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These contours are significantly more constraining than those derived by Podariu Ratra (2000. 11) from earlier SMa data.
These contours are significantly more constraining than those derived by Podariu Ratra (2000, 1) from earlier SNIa data.
Figure 2 shows the combined SNla and galaxy cluster constraints on the ACDM model.
Figure 2 shows the combined SNIa and galaxy cluster constraints on the $\Lambda$ CDM model.
These joint constraints are significantly tighter (han those derived using either the SNIa data (ROF. 88) or the galaxy cluster data (304. 44 and 8: CR. LL) alone.
These joint constraints are significantly tighter than those derived using either the SNIa data (R04, 8) or the galaxy cluster data (A04, 4 and 8; CR, 1) alone.
This is because the galaxy. cluster data tend to tightly constrain Qa, CAO4. CR). while the SNla data tend to tightly constrain a linear combination of 4 and O3; (04).
This is because the galaxy cluster data tend to tightly constrain $\Omega_M$ (A04, CR), while the SNIa data tend to tightly constrain a linear combination of $\Omega_\Lambda$ and $\Omega_M$ (R04).
Together. the data focus attention on a small part of parameter space near £24;~0.3 and O4~0.7 where the Universe is spatially Lat.
Together, the data focus attention on a small part of parameter space near $\Omega_M \sim 0.3$ and $\Omega_\Lambda \sim 0.7$ where the Universe is spatially flat.
Figure 3 shows the joint data constraints on the NCDAL parametrization.
Figure 3 shows the joint data constraints on the XCDM parametrization.
Models close to ο~0.3 and i6e—1 are favored.
Models close to $\Omega_M \sim 0.3$ and $w \sim -1$ are favored.
We emphasize that we=—1 corresponds (o the spatiallv-Hat. ACDAL model.
We emphasize that $w=-1$ corresponds to the spatially-flat $\Lambda$ CDM model.
Figure 4 shows the joint data constraints on (he ó0CDM model with We)xo.
Figure 4 shows the joint data constraints on the $\phi$ CDM model with $V(\phi) \propto \phi^{-\alpha}$.
Models close to Qa,~0.25 and à~0 are favored.
Models close to $\Omega_M \sim 0.25$ and $\alpha \sim 0$ are favored.
We note that a=0 corresponds to (he ACDAI moclel.
We note that $\alpha = 0$ corresponds to the spatially-flat $\Lambda$ CDM model.
The SNla aud galaxy cluster data together favor a spatially-flat ACDM model with Q1;220.25— 0.3.
The SNIa and galaxy cluster data together favor a spatially-flat $\Lambda$ CDM model with $\Omega_M \approx 0.25 - 0.3$ .
Ht might be significant that other data also favor this value of Oy; (see. e.g.. Chen atra 2003b: Spergel et 22003).
It might be significant that other data also favor this value of $\Omega_M$ (see, e.g., Chen Ratra 2003b; Spergel et 2003).
We emphasize. however. (hat slowly-evolvinge dark enerev models are also consistent with this data.
We emphasize, however, that slowly-evolving dark energy models are also consistent with this data.
example, the dependence of AGN fraction on stellar mass can be opposite if galaxy morphology is considered (increases with decreasing mass in the early-type galaxy population).
example, the dependence of AGN fraction on stellar mass can be opposite if galaxy morphology is considered (increases with decreasing mass in the early-type galaxy population).
In this work, we estimate the fraction of heavily-obscured AGN in mid-IR-luminous and massive galaxies at high redshift, few of which are individually detected in X-rays.
In this work, we estimate the fraction of heavily-obscured AGN in mid-IR-luminous and massive galaxies at high redshift, few of which are individually detected in X-rays.
The main goal is to constrain the amount of obscured SMBH accretion happening in distant galaxies.
The main goal is to constrain the amount of obscured SMBH accretion happening in distant galaxies.
This can be done thanks to the very deep X-ray observations available in the Chandra Deep Fields and the very low and stable Chandra background, which allows for the efficient stacking of individually undetected sources.
This can be done thanks to the very deep X-ray observations available in the Chandra Deep Fields and the very low and stable Chandra background, which allows for the efficient stacking of individually undetected sources.
Throughout this letter, we assume a ACDM cosmology with hg=0.7, Q,=0.27 and Q,4=0.73, in agreement with the most recent cosmological observations (Hinshawetal.2009).
Throughout this letter, we assume a $\Lambda$ CDM cosmology with $h_0$ =0.7, $\Omega_m$ =0.27 and $\Omega_\Lambda$ =0.73, in agreement with the most recent cosmological observations \citep{hinshaw09}.
. By stacking individually-undetected sources selected at longer wavelengths, it is possible to detect very faint X-ray emitters using Chandra observations.
By stacking individually-undetected sources selected at longer wavelengths, it is possible to detect very faint X-ray emitters using Chandra observations.
For example, this technique was used successfully by Brandtetal. in the Chandra Deep Field North (CDF-N) to measure(2001) the average X-ray emission from a sample of Lyman break galaxies at zc:2-4 and by Rubinetal.(2004) to detect X-rays from red galaxies at z~2.
For example, this technique was used successfully by \citet{brandt01} in the Chandra Deep Field North (CDF-N) to measure the average X-ray emission from a sample of Lyman break galaxies at $z$$\simeq$ 2–4 and by \citet{rubin04} to detect X-rays from red galaxies at $z$$\sim$ 2.
More recently, samples of heavily-obscured AGN candidates selected based on their mid-IR properties have been studied in X-rays via Chandra stacking (e.g., Daddietal.2007;Fioreetal.2008;Treister 2009b)).
More recently, samples of heavily-obscured AGN candidates selected based on their mid-IR properties have been studied in X-rays via Chandra stacking (e.g., \citealp{daddi07,fiore08,treister09c}) ).
The 4 Msec Chandra observations of the Chandra Deep Field South (CDF-S), are currently the deepest view of the X-ray sky.
The 4 Msec Chandra observations of the Chandra Deep Field South (CDF-S), are currently the deepest view of the X-ray sky.
In addition, the CDF-S has been observed extensively at many wavelengths.
In addition, the CDF-S has been observed extensively at many wavelengths.
The multiwavelength data available on the (E)CDF-S were presented by Treisteretal.(2009b).
The multiwavelength data available on the (E)CDF-S were presented by \citet{treister09c}.
. Very relevant for this work are the deep Spitzer observations available in this field, using both the Infrared Array Camera (IRAC) and the Multiband Imaging Photometer for Spitzer (MIPS), from 3.6 to 24 yum. Also critical is the availability of good quality photometric redshifts (Az/(1+z)=0.008 for obtained thanks to deep observations in 18 medium-bandR<25.2) optical filters performed using Subaru/Suprime-Cam (Cardamoneetal. 2010a).
Very relevant for this work are the deep Spitzer observations available in this field, using both the Infrared Array Camera (IRAC) and the Multiband Imaging Photometer for Spitzer (MIPS), from 3.6 to 24 $\mu$ m. Also critical is the availability of good quality photometric redshifts $\Delta$$z$ $z$ )=0.008 for $R$$<$ 25.2) obtained thanks to deep observations in 18 medium-band optical filters performed using Subaru/Suprime-Cam \citep{cardamone10}. .
. We generated our sample starting with the 4959 24 ym sources in the region covered by the Chandra Spitzer/MIPSobservations that have photometric redshift 20.5, and hence rest-frame E>4 keV emission falling in the high-sensitivity Chandra range.
We generated our sample starting with the 4959 Spitzer/MIPS 24 $\mu$ m sources in the region covered by the Chandra observations that have photometric redshift $z$$>$ 0.5, and hence rest-frame $>$ 4 keV emission falling in the high-sensitivity Chandra range.
In addition, sources individually detected in X-rays and reported in the catalogs of Luoetal.(2008),, Lehmeretal.(2005) or Viranietal.(2006) were removed from our sample, as these sources will otherwise dominate the stacked spectrum.
In addition, sources individually detected in X-rays and reported in the catalogs of \citet{luo08}, \citet{lehmer05} or \citet{virani06} were removed from our sample, as these sources will otherwise dominate the stacked spectrum.
We then inspected the remaining sources to eliminate individual detections in the 4 Msec data not present in the 2 Msec catalog of Luoetal.(2008).
We then inspected the remaining sources to eliminate individual detections in the 4 Msec data not present in the 2 Msec catalog of \citet{luo08}.
We further excluded 28 sources that meet the selection criteria of Fioreetal.(2008) for heavily obscured AGN, foa/fr>1000 and R-K 74.5 (Vega), because we expect these sources to contain an intrinsically luminous AGN while the aim of this work is to find additional hidden (quasar),accretion in less luminous objects.
We further excluded 28 sources that meet the selection criteria of \citet{fiore08} for heavily obscured AGN, $f_{24}$ $f_R$$>$ 1000 and $R$ $K$$>$ 4.5 (Vega), because we expect these sources to contain an intrinsically luminous AGN (quasar), while the aim of this work is to find additional hidden accretion in less luminous objects.
The median redshift of the sources in our final sample is 1.32 (average z=1.5) with a standard deviation of 0.77.
The median redshift of the sources in our final sample is 1.32 (average $z$ =1.5) with a standard deviation of 0.77.
In order to perform X-ray stacking in the rest-frame, we started with the regenerated level 2 merged event files created by the Chandra X-rayCenter®.
In order to perform X-ray stacking in the rest-frame, we started with the regenerated level 2 merged event files created by the Chandra X-ray.
. For each source, we extracted all events in a circle of 30’ radius centered in the optical position.
For each source, we extracted all events in a circle of $''$ radius centered in the optical position.
The energy of each event was then converted to the rest frame using the photometric redshift of the source.
The energy of each event was then converted to the rest frame using the photometric redshift of the source.
Using standard CIAO (Fruscioneetal.2006) tools we then generated seven X-ray images for each source covering the energy range from 1-8 keV in the rest-frame with a fixed width of 1 keV. Images for individual sources were then co-added to measure the stacked signal.
Using standard CIAO \citep{fruscione06} tools we then generated seven X-ray images for each source covering the energy range from 1-8 keV in the rest-frame with a fixed width of 1 keV. Images for individual sources were then co-added to measure the stacked signal.
Total counts were measured in a fixed 5” aperture, while the background was estimated by randomly placing apertures with same the area, 5” to 30” away from the center.
Total counts were measured in a fixed $''$ aperture, while the background was estimated by randomly placing apertures with same the area, $''$ to $''$ away from the center.
Several groups have found (e.g, Kartaltepeetal.2010 and references therein) that the fraction of galaxies containing an AGN is a strong function of their IR luminosity.
Several groups have found (e.g, \citealp{kartaltepe10} and references therein) that the fraction of galaxies containing an AGN is a strong function of their IR luminosity.
Hence, it is a natural choice to divide our sample in terms of total IR luminosity.
Hence, it is a natural choice to divide our sample in terms of total IR luminosity.
The infrared luminosity was estimated from the observed 24 µπι luminosity assuming the relation found by Takeuchietal.(2005):: log(Lrr)=1.02+0.972 log(Li2
The infrared luminosity was estimated from the observed 24 $\mu$ m luminosity assuming the relation found by \citet{takeuchi05}: $\log$ $_{IR}$ )=1.02+0.972 $\log$ $_{12~\mu m}$ ).
We further assumed that the k correction between ym).observed-frame 24 jum and rest-frame 12 jum luminosity for these sources is negligible, as shown by Treisteretal.(2009b).
We further assumed that the $k$ correction between observed-frame 24 $\mu$ m and rest-frame 12 $\mu$ m luminosity for these sources is negligible, as shown by \citet{treister09c}.
. We then separated our sample in 4 overlapping bins: Lrg»10!! Lrg»5x10!9 Lo, 5x10 Lo>Lrr>10!Le and Lrg»10!9Lo,Lo and stacked them independently.
We then separated our sample in 4 overlapping bins: $L_{IR}$$>$ $^{11}$$L_\odot$, $L_{IR}$$>$ $\times$ $^{10}$$L_\odot$, $\times$ $^{10}$$L_\odot$$>$$L_{IR}$$>$ $^{10}$$L_\odot$ and $L_{IR}$$>$ $^{10}$$L_\odot$ and stacked them independently.
The number of sources in each sample is 670, 1545, 2342 and 3887 respectively.
The number of sources in each sample is 670, 1545, 2342 and 3887 respectively.
In Fig.
In Fig.
1 wepresent the stacked spectra as a function of rest-frame energy, both in total counts and normalized at 1 keV to highlight the difference in spectral shape among the different samples.
\ref{obs_spec} wepresent the stacked spectra as a function of rest-frame energy, both in total counts and normalized at 1 keV to highlight the difference in spectral shape among the different samples.
At E25 keV, the spectra begin to diverge, where we expect the AGN emission to dominate even for heavily-obscured sources.
At $\gtrsim$ 5 keV, the spectra begin to diverge, where we expect the AGN emission to dominate even for heavily-obscured sources.
There is a clear trend,
There is a clear trend,
We cannot prove that they are not just outliers. but we can assemble circumstantial evidence for this assertion.
We cannot prove that they are not just outliers, but we can assemble circumstantial evidence for this assertion.
ln Fig Ll we plot XM vs. Fell] for the known multiple stars (identified as such by In this figure. multiple systems are seen to be brighter than their single counterparts at the same metallicity by approximately 0.75. magnitude. precisely as one would expect.
In Fig 11 we plot $\Delta M_V$ vs. $[{\rm Fe/H}]$ for the known multiple stars (identified as such by In this figure, multiple systems are seen to be brighter than their single counterparts at the same metallicity by approximately 0.75 magnitude, precisely as one would expect.
Further evidence for this assertion is shown in Fig 12. where we plot the colour-colour relation for the single stars using the same symbols as in Fig 10.
Further evidence for this assertion is shown in Fig 12, where we plot the colour-colour relation for the single stars using the same symbols as in Fig 10.
Phe suspected multiple stars tend to lie above the sequence defined. by the rest of the stars. ancl this is also where the known multiple stars lie when plotted in this plane.
The suspected multiple stars tend to lie above the sequence defined by the rest of the stars, and this is also where the known multiple stars lie when plotted in this plane.
The L4 suspected multiples Dageed in figure 10. would represent approximately a further in multiple svstems in addition to the removed on the basis of Hipparcos.
The 14 suspected multiples flagged in figure 10, would represent approximately a further in multiple systems in addition to the removed on the basis of Hipparcos.
We suspect that the stars marked by filled circles in Figs 10 and 12 are unrecognized multiple stars. but this would. require further study to prove.
We suspect that the stars marked by filled circles in Figs 10 and 12 are unrecognized multiple stars, but this would require further study to prove.
From Fig 10 it is clear that the luminosity of the bulk of the stars (1.0. those marked. by crosses) relative to JEWKO correlates well with the metallicity.
From Fig 10 it is clear that the luminosity of the bulk of the stars (i.e. those marked by crosses) relative to JFK0 correlates well with the metallicity.
We have fitted. this correlation with the following relation (shown hy a solid line in Fig 10): which can be inverted. to give a metallicity index. KE To test that this relation is independent of the colour of the star we plot the difference between the photometrically dened abundance and the metallicity based οἱ the IuminosiE Ael]=ονι:1Η. vs. the 2BV colour.
We have fitted this correlation with the following relation (shown by a solid line in Fig 10): which can be inverted to give a metallicity index, $_{\rm KF}$: To test that this relation is independent of the colour of the star we plot the difference between the photometrically defined abundance and the metallicity based on the luminosity, $\Delta [{\rm Fe/H}] = [{\rm Fe/H}]_{\rm KF} - [{\rm Fe/H}]$, vs. the $B - V$ colour.
As can be seen in Fie 13. the cliflerence is independent. of colour. indicating no residual temperature ellects are present in the calibration.
As can be seen in Fig 13, the difference is independent of colour, indicating no residual temperature effects are present in the calibration.
Equation (1) was used to produce isochrones of various metallicity. simply offset from JEINO.
Equation (1) was used to produce isochrones of various metallicity, simply offset from JFK0.
In Fig 14 is shown the JEIXO. Ll Gyr isochrone and the artificial isochrones with FefM]-— Ls. L5. LO. QS. 0.6. 0.4. 0.2. 0.0. 0.2. 0.4 ancl 0.6 over-plotted with our single stars.
In Fig 14 is shown the JFK0 11 Gyr isochrone and the artificial isochrones with $[{\rm Fe/H}]$ = $-$ 1.8, $-$ 1.5, $-$ 1.0, $-$ 0.8, $-$ 0.6, $-$ 0.4, 0.2, 0.0, 0.2, 0.4 and 0.6 over-plotted with our single stars.
Most of the nearby stars lie between. Fe/1l] = 0.6 and 0.2 with very few metal weak stars.
Most of the nearby stars lie between $[{\rm Fe/H}]$ = $-$ 0.6 and 0.2 with very few metal weak stars.
There are three apparent sub-cwarls which happen to lit the Fe/H} = — L8 isochrone: one is certainly a bone fide halo subdwarl (LLDIO3095) because it has high space velocities: the other two have modest. velocities and are probably not subciwarfs (11D. 120559 and HD. 145417).
There are three apparent sub-dwarfs which happen to fit the $[{\rm Fe/H}]$ = $-$ 1.8 isochrone: one is certainly a bone fide halo subdwarf (HD103095) because it has high space velocities; the other two have modest velocities and are probably not subdwarfs (HD 120559 and HD 145417).
The photometric metallicities used to derive the luminosity relation (leq.
The photometric metallicities used to derive the luminosity relation (Eq.
1) are ultimately based. on spectroscopicallv obtained metallicities for €. and Ix. dwarfs.
1) are ultimately based on spectroscopically obtained metallicities for G and K dwarfs.
All of these dwarfs have accurate metallicities. colours and. parallaxes.
All of these dwarfs have accurate metallicities, colours and parallaxes,
and (2010).. which selected spirals o£ similar morphologies to the Virgo spirals to enable comparison between the clusters.
and , which selected spirals of similar morphologies to the Virgo spirals to enable comparison between the clusters.
We may tlerefore compare our results to those of (1996).. but doing so requires atteution to |ow our Characterization of the H I deficieucy compares to that used by Skillman et al.
We may therefore compare our results to those of , but doing so requires attention to how our characterization of the H I deficiency compares to that used by Skillman et al.
Iuterestit[n]ly. the ϱ.1:) dex difference between mean (O/H) vaues for our H deficient" and "H uormal' [n]e$"OUds is approximately the same as the offsets between the hydrogeu "deficient" aud "juterimediate tid between the "intermediate" and “normal” groips of Vireo galaxies found in (1996).
Interestingly, the 0.13 dex difference between mean (O/H) values for our “H deficient” and “H normal” groups is approximately the same as the offsets between the hydrogen “deficient” and “intermediate” and between the “intermediate” and “normal” groups of Virgo galaxies found in .
. A closer examination of their galaxy. selection shows that the similarity between tjese ofsets is not coiicidental.
A closer examination of their galaxy selection shows that the similarity between these offsets is not coincidental.
compute DEF values for Virgo galaxies. :illosviug us to compare the eas content of the two sampes directly.
compute DEF values for Virgo galaxies, allowing us to compare the gas content of the two samples directly.
While the Virgo cluster coitalus many more gaaxies with very high values of DEF (and. therefore presumably higher abuauces) than Pegasus. onlv oue galaxy. (NCC. 1680) in the sample has a hierer DEF (0.90 than Pegasus NGC 7613.
While the Virgo cluster contains many more galaxies with very high values of DEF (and therefore presumably higher abundances) than Pegasus, only one galaxy (NGC 4689) in the sample has a higher DEF (0.90) than Pegasus' NGC 7643.
We plot the abuudances of the seven Vireo spirals frou the survey with DEF values measured by eavazzi0d alongside those of Pegasus in Figure {αλ the two samples have a fae)great deal of overlap in metallicitv-DEF space.
We plot the abundances of the seven Virgo spirals from the survey with DEF values measured by alongside those of Pegasus in Figure \ref{avgvdef_comp}; the two samples have a great deal of overlap in metallicity-DEF space.
Apparently. we are samplingOm a raneeOm of DEF fo: Pegasus in which our gas-normal group is analogous to the "intermediate" Virgo galaxies. witl the "deficieut' groups being similar lor both eusters.
Apparently, we are sampling a range of DEF for Pegasus in which our “gas-normal” group is analogous to the “intermediate” Virgo galaxies, with the “deficient” groups being similar for both clusters.
οw metallicity offset is therefore consistent wlth what we know of the abundance - DEF correlation in Virgo.
Our metallicity offset is therefore consistent with what we know of the abundance - DEF correlation in Virgo.
For the Virgo spirals plotted ii Figure [(a).. the H I deficieut galaxies (as cdeined by |aving DEF >0.3) have an average 12 + log(O/H) of 9.25 zEO.0:)). while the H I normal (DEF <0. 3) spirals have an average 12 + log(O/H 9.11+0.06.
For the Virgo spirals plotted in Figure \ref{avgvdef_comp}, the H I deficient galaxies (as defined by having DEF $> 0.3$ ) have an average 12 + log(O/H) of 9.25 $\pm 0.03$, while the H I normal (DEF $< 0.3$ ) spirals have an average 12 + log(O/H) = $9.11 \pm 0.06$.
TIis offset. 0.11x0.00. ls nelher larger uor more significant than the offset. [or our Pegasts sample.
This offset, $0.14 \pm 0.09$, is neither larger nor more significant than the offset for our Pegasus sample.
Evidentls. tje. larger ineallicity offset. observed for Virgo is not a result of sampling more stripped. metal-rich galaxies. jit from choosing a more remote. gas-rich contro sample.
Evidently, the larger metallicity offset observed for Virgo is not a result of sampling more stripped, metal-rich galaxies, but from choosing a more remote, gas-rich control sample.
Figure I of shows that two of the hree H I normal Virgo galaxies examinel-NGC. 1651 aud NGC [r13-are so far from the cluster center as to essentially be fiek galaxies.
Figure 1 of shows that two of the three H I normal Virgo galaxies examined–NGC 4651 and NGC 4713–are so far from the cluster center as to essentially be field galaxies.
Lucdeecl. neither of these objects appears in the Virgo Chister Catalog1985).. hence their absence in the Histrvey.
Indeed, neither of these objects appears in the Virgo Cluster Catalog, hence their absence in the H I survey.
As expected based on the observed correlation. they display very low oxygen content (12 + lo»(O/H) uw 0.00). contributiug to the 0.3) dex iietalliciy oflset for Virgo.
As expected based on the observed correlation, they display very low oxygen content (12 + log(O/H) $<$ 9.00), contributing to the 0.3 dex metallicity offset for Virgo.
We conclude. then. that te process of nebular metallicily enlianceiment obse"ved iu the Virgo cluster has occurred. to a similar ¢egree in Pegasus at fixed DEF.
We conclude, then, that the process of nebular metallicity enhancement observed in the Virgo cluster has occurred to a similar degree in Pegasus at fixed DEF.
When evaltallig galactic HI deficiency quantitatively with DEF ratler than the more qualitative erouplings of(1996).. it becomes apparent that evaluatit& the influence of the cluster environlelit on galactic metallicity with au offset betweei hyd'OgeliI-DOOF axl groupS be inisleacine.
When evaluating galactic H I deficiency quantitatively with DEF rather than the more qualitative groupings of, it becomes apparent that evaluating the influence of the cluster environment on galactic metallicity with an offset between hydrogen-poor and hydrogen-normal groups can be misleading.
As each sample is likely to have a different ruge of DEF values. as seel1 our sample compared to Virgo. such a bim€a separation leads o ambiguous conclusious.
As each sample is likely to have a different range of DEF values, as seen for our sample compared to Virgo, such a bimodal separation leads to ambiguous conclusions.
A er solution is to examine the correlation beW'eeu logíO/H) and DEF. which more accurately describes the continuous progression towards hielel netalicities with increasiug H I deficieucs.
A better solution is to examine the correlation between log(O/H) and DEF, which more accurately describes the continuous progression towards higher metallicities with increasing H I deficiency.
From Figure Ifa). we see a slrong correlation ist log(O/H) versus DEF for he combined Pegasus-Virgo sample.
From Figure \ref{avgvdef_comp}, we see a strong correlation in log(O/H) versus DEF for the combined Pegasus-Virgo sample.
Performing a linear least-squa‘es fit to tle treud. we derive the relation 12+loe[n] (O/H)=9.120+0.223 DEF. with uncertainties of 0.015 dexio;j;/dexpir O1 the slope and 0.02 dexio;5; on the intercept.
Performing a linear least-squares fit to the trend, we derive the relation $12 + \log $ $ = 9.120 + 0.223 \times$ DEF, with uncertainties of 0.045 $_{(O/H)}$ $_{DEF}$ on the slope and 0.02 $_{(O/H)}$ on the intercept.
The resulting Pearson correlation coefficient to he data is 0:51.
The resulting Pearson correlation coefficient to the data is 0.84.
Thus. while the metallicity offsets of the samples above aud below DEF = 0.) are
Thus, while the metallicity offsets of the samples above and below DEF = 0.3 are
is achieved by averaging the spectrum, but to have a sufficient number of points to complete an accurate amplitude fitting.
is achieved by averaging the spectrum, but to have a sufficient number of points to complete an accurate amplitude fitting.
To estimate reliably the amount of scattered starlight at a given angular position, it is necessary that at least one data element that is uncontaminated by the light of the companion.
To estimate reliably the amount of scattered starlight at a given angular position, it is necessary that at least one data element that is uncontaminated by the light of the companion.
The more data points used to fit the linear average spectrum, the more accurate the estimation of the stellar contribution, and the more reliable the characterization of a companion will be.
The more data points used to fit the linear average spectrum, the more accurate the estimation of the stellar contribution, and the more reliable the characterization of a companion will be.
Secondly, for very bright companions, a mask is applied to the data being fitted to ensure that the light of the companion does not contaminate the amplitude fit.
Secondly, for very bright companions, a mask is applied to the data being fitted to ensure that the light of the companion does not contaminate the amplitude fit.
If a companion is very bright compared to the scattered starlight that we wish to evaluate, the fitting will be completely biased: the reconstructed spectrum will be overestimated and the companion spectrum continuum will be underestimated.
If a companion is very bright compared to the scattered starlight that we wish to evaluate, the fitting will be completely biased: the reconstructed spectrum will be overestimated and the companion spectrum continuum will be underestimated.
When subtracting the reconstructed spectrum from the observed data, part of the companion light is then removed.
When subtracting the reconstructed spectrum from the observed data, part of the companion light is then removed.
This issue is important only for very bright companions, whose position can be easily ascertained from the data to create a mask at the appropriate position.
This issue is important only for very bright companions, whose position can be easily ascertained from the data to create a mask at the appropriate position.
The exact size of this mask can be deduced from the instrumental PSF.
The exact size of this mask can be deduced from the instrumental PSF.
We choose to consider a size of 2.54/D to completely mask the PSF core (see Sect.
We choose to consider a size of $2.5\lambda/D$ to completely mask the PSF core (see Sect.
/4-2.]] for a more complete analysis).
\ref{section:planet_mask_size} for a more complete analysis).
Finally, depending on the slit size and spectral range, it is important to consider that speckles located at the edge of the slit move out of the slit as the wavelength increases.
Finally, depending on the slit size and spectral range, it is important to consider that speckles located at the edge of the slit move out of the slit as the wavelength increases.
For the slit sizes and 0.12", see Sect. B))
For the slit sizes and , see Sect. \ref{section:lss_simulations}) )
and wavelength range (J, H and K bands) considered, a significantly large number of speckles move out of the slit (~50%)).
and wavelength range (J, H and K bands) considered, a significantly large number of speckles move out of the slit $\sim$ ).
The impact of this effect is identical at all positions in the slit, so the final influence is negligible on data analysis.
The impact of this effect is identical at all positions in the slit, so the final influence is negligible on data analysis.
However it means that the measured model spectrum is not equivalent to the true star spectrum.
However it means that the measured model spectrum is not equivalent to the true star spectrum.