X-shooter spectroscopy of young stellar objects in Lupus: Lithium, iron, and barium elemental abundances
K. Biazzo, A. Frasca, J. M. Alcalá, M. Zusi, E. Covino, S. Randich, M. Esposito, C. F. Manara, S. Antoniucci, B. Nisini, E. Rigliaco, F. Getman
aa r X i v : . [ a s t r o - ph . S R ] J un Astronomy & Astrophysics manuscript no. Biazzoetal_Lupus_LangEditCorr_noref c (cid:13)
ESO 2018September 25, 2018
X-shooter spectroscopy of young stellar objects in Lupus:
Lithium, iron, and barium elemental abundances ⋆ , ⋆⋆ K. Biazzo , A. Frasca , J. M. Alcalá , M. Zusi , E. Covino , S. Randich , M. Esposito , C. F. Manara , S.Antoniucci , B. Nisini , E. Rigliaco , and F. Getman INAF - Osservatorio Astrofisico di Catania, Via S. Sofia 78, I-95123 Catania, Italy INAF - Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy INAF - Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere 100, I-00133 Rome, Italy INAF - Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, I-50125 Firenze, Italy Scientific Support Office, Directorate of Science, European Space Research and Technology Centre (ESA/ESTEC),Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands INAF - Osservatorio Astronomico di Roma, via Frascati 33, I-00078 Monte Porzio Catone, Italy INAF - Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, I-35122 Padova, ItalyReceived .../ accepted ...
ABSTRACT
Aims.
With the purpose of performing a homogeneous determination of elemental abundances for members of the LupusT association, we analyzed three chemical elements: lithium, iron, and barium. The aims were: 1) to derive the lithiumabundance for the almost complete sample ( ∼ ) of known class II stars in the Lupus I, II, III, and IV clouds; 2) toperform chemical tagging of a region where few iron abundance measurements have been obtained in the past, and nodetermination of the barium content has been done up to now. We also investigated possible barium enhancement atthe very young age of the region, as this element has become increasingly interesting in the last few years following theevidence of barium over-abundance in young clusters, the origin of which is still unknown. Methods.
Using the X-shooter spectrograph mounted on the Unit 2 (UT2) at the Very Large Telescope (VLT), weanalyzed the spectra of 89 cluster members, both class II (82) and class III (7) stars. We measured the strength of thelithium line at λ Results.
We find a dispersion in the strength of the lithium line at low effective temperatures and identify three targetswith severe Li depletion. The nuclear age inferred for these highly lithium-depleted stars is around Myr, which exceedsby an order of magnitude the isochronal one. We derive a nearly solar metallicity for the members whose spectra couldbe analyzed. We find that Ba is over-abundant by ∼ . dex with respect to the Sun. Since current theoretical modelscannot reproduce this abundance pattern, we investigated whether this unusually large Ba content might be related toeffects due to stellar parameters, stellar activity, and accretion. Conclusions.
We are unable to firmly assess whether the dispersion in the lithium content we observe is a consequence ofan age spread. As in other star-forming regions, no metal-rich members are found in Lupus, giving support to a recenthypothesis that the iron abundance distribution of most of the nearby young regions could be the result of a commonand widespread star formation episode involving the Galactic thin disk. Among the possible causes or sources for Baenhancement examined here, none is sufficient to account for the over-abundance of this element at a ∼ . dex level.. Key words.
Stars: abundances – Stars: pre-main sequence – Stars: low-mass – Techniques: spectroscopic – open clustersand associations: individual: Lupus
1. Introduction
The determination of elemental abundances in nearby( < pc) star-forming regions (SFRs) is important fora variety of astrophysical problems, in both exo-planetary Send offprint requests to : K. Biazzo ⋆ Based on observations collected at the European Organi-zation for Astronomical Research in the Southern Hemisphere(Paranal, Chile) under programs 084.C-0269(A), 085.C-0238(A),085.C-0764(A), 086.C- 0173(A), 087.C-0244(A), 089.C-0143(A),093.C-0506(A), 095.C-0134(A), and 097.C-0349(A). ⋆⋆ This paper is dedicated to the memory of Prof. FrancescoPalla, who passed away in 2016. and stellar contexts. The members of these regions are stillclose to their birthplace. Their elemental abundances arethus fundamental to trace the present chemical pattern ofthe Galactic thin disk in the solar neighborhood and theinterstellar medium in which they are immersed.In the last three decades, an increasing number ofstudies have focused on the abundance measurements ofiron and other elements in SFRs, young clusters, andassociations, and specifically in their low-mass mem-bers (e.g., Padgett 1996; Cunha et al. 1998; James et al.2006; Santos et al. 2008; González-Hernández et al. 2008;Viana Almeida et al. 2009; D’Orazi & Randich 2009;Biazzo et al. 2011a,b; D’Orazi et al. 2011; Tabernero et al.
Article number, page 1 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref ∼ × K (see, e.g., Bildsten et al. 1997). Temper-atures of this order can be easily reached in the interior of alow-mass pre-main sequence (PMS) star as it contracts to-wards the main sequence (Bodenheimer et al. 1965). There-fore, low-mass stars ( ∼ . − . M ⊙ ) deplete their initial Licontent during the PMS phase; the depletion timescale de-pends on stellar mass, with stars of ∼ . − . M ⊙ startingto burn it after ∼ − Myr and stars of < . M ⊙ after ∼ − Myr (see Baraffe et al. 2015, and references therein).Therefore, lithium abundance has been widely used as anindependent and reliable method to estimate the ages oflow-mass members of young regions (see, e.g., Song et al.2002; White & Hillenbrand 2005; Palla et al. 2005, 2007;Sacco et al. 2007; Yee & Jensen 2010; Sergison et al. 2013;Lim et al. 2016).The determination of the abundance of iron, iron-peak,and alpha elements is important in the field of star for-mation. In fact, several studies have provided hints thatregions where star formation has ceased generally share ametallicity close to the solar value, while SFRs where themolecular gas is still present seem to be characterized by aslightly lower iron content (Biazzo et al. 2011a; Spina et al.2014b, and references therein). Whether this is due to low-number statistics (both in regions and in number of ana-lyzed targets per region) or inhomogeneous and uncertainmethodologies is still debated. Spina et al. (2014b) recentlyclaimed that the metal-poor nature of these young envi-ronments could be the result of a common and widespreadstar formation episode involving the Gould Belt, and givingbirth to most of the SFRs, and stars in the solar neighbor-hood (see also Guillout et al. 1998). However, the possibil-ity of a more complex process of chemical evolution thatinvolved a much larger area in the disk of the Milky WayGalaxy is not excluded (Spina et al. 2017).Barium is synthesized by neutron capture reactionsmostly by the so called s -process occurring in asymp-totic giant branch (AGB) stars and represents an excellenttracer of chemical enrichment mechanisms in the Galaxy;for this reason, many studies have been focused on deriv-ing barium abundance in both field stars in the halo andthick/thin disk, and in stellar clusters older than ∼ ∼ . dex for open clusters in the age range ∼ . − . Gyr. D’Orazi et al. (2009) reproduced such be-havior by assuming higher Ba yields from low-mass AGBstars in their Galactic chemical evolution models (see alsoMaiorca et al. 2014). A further increase in still youngerclusters, up to ∼ . − . dex at ∼ Myr, was also foundby D’Orazi et al. (2009); this behavior is not reproducedby the same models. They argued that a process creat-ing Ba in the last dozen Myr through Galactic chemicalevolution is quite unlikely, unless local enrichment is in-voked. Other recent studies confirmed the presence of highBa abundance in ∼ − Myr old stars (Desidera et al.2011; D’Orazi et al. 2012). Several hypotheses (high level of chromospheric activity, uncertainty in stellar parame-ters, effects of the stratification in temperature of the modelatmosphere, non local thermodynamic equilibrium, NLTE,corrections) were proposed to reproduce the high Ba con-tent in young clusters, but all of them failed in explainingthe observed over-abundance.Here, we present a systematic and homogeneous analysisof elemental abundances of PMS stars in the Lupus cloudcomplex. Lupus is one of the most nearby ( d ∼ − pc) and largest low-mass star-forming regions (see Comerón(2008) for a review). Similarly to other regions (e.g., Taurus,Chamaeleon, Ophiucus, Corona Australis), a large varietyof objects in various stages of evolution are present in Lu-pus. The region of the sky occupied by the Lupus cloudsis almost devoid of early-type stars and shows no sign ofongoing high-mass star forming activity, although the largenumber of OB members of the Scorpius-Centaurus SFR inthe vicinity of the Lupus complex implies the existence of anambient field of high energy sources, which are likely to haveplayed an important role in the evolution and possibly theorigin of the Lupus complex (Tachihara et al. 2001). Thismakes the Lupus stellar population of particular interestfor comparative studies with other nearby regions, such asTaurus-Auriga and Chamaeleon, which have a similar massof molecular gas and low-mass star formation activity, butwhich evolved more in isolation and relatively unperturbed(Comerón 2008).This is a companion paper to previous studies focusedon the investigation of the accretion properties of LupusPMS stars (Alcalá et al. 2014, 2017), as well as the de-termination of their stellar parameters and activity indi-cators (Frasca et al. 2017). We used the same data as inthose works, namely spectra acquired with the X-shooterspectrograph on the Very Large Telescope (VLT; Paranal,Chile), in order to study the abundance of lithium, iron,and barium of the low-mass ( ∼ . − . M ⊙ ) PMS starsin Lupus. While a few studies of elemental abundance ofiron, silicon, and nickel in a handful of class III stars in thisregion have been performed in the past (see, Santos et al.2008, and references therein), and only one class II wasanalyzed in terms of iron abundance (Padgett 1996), a ho-mogeneous and self-consistent analysis of Li, Fe, and Ba ina significant sample of the class II sources is still lacking.In this paper, we aim to cover this gap.The outline of this paper is as follows. The data sampleis presented in Sect. 2. In Sect. 3, we derive the elementalabundances of lithium, iron, and barium. We then discussthe implications of our findings in Sect. 4. In Sect. 5 a sum-mary of our results is presented.
2. Data set
The data set for this paper is exactly the same as in our pre-vious works (Alcalá et al. 2014, 2017; Frasca et al. 2017).All the criteria for the target selection, as well as observa-tional strategy, are provided in those papers. We adopt thesame listing order as in Frasca et al. (2017) in our Table 1.The final sample includes 82 class II and seven classIII sources in Lupus clouds I, II, III, and IV. Forty-threeobjects were observed in 2010-2012 during the INAF (
Isti-tuto Nazionale di Astrofisica ) guaranteed time observations(GTO; Alcalá et al. 2011, 2014), forty sources were ob-served in 2015 and 2016 during the ESO (European South-ern Observatory) periods 95 and 97, and the remaining six
Article number, page 2 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs were taken from the ESO archive (see Alcalá et al. 2017;Frasca et al. 2017). All targets were observed using the 0 ′′ . R ∼ in the VIS (visible) arm,with the exception of four (Sz 68, Sz 74, Sz 83, and Sz 102)in P95 and P97, and those of the ESO archive, which wereobserved using the 0 ′′ . R ∼
17 400 . The sample comprises 82 class II and sevenclass III sources. For details about data reduction, observ-ing log-book, and selection criteria, we refer to Alcalá et al.(2014, 2017) and Manara et al. (2013). Throughout this pa-per, stellar parameters (effective temperature T eff , surfacegravity log g , projected rotational velocity v sin i , veiling r ) and membership information (radial velocity v rad ) weretaken from Frasca et al. (2017), while spectral types weretaken from Alcalá et al. (2017) and Manara et al. (2013).The sample of class II objects in the aforementioned Lupusclouds is complete at more than ∼ % level (Alcalá et al.2017).
3. Analysis
Lithium abundances were measured from the line equi-valent widths and by using appropriate curves of growth(Sect. 3.1.2), while iron and barium abundances were deter-mined through the spectral synthesis method and the synth driver within the MOOG code (Sneden 1973; see Sect. 3.2).This is the most adequate strategy to derive the abun-dances, given the relatively low resolution of our spectra.
Equivalent widths of the lithium line ( EW Li ) at λ =6707 . Å were measured by direct integration or by Gaus-sian fit using the IRAF task splot . At the X-shooter reso-lution the Li i line is blended with the Fe i λ EW Fe and the B − V color bySoderblom et al. (1993). To estimate the correction, weconsidered the T eff determinations by Frasca et al. (2017)and the calibration by Pecaut & Mamajek (2013). For starscooler than 4000 K, the real continuum is no longer visible,due to the increasing strength of molecular bands. There-fore, the lithium equivalent widths are referred to the localpseudo-continuum, and the molecular bands are the majorblending sources. However, we integrated the lithium lineincluding the blends, as in Palla et al. (2007) to be consis-tent with their curves of growth (see Sect. 3.1.2).Errors in EW Li were evaluated by determining thesignal-to-noise ( S/N ) ratio at wavelengths adjacent to thelithium line and by multiplying its reciprocal by the widthof the integration range. Typical errors in lithium equiva-lent widths are of 20-25 mÅ (see Table 1).Our spectra are affected by spectral veiling ( r ), thatis, the amount of continuum excess emission, which fills inthe lines. Measured EW Li were corrected for this contri-bution using the r values expressed in units of the photo-spheric continuum and determined by Frasca et al. (2017) IRAF, Image Reduction and Analysis Facility, is distributedby the National Optical Astronomy Observatory, which is op-erated by the Association of the Universities for Research inAstronomy, Inc. (AURA) under cooperative agreement with theNational Science Foundation. in several spectral regions. We applied the relationship EW corrLi = EW Li (1 + r ) , where EW corrLi is the correctedvalue for the lithium equivalent width. For the Li i line at λ = 6707 . Å, we adopted the mean value ( r ) between λ = 6200 Å and λ = 7100 Å (see Table 1). As in Frasca et al.(2017), all values of veiling ≤ . are considered non de-tectable and set equal to zero. This is the case of about 65%of the targets, while, for the rest of the sample, r rangesfrom ∼ . up to ∼ . Figure 1 shows the EW Li versus T eff plot for the 89 Lupus members listed in Table 1, as obtainedfrom our measurements and after the correction both forthe blending with the iron line and the veiling contributionas explained above. The distribution of the corrected equiv-alent width peaks at ∼ mÅ (see also Fig. 2), with mostof the spread at a given T eff reduced after the aforemen-tioned corrections. The residual scatter in EW corrLi couldbe due to measurement errors, but we cannot exclude thepossibility that part of the dispersion may be due to starswith Li depletion. For five targets (Lup 706, Par-Lup3-4,2MASS J16085953-3856275, SSTc2d J154508.9-341734, and2MASS J16085373-3914367) we were not able to measure EW Li because of low S/N and/or high veiling contribu-tion. However, the lithium content of these objects is belowthe level of other stars with similar effective temperature.For one target (Sz 94) we did not detect the line (see Ta-ble 1 and discussion in Sect. 4.1). We consider these six tar-gets as upper limits, since the error in equivalent width islarger than the EW Li value. The rest of the targets with lowlithium content ( EW corrLi < ∼ mÅ) have negligible veiling( r < ∼ . ) and suggest a possible large amount of Li de-pletion. They will be discussed in Sect. 4.1.Figure 2 shows the position of all members in an EW corrLi versus radial velocity (RV) plot, where RVs by Frasca et al.(2017) were transformed to the local standard of rest (LSR).In the same plot, the RV distribution of the gas derived byVilas-Boas et al. (2000) from the CO ( J = 1 − ) transi-tion in 35 dense molecular cores in Lupus I, II, III, and IVis shown. Our RV distribution is in good agreement, withinthe errors, with the average velocity of the gas, that is, < V LSR > gas = 4 . ± . km/s, as also found in other regions(Biazzo et al. 2012a; Da Rio et al. 2017). All but six stars(Sz 66, Sz 91, SSTc2d160901.4-392512, Sz 123A, Sz 102, andSz 122) are confined inside ± σ from the peak at 9.6 km/sof the RV distribution, where σ = 5 . km/s. The six starsoutside the < V LSR > ± σ distribution could be spec-troscopic binaries because both their high lithium contentand proper motions (Girard et al. 2011; López Martí et al.2011) strongly suggest membership. Five of them havelarge EW corrLi , which gives support to their membershipto the Lupus SFR. One of these, Sz 102, shows a large V LSR error, maybe due to the high v sin i and veiling (seeFrasca et al. 2017). The star with low EW corrLi and outsidethe < V LSR > ± σ distribution is Sz 122, a class III objectwith v sin i ∼ km/s. This high-rotational velocity tar-get is suspected to be a spectroscopic binary, as reportedby Stelzer et al. (2013). Lithium abundances, A (Li), were estimated from the EW corrLi measured in this work, the atmospheric para-meters ( T eff , log g ) taken from Frasca et al. (2017), andusing the NLTE curves of growth (COGs) reported by Article number, page 3 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref
Fig. 1.
Lithium equivalent width versus effective temperaturefor the studied sample. Open symbols refer to measured EW Li ,while filled red symbols represent the lithium equivalent widthsafter correction for blending with the iron line and spectral veil-ing. Overplotted as black dashed and blue solid lines are theCOGs at log g = 3 . , . and lithium abundance of 3.5 dexfrom Palla et al. (2007) and Pavlenko & Magazzù (1996), re-spectively. Arrows indicate upper limits (see text). Spectraltypes as in Luhman et al. (2003) for M-type young stellar ob-jects (YSOs) and Kenyon & Hartmann (1995) for K-type YSOsare also marked (see also Alcalá et al. 2017 and Table 1). Pavlenko & Magazzù (1996) for T eff > K, the COGsby Palla et al. (2007) for T eff < K, and the averageof these COGs for < T eff < K. The mainsource of error in A (Li) comes from the uncertainty instellar parameters ( T eff and log g ), listed in Frasca et al.(2017), in our measurements of lithium equivalent widths,and from the Soderblom et al. (1993) relationship. Wetherefore took into account all these four error sources andestimated the error in A (Li) by adding them in quadrature.The global uncertainties range from ∼ . − . dex upto ∼ . − . dex for cool stars ( T eff ∼ − K),and are around . − . dex for targets at ∼ K.Finally, uncertainty of ∼ . in r translates into errorsaround ∼ . dex and ∼ . dex in A (Li) for warm and coolstars, respectively. In Figs. 3 and 4 we show the lithiumabundance as a function of the effective temperature andthe rotational velocity, respectively (see also Table 1).Upper and lower limits in A (Li) result from the rangeof validity of the COGs as a function of T eff and log g .Furthermore, upper limits in EW Li translate also intoupper limits in A (Li) . Most of the stars have Li abundancesbetween A (Li) ∼ and 4 dex, with a peak at around 3.1dex, independently of their classification in class II, classIII, transitional disks, or sub-luminous (see Alcalá et al.2017, and their references therein, for the classification). Aspread of Li abundance appears for stars cooler than about3500 K, regardless of the uncertainties and upper or lowerlimits. As shown in Fig. 4, the scatter cannot be ascribedto a spread in projected rotational velocity as the starswith low-Li content ( A (Li) < ∼ dex) have v sin i spanningfrom ∼ km/s down to less than 8 km/s. The only ex- Fig. 2.
Corrected lithium equivalent width versus radial velo-city (from Frasca et al. 2017) in the LSR. Different symbols areused for class III (open green squares), transitional disk (violetcircles), and sub-luminous or flat spectral energy distribution ob-jects (cyano pentagons), as defined in Alcalá et al. (2017). Thevertical solid line indicates the mean V LSR ( . ± . km/s) of ourtargets, while the dotted ones represent the < V LSR > ± σ va-lues. Upper limits are marked by arrows. The magenta hatchedhistogram in the background of the central panel represents thevelocity distribution of gas condensations in Lupus as derivedby Vilas-Boas et al. (2000). The histogram on the right panelshows our EW Li distribution, with a peak around ∼ mÅ. ception is Sz 122, which we do not consider in the analysisbecause it may be an unresolved spectroscopic binary (seeStelzer et al. 2013). The lack of A (Li) - v sin i connection isin line with the fact that, at the young ages of our targets,there is no evidence for lithium-rich fast rotators andlithium-depleted slow rotators; Li abundance appears tobe poorly affected by rotationally induced mixing arisingfrom angular momentum loss (see Bouvier et al. 2016 fora recent discussion about the lithium-rotation connectionat very young ages). Moreover, since all members are sup-posed to have formed from the same molecular cloud, weexpect our analysis to be free from significant star-to-stardifferences in the initial chemical composition. Therefore,we do not exclude a priori the possibility that some of thelate-type stars underwent lithium depletion. This issue willbe discussed in Section 4.1. The spectral synthesis for deriving iron and barium abun-dances was carried out by employing the code MOOG(Sneden 1973; 2013 version) and the Kurucz (1993) setof model atmospheres. We then adopted the stellar pa-rameters ( T eff , log g , v sin i ) determined by Frasca et al.(2017) and considering the stars with: i ) T eff > K,to avoid the contribution of molecular bands, which wouldseverely affect the determination of [Fe/H] through theiruncertain opacities (see, e.g., Appendix B in Biazzo et al.2011a); ii ) low veiling ( r < . ), as it reduces the Article number, page 4 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs
Fig. 3.
Lithium abundance versus effective temperature. The“lithium isochrones” by Baraffe et al. (2015) in the 2–100 Myrrange are overlaid with dotted lines. Arrows refer to upper andlower limits. Open squares, circles, and pentagons represent theposition of the class III, transitional disks, and sub-luminous orflat energy distribution targets, respectively. Spectral types asin Fig. 1 are also shown.
Fig. 4.
Lithium abundance versus projected rotational veloc-ity. Symbols as in Fig. 3. The dashed and dotted lines representthe upper limits at 8 and 6 km/s of our VIS spectra acquiredwith the slit width at 0 ′′ . ′′ .
4, respectively (see Frasca et al.2017). depth of absorption lines, introducing a further uncer-tainty in [Fe/H] determinations; iii ) low v sin i ( < km/s) to avoid severe line blending. In the end, six tar-gets fulfill these requirements (SSTc2d J160830.7-382827,RY Lup, MY Lup, Sz 68, Sz 133, and SSTc2d J160836.2-392302). SSTc2d J160830.7-382827 and MY Lup are weakaccretors with transitional disks, RY Lup has a transitionaldisk, Sz 68 is a weak accretor, Sz 133 is a sub-luminous object, and SSTc2d J160836.2-392302 has a most probabletransitional disk (see Ansdell et al. 2016; Alcalá et al. 2017for details). Despite the extremely wide wavelength coverage, the re-latively low resolution of the X-shooter spectra preventedus from using a very wide spectral range with unblendedand isolated iron lines. For this reason, we chose to derivethe iron abundance using the spectral synthesis methodin a wavelength window of ∼ Å around the Li i line at6707.8 Å. This spectral range proved to be very suitable forreliable iron abundance measurements (see D’Orazi et al.2011, and references therein, for details). We consideredthe line list employed in D’Orazi et al. (2011), with care-fully derived atomic parameters. As solar iron abundancewe considered the value of Asplund et al. (2009), that is, log n (Fe) ⊙ = 7 . ± . dex. We refer to D’Orazi et al.(2011), and references therein, for detailed explanations ofthe method.For the six selected targets, we fixed within theMOOG code the appropriate spectral resolution, the limb-darkening coefficients (taken from Claret et al. 2012), theveiling r , and the microturbulence ξ = 1 . km/s, whichis typical of young stars similar to our targets (see, e.g.,D’Orazi et al. 2011; Biazzo et al. 2011a,b). Then, we pro-gressively changed the iron abundance until the best-fit(minimum of residuals) to the observed spectrum was ob-tained (see Fig. 5). As a by-product, the spectral synthesisaround the Li line also allowed us to estimate the Li abun-dance, A (Li) synth , for the six targets, that is found to beconsistent within errors with the one obtained from theequivalent widths and the COGs (see Table 2 and Fig. 5).This allowed us to make an independent assessment of thelithium abundance for these stars.The Fe abundance uncertainties are related both to theuncertainty in the best-fit model (we call this σ ) and theerrors in stellar parameters (which we call σ ). Besides theuncertainty coming from the best-fit, σ also includes er-rors in the continuum placement, that we estimated to betwice as large as the standard deviation obtained for thefit. We found that, at the average T eff of 4800 K of ourtargets, σ varies between 0.07 dex and 0.11 dex for typicaluncertainties of ±
120 K and ± T eff and log g , re-spectively. Microturbulence velocity was fixed at 1.5 km/s,but typical errors of ∼ ± . dex. Other sources of error are the indetermina-tion in v sin i and r , which were fixed in our analysis. Anuncertainty in v sin i of about 3 km/s may lead to errors of ± . dex in iron abundance. The last source of uncertaintyis veiling, that could be the largest for highly veiled spectra.However, for the low veiling values of the six targets hereanalyzed, an uncertainty of ∼ r translates into anerror in [Fe/H] of ± . dex. Final errors can be obtained bysumming in quadrature the uncertainties from spectral syn-thesis and stellar parameters, plus additional contributionsfrom rotational velocity and veiling. Typical uncertaintiesof . − . dex for [Fe/H] are derived, depending on target.The major source of error is the best-fit procedure, mainlybecause of the uncertainties due to continuum placement.Systematic (external) errors, caused for instance by thecode and/or model atmosphere, should not strongly in- Article number, page 5 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref fluence our abundance analysis, as widely discussed byD’Orazi et al. (2011).
We determined the abundance of the s -process elementbarium through spectral synthesis of the Ba ii line at λ = 5853 . Å. We checked for other Ba lines, suchas the Ba ii lines at λ λ λ i line at λ ii line and the Ba i line are also strongly blended withiron lines (Mashonkina & Gehren 2000; Reddy & Lambert2015; Korotin et al. 2015). We also tried to analyze othersuitable lines of additional s -process elements (e.g., Y ii λλ ii λλ ii λλ ii λλ i ) the lines are too weak and/or blended with other nearbylines (due to the relatively low resolution of our data andthe stellar rotation); ii ) most of those lines are in the UV-Blue (UVB) arm of the X-shooter spectrograph, where thecontinuum placement has a strong impact on the abun-dance estimate; iii ) the veiling contribution, even if low,and added to the aforementioned effects, makes the abun-dance measurement very uncertain. In addition, the S/N ratio of the spectra is lower in the UVB arm than in theother spectrograph armsAlthough the Ba line at λ . Å does not experi-ence severe hyperfine structure (hfs) and isotopic shifts,we decided to include them and employ those providedby McWilliam (1998) with the aim of obtaining the bestpossible result with the spectra at our disposal. We thenadopted the isotopic solar mixtures by Anders & Grevesse(1989), that is, 2.42% for
Ba, 7.85% for
Ba, 71.94% for
Ba, 6.58% for
Ba, and 11.21% for
Ba. As expected,the results do not depend on the abundance ratio for evenand odd isotopes. As for the iron abundance, we consideredthe solar barium abundance by Asplund et al. (2009), thatis, log n (Ba) ⊙ = 2 . ± . dex, and the stellar parametersby Frasca et al. (2017). Then, we ran MOOG fixing thespectral resolution, the limb-darkening coefficients (takenby Claret et al. 2012), the veiling r (assuming a meanvalue between r and r ; see Table 1), and the mi-croturbulence ξ = 1 . km/s. The fitting procedure and theerror estimates are the same as for the iron abundance de-termination described in the previous Section, and for thesame six selected stars.In Fig. 6 we show the spectral synthesis of the six tar-gets in the wavelength region around the Ba ii line, withthe corresponding error σ (see Table 2). As done for theFe abundance, to evaluate the impact of the variation ofthe stellar parameters ( T eff , log g , and ξ ) and of v sin i and r , we varied each quantity separately (leaving the othersunchanged) and checked the abundance sensitivity to thatvariation. A change of ±
120 K in T eff , ± log g ,and ± ξ , leads to Ba abundance variations of0.07 dex, 0.03 dex, and 0.15 dex, respectively. This meansthat the barium abundance measurement is strongly in-fluenced by the microturbulence (also noted by other au-thors, for example, D’Orazi et al. 2012), as the barium lineis close to the flat part of the curve of growth. Variationsof 3 km/s in v sin i lead to 0.01 dex uncertainty in [Ba/H], while an uncertainty of about 0.1 in veiling yields an er-ror of ∼ ξ and r measurement on the determination ofbarium abundance in Sect. 4.4. The cumulative uncertaintyin [Ba/H] can be obtained by summing in quadrature theuncertainties from the fit, those on the stellar parameters,and the additional contribution of the v sin i error (negli-gible) and veiling. The cumulative uncertainties in [Ba/H]measurements for our targets are of ∼
4. Results and discussion
Our sample comprises stars with M ⋆ ∼ . − . M ⊙ (see Alcalá et al. 2017; Frasca et al. 2017). Considering thesub-sample of stars with masses > . M ⊙ , we do not findan indication of Li depletion, ∼ . dex being the meanabundance of these stars. The situation is different for thesub-sample of stars with masses in the range . − . M ⊙ (Fig. 7). For these stars we can apply the so-called lithiumtest, as first proposed by Palla et al. (2005) for targets inOrion. Briefly, this is a useful clock based on the possibilitythat stars deplete their initial lithium content during theearly phases of PMS contraction. It has been demonstratedthat the theoretical assumptions required to study Li deple-tion history have little uncertainty, as for fully convectivestars the depletion mostly depends on T eff (Bildsten et al.1997). At the same time, models show that stars withmass in the range ∼ . − . M ⊙ start to deplete Li af-ter 4–15 Myr and completely destroy it after a further 10–15 Myr (e.g., Baraffe et al. 2015, and references therein).We therefore can compare the nuclear ages derived fromthe lithium test with the isochronal ages derived from theHertzsprung-Russell (HR) diagram. First evidence of a Lidepletion boundary for PMS stars have been reported bySong et al. (2002) and White & Hillenbrand (2005), wherethe timescale of Li depletion turned out to be larger thanthe isochronal age.In order to investigate the lithium depletion in oursample, we applied the procedure already tested in somesub-groups of the Orion SFR (see Palla et al. 2005, 2007;Sacco et al. 2007) for objects with mass in the range .
Fig. 5.
Observed spectrum (black line) and best-fit synthetic spectrum (red line) for the six targets analyzed for abundancemeasurements. The blue dashed and green dotted lines represent the [Fe/H] values due to uncertainties in the best-fit procedure(see text). Iron lines are indicated with short vertical lines in the upper part of each panel.
Fig. 6.
Visualization of spectral synthesis as in Fig. 5 but for the Ba ii line at λ . Å. The barium line is marked with a shortvertical line in the upper part of each panel. an effective temperature of 3550 K, that is, in the T eff withinthe range of overlap between the two curves of growth usedto derive abundance (see Sect. 3.1.2), hence its A (Li) is rather uncertain. Then, for SSTc2d J160927.0-383628 andSSTc2d J154508.9-341734 a veiling of r ∼ is measured,hence their lithium abundances may be strongly influenced Article number, page 7 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref
Fig. 7.
Hertzsprung-Russell diagram for the sample stars withmasses ∼ . − . M ⊙ . The shaded regions indicate differentlevels of predicted Li depletion: up to a factor of 10 (light gray)and more (dark gray) below the initial value and according tothe models of Baraffe et al. (2015). Masses from evolutionarytracks and isochronal ages are labeled. Black diamonds refer totargets showing depletion (i.e., Sz 99, Sz 69, and Sz 94; see textand Fig. 8). Open squares, circles, and pentagons represent theposition of the class III, transitional disk, and sub-luminous orflat targets, respectively. by strong continuum excess emission, while the class IIIstar Sz 122 could be a spectroscopic binary, as stressed inSect. 3.1.2. In summary, among the six YSOs below ourlithium depletion threshold, there are only two cases inwhich lithium depletion can be clearly assessed; these areSz 99 and Sz 69. We thus consider these two YSOs as themost probable lithium depleted targets in our sample. Inaddition, we also include Sz 94, a class III object for whichlithium has not been detected either by us or other au-thors (Mortier et al. 2011; Manara et al. 2013; Stelzer et al.2013), but its radial velocity and the proper motion re-ported by López Martí et al. (2011) are consistent with theLupus SFR. The profile of the Li i λ T eff , log g , v sin i , and veil-ing.The existence of very young Li-poor low-mass stars inLupus allowed us to perform a comparison between theo-retical and analytical models of early nuclear burning. Wetherefore adopted the stellar masses and isochronal agesdetermined by Frasca et al. (2017) based on the PMS mo-dels of Baraffe et al. (2015), and applied the proceduredescribed in Palla et al. (2005). Errors on both quanti-ties were estimated from the uncertainties on luminos-ity and effective temperature provided by Alcalá et al.(2017) and Frasca et al. (2017), respectively. As stressedby Frasca et al. (2017), individual ages can be affected byuncertainties depending on the data errors and the adoptedset of evolutionary tracks. As such, these ages must be taken Fig. 8.
Li abundance versus age for stars with mass in the range . − . M ⊙ . Symbols are the same as in Fig. 3. The dashed hori-zontal lines mark the region of the interstellar lithium abundance(3.1–3.3 dex). Dotted lines represent ± σ from the mean lithiumabundance of this sub-sample. The labeled stars are those dis-cussed in the text. with care. We can compare these results with the analyticestimates derived by Bildsten et al. (1997) for fully con-vective stars undergoing gravitational contraction at nearlyconstant T eff , assuming fast and complete mixing, and withnegligible influence of degeneracy during the depletion, thatis, in the mass range . M ⊙ < ∼ M ⋆ < ∼ . M ⊙ . Bildsten et al.(1997) derived relations for the time variation of the lu-minosity (see their Eq. 4) and of the amount of Li deple-tion (see their Eq. 11). Taking into account the measuredluminosity and effective temperature, we can apply thoseequations and obtain a mass-depletion time plot. The re-sults for the three Li-depleted stars are shown in Fig. 10.The line with positive slope represents the mass-age rela-tion for each star at given T eff and L ⋆ . The line with nega-tive slope is the mass-age relation for a fixed Li abundanceequal to that measured in each target. The intersection ofthese two lines yields the combination of mass and age atgiven T eff , luminosity, and lithium depletion. The derivedvalues of nuclear masses and ages ( M Li , t Li ), together withthose obtained by Frasca et al. (2017) through HR diagram( M HRD , t HRD ) for the three stars, are listed in Table 3.The three stars are within the bounded region predictedby the Bildsten et al. (1997) analysis, but both masses andages from Baraffe et al. (2015) models are discrepant whencompared to the nuclear masses and ages. In fact, whilethe HR diagram indicates masses of ∼ . − . M ⊙ andages of ∼ − Myr, the amount of lithium depletion canonly be explained by more massive ( ∼ . M ⊙ ) and older( ∼ Myr) stars (see Fig. 10). In all cases, the derived val-ues of lithium abundance yield ages that are inconsistentwith the isochronal ones: these targets have experiencedtoo much burning for the estimated isochronal ages. In or-der to reconcile the two estimates, the stellar luminosityshould be decreased by a factor of ∼ − and/or the effec-tive temperature increased by several hundreds of Kelvin Article number, page 8 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs (see Fig. 7), which is several times more than the errorsin these quantities. As pointed out by several authors (seeHartmann 2003; Palla et al. 2007, and references therein),the discrepancies may be explained as being due to a com-bination of effects, such as poorly known stellar (e.g., bina-rity, photometric variability, atmospheric parameters) andcluster (e.g., distance, extinction) properties. At the sametime, an increase of the Li abundance up to the interstellarvalue would require an initial equivalent width a factor of ∼ − larger than measured, which seems inconsistentif we take into account the uncertainties estimated in Sec-tion 3.1.2. Moreover, the influence of veiling in the EW Li measurements is excluded because the three targets show r close to zero. In particular, to obtain A (Li) ∼ thesetargets should have a veiling of at least ∼
3, which is notpossible given their low level of continuum excess emission(see Alcalá et al. 2014, 2017).We also compared our results with recent self-consistentcalculations coupling numerical hydrodynamics simulationsof collapsing pre-stellar cores and stellar evolution mo-dels of accreting objects (Baraffe et al. 2017). These mo-dels predict that early accretion of material with low inter-nal energy (‘cold accretion’) or early accretion of materialwith energy depending on the accretion rate (‘hybrid ac-cretion’) can produce objects with abnormal Li depletion,at odds with predictions from non-accreting stellar evolu-tion models (Baraffe et al. 2017, and references therein).In particular, accretion bursts with typical accretion rates ˙ M burst > − M ⊙ yr − may gravitationally compress thestar, increasing its core temperature and pressure and trig-gering the early onset of lithium burning within a few Myr.Efficient large-scale convection, such as in low-mass PMSstars, would then rapidly deplete lithium throughout thestar. Figure 11 shows the surface lithium abundances asa function of effective temperature in accreting models byBaraffe et al. (2017) under cold and hybrid accretion sce-narios for ages at 2, 5, 10, and 20 Myr (the median age ofour data was estimated to be ∼ Myr; Frasca et al. 2017).Our Li depleted targets show a content of lithium which isnot reproduced by early accretion models. The latter pre-dict less lithium depletion at the ages and masses (effectivetemperatures) of our targets. Only for one source do the mo-dels seem to reproduce the observed lithium abundance butfor an age ( ∼ Myr) inconsistent with the Lupus clouds.We conclude that early accretion models are not able to re-produce the lithium depletion of such a rare object (3/89,i.e. ∼ ).Therefore, we are not able to draw final conclusionsabout the origin of the lithium depletion in the three tar-gets. We support the recent suggestion by Baraffe et al.(2017) to devote more observational effort to characterizeobjects with abnormal Li depletion in young clusters, nor-mally considered as outliers and often rejected from analy-ses. Only one of our targets (namely, RY Lup) is in commonwith previous studies of iron abundance in the literature.For this star, we obtain a best-fit at [Fe/H]= . ± . ,which is in agreement with the value of [Fe/H]= − . ± . derived by Padgett (1996). Fig. 9.
Portions of spectra for the three Li depleted stars (solidlines) around the lithium line at λ = 6707 . Å. Dashed linesrepresent the spectra of three non-Li depleted stars with similarstellar parameters (i.e., T eff , log g , v sin i , and r ). Fig. 10.
Mass versus age for the three stars with evidence ofLi depletion. The curves at a given luminosity (solid green linewith positive slope) and at a given lithium abundance (solid blueline with negative slope) were computed for the values of T eff ,luminosity, and lithium depletion given in Table 3. The dashedand dotted curves represent the uncertainty ranges in the ob-served luminosity and in the measured abundance, respectively.They define the locus where the values of mass and time areconsistent with the observations. For Sz 69 and Sz 94 no errorsin Li abundances are reported because their values, consistentwith zero, are upper limits. The diamonds give the mass and agefrom theoretical PMS tracks and isochrones, with typical errorsof ∼ . M ⊙ and ∼ Myr, respectively (see Frasca et al. 2017).Article number, page 9 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref
Fig. 11.
Surface lithium abundance as a function of effectivetemperature in accreting models under the cold (upper panel)and hybrid (bottom panel) accretion scenarios computed byBaraffe et al. (2017). Black, blue, light blue, and green symbolsrefer to predictions computed at 2, 5, 10, and 20 Myr, respec-tively. Filled symbols refer to models that predict a final stellarmass close to that of our Li depleted targets (i.e., ∼ . − . M ⊙ ).The positions of three Li depleted targets are shown with big reddiamonds. In Fig. 12 the distribution of our [Fe/H] measurementsin Lupus is compared with the previous estimates byPadgett (1996) and Santos et al. (2008). In the first case,only the abundance of RY Lup was measured. In the secondwork, the average value obtained by analyzing four classIII stars was <[Fe/H]>= − . ± . , which is in agree-ment with our estimate. We note that, even if our measure-ments have larger uncertainties when compared with pre-vious works (mainly because of the relatively low spectralresolution of the instrument used here), our distribution isnarrow, peaked at ∼ . dex with a standard deviation of0.05 dex.We stress that our abundance determination on class IItargets is one of the few in which the contribution of veilinghas been taken into account. A previous paper providingabundance determination for “deveiled” class II targets isthat by D’Orazi et al. (2011) focused on the Taurus-AurigaSFR, where the authors find that class II and class III ob-jects share the same chemical composition, indicating thatthe presence of a circumstellar accretion disk does not affectthe stellar photospheric abundances. Other older analyses Fig. 12.
Comparison of our [Fe/H] with previous estimates byPadgett (1996) and Santos et al. (2008). in class II targets did not consider the correction for theveiling, claiming that the derived iron abundance had to betreated as a lower limit (see, e.g., Padgett 1996).
Recent works have shown that nearby ( < pc) youngopen clusters span a range in [Fe/H] from ∼ − . to ∼ +0 . dex, but the youngest associations ( < ∼ Myr)are generally clustered around the low metallicity values(see Fig. 13 in Biazzo et al. 2011a and Fig. 10 in Spina et al.2014b). Because of their young ages, these regions did nothave time to migrate through the Galactic disk. Therefore,their metal content should be representative of the presentchemical pattern of the nearby interstellar gas from whichtheir members formed, with negligible effects of chemi-cal evolution (Biazzo et al. 2011a, and references therein).Spina et al. (2014b) concluded that since the chemical con-tent provides a powerful tool for tagging groups of starsto a common formation site, these different young regionsshould share the same origin. They also report the metallic-ity distribution of regions younger than 100 Myr, separatingclusters associated and not associated with the Gould Belt(see their Fig. 11), a disk-like structure made up of gas,young stars, and associations, whose origin remains some-what controversial (see, e.g., Guillout et al. 1998; Bally2008, and references therein). Spina et al. (2014b) alsoclaim that star-forming regions and young open clustersassociated with the Gould Belt show a metallicity lowerthan the solar value, and this could be the reason for the“non metal-rich” nature of the youngest stars in the solarvicinity.Our determination of iron abundance for the Lupus tar-gets with the X-shooter spectrograph is not as accurate asin the cited works, mainly because of the relatively low re-solution of our spectra, but, within the errors, it is in linewith recent results. However, we caution about the conclu-sion related to the Gould Belt for a number of reasons: thenumber statistics of the YSOs in the regions studied so far
Article number, page 10 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs is still too low, the abundance determinations are ratherheterogeneous because of the different methodologies usedto derive them, and the uncertainties in the distance andproper motion of the studied YSOs are still rather high.Therefore, additional accurate and homogeneous determi-nations of elemental abundance and kinematics are neededto further investigate this issue. Present and future facilitiesboth from space (e.g.,
Gaia ) and from the Earth (e.g., the
Gaia-ESO Survey ) will allow us to trace dynamically andchemically our Galactic disk with unprecedented accuracy.
In Fig. 13 we show the [Ba/Fe] ratio versus mean age fortargets in clusters studied by several authors (D’Orazi et al.2009, 2012; De Silva et al. 2013; Reddy & Lambert 2015).For homogeneity reasons, we considered only dwarf tar-gets analyzed with similar methods to ours. The Ba abun-dance shows an increasing trend with decreasing age, as al-ready reported in previous works (e.g., D’Orazi et al. 2009;De Silva et al. 2013; Yong et al. 2012; Jacobson & Friel2013). To our knowledge, the sample of dwarfs in Lupusis the youngest one analyzed so far for barium abundancedeterminations.For targets older than ∼
500 Myr, the trend of increa-sing [Ba/Fe] with decreasing cluster age was interpretedby D’Orazi et al. (2009) as an indication that low-mass( < . M ⊙ ) AGB stars contributed more importantly to thechemical evolution of the Galactic disk than previously as-sumed by models (see Fig. 13). Applying s -process yields inwhich the neutron source was larger by a factor six than inprevious models, D’Orazi et al. (2009) could reproduce theobserved trend (see also Maiorca et al. 2014). On the otherhand, as the same authors acknowledge, it would be diffi-cult to imagine a process capable of creating barium in thelast 500 Myr of Galactic evolution, unless local enrichmentis assumed. Thus, in agreement with other works analyz-ing dwarf stars in 30-50 Myr old associations and clusters(e.g., D’Orazi et al. 2009, 2012, 2017), we are still facing abarium conundrum. In our case, the behavior is even moreextreme, because for the Lupus SFR our [Ba/Fe] valuesare > ∼ . dex (Fig. 13). If, on one side, the sharp functionwith age is evident, on the other, whether this correspondsto a real increase in the Ba abundance or whether it de-pends on the methodologies used to derive the abundanceis still a matter of debate (see also D’Orazi et al. 2017). Tospot possible artifacts in our analysis of Ba abundance, wechecked for plausible dependence of [Ba/Fe] on stellar pa-rameters, chromospheric activity levels, and accretion prop-erties. This is depicted in Fig. 14 and discussed next.At solar metallicity, the formation of the λ ii line occurs in atmospheric layers that are mostly at effectivedepths below log τ = − . (Mashonkina & Zhao 2006),therefore quite deep to expect a strong impact from theabove hot chromosphere. Indeed, we do not see any clearrelationship between the activity indicators Ca ii fluxes (orrotational velocity) derived by Frasca et al. (2017) and Baover-abundance. This is in agreement with the findings byD’Orazi et al. (2012) for young clusters of ∼ α flux seems to be instead present, mayberelated to the different physical conditions of the emittingregions in Ca ii and H α lines (see Frasca et al. 2017, andreferences therein). In fact, for the six stars, the Ca ii -IRT flux ratio is around 1.2-1.6, typical of optically thick emis-sion sources, while the Balmer decrement is around 3-10,typical of optically thin emission (see Frasca et al. 2017 fordetails).No log g -[Ba/Fe] relation seems to be present, whilesome T eff -[Ba/Fe] trend appears, with the exception of onetarget (RY Lup). This trend could reflect some effect ofoverionization, with the consequence of abundance diffe-rences between the neutral species and the singly-ionizedones, as found for several iron-peak and α - elements fordwarf targets in ∼ ∼ ii abundances of cooler stars, we shouldalso compute the Ba abundances using Ba i lines. In ourcase, we could exploit the Ba i L acc (and mass accretion rate ˙ M acc ) derived byAlcalá et al. (2017). Similar behavior is seen with the veil-ing, as weak accreting targets show also lower r values,and in our case smaller Ba abundance. We tentatively at-tribute this trend to the effects of strong, warm flux fromthe upper chromosphere due to strong accreting phenomenaon the structure of the stellar atmosphere.The two targets which suffer less from all these effectsare the warmest and “unveiled” SSTc2d J160830.7-382827and MY Lup, both of them having transitional disks andbeing weak (or dubious) accretors, because their accretionluminosity is comparable to the chromospheric level (seeAlcalá et al. 2017). In summary, with the aim of avoidingproblems related to possible dependence of [Ba/Fe] on ac-cretion or activity diagnostics and stellar parameters, weconsider as the most reliable those derived for the twowarmest weak accretors (i.e., SSTc2d J160830.7-382827 andMY Lup), with an average [Ba/Fe] ∼ . dex (red diamondin Fig. 13).In conclusion, we are not able to provide an explana-tion for the peculiar trend of the Ba abundance in youngSFRs, clusters, and associations. We also do not think thatany of the examined possibilities can explain the observedBa over-abundance at the ∼ i -processas a promising mechanism of production of heavy elements(see also Mishenina et al. 2015; Hamper et al. 2016). Howe-ver, further theoretical work is needed. Future observationalsurveys for the determination of Ba elemental abundancewith homogeneous methodologies in large samples of stars Article number, page 11 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref
Fig. 13. [Ba/Fe] abundance as a function of cluster age fordwarf stars in Lupus (this work) and other regions (from theliterature). The red diamond represents the mean value of themost reliable Ba abundances obtained for the two warmest tar-gets (see text and Table 2). Cluster abundances and ages weretaken from D’Orazi et al. (2009, 2012), De Silva et al. (2013),and Reddy & Lambert (2015). The dashed line represents theGalactic chemical evolution model developed by D’Orazi et al.(2009) adopting enhanced s -process yields from AGB stars. in young clusters will be of paramount importance for goodstatistics at ages of < ∼ Myr.
5. Conclusions
We have presented the results of a study on elemental abun-dances in the Lupus star-forming region using spectroscopicdata acquired with X-shooter at the VLT. The studied sam-ple comprises of almost all class II objects in the Lupus I,II, III, and IV clouds (82 over 89 sources; see Alcalá et al.2017) and seven class III objects. Three elements were an-alyzed: lithium, iron, and barium.Our main results can be summarized as follows: – We detected the lithium line at λ =6707.8 Å in all tar-gets but six. The class III object Sz 94 does not showany hint of the absorption line, while for the other fiveclass II targets we could only measure upper limits forthe lithium equivalent widths due to the low S/N of thespectra. – Three objects appear to be highly lithium depleted.They represent only a few percent of the Lupus popu-lation, hence they are extremely rare, as found in otherstar-forming regions. The depletion in the lithium ele-mental abundance observed in such objects is still notreproduced by pre-main sequence evolutionary modelsin the literature, which makes them appealing for futuredetailed studies. – For six class II targets we measured iron and bariumabundance through spectral synthesis. The mean ironabundance in the Lupus star-forming region is consi-stent, within the errors, with the chemical pattern ofthe Galactic thin disk in the solar neighborhood.
Fig. 14.
Dependence of the [Ba/Fe] abundance on activity(Ca ii -8542, H α , Ca ii -K fluxes) and accretion ( L acc , L acc /L ⋆ , ˙ M acc , r ) diagnostics and on stellar parameters ( v sin i , T eff , log g ). Stars are enumerated: SSTc2d J160830.7-382827(1), RY Lup (2), MY Lup (3), Sz 68 (4), Sz 133 (5), andSSTc2d J160836.2-392302 (6). Open circles and pentagons rep-resent transitional disk and sub-luminous targets, respectively.Error bars of the [Ba/Fe] abundances are not plotted for clarityreasons. – We found enhancement in barium abundance up to ∼ . dex level. Our targets thus confirm and extend to ayounger age that previously found by other authors. Wediscussed several possible explanations for this puzzlingbehavior, including chromospheric and accretion effects,uncertainties in stellar parameters, and departure fromLTE approximation, but none of these seems to com-pletely justify the barium over-abundance. The bariumproblem is still an open issue and deserves further work,both theoretical and observational, in particular at clu-ster ages < ∼ Myr.
Acknowledgements.
The authors are very grateful to the referee forhis/her useful remarks that allowed us to improve the previous versionof the manuscript. The authors wish to dedicate this paper in remem-brance of Francesco Palla; in particular KB is grateful to Francesco forhis enriching teaching in the field of star formation, his extraordinarysimplicity and his humility. KB also thanks Valentina D’Orazi andChris Sneden for fruitful discussions on the barium issue. CFM grate-fully acknowledges an ESA Research Fellowship. This research has
Article number, page 12 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs made use of the SIMBAD database, operated at CDS (Strasbourg,France).
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Table 1.
Stellar parameters. Object name, spectral type, effective temperature, surface gravity, rotational velocity, radial velocity,veiling at λ =5400 Å, Å, and 7100 Å, measured and corrected lithium equivalent width and lithium abundance.
Name SpT T eff log g v sin i V rad r r r EW Li EW corrLi A (Li)(K) (dex) (km/s) (km/s) (mÅ) (mÅ) (dex)Sample of class II YSOs by Alcalá et al. (2014)Sz 66 M3 3351 ±
47 3.81 ± ≤ ± ≤ ±
10 481 2.86 ± ±
34 4.45 ± ≤ ± ≤ ≤ ±
21 631 3.34 ± ±
119 3.50 ± ± ± ≤ ≤ ±
10 122 < − Sz 71 M1.5 3599 ±
35 4.27 ± ≤ ± ±
12 720 3.00 ± ±
70 4.18 ± ≤ ± ≤ ≤ ±
10 392 1.87 ± ±
33 4.40 ± ± ± ±
11 832 3.32 ± ±
79 3.98 ± ± ± ≤ ±
12 535 3.39 ± ±
96 3.5 ± ± ± ±
10 988 > a M5 3058 ±
82 4.20 ± ± ± ≤ ±
12 531 2.69 ± ±
62 4.48 ± ≤ ± ≤ ±
10 579 > ±
19 3.77 ± ≤ ± ±
10 777 3.17 ± ±
111 4.08 ± ≤ ± ±
10 704 > a M1 3664 ±
45 4.34 ± ≤ ± ±
10 681 3.00 ± ±
109 3.89 ± ± ± ≤ ±
25 323 1.04 ± ±
62 3.89 ± ± ± ≤ ≤ ±
25 546 2.71 ± ±
78 4.14 ± ± ± ≤ ≤ ±
15 497 2.64 ± ±
98 3.89 ± ± ± ≤ ≤ ±
15 186 0.43 ± a M5.5 3037 ±
44 3.87 ± ± ± ≤ ≤ ±
15 496 2.39 ± ±
36 3.97 ± ± ± ≤ ±
15 564 > ±
73 3.96 ± ≤ ± ≤ ≤ ±
24 542 2.83 ± b M7.5 2750 ±
82 3.93 ± ± ± ≤ < d < < − Sz 106 b M0.5 3691 ±
35 4.82 ± ≤ ± ≤ ±
12 612 < ±
49 4.47 ± ≤ ± ≤ ≤ ±
25 534 3.20 ± b M4.5 3089 ±
246 3.56 ± ± ± < d < < − Sz 110 M4 3215 ±
162 4.31 ± ≤ ± ±
10 583 3.40 ± a M1 3683 ±
34 4.66 ± ≤ ± ±
12 650 < a M5 3079 ±
47 3.96 ± ≤ ± ≤ ≤ ±
12 562 3.03 ± ±
114 3.76 ± ≤ ± ±
15 272 0.93 ± ±
31 3.98 ± ≤ ± ≤ ≤ < d < < − SSTc2d 160901.4-392512 M4 3305 ±
57 4.51 ± ≤ ± ≤ ≤ ±
20 538 > ±
35 3.92 ± ≤ ± ±
10 698 > ±
42 3.90 ± ± ± ≤ ≤ ±
15 551 0.93 ± ±
59 3.97 ± ± ± ≤ ≤ ± ± a M1 3521 ±
70 4.46 ± ± ± ≤ ±
12 499 2.34 ± b M2 3513 ±
45 4.17 ± ≤ ± ≤ ± ± ±
43 3.95 ± ≤ ± ≤ ≤ ±
15 501 2.43 ± ±
75 3.85 ± ≤ − ± ≤ ±
10 667 3.03 ± ±
45 4.46 ± ≤ ± ≤ ≤ ±
100 515 2.23 ± ±
205 3.33 ± ≤ − ± < d < < − Sz 68 K2 4506 ±
82 3.68 ± ± − ± ±
10 571 3.64 ± ±
100 3.45 ± ± ± ≤ ≤ ±
15 493 < ±
151 3.48 ± ≤ − ± ≤ ±
20 554 2.96 ± ±
76 3.53 ± ± ± ≤ ±
15 478 2.14 ± ±
45 4.49 ± ≤ ± ≤ ±
11 627 2.76 ± ±
82 4.41 ± ≤ ± ≤ ≤ ±
40 631 3.37 ± a K2 5082 ±
118 3.87 ± ± ± ≤ ±
10 455 3.70 ± ±
82 4.03 ± ≤ ± ≤ ≤ ±
20 552 2.96 ± ±
140 4.00 ± ≤ ± ≤ ≤ ±
80 632 > ±
221 3.97 ± ≤ − ± ≤ ±
50 610 3.22 ± a K0 4968 ±
200 3.72 ± ± ± ≤ ≤ ≤ ±
10 454 3.67 ± ±
122 4.29 ± ≤ ± ≤ ±
30 584 > b K5 4420 ±
129 3.96 ± ≤ ± ±
25 643 3.56 ± b M4.5 3072 ±
55 4.01 ± ≤ ± ≤ ≤ ±
50 652 > c ... 3474 ±
206 4.18 ± ≤ − ± ±
65 932 > ±
52 4.24 ± ≤ ± ≤ ±
10 587 2.71 ± ±
53 4.37 ± ≤ − ± ≤ ≤ ±
10 559 3.49 ± ±
57 4.50 ± ≤ − ± ≤ ±
10 683 2.99 ± a M5.5 3024 ±
46 3.96 ± ± ± ≤ ≤ ±
100 496 2.43 ± ±
71 4.10 ± ≤ − ± ≤ ±
10 633 2.97 ± ±
46 4.48 ± ≤ ± ≤ ≤ ±
20 511 2.43 ± b K2 5145 ±
50 4.10 ± ± ± ±
50 595 3.98 ± ±
145 4.09 ± ≤ ± ≤ ≤ ±
10 421 3.40 ± a K6 4429 ±
83 4.04 ± ≤ ± ≤ ±
35 501 3.09 ± ±
59 3.86 ± ≤ ± ≤ ≤ ±
25 467 2.28 ± ±
85 4.24 ± ≤ ± ≤ ±
12 663 3.50 ± ±
200 3.60 ± ≤ ± ≤ ≤ < d < < − ±
55 3.99 ± ≤ − ± ≤ ≤ ±
40 561 2.51 ± a M4.5 3147 ±
58 4.00 ± ≤ ± ±
15 209 < ±
51 4.10 ± ≤ − ± ≤ ±
15 651 > ±
82 4.47 ± ≤ − ± ≤ ±
10 671 2.95 ± ±
72 3.77 ± ± ± ≤ ≤ ±
50 521 2.53 ± ±
48 3.98 ± ≤ − ± ≤ ≤ ±
15 541 2.69 ± ±
69 4.00 ± ± ± ≤ ≤ ±
50 630 3.10 ± a M4.5 3098 ±
59 4.02 ± ± − ± ≤ ≤ ±
20 522 2.73 ± ±
22 4.57 ± ≤ − ± ≤ ±
10 585 2.79 ± ±
78 4.25 ± ≤ − ± ±
15 503 2.39 ± Article number, page 14 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs
Table 1.
Continued.
Name SpT T eff log g v sin i V rad r r r EW Li EW corrLi A (Li)(K) (dex) (km/s) (km/s) (mÅ) (mÅ) (dex)Sample of class II YSOs from the ESO ArchiveGQ Lup K6 4192 ±
65 4.12 ± ≤ ± ±
10 647 3.17 ± a M4 3440 ±
60 4.41 ± ≤ ± ≤ ≤ ±
50 583 > ±
48 4.28 ± ± ± ≤ ±
15 663 3.08 ± ±
70 4.75 ± ± ± ±
20 671 < a K5 4146 ±
95 3.80 ± ± − ± ≤ ≤ ≤ ±
10 545 2.91 ± ±
62 4.31 ± ± ± ≤ ±
15 642 3.06 ± ±
59 4.21 ± ± ± ≤ ≤ < e < < − Par-Lup3-1 M6.5 2766 ±
77 3.48 ± ± ± ≤ ≤ ±
80 654 < ±
61 3.91 ± ± ± ≤ ≤ ±
35 574 3.09 ± ±
100 3.52 ± ± ± ≤ ≤ ±
30 540 2.52 ± ±
34 4.50 ± ≤ ± ≤ ±
10 723 3.02 ± ±
61 4.08 ± ± ± ≤ ≤ ±
25 607 > ±
27 4.58 ± ± ± ≤ ≤ ±
25 274 0.98 ± a YSO with transitional disk; b Sub-luminous YSO; c Flat source (see Alcalá et al. 2017); d Lithium non detected because of low
S/N ; e star with nolithium (see text for details).Notes: Spectral types for class II and III YSOs were taken from Alcalá et al. (2017) and Manara et al. (2013), respectively, while effective temperatures,surface gravities, projected rotational velocities, radial velocities, and veiling in three spectral regions from Frasca et al. (2017). Article number, page 15 of 17 &A proofs: manuscript no. Biazzoetal_Lupus_LangEditCorr_noref
Table 2.
Iron, barium, and lithium abundances measuredthrough spectral synthesis. Errors refer to the best-fit procedure( σ ), the main source of uncertainty (see text for details).Name [Fe/H] [Ba/H] A (Li) synth (dex) (dex) (dex)SSTc2d J160830.7-382827 . ± .
20 0 . ± .
20 3 . ± . RY Lup . ± .
25 1 . ± .
30 3 . ± . MY Lup . ± .
20 0 . ± .
25 3 . ± . Sz 68 . ± .
30 1 . ± .
10 3 . ± . Sz 133 . ± .
70 1 . ± .
70 4 . ± . SSTc2d J160836.2-392302 . ± .
30 1 . ± .
10 3 . ± . Article number, page 16 of 17. Biazzo et al.: Lithium, Iron, and Barium abundances in Lupus YSOs
Table 3.
Lithium depleted stars and their properties.
Name L ⋆ T eff A (Li) M HRD t HRD M Li t Li ( L ⊙ ) (K) (dex) ( M ⊙ ) (Myr) ( M ⊙ ) (Myr)Sz 69 . ± .
041 3163 ± < − . . ∼ . ∼ Sz 94 . ± .
061 3205 ± < − . . ∼ . ∼ Sz 99 . ± .
034 3297 ±
98 0 . ± .
14 0 . . ∼ . ∼16