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A&A
Volume 634, February 2020
Article Number A33
Number of page(s) 6
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/201936881
Published online 31 January 2020

© ESO 2020

1. Introduction

It has been customarily assumed that star formation cannot proceed within low gas density environments, such as those that characterize the outermost regions of galactic disks (Ferguson 2002; Bianchi 2009). However, the Galaxy Ultraviolet Explorer (GALEX, Martin et al. 2005) revealed the presence of young stellar populations in the extreme outskirts of spiral galaxies, well beyond where the bulk of the galactic-scale star formation was assumed to take place (Bianchi et al. 2005; Thilker et al. 2005, 2007). A common feature of all these galaxies is that they lie in groups, as do most of the galaxies in the Local Universe, including the Milky Way, and hence extended ultraviolet emission could be a natural consequence (Marino et al. 2010). Nevertheless, the presence of young massive stars in the outskirts of our Galaxy is very difficult to confirm in the ultraviolet range because of the high extinction areas close to the Galactic plane (Schultheis et al. 2015; Schlafly et al. 2016, 2017). It is therefore necessary to follow an alternative approach and look for young stars in other spectral ranges.

With the second data release of the Gaia mission (Gaia DR2, Gaia Collaboration 2016, 2018a), an extensive database of astrometric measurements for sources up to 90 kiloparsecs (kpc) from the solar system, including proper motions, radial velocity, and photometry in blue GBP, red GRP, and green G passbands, together with extinction AG and reddening E(GBP − GRP), is now available. This unique database provides a refined characterization of the structure and evolution of the Milky Way (Gaia Collaboration 2018b; Helmi et al. 2018; Antoja et al. 2018) and could provide useful data for the search of very young stars in the halo region, beyond the nominal edge of the disk (15 kpc from the Galactic center, Armentrout et al. 2017). In this work we present our efforts to search for recent star formation in the outskirts of the Milky Way using Gaia DR2 data. The sample selection is explained in Sect. 2, the kinematics of the potentially young candidates are studied in Sect. 3, and a discussion of the results is presented in Sect. 4. Finally, the main conclusions of our work are summarized in Section 5.

2. Sample

Young stars are characterized by their strong ultraviolet emission. However, since in the early phases of stellar evolution they are still embedded in a dust dense envelope, light at ultraviolet and optical wavelengths is attenuated. In addition, if they are far away, the presence of interstellar clouds in the lines of sight to stars contributes to the overall extinction and an apparent redder color is observed. We do not expect to be able to resolve stars of the first type in the outskirts of the Milky Way because they will be severely extincted and their detection unfeasible. Nevertheless, we can try to find distant young stars, of a few hundred Myr1, that will be naturally reddened by the interstellar material of the disk.

Keeping these ideas in mind, we searched for stars with unusually high reddening values in the vast database of Gaia DR2.

We selected all the stars with strictly positive values of parallax and non-null values of reddening E(GBP − GRP) derived by the Apsis data processing pipeline (Andrae et al. 2018), resulting in a catalogue of 87 733 672 stars.

First, we plotted all the stars in slices of a few hundred parsecs estimating the distance as the inverse of the parallax and examined Gaia DR2 data in search of regions with unusual patterns of reddening. The most interesting results were found at parallax values between 1/60 and 1/30 milliarcseconds (mas), which would ideally correspond to distances in the range 30–60 kpc, so we focused our attention on this subsample. In addition, these Gaia DR2 sources were systematically ignored in previous studies because of the extremely large parallax errors, usually on the order of the parallax value itself. In this way our study can be considered both a search for a young population in the Milky Way halo and an independent assessment of the reliability of extreme Gaia DR2 measurements. For stars with parallax values between 1/60 and 1/30 mas, we searched for regions detached from the Galactic disk (Galactic latitudes |b| > 15°) where a moderate number of extincted sources with reddening greater than 0.8 are present. We identified four regions that meet these requirements (see Fig. 1); we labeled them Region 1 (l, b) = (135° , − 20° ), Region 2 (l, b) = (195° , − 15° ), Region 3 (l, b) = (230° , − 20° ), and Region 4 (l, b) = (325° , + 20° ). From these four overdensities we selected Regions 1 and 4 because they are significantly redder than their mirror Galactic counterparts (same longitude, opposite latitude; see Fig. 2).

thumbnail Fig. 1.

Reddening map for distant sources in Galactic coordinates. Scatter plot of all Gaia DR2 sources with parallaxes between 1/60 and 1/30 mas (nominal heliocentric distances between 30 and 60 kpc) in Galactic coordinates. The color-coding corresponds to the value of the reddening, E(GBP − GRP). The black rectangles enclose Region 1 (lower left) and Region 4 (upper right).

thumbnail Fig. 2.

Distribution of sources with reddening values between 0.8 and 1.2 towards the four Regions discussed in Section 2 in black, and their mirror Galactic counterparts (same longitude, opposite latitude) in red.

Since the parallax errors are extremely large, if we apply any cut based on them the resulting sample will be practically nonexistent. On the other hand, we can use the renormalized unit weight error (RUWE) measurement associated with each Gaia DR2 source to determine the quality and reliability of the astrometric parameters (see Gaia Collaboration 2018a; Lindegren 2018, for details). Basically the RUWE value indicates whether the derived parameters for the source are reliable or if there are any problems with the astrometric solution. For the stars in our sample, the RUWE value is less than 1.4 for more than 95% of the objects, meaning that their astrometric solution is valid. We also did not apply the zero-point correction since it is discouraged by Lindegren et al. (2018) and there are serious discrepancies in the exact value (see, e.g., the discussion in de la Fuente Marcos & de la Fuente Marcos 2019, and references therein). In addition, it is not clear how our regions might be affected, and the correction value is the same order of magnitude as the parallaxes. However, we were able to confirm independently that some of them are actually that far away (see Sect. 4).

We note that contamination may arise from the Galactic disk but also from stellar and Galactic streams. Towards Region 1 and at heliocentric distances of ∼18–20 kpc lies the Triangulum-Andromeda (TriAnd) overdensity (Bergemann et al. 2018; Hayes et al. 2018), which according to recent studies has an origin in the Milky Way disk, but whose stars have ages ranging from 6 to 10 Gyr (Bergemann et al. 2018); there is no known stellar stream or overdensity associated with Region 4. In both cases, the peculiar stellar population seems to be well detached from the bulk of the disk population (see Fig. 1).

3. Kinematics

Motivated by the high values of reddening of our stars in Gaia DR2, we studied their kinematics in order to search for evidence of internal coherence. As we want to determine if they were formed inside the Milky Way or, on the other hand, are part of an external stellar stream, we compared the data with synthetic data from the Galactic model GALAXIA (Sharma et al. 2011), which takes into account the warp and flare of the disk.

First, we studied the proper motions of both regions, which are displayed in Fig. 3. In the analysis, we distinguished between sources with color (GBP − GRP)−E(GBP − GRP) < 0.5 (blue points) and > 0.5 (black points) since we expected that any young stellar candidates would lie within the blue population. For an A7V star, B − R = 0.5 is the turnoff point of a 165 Myr old metal-rich cluster, so it makes sense to assign this arbitrary boundary when looking for relatively young populations and it is also consistent with our own definition of youth (see, e.g., Howell et al. 2005). Although we observe that there is no apparent difference between the black and blue populations (see Table 1), stars in Region 4 are far more dispersed and their proper motions are five times higher than those in Region 1.

thumbnail Fig. 3.

Proper motions of Gaia DR2 sources in Region 1 and Region 4. Blue points (376 in Region 1; 741 in Region 4) correspond to sources with values of GBP − GRP <  0.5, while black points (83 in Region 1; 690 in Region 4) correspond to sources with GBP − GRP >  0.5.

Table 1.

Median and interquartile range (IQR) values in mas yr−1 for the proper motions of stars in Regions 1 and 4.

The key parameter left to fully determine the kinematics of the populations is the radial velocity. Unfortunately, Gaia DR2 only provides measurements for 13 sources in Region 1; for Region 4 we have a statistically more significant sample including 51 stars. Due to the scarcity of available data, we performed a very simple, non-parametric statistical study based on the median value of radial velocity given by the GALAXIA model and the data, and the interquartile range (IQR)2. The results are summarized in Table 2 and complementary histograms are shown in Fig. 4. Although the IQRs are wide, it is clear that at least Region 1 shows an anomalous distribution in radial velocities, while Region 4 data could suffer from disk contamination.

thumbnail Fig. 4.

Histogram of radial velocities of sources from Regions 1 and 4. Red bars correspond to GALAXIA predictions for the disk of the Milky Way, while gray bars correspond to predictions for the halo; blue dashed bars correspond to probability densities of Gaia DR2 data (13 sources in Region 1; 51 in Region 4). The probability density is an amount such that the sum of the values times the bin size is equal to 1. Bins were computed according to the Freedman–Diaconis rule.

Table 2.

Radial velocity statistics (in km s−1) for the sources in the regions of interest.

Finally, for the samples with radial velocity, we computed the heliocentric Galactic velocities UVW following the standard procedure (see, e.g., Johnson & Soderblom 1987) and compared them with predictions from GALAXIA (Fig. 5); the associated errors were also computed, but are not displayed due to the extremely large uncertainties. From these plots we interpret that Region 1 data are compatible with a Galactic origin, perhaps due to a possible contamination arising from TriAnd, while the trends in Region 4 indicate that these sources were not born in the place where they are observed now. What we conclude in view of the very different kinematics is that these two regions do not share a common origin.

thumbnail Fig. 5.

Heliocentric velocities for sources towards Regions 1 and 4. Gray dots are GALAXIA predictions (7065 in Region 1; 26 031 in Region 4), black dots are Gaia DR2 sources with radial velocity data (13 in Region 1; 51 in Region 4).

The Galactic orbits of stars in Gaia DR2 with full data sets (i.e., also including radial velocity) can be estimated using Galpy (Bovy 2015). For those stars in Regions 1 and 4 with complete kinematic information (13 and 52 sources, respectively), we computed the orbital motions by applying Monte Carlo sampling. We generated 104 instances of the input data set required to calculate one orbit using values and uncertainties from Gaia DR2, then extracted relevant parameters such as the maximum value of the Z coordinate (Zmax) and the eccentricity (e). For each source and using the 104 orbital realizations, we computed median values and the 16th and 84th percentiles to estimate the uncertainties, as we describe in the previous sections. The orbits were calculated using the gravitational potential recommended in the Galpy documentation, MWPotential2014 (Bovy 2015), which includes a bulge, a disk, and a dark matter halo component, but neither spiral arms nor giant molecular clouds. Our results are shown in Fig. 6. Most sources are fully consistent with halo membership, but the samples from Regions 1 and 4 clearly have different provenance. The sample in Region 4, if located as far away as the data suggest, might not have an origin in the Milky Way. A possible formation scenario for these stars would be the collision of a high velocity cloud with the disk (Eggen et al. 1962).

thumbnail Fig. 6.

Galactic orbits characteristic of sources in Regions 1 and 4. Maximum height above the plane of the orbit as a function of the eccentricity for relevant sources in Region 1 (13, in blue) and Region 4 (52, in red). Median values and error bars displaying the 16th and 84th percentiles are shown.

4. Discussion

In the first approach, we plotted the CM diagram for Regions 1 and 4, taking Gaia DR2 parallax, extinction, and reddening values; the results are displayed in Fig. 7 with some overlaid PARSEC+COLIBRI PR16 isochrones of solar metallicity (Marigo et al. 2017; Bressan et al. 2012) and GALAXIA predictions. In both cases the data suggest the presence of a blue population that is evolving towards the red branch of the diagram, much younger than the current ∼2 Gyr population already known and predicted by the models. However, while the errors in apparent magnitudes are not significant, the errors in the parallax are considerable. The most suggested line of action in these cases is to apply Bayesian inference to predict distances from the parallaxes (Luri et al. 2018; Bailer-Jones et al. 2018), but due to the large uncertainties, the posterior distribution will be strongly affected by the prior, and hence the predicted distances will be shorter than they really are. Instead, we followed the approach outlined by de la Fuente Marcos & de la Fuente Marcos (2019) and estimated the heliocentric and Galactocentric distances via Monte Carlo sampling, assuming a distance to the Galactic Center of d = 8.18 kpc (Abuter 2019); for each source, we performed 105 Monte Carlo simulations. Since Gaia DR2 values for extinction and reddening were derived neglecting the parallax uncertainties (Andrae et al. 2018), we determined the absolute magnitudes solely from our Monte Carlo parallaxes. Figure 8 shows the sources of Region 4 for which the Monte Carlo estimates are compatible with a young age, although it is clear that the errors in the parallaxes are so large that we cannot be certain about the values. The most promising candidate in Region 4 is Gaia DR2 6116708946261654272, with lower bar error in absolute magnitude and 16th Galactocentric distance percentile3 greater than 15 kpc. Thus, we performed an extensive search in both Vizier4 (Ochsenbein et al. 2000) and SIMBAD (Wenger et al. 2000) looking for any available information about the samples that could shed some light on the question of their age. What we found is that in general, there is not much information about sources in Region 4, which we ascribe to their location in the southern celestial hemisphere, while for sources in Region 1 there are some LAMOST DR45 (Luo et al. 2018) spectra available that we used for the purpose of estimating spectroscopic parallaxes. Eventually, we found one star, Gaia DR2 375075920547984000, which is classified as an A2IV by LAMOST DR4. We estimated the Galactocentric distance to the source spectroscopically and via Monte Carlo sampling, and the results are compatible. For the spectroscopic estimate, we took YZ Cassiopeiae as an A2IV reference star, with a visual magnitude MV = 0.251 mag (Bilir et al. 2008), and we obtained a heliocentric distance of 21.2 kpc with an estimated error of 0.9 kpc6, which corresponds to a Galactocentric distance of 26.3 kpc for this object; the Monte Carlo estimates are compatible with a median Galactocentric distance of 17.5 kpc, and percentiles p16 = 11.1 kpc and p84 = 49.6 kpc. This star is also included in the Starhorse catalog recently published by Anders et al. (2019) although the distance estimate is not very reliable; the resulting distance probability density function is very broad. Nevertheless, their Galactocentric distance is also compatible with our results, with a value of 26.3 kpc. In summary, we have an A2IV star located farther than 15 kpc from the Galactic center. This star is very likely to be young since an A2 star spends little time in the subgiant stage, and in consequence there should be more young stars with it, unless it is a runaway star (see, e.g., Hoogerwerf et al. 2000); no conclusions can be drawn without a better characterization of the star (radial velocity, reddening, and precise parallax).

thumbnail Fig. 7.

Color–magnitude diagrams for Regions 1 and 4 for Gaia DR2 data, corrected from extinction and reddening and with distance estimated inverting the nominal parallax; for comparison, GALAXIA predictions are shown as gray dots. Shown are (from top to bottom) PARSEC+COLIBRI PR 16 isochrones with solar metallicity of 10 Myr (gold line), 50 Myr (dashed blue line), 100 Myr (solid red line), 150 Myr (dashed green line), and 2 Gyr (solid gray line).

To determine whether there are more young star candidates like the one just discussed, we need to be sure that Gaia DR2 nominal parallaxes are valid despite the large errors. Our search in Vizier has reported two more encouraging results. Gaia DR2 396558526625729152 is classified in LAMOST DR4 as an A2V that gives a spectroscopic Galactocentric distance of 17.4 kpc (taking MV = 1.3 mag, Drilling & Landolt 2000), and Gaia DR2 338768431691725824 is an RR Lyrae star (Sesar et al. 2017) with a distance modulus of 16.31 mag. Although the RR Lyrae is not a young star, both of them are well beyond the nominal edge of the Galactic disk. We are thus convinced that Gaia DR2 parallaxes for distant sources are more reliable than previously thought.

However, there could be some contamination arising from TriAnd suggested by the presence of two stars in Region 1, Gaia DR2 374544482769997056 and Gaia DR2 330307728370143360, that were previously identified by Sheffield et al. (2014) as bona fide members of TriAnd. In addition, an old population is clearly revealed by LAMOST: Gaia DR2 339458031639126016 is classified as a M1 star, and Gaia DR2 388342971445046272 as a K7. The list of the discussed stars is shown in Table 3.

Table 3.

Stars with additional data discussed in Sect. 4.

5. Conclusions

If we take into account the analysis presented in the previous section, we are convinced that nominal Gaia DR2 parallaxes may be valid even for very distant sources (d >  15 kpc), since our Monte Carlo estimated values are always compatible with the few spectroscopic estimates available. Nevertheless, the topic of the age of the sources is still debatable. In view of the corrected data shown in the CMD in Fig. 8, and assuming (based on the few confirmed cases) that most of the stars are really that far away, we conclude that within the samples there must be some young stars, the most promising candidate being Gaia DR2 375075920547984000. On the other hand, although the stars are likely to be distant, they are not located farther than 30 kpc, meaning that the errors in the parallax are not negligible. Hence, reddening and extinction values derived by Apsis are likely to change, and we have to be cautious with respect to the CMD and the results that can be drawn from them. We are hopeful that with the arrival of Gaia DR3 we will be able to select a reliable sample of candidates in both regions to be studied in more detail.

thumbnail Fig. 8.

Zoom-in of the color-magnitude diagram for Region 4 after applying the Monte Carlo sampling. For better illustration, the axes have been rotated and the error bars are displayed in gray. The big black star is one of the most promising young star candidates, Gaia DR2 6116708946261654272, because of the bounded errors in absolute magnitude (no additional information in Vizier has been found).

1

Hereafter, when we refer to young stars we mean stars with an age of ∼100 − 200 Myr, independently of the evolutionary stage.

2

The IQR is a measure of statistical dispersion that indicates the width of the interval where 50% of the data are included.

3

We computed the 16th (p16) and 84th (p84) percentiles following Apsis convention. They can be considered asymmetric error bars for the median estimates.

4

All the matches in Vizier were carried out in a search radius of 3″.

6

We used LAMOST g and r magnitudes for the estimation of the V magnitude via the relationship V = r + 0.44(g − r) − 0.02, considering an error of 0.05 mag for each magnitude.

Acknowledgments

We want to thank an anonymous referee for a careful revision that has greatly improved this manuscript. This work has been partly funded through grants ESP2015-68908-R and ESP2017-87813-R. LBA acknowledges Complutense University of Madrid and Banco Santander for the grant “Personal Investigación en Formación CT17/17-CT18/17”. The authors acknowledge Javier Yáñez for useful discussions on statistical strategies. In preparation of this article, we made use of the NASA Astrophysics Data System and the ASTRO-PH e-print server. This research has made use of the SIMBAD and VizieR databases operated at CDS, Strasbourg, France. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013, 2018). Some of the plots included in this article as well as the statistical treatment of the data have been performed with R (R Core Team 2015).

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All Tables

Table 1.

Median and interquartile range (IQR) values in mas yr−1 for the proper motions of stars in Regions 1 and 4.

In the text
Table 2.

Radial velocity statistics (in km s−1) for the sources in the regions of interest.

In the text
Table 3.

Stars with additional data discussed in Sect. 4.

In the text

All Figures

thumbnail Fig. 1.

Reddening map for distant sources in Galactic coordinates. Scatter plot of all Gaia DR2 sources with parallaxes between 1/60 and 1/30 mas (nominal heliocentric distances between 30 and 60 kpc) in Galactic coordinates. The color-coding corresponds to the value of the reddening, E(GBP − GRP). The black rectangles enclose Region 1 (lower left) and Region 4 (upper right).
In the text
thumbnail Fig. 2.

Distribution of sources with reddening values between 0.8 and 1.2 towards the four Regions discussed in Section 2 in black, and their mirror Galactic counterparts (same longitude, opposite latitude) in red.
In the text
thumbnail Fig. 3.

Proper motions of Gaia DR2 sources in Region 1 and Region 4. Blue points (376 in Region 1; 741 in Region 4) correspond to sources with values of GBP − GRP <  0.5, while black points (83 in Region 1; 690 in Region 4) correspond to sources with GBP − GRP >  0.5.
In the text
thumbnail Fig. 4.

Histogram of radial velocities of sources from Regions 1 and 4. Red bars correspond to GALAXIA predictions for the disk of the Milky Way, while gray bars correspond to predictions for the halo; blue dashed bars correspond to probability densities of Gaia DR2 data (13 sources in Region 1; 51 in Region 4). The probability density is an amount such that the sum of the values times the bin size is equal to 1. Bins were computed according to the Freedman–Diaconis rule.
In the text
thumbnail Fig. 5.

Heliocentric velocities for sources towards Regions 1 and 4. Gray dots are GALAXIA predictions (7065 in Region 1; 26 031 in Region 4), black dots are Gaia DR2 sources with radial velocity data (13 in Region 1; 51 in Region 4).
In the text
thumbnail Fig. 6.

Galactic orbits characteristic of sources in Regions 1 and 4. Maximum height above the plane of the orbit as a function of the eccentricity for relevant sources in Region 1 (13, in blue) and Region 4 (52, in red). Median values and error bars displaying the 16th and 84th percentiles are shown.
In the text
thumbnail Fig. 7.

Color–magnitude diagrams for Regions 1 and 4 for Gaia DR2 data, corrected from extinction and reddening and with distance estimated inverting the nominal parallax; for comparison, GALAXIA predictions are shown as gray dots. Shown are (from top to bottom) PARSEC+COLIBRI PR 16 isochrones with solar metallicity of 10 Myr (gold line), 50 Myr (dashed blue line), 100 Myr (solid red line), 150 Myr (dashed green line), and 2 Gyr (solid gray line).
In the text
thumbnail Fig. 8.

Zoom-in of the color-magnitude diagram for Region 4 after applying the Monte Carlo sampling. For better illustration, the axes have been rotated and the error bars are displayed in gray. The big black star is one of the most promising young star candidates, Gaia DR2 6116708946261654272, because of the bounded errors in absolute magnitude (no additional information in Vizier has been found).
In the text

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