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Again. we cannot judge on a difference between UCDs and massive Fornax GCs due to the brighter sensitivity limit in the FUV. which excludes most of the GCs detected in the NUV.
Again, we cannot judge on a difference between UCDs and massive Fornax GCs due to the brighter sensitivity limit in the FUV, which excludes most of the GCs detected in the NUV.
A UV excess in an old stellar population is likely due to EHB stars.
A UV excess in an old stellar population is likely due to EHB stars.
As pointed out in Sect. 1..
As pointed out in Sect. \ref{introduction},
an EHB may be linked to helium-enriched stars (e.g. Ventura et al.
an EHB may be linked to helium-enriched stars (e.g. Ventura et al.
2001. D'Antona et al.
2001, D'Antona et al.
2002).
2002).
The strong UV excess of the seven massive Fornax GCs beyond the He-enhanced isochrones. especially the NUV excess of the three most extreme GCs with V)«2.4 mag (see Fig. 3)).
The strong UV excess of the seven massive Fornax GCs beyond the He-enhanced isochrones, especially the NUV excess of the three most extreme GCs with $<$ 2.4 mag (see Fig. \ref{colcol}) ),
suggests that in these objects. EHB formation is also driven by other processes.
suggests that in these objects, EHB formation is also driven by other processes.
In this context. a plausible explanation may be enhanced mass loss of evolved stars. triggered by high stellar densities (Decressin et al.
In this context, a plausible explanation may be enhanced mass loss of evolved stars, triggered by high stellar densities (Decressin et al.
2007: Huang Gies 2006) and/or large binary fractions.
2007; Huang Gies 2006) and/or large binary fractions.
Excess radiation at short wavelengths can in principle also arise from accretion onto a black hole (King et al.
Excess radiation at short wavelengths can in principle also arise from accretion onto a black hole (King et al.
1993). which can be traced by low-mass X-ray binaries (Jordánn et al.
1993), which can be traced by low-mass X-ray binaries (Jordánn et al.
2004).
2004).
We have cross-checked the positions of all GALEX UV detections with X-ray source detections in the Chandra Fornax Survey data (Scharf et al.
We have cross-checked the positions of all GALEX UV detections with X-ray source detections in the Chandra Fornax Survey data (Scharf et al.
2005 and private communication). the deepest available wide-field X-ray survey of Fornax (50ks integration with ACIS).
2005 and private communication), the deepest available wide-field X-ray survey of Fornax (50ks integration with ACIS).
The sensitivity of these images is a few 10? erg/see. allowing to detect the most luminous LMXBs (Jordánn et al.
The sensitivity of these images is a few $ 10^{38}$ erg/sec, allowing to detect the most luminous LMXBs (Jordánn et al.
2004).
2004).
In Fig.
In Fig.
1. we indicate the (V- optical colours of those compact objects with X-ray matches.
\ref{map} we indicate the (V-I) optical colours of those compact objects with X-ray matches.
At a given magnitude. the X-ray matches are biased towards red optical colours (see also Jordann et al.
At a given magnitude, the X-ray matches are biased towards red optical colours (see also Jordánn et al.
2004). while GALEX UV detections are biased towards blue optical colours,
2004), while GALEX UV detections are biased towards blue optical colours.
This suggests that generally. the UV- and X-ray-emission of the compact stellar systems are not caused by the same physical processes.
This suggests that generally, the UV- and X-ray-emission of the compact stellar systems are not caused by the same physical processes.
However. there is one GALEX UV detection with an X-ray counterpart (Fig.
However, there is one GALEX UV detection with an X-ray counterpart (Fig.
| and 35. which happens to be one of the three GCs with largest UV excess.
\ref{map} and \ref{colcol}) ), which happens to be one of the three GCs with largest UV excess.
We can therefore not exclude that the UV excess in some of the GCs is linked to accretion processes.
We can therefore not exclude that the UV excess in some of the GCs is linked to accretion processes.
We finally note that comparing the probability of. UV excess between UCDs anc GCs allows to test. whether EHBs are more likely associated with present-day deep potential wells (ie. UCDs) or high stellar densities (ie. GCs: Dabringhausen et al.
We finally note that comparing the probability of UV excess between UCDs and GCs allows to test whether EHBs are more likely associated with present-day deep potential wells (i.e. UCDs) or high stellar densities (i.e. GCs; Dabringhausen et al.
2008. Mieske et al.
2008, Mieske et al.
2008).
2008).
One would expect deep potential wells to favour self-enrichment (e.g. Ventura et al.
One would expect deep potential wells to favour self-enrichment (e.g. Ventura et al.
2001. D'Antona et al.
2001, D'Antona et al.
2002). and high stellar densities to favour mass-loss scenarios (Decressin et al.
2002), and high stellar densities to favour mass-loss scenarios (Decressin et al.
2007; Huang Gies 2006).
2007; Huang Gies 2006).
Such à comparison may therefore help to constrain the efficieney of EHB formation channels. provided that the present-day density and mass of the systems investigated have not experienced significant changes during
Such a comparison may therefore help to constrain the efficiency of EHB formation channels, provided that the present-day density and mass of the systems investigated have not experienced significant changes during
Alachin ct) al. (
Machin et al. (
1990) obtained optical photometry simultaneously with the ~ Ith EXOSAT observation.
1990) obtained optical photometry simultaneously with the $\sim$ 14h EXOSAT observation.
This enabled. them to conclude. that the. optical anc X-ray emission are anticorrelated.
This enabled them to conclude that the optical and X-ray emission are anticorrelated.
We have re-examined carefully the ENOSAT ME data (Pig 3) in search ofllickering and/or short. periods.
We have re-examined carefully the EXOSAT ME data (Fig 3) in search of flickering and/or short periods.
Phe EXNOSAT ALE observation in question consists of three parts.
The EXOSAT ME observation in question consists of three parts.
During the first part the X-ray [lux is low and there is no evidence for any Hickering.
During the first part the X-ray flux is low and there is no evidence for any flickering.
During the second part the X-ray [lux is rising and simultaneously the Ilickering is emerging.
During the second part the X-ray flux is rising and simultaneously the flickering is emerging.
During the final part the X-ray flux is constantly high and there appears to be a clear ‘period’ of Uickering with a characteristic time scale of 24-25 min.
During the final part the X-ray flux is constantly high and there appears to be a clear 'period' of flickering with a characteristic time scale of 24-25 min.
We have performed period analysis of the three parts of EXOSAT observations and whilst the first two do not show any evidence for any short period. the final part does seem to show the aforementioned behaviour.
We have performed period analysis of the three parts of EXOSAT observations and whilst the first two do not show any evidence for any short period, the final part does seem to show the aforementioned behaviour.
In order to evaluate the significance of the 24-25min period. we have performed red noise analysis in the same manner as we did with the optical data above.
In order to evaluate the significance of the 24-25min period, we have performed red noise analysis in the same manner as we did with the optical data above.
The results are plotted in Fig 3 (bottom. panel).
The results are plotted in Fig 3 (bottom panel).
Lt is quite clear from our red noise analysis that the apparent perioclicity can be attributed to the presence of rec noise (i.c. the significance of the 24-25min period is only ~ )).
It is quite clear from our red noise analysis that the apparent periodicity can be attributed to the presence of red noise (i.e. the significance of the 24-25min period is only $\sim$ ).
It is worth noting though. that the EENOSAT perice is very close to being half of the 51 min period reported by Shahhaz ct al. (
It is worth noting though, that the EXOSAT period is very close to being half of the 51 min period reported by Shahbaz et al. (
2008) and the 50 min period seen in one of Our Catasets.
2008) and the 50 min period seen in one of our datasets.
Given that Alachin ct al. (
Given that Machin et al. (
1990). reported: X-ray vs. optical anticorrelation for the source. we have also studie the optical vs. X-ray anticorrelation by extracting the RAPE ASAL daily average count rates and comparing those with the average delta magnitudes in our optical data.
1990) reported X-ray vs. optical anticorrelation for the source, we have also studied the optical vs. X-ray anticorrelation by extracting the RXTE ASM daily average count rates and comparing those with the average delta magnitudes in our optical data.
We must interpret the results with caution though. since even if all our optical data is in white light. the earlier observations are obtained with a dillerent CCD detector than the [atter ones.
We must interpret the results with caution though, since even if all our optical data is in white light, the earlier observations are obtained with a different CCD detector than the latter ones.
oth. CCDs in question are blue sensitive and. AR coated though.
Both CCDs in question are blue sensitive and AR coated though.
The results from the optical vs- X-ray comparison are shown in Fig 4.
The results from the optical vs- X-ray comparison are shown in Fig 4.
The plot shows the optical magnitude vs. the X-ray lux.
The plot shows the optical magnitude vs. the X-ray flux.
During most of the optical observations the X-ray fux is not correlated with the mean optical brightness.
During most of the optical observations the X-ray flux is not correlated with the mean optical brightness.
Only during the two observations when the source is at it’s faintest in the optical. can we see some evidence for the optical vs. X-rav anticorrelation.
Only during the two observations when the source is at it's faintest in the optical, can we see some evidence for the optical vs. X-ray anticorrelation.
The formal correlation coellicient. lor the whole set of twelve observations is 0.7 and the rank correlation test (Spearman's p) reveals that eiven the small number of points the apparent anticorrelation of [Iuxes is not very significant random chance probability) and if we remove the two rightmost points [rom Πο. there is no correlation left.
The formal correlation coefficient for the whole set of twelve observations is 0.7 and the rank correlation test (Spearman's $\rho$ ) reveals that given the small number of points the apparent anticorrelation of fluxes is not very significant random chance probability) and if we remove the two rightmost points from fig.4, there is no correlation left.
Llowever. given the short term (within a day) variability of the RATE ASAT Fux together with sampling elfects and only changes in the X-ray lux level during the ENOSAT observations. a clear correlation would not be expected.
However, given the short term (within a day) variability of the RXTE ASM flux together with sampling effects and only changes in the X-ray flux level during the EXOSAT observations, a clear correlation would not be expected.
“Phe scatter in ASAL count rates (excluding the two rightmost points in Fig.
The scatter in ASM count rates (excluding the two rightmost points in Fig.
4) is (le) which could hide the anticorrelation observed by Alachin et al. (
4) is $\sigma$ ) which could hide the anticorrelation observed by Machin et al. (
1990).
1990).
Interestingly the two outlier points in Fie.
Interestingly the two outlier points in Fig.
4 have peculiar values both in their optical magnitude and their X-ray Hux.
4 have peculiar values both in their optical magnitude and their X-ray flux.
These points correspond to the two first observations of our dataset and thus could be caused by some longer term trend.
These points correspond to the two first observations of our dataset and thus could be caused by some longer term trend.
Finally. we have examined the results of the AR(2) time series fits to the individual datasets in order to gain insight into the underlving cause of variability.
Finally, we have examined the results of the AR(2) time series fits to the individual datasets in order to gain insight into the underlying cause of variability.
La particular. we have investigated whether the amount of red noise (or correlated behaviour in the light curves) depends on the mean optical Dux level.
In particular, we have investigated whether the amount of red noise (or correlated behaviour in the light curves) depends on the mean optical flux level.
As noted earlier. the X-ray fHickering in the ENOSAT data seemed to appear when the X-ray fux level reached its maximum.
As noted earlier, the X-ray flickering in the EXOSAT data seemed to appear when the X-ray flux level reached its maximum.
However. in case of optical data we do not see any evidence for correlation in between the red noise level (as estimated from the AR(2) fits) and the mean differential magnitude.
However, in case of optical data we do not see any evidence for correlation in between the red noise level (as estimated from the AR(2) fits) and the mean differential magnitude.
Furthermore. the optical tux during the only epoch of significant periodicity (dataset #277) is not different from the mean optical [ux level.
Furthermore, the optical flux during the only epoch of significant periodicity (dataset 7) is not different from the mean optical flux level.
The same applies to the red noise properties.
The same applies to the red noise properties.
We have analysed: 12 sets of optical fast. photometry of 400614|0901 together with reanalysis of old. EXOSAT ALE data.
We have analysed 12 sets of optical fast photometry of 4U0614+091 together with reanalysis of old EXOSAT ME data.
Despite the large amount of data. we do not have conclusive evidence for the orbital period. of this peculiar system.
Despite the large amount of data, we do not have conclusive evidence for the orbital period of this peculiar system.
However. in one case out of 13 we detect a strong periodic signal at around 50 min. which is compatible with the tentative 48.5 min period reported by Nelemans (2006) and the 51.3 min period reported by Shahbaz ct al. (
However, in one case out of 13 we detect a strong periodic signal at around 50 min, which is compatible with the tentative 48.5 min period reported by Nelemans (2006) and the 51.3 min period reported by Shahbaz et al. (
2008) and annot be explained. by the presence of red noise.
2008) and cannot be explained by the presence of red noise.
We can also conclude that none of our 12 optical datasets show any evidence for eclipses (not even partial ones).
We can also conclude that none of our 12 optical datasets show any evidence for eclipses (not even partial ones).
This means ju svstem inclinations above 75 degrees are very unlikely uxl there appears to be a controversy in between modelling we N-rav spectra (Schulz ct al.
This means that system inclinations above 75 degrees are very unlikely and there appears to be a controversy in between modelling the X-ray spectra (Schulz et al.
2010) ancl understanding 16 optical ancl X-ray light curves.
2010) and understanding the optical and X-ray light curves.
The variety of optical ight curves presented in this paper underline the multitude X variability present in this source.
The variety of optical light curves presented in this paper underline the multitude of variability present in this source.
The fact that most of 10 seemünelv significant (in terms of white noise alone) »eriodice signals can be explained. using the red noise mocel oes not mean that the variability is not real.
The fact that most of the seemingly significant (in terms of white noise alone) periodic signals can be explained using the red noise model does not mean that the variability is not real.
Ht rather uncerlines the characteristic time scales at which the svsteni exhibits nonperiodic variability such as llickering.
It rather underlines the characteristic time scales at which the system exhibits nonperiodic variability such as flickering.
We have tried to estimate whether these variability time scales would be correlated with different levels of N-rav or optical activity.
We have tried to estimate whether these variability time scales would be correlated with different levels of X-ray or optical activity,
2..
\ref{CF}.
The average density aud the rms velocity in FIR 5 can be obtained from previous works.
The average density and the rms velocity in FIR 5 can be obtained from previous works.
Emprechtingeretal.(2009) modeled the morphology of NGC 2024 based on APEX observations of CO isotopologues.
\citet{Empre09} modeled the morphology of NGC 2024 based on APEX observations of CO isotopologues.
The various line profiles obtained lor different transitions are consistent. will a complex structure composed by a Photo Dominated Reeion (PDHR) foreeround to the molecular gas where the lar inlvared cores are found.
The various line profiles obtained for different transitions are consistent with a complex structure composed by a Photo Dominated Region (PDR) foreground to the molecular gas where the far infrared cores are found.
In their models. ihe dense molecular cloud must be warm (7!5 Ix) and dense (9x10? 7) to reproduce the velocity gradients observed Lor distinct cloud components.
In their models, the dense molecular cloud must be warm (75 K) and dense $9 \times 10^{5}$ $^{-3}$ ) to reproduce the velocity gradients observed for distinct cloud components.
These results agree will the previous work of Mangunmetal.(1999).. based on formaldehyde observations.
These results agree with the previous work of \citet{Mangum99}, based on formaldehyde observations.
These authors derived a kinetic temperature of Ty,>40 Ix for FIR 3-7 ancl estimate densities ab the same order of magnitude (7j,2xLO’ 7).
These authors derived a kinetic temperature of $T_{K} > 40$ K for FIR 3-7 and estimate densities at the same order of magnitude $n_{\mathrm{H_{2}}} \approx 2 \times 10^{6}$ $^{-3}$ ).
We adopt my,=1.5x10" oE as an average value for the density.
We adopt $n_{\mathrm{H_{2}}} = 1.5 \times 10^{6}$ $^{-3}$ as an average value for the density.
For (he velocity dispersion. we adopt 0Vios of 0.87 + 0.03J|... which is the value derived by Mangunmetal.(1999). [rom the formaldehyde observations.
For the velocity dispersion, we adopt $\delta V_{LOS}$ of 0.87 $\pm$ 0.03, which is the value derived by \citet{Mangum99} from the formaldehyde observations.
This molecule is a good tracer of dense gas. and for the single-dish data οἱ Alangumetal.(1999)... it traces the gas kinetic temperature in a scale of ~8000 AU. hence ib ds well correlated to the turbulent motions of the core.
This molecule is a good tracer of dense gas, and for the single-dish data of \citet{Mangum99}, it traces the gas kinetic temperature in a scale of $\sim 8000$ AU, hence it is well correlated to the turbulent motions of the core.
Finally. applving those inputs to the equation 2.. together with the 96;,, previously obtained. we estimate that the POS magnetic field strength is 2.2 mG. which is in good agreement with the value estimated in LOGRO2.
Finally, applying those inputs to the equation \ref{CF}, together with the $\delta\phi_{int}$ previously obtained, we estimate that the POS magnetic field strength is 2.2 mG, which is in good agreement with the value estimated in LCGR02.
The uncertainty in the magnetic field strength is determined mainly by the error in (he volume densitv ». which is a factor of ~ 2 due to the distinct assumptions on the cloud temperature.
The uncertainty in the magnetic field strength is determined mainly by the error in the volume density $n$, which is a factor of $\sim$ 2 due to the distinct assumptions on the cloud temperature.
This [actor implies an uncertainty of for the derived field strength.
This factor implies an uncertainty of for the derived field strength.
As mentioned earlier. the dispersion used as input in equation 2. carries the combined effects of changes on the large-scale field directions plus turbulent motions.
As mentioned earlier, the dispersion used as input in equation \ref{CF} carries the combined effects of changes on the large-scale field directions plus turbulent motions.
In this case. the derived field strength is only a lower limit since the augle dispersion is not generated purely by Alfvénnie motions.
In this case, the derived field strength is only a lower limit since the angle dispersion is not generated purely by Alfvénnic motions.
On the other hand. beam averaging and line-of-sight effects due to field twisting of multiple gas components usually underestimates (hie real value of the turbulent component. and the estimated field strength in this case would be an upper
On the other hand, beam averaging and line-of-sight effects due to field twisting of multiple gas components usually underestimates the real value of the turbulent component, and the estimated field strength in this case would be an upper
uodels by Conrovctal.(2009).. we find that the oimetrze hDnuuinositv of TP-AGD stars is a factor of 3 lower than predicted by the latest Padova TP-ACGD nodels. when assuming solar metallicity.
models by \cite{co09}, we find that the bolometric luminosity of TP-AGB stars is a factor of $\sim$ 3 lower than predicted by the latest Padova TP-AGB models, when assuming solar metallicity.
Ioxwever. an independent abundance measurement is needed to break he degeneracy between iuetallicitv aud the TP-ACDB dase parameters.
However, an independent abundance measurement is needed to break the degeneracy between metallicity and the TP-AGB phase parameters.
The significant reduction in the bolometric huuinosity of TP-AGB stars that is required to fit the observed xost-starburst SED reflects one or more failures of the curent generation of SPS models.
The significant reduction in the bolometric luminosity of TP-AGB stars that is required to fit the observed post-starburst SED reflects one or more failures of the current generation of SPS models.
This reduction cau be wsically achieved in the models by reduciug TP-ACB Ποιος, reducing bolometric Iuuinosities of stars in the TP-AGB phase. and/or cmbedding a significant fraction of TP-AGD stars within optically thick circtuestcllar dust shells.
This reduction can be physically achieved in the models by reducing TP-AGB lifetimes, reducing bolometric luminosities of stars in the TP-AGB phase, and/or embedding a significant fraction of TP-AGB stars within optically thick circumstellar dust shells.