation for Research in Astronomy (ASTRON) through grant 782–372–033 and 782--372--035.
Pvll wishes to thank ESO for their hospitality. and inancial support during his stay in Santiago where part of this paper was written.
PvH wishes to thank ESO for their hospitality and financial support during his stay in Santiago where part of this paper was written.
Ον is currently supported. by he NSE through grant AST 9617083.
PvH is currently supported by the NSF through grant AST 96–17083.
‘The photo-ionization code wavas used. written by Gary Ferlanc and obtained from the University of Kentucky. USA.
The photo-ionization code was used, written by Gary Ferland and obtained from the University of Kentucky, USA.
We thank Cary Ferlancl for his invaluable help in debugging ancl mocilving the code.
We thank Gary Ferland for his invaluable help in debugging and modifying the code.
Atomic diffusion m stars. when efficient enough to overcome mixing processes. leads to inhomogeneous abundance distributions of chemical elements.
Atomic diffusion in stars, when efficient enough to overcome mixing processes, leads to inhomogeneous abundance distributions of chemical elements.
This is probably what happens in the atmospheres of upper main-sequence. chemically peculiar (CP) stars which exhibit a wide variety of strong abundance anomalies. since the outer layers of these stars can be considered stable enough to allow element diffusion to take place.
This is probably what happens in the atmospheres of upper main-sequence, chemically peculiar (CP) stars which exhibit a wide variety of strong abundance anomalies, since the outer layers of these stars can be considered stable enough to allow element diffusion to take place.
A considerable number of papers. starting with Michaud (1970)). have examined to what extent the diffusion model ts able to explain the observed anomalies.
A considerable number of papers, starting with Michaud \cite{mic70}) ), have examined to what extent the diffusion model is able to explain the observed anomalies.
Often. but not always. these anomalies appear to be quite well correlated with the respective radiative accelerations (frequently the leading contribution to the diffusion velocity) of each element.
Often, but not always, these anomalies appear to be quite well correlated with the respective radiative accelerations (frequently the leading contribution to the diffusion velocity) of each element.
However. the CP phenomenon involves so many complex processes that a direct comparison of observed apparent abundance anomalies and calculated radiative accelerations will not be sufficient for fully describing the build-up of abundance peculiarities.
However, the CP phenomenon involves so many complex processes that a direct comparison of observed apparent abundance anomalies and calculated radiative accelerations will not be sufficient for fully describing the build-up of abundance peculiarities.
On the one hand. abundance determinations in the past did not take stratification of the chemical elements into account; on the other. these stratifications are built up by a time-dependent. non-linear diffusior process which ts quite sensitive to magnetic fields and to macroscopic motions (residual turbulence or stellar wind for instance) and depends not only on radiative accelerations.
On the one hand, abundance determinations in the past did not take stratification of the chemical elements into account; on the other, these stratifications are built up by a time-dependent, non-linear diffusion process which is quite sensitive to magnetic fields and to macroscopic motions (residual turbulence or stellar wind for instance) and depends not only on radiative accelerations.
Despite the fact that full modelling of atmospheres of CP stars Is still out of reach. notable progress has been made in the field ofdiffusion.
Despite the fact that full modelling of atmospheres of CP stars is still out of reach, notable progress has been made in the field of diffusion.
A first study addressing the special behaviour of silico in magnetic atmospheres was carried out by Vauclair et al. (1979)).
A first study addressing the special behaviour of silicon in magnetic atmospheres was carried out by Vauclair et al. \cite{vhp79}) ),
followed by the quantitative modelling of Si stratification by Alecian Vauclair (1981)).
followed by the quantitative modelling of Si stratification by Alecian Vauclair \cite{alv81}) ).
. À theoretical prediction of manganese accumulation in hot CP stars was proposed by Alecian Michaud (1981)). and a first attempt at detecting such a stratification was carried out by. Alecian (1982)) in the HgMn star v HHer using à method based on the curve of growth of Mn resonance lines.
A theoretical prediction of manganese accumulation in hot CP stars was proposed by Alecian Michaud \cite{alm81}) ), and a first attempt at detecting such a stratification was carried out by Alecian \cite{ale82}) ) in the HgMn star $\upsilon$ Her using a method based on the curve of growth of Mn resonance lines.
Abundances of tron peak elements in several HgMn stars were analysed in detail by Smith Dworetsky (1993)).
Abundances of iron peak elements in several HgMn stars were analysed in detail by Smith Dworetsky \cite{smi93}) ).
In their study. these authors used what were state of the art methods at that time. applying schematic corrections for the chemical stratifications.
In their study, these authors used what were state of the art methods at that time, applying schematic corrections for the chemical stratifications.
Within the framework of this approximate treatment. they found that their results were in excellent agreement with the predictions of the diffusion model.
Within the framework of this approximate treatment, they found that their results were in excellent agreement with the predictions of the diffusion model.
A detailed study of the stratifications of several metals in the magnetic star 53 Cam. assuming the presence of a stellar wind. is due to Babel Michaud (1991 )) and to Babel (1992)).
A detailed study of the stratifications of several metals in the magnetic star 53 Cam, assuming the presence of a stellar wind, is due to Babel Michaud \cite{bam91}) ) and to Babel \cite{bam92}) ).
Their main conclusion for that particular star was that diffusion alone cannot account for the observations and that more sophisticated models have to be developed. including mass loss confined by magnetic fields.
Their main conclusion for that particular star was that diffusion alone cannot account for the observations and that more sophisticated models have to be developed, including mass loss confined by magnetic fields.
These early studies were limited by technical. constraints. such as insufficient computing power. and by the lack of atomic and observational data.
These early studies were limited by technical constraints, such as insufficient computing power, and by the lack of atomic and observational data.
Therefore no firm conclusions could be reached concerning the chemical stratifications produced by diffusion processes.
Therefore no firm conclusions could be reached concerning the chemical stratifications produced by diffusion processes.
Fortunately. the situation has improved. drastically.
Fortunately, the situation has improved drastically.
Thanks to high. performance detectors. inhomogeneous element distributions appear to be established beyond reasonable doubt; see e.g. Kochukhov et al. (2004))
Thanks to high performance detectors, inhomogeneous element distributions appear to be established beyond reasonable doubt; see e.g. Kochukhov et al. \cite{koc04}) )
for horizontal distributions in the magnetic atmosphere of 53 Cam and Kochukhov et al. (2006))
for horizontal distributions in the magnetic atmosphere of 53 Cam and Kochukhov et al. \cite{koc06}) )
for vertical distributions in the Ap star 1133792.
for vertical distributions in the Ap star 133792.
Significant progress has also been made in modelling. since self-consistent atmospheric models for non-magnetic stars. including abundance stratifications compatible with the amount of elements which can be supported by radiative forces. are in an advanced stage of development Bon-Hoa et al. 2002).
Significant progress has also been made in modelling, since self-consistent atmospheric models for non-magnetic stars, including abundance stratifications compatible with the amount of elements which can be supported by radiative forces, are in an advanced stage of development (Hui-Bon-Hoa et al. \cite{hbh02}) ).
In the present paper. we attempt to advance one further step on the long path towards the complete modelling of the migration. process of chemical elements. by looking for an equilibrium solution to the stratification of metals 1n magnetic atmospheres.
In the present paper, we attempt to advance one further step on the long path towards the complete modelling of the migration process of chemical elements, by looking for an equilibrium solution to the stratification of metals in magnetic atmospheres.
We rely on the physics and methods presented in previous papers. where we have computed the Zeeman amplification of radiative accelerations in detail (Alecian. Suft 2004)). and studied diffusion velocities in. magnetic atmospheres (Alecian Stift. 2006)).
We rely on the physics and methods presented in previous papers, where we have computed the Zeeman amplification of radiative accelerations in detail (Alecian Stift \cite{als04}) ), and studied diffusion velocities in magnetic atmospheres (Alecian Stift, \cite{als06}) ).
The present work still assumes LTE and the temperature/pressure structure of the atmosphere is computed with solar abundances (ATLAS9. Kuruez. 1993)).
The present work still assumes LTE and the temperature/pressure structure of the atmosphere is computed with solar abundances (ATLAS9, Kurucz, \cite{kur93}) ).
The CARAT code is used in the improved version discussed by Alecian Stift (2006)).
The CARAT code is used in the improved version discussed by Alecian Stift \cite{als06}) ).
It carries out full
It carries out full
clusters. For which we need only the observed V. and J magnitudes. as well as the sharpness measurement.
clusters, for which we need only the observed $V$ and $I$ magnitudes, as well as the sharpness measurement.
Photometry uncertainties are not needed. as these are simulated. using the artificial clusters.
Photometry uncertainties are not needed, as these are simulated using the artificial clusters.
The second data set is the artificial cluster library. which contains input inlegrated V. and / magnitudes and core radii. as well as the observed properties (or a flag if the cluster was either not recovered or did not meet our cluster selection criteria).
The second data set is the artificial cluster library, which contains input integrated $V$ and $I$ magnitudes and core radii, as well as the observed properties (or a flag if the cluster was either not recovered or did not meet our cluster selection criteria).
The CAMRD modeling then proceeded according to the steps described in the previous sections.
The CMRD modeling then proceeded according to the steps described in the previous sections.
Although this modeling is not quite as sophisticated as the stellar population CMD- method of Dolphin&Nennicutt(2002) due to a number of approximations in our svnthetic CAIRD construction. we believe that it is useful to look at the fit quality. to determine how well we matched (he observed data.
Although this modeling is not quite as sophisticated as the stellar population CMD-fitting method of \citet{dol02} due to a number of approximations in our synthetic CMRD construction, we believe that it is useful to look at the fit quality to determine how well we matched the observed data.
The maxinum likelihood fit parameter (ecuation 5)) of our best solution was 924.7.
The maximum likelihood fit parameter (equation \ref{eq_fitparam}) ) of our best solution was 924.7.
For comparison. the tvpical data set randomly drawn from our best model would produce a fit parameter of 850.9. will a lo scatter of 38.2.
For comparison, the typical data set randomly drawn from our best model would produce a fit parameter of 850.9, with a $1 \sigma$ scatter of 38.2.
Thus the mateh between the observed data and our best model is roughly 2 σ from ideal.
Thus the match between the observed data and our best model is roughly 2 $\sigma$ from ideal.
Given the approximations involved in using functional forms for differential extinction and {he mass and radius distributions. we consider this a good fit.
Given the approximations involved in using functional forms for differential extinction and the mass and radius distributions, we consider this a good fit.
A comparison between the top and bottom panels of Figure 1. shows that our best model matched the observed sharpness distribution very well.
A comparison between the top and bottom panels of Figure \ref{fig_sharp_dist} shows that our best model matched the observed sharpness distribution very well.
A comparison of the CMDs is given in Figure 2..
A comparison of the CMDs is given in Figure \ref{fig_cmrds}.
The top panels show the observed (left) and model (right) CMDs in grevscale: the break at V=23.5 is caused by (he higher cutoff magnitude in WEC? (hat was imposed {ο combat contamination. (
The top panels show the observed (left) and model (right) CMDs in greyscale; the break at V=23.5 is caused by the higher cutoff magnitude in WFC2 that was imposed to combat contamination. (
Since the magnitude limits were also applied to the synthetic data. il is possible to include photometry of varving depth without anv extra effort.)
Since the magnitude limits were also applied to the synthetic data, it is possible to include photometry of varying depth without any extra effort.)
The bottom left panel shows the difference of the top panels. with darker squares sienilvine more observed than svnthetic clusters and lighter squares the reverse.
The bottom left panel shows the difference of the top panels, with darker squares signifying more observed than synthetic clusters and lighter squares the reverse.
The important. panel of Figure 2 is the bottom right panel. which shows (he significance of the dillerences (essentially a \ measurement. though using Poisson statistics).
The important panel of Figure \ref{fig_cmrds} is the bottom right panel, which shows the significance of the differences (essentially a $\chi$ measurement, though using Poisson statistics).
In no part of the CMD is that [it worse (han 3c. and only two CAID regions are worse than 2e. (
In no part of the CMD is that fit worse than $3 \sigma$, and only two CMD regions are worse than $2 \sigma$. (
Note that the CAIRD as fit bv our algorithm is (ruly a three-dimensional diagram: we have divided it into à CMD and a radius histogram in this section merely [or ease of display.)
Note that the CMRD as fit by our algorithm is truly a three-dimensional diagram; we have divided it into a CMD and a radius histogram in this section merely for ease of display.)
Of the free parameters (hat enter into the solution. two are quite well-constrained.
Of the free parameters that enter into the solution, two are quite well-constrained.
The
The
The first paper (Rucinski&Duerbeck2006.PaperI). presented data obtained on 4 nights in August 1998 for 17 targets. a mixture of contact binaries possibly offering reasonable orbital solutions with a selection of variables suspected to be contact binaries.
The first paper \citep[ Paper~I]{RD06} presented data obtained on 4 nights in August 1998 for 17 targets, a mixture of contact binaries possibly offering reasonable orbital solutions with a selection of variables suspected to be contact binaries.
The current paper is verv similar in spirit: It gives radial velocity data and preliminary orbital solutions for 14 similar (targets observed on 5 nights of November 28 to December 2. 1996. in the later part ol the southern sky.
The current paper is very similar in spirit: It gives radial velocity data and preliminary orbital solutions for 14 similar targets observed on 5 nights of November 28 to December 2, 1996, in the later part of the southern sky.
The goals of (hiis paper. similarly to those of Paper I. are close to the series of racial velocily studies currently conducted at the David Dunlap Observatory (DDO).
The goals of this paper, similarly to those of Paper I, are close to the series of radial velocity studies currently conducted at the David Dunlap Observatory (DDO).
This series has recently reached. with the eleventh paper (Pribullaetal.2006) (where relerences {ο ihe previous papers and many common details can be found). the round number of 100 well covered RV orbits.
This series has recently reached, with the eleventh paper \citep{ddo11} (where references to the previous papers and many common details can be found), the round number of 100 well covered RV orbits.
The instrumentation ancl data analvsis techniques. are explained in Paper I. The observations were carried with the ESO La Silla 1.52-m telescope and a D&CCh Casseerain specirograph.
The instrumentation and data analysis techniques, are explained in Paper I. The observations were carried with the ESO La Silla 1.52-m telescope and a Ch Cassegrain spectrograph.
The broadening functions were extracted [rom the wavelength region of 401.6 — 499.8 nm.
The broadening functions were extracted from the wavelength region of 401.6 – 499.8 nm.
The spectra have a BF resolution of tvpically c23—27 km |.
The spectra have a BF resolution of typically $\simeq 23 - 27$ km $^{-1}$.
Stellar exposure times ranged between 10 and 20 min. depending on brightness: each stellar exposure was followed bv a IHe-Àr spectrum.
Stellar exposure times ranged between 10 and 20 min, depending on brightness; each stellar exposure was followed by a He-Ar spectrum.
Spectrum extraction and wavelength calibration was carried out with ESO MIDAS softwaresvstem?.
Spectrum extraction and wavelength calibration was carried out with ESO MIDAS software.
. As a comparison star template. we used 68 Eri (F5 V). for which a radial velocity of +10.1kams.| was adopted 2004).
As a comparison star template, we used 68 Eri (F5 V), for which a radial velocity of $+10.1~\rm km~s^{-1}$ was adopted \citep{nord04}.
In terms of the presentation. we again stav close to the format of Paper I. the only difference being that the Cargels are discussed simply in the constellation order in Section 2..
In terms of the presentation, we again stay close to the format of Paper I, the only difference being that the targets are discussed simply in the constellation order in Section \ref{sec2}.
We see no obvious cases of pulsating stars in this eroup of the targets: only CU Eri A and CU Eri B do not appear to be close binaries.
We see no obvious cases of pulsating stars in this group of the targets; only CU Eri A and CU Eri B do not appear to be close binaries.
We describe our results in the context of existing photometric data [rom the literature and the Hipparcos project.
We describe our results in the context of existing photometric data from the literature and the Hipparcos project.
We also utilize the mean (5—V) color indexes taken [rom the Tveho-2 catalog (Hogetal.2000) and the photometric estimates of the spectral types using the relations published by Bessell(1979).
We also utilize the mean $(B-V)$ color indexes taken from the Tycho-2 catalog \citep{tycho2} and the photometric estimates of the spectral types using the relations published by \citet{bessell79}.
. Spectral (vpes are (taken. uniformly from the 5 volumes of the Michigan Catalogue of IID Stars (Πο&Cowley1975:Houk1978.1982:HloukSmith-Moore 1999):: hereafter quoted as IIDII.
Spectral types are taken uniformly from the 5 volumes of the Michigan Catalogue of HD Stars \citep{houketal75,houk78,houk82,houketal88,houketal99}; hereafter quoted as HDH.
Because of the high incidence of companions to contact binary stars (Pribulla&Iucinski2006).. we checked. all stars for possible membership in visual svstems using the Washington Double Star Catalog
Because of the high incidence of companions to contact binary stars \citep{priruc2006}, we checked all stars for possible membership in visual systems using the Washington Double Star Catalog.
(WDS)*.. Fieures 1 and 2. present the broadening funictions for all targets at phases selected for best visibility of both components.
Figures \ref{fig1} and \ref{fig2} present the broadening functions for all targets at phases selected for best visibility of both components.
Figures 3. and 4. show the preliminary radial velocity
Figures \ref{fig3} and \ref{fig4} show the preliminary radial velocity
dosca το coronagraph. a well developecLstreamer is delineated by a sharp xiehtuess boundary.
coronagraph, a well developed streamer is delineated by a sharp brightness boundary.
The boundary separates the streamer from its surrouncdines.
The boundary separates the streamer from its surroundings.
Besides the boundary. a typical streamer also includes a bunch of closed field arcades. a streamer CUS). and a high density plasma sheet (also called the streamer stalls or streamer belt) within which a long thin curent sheet e1ibedded (see. e.g. Phemman Kopp. 1971: Stress Nerney. 2006).
Besides the boundary, a typical streamer also includes a bunch of closed field arcades, a streamer cusp, and a high density plasma sheet (also called the streamer stalk or streamer belt) within which a long thin current sheet embedded (see, e.g, Pneuman Kopp, 1971; Suess Nerney, 2006).
On the other haud. coronal lass ejections (CMES). represeuntius the largest and most euergetic dynamical process in the corona. may cause elobal perturbations wit inescale of miuutes to hours.
On the other hand, coronal mass ejections (CMEs), representing the largest and most energetic dynamical process in the corona, may cause global perturbations with a timescale of minutes to hours.
Therefore. cloSC interactioas between CATES aud streaners can frequentlv QCCHT. οςeciallv during the active phase of a solar οσοe when CAIEs aud streamers are xeseut at virtually all. heliolatitudes.
Therefore, close interactions between CMEs and streamers can frequently occur, especially during the active phase of a solar cycle when CMEs and streamers are present at virtually all heliolatitudes.
Iu eenoeral. CAIE-streamer relevant events can be classified into two eroups.
In general, CME-streamer relevant events can be classified into two groups.
One comprises hose eveuts of CMES originaine and erupting from the streamer interior. ike the so-called streamer blowouts (Toward et al.
One comprises those events of CMEs originating and erupting from the streamer interior, like the so-called streamer blowouts (Howard et al.,
1985: Uundhausen 1993) or streamer muffs (Bemporacl e al.
1985; Hundhausen 1993) or streamer puffs (Bemporad et al.
2005).
2005).
Ou the other haud. the events iu the second eroup result from the streamers cine hit ou fie sides by citrer CAIEs with expanding Structures or by CAIE-dviven disturbances like shock waves.
On the other hand, the events in the second group result from the streamers being hit on the sides by either CMEs with expanding structures or by CME-driven disturbances like shock waves.
The collision may caise appareut deflections or siuks of streamer raves tracing the passage of CALE disturbances (Sheclev et al.
The collision may cause apparent deflections or kinks of streamer rays tracing the passage of CME disturbances (Sheeley et al.
2000).
2000).
Ii some cases. the collision iiw have trigecrec reconnections across the streamer current sheet as dudicated by the observed streamer disconnection (0.2. Deuporad. et al;
In some cases, the collision may have triggered reconnections across the streamer current sheet as indicated by the observed streamer disconnection (e.g., Bemporad, et al.,
2008). the release of plasina blobs alouso: the streamer stalls. or the formation of streamer in/out airs (ce... Shecley Wane. POOT).
2008), the release of plasma blobs along the streamer stalk, or the formation of streamer in/out pairs (e.g., Sheeley Wang, 2007).
Given the fundamental role plaved by wave excitatious in a disturbed plasiuaauagnueic field svsteii. one natural
Given the fundamental role played by wave excitations in a disturbed plasma-magnetic field system, one natural
integrating to a fixed logNy;=16.0.
integrating to a fixed $\logNHI = 16.0$.
We used the updated “HM05” UV background spectrum, which is the average UV background produced by AGN and starburst galaxies (?);; we did not include a contribution from nearby star-formation.
We used the updated “HM05” UV background spectrum, which is the average UV background produced by AGN and starburst galaxies \citep{haardt-madau-96-UV-background}; we did not include a contribution from nearby star-formation.
? give a relation to derive the contribution of star-forming regions to the UV background, as a function of escape fraction, distance and SFR.
\citet{tumlinson-etal-11-J1009-LLS} give a relation to derive the contribution of star-forming regions to the UV background, as a function of escape fraction, distance and SFR.
At a distance of d>95kpc and SFR=0.95+0.04 Myr, star formation contributes to the ionization field at the level of a few percent for reasonable values of the escape fraction of ionizing photons (i.e.fese <0.04;739).
At a distance of $d > 95\kpc$ and $\mbox{SFR} = \SFRnum\,\Myr$ , star formation contributes to the ionization field at the level of a few percent for reasonable values of the escape fraction of ionizing photons \citep[i.e. $f_{esc} <.
Note also that photoionization of tto rrequires photons with energies of oor greater, which cannot be supplied by star-formation.
Note also that photoionization of to requires photons with energies of or greater, which cannot be supplied by star-formation.
Figure 5 shows contours of the predicted column densities in sspace for these cloudy models.
Figure \ref{fig: metal_poor} shows contours of the predicted column densities in space for these cloudy models.
As can be seen, the aand nnon-detections provide tight constraints on the metallicity, although this restriction relaxes at high ionization parameter (low density).
As can be seen, the and non-detections provide tight constraints on the metallicity, although this restriction relaxes at high ionization parameter (low density).
The nnon-detection limits the allowed region of parameter space to logU«—1.5, but the combined detection of aand non-detection of iis the most restrictive.
The non-detection limits the allowed region of parameter space to $\logU < -1.5$, but the combined detection of and non-detection of is the most restrictive.
These combine to limit the metallicity of the cloud to [M/H]«—1.5.
These combine to limit the metallicity of the cloud to $\MH < -1.5$.
At this point, it is worth emphasizing the value of the follow-up HIRES optical spectrum.
At this point, it is worth emphasizing the value of the follow-up HIRES optical spectrum.
Without it, the metallicity would be significantly less constrained, and our interpretation much less certain.
Without it, the metallicity would be significantly less constrained, and our interpretation much less certain.