EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 633 (January 2020) A&A, 633 (2020) A119 Full HTML
Open Access
Issue
A&A
Volume 633, January 2020
Article Number A119
Number of page(s) 9
Section Planets and planetary systems
DOI https://doi.org/10.1051/0004-6361/201936891
Published online 21 January 2020

© A. Zurlo et al. 2020

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

Detecting forming protoplanets is the cornerstone to understanding planet formation (Fortney et al. 2008; Spiegel & Burrows 2012; Mordasini et al. 2017). Approximately 30% of Herbig Ae/Be disks should host giant planets of ~0.1 to 10 MJup (Kama et al. 2015). Some Herbig stars host transitional disks (TDs), which are peculiar disks with cavities, gaps, and spiral structures that can be induced by the presence of a companion (e.g., Dodson-Robinson & Salyk 2011). Supporting this theory, hydrodynamical simulations predicted the presence of substellar companions around some observed TDs (e.g., Dong et al. 2016a).

Accreting giant planets are thought to develop a circumplanetary disk (CPD) as they interact with the circumstellar material (e.g., Miki 1982; Gressel et al. 2013; Perez et al. 2015). While a fraction of the mass flows through the CPD at midplane latitudes, a substantial amount of gas falls onto the surface of the CPD via meridional flows (Tanigawa et al. 2012; Morbidelli et al. 2014; Szulágyi et al. 2014). The vertical accretion flow happens at near free-fall speeds, thus shocking the CPD surface and heating the gas to thousands of kelvin. Hα emission is thought to arise from the hot, shocked surface (Aoyama et al. 2018). Additionally, the intrinsic planet luminosity also contributes to heat up the circumplanetary environment, while enhancing the stellocentric accretion rate (Montesinos et al. 2015).

Breakthroughs in Hα high-contrast imaging, which is an indicator of accretion on compact bodies (Rigliaco et al. 2012; Tambovtseva et al. 2014; Zhu 2015a), have recently triggered intense interest. Notably, the first successful Hα detection of a close stellar companion was found around the star HD 142527 (Close et al. 2014), and was followed by the detection of an accreting protoplanet candidate around the transition disk LkCa15 (Sallum et al. 2015). The emission from the potential protoplanet is at the same location of the scattered light of the interior part of the disk (Thalmann et al. 2016), and it is hard to disentangle the two components. The presence of such a protoplanet is a subject of debate (see Mendigutía et al. 2018; Currie et al. 2019). The most evident case of an accreting protoplanet in a transition disk is PDS70b, where a clear detection in the near-infrared (NIR) has been recovered (Keppler et al. 2018; Müller et al. 2018; Christiaens et al. 2019; Mesa et al. 2019). Subsequently, Wagner et al. (2018) found Hα emission coming fromthe location of the protoplanet, demonstrating that the object is currently actively accreting with a mass accretion rate of Ṁ = 10−8±1 MJup yr−1. Recently, a second accreting protoplanet around the same star was detected in Hα emission with the multi-unit spectroscopic explorer (MUSE) instrument by Haffert et al. (2019). Finally, Cugno et al. (2019) published a first Hα survey focusing on targets that are suspected of hosting forming companions; they were unable to find any new unknown accretion signals.

Imaging young (a few Myr old) disks in Hα could potentially be sensitive to low-mass planets, even if they have moderate accretion rates (Ṁ > 10−5 MJup yr−1; Close et al. 2014). These values were estimated by converting the accretion rate of a low-mass object to Hα luminosity by using the TTauri stars relation presented in Rigliaco et al. (2012) in order to predict the contrast in Hα, showing that observations are more favorable for planetary objects with respect to any other NIR observation (Fig. 5 of Close et al. 2014). This is the case even though the two recent papers by Thanathibodee et al. (2019) and Aoyama & Ikoma (2019) demonstrate that the Hα emission from the planets comes from different mechanisms.

For this purpose, we selected all the known TDs that are closer than 200 pc and observable with the SPHERE planet finder at the Very Large Telescope (VLT, Chile). We found 11 targets, and for completeness we added three targets from the archive. The selected stars are young (age <20 Myr), and all of them have very low extinction in the visible (a fraction of a magnitude). Our targets are young stars with disks that present spirals, cavities, or gaps, and even in some cases, in both gas and dust. Material ought to flow from the outer disk reservoir and cross the gap to replenish the material being accreted by these stars. Hydro simulations suggest that the most obvious gap crossing mechanism is through the protoplanet’s wake streams.

The outline of the paper is as follows. In Sect. 2, we describe our target selection and present the SPHERE Hα observations of the systems; in Sect. 3, we describe the reduction methods applied and the results that we obtained in Sect. 4. We give the discussion and conclusions in Sect. 5.

2 Observations

2.1 Target selection

Our survey targets all the known TDs that are closer than 200 pc and observable from the VLT. Some of them had already been observed in a similar configuration of the instrument and for the same purpose. The data were already available in the archive. The individual target PDS 70, which was initially selected as part of the sample before the discovery of the companions b and c (Keppler et al. 2018; Haffert et al. 2019), has been removed from this analysis, as it will be presented in a separate and future paper (Zurlo et al., in prep.). The stars included in the sample are listed – together with their main properties – in Table 1.

The disks around MWC 758, HD 135344 B, HD 100546, AK Sco, and HD 100453 present spirals that can be induced by the presence of a companion. For MWC 758, Dong et al. (2015) predict the presence of a ~ 10 MJup companion at 160 au, which could have shaped the disk. Then, Reggiani et al. (2018) present a companion candidate at 20 au, and their contrast curves rule out companions larger than 5 MJup beyond the spirals (~ 0.′′6), assuming hot-start models. For HD 135344 B, similarly, van der Marel et al. (2016) and Hammer et al. (2019), connect the presence of the spiral features of the disk with the dynamical interaction with companions. For HD 100546, protoplanet candidates were actually found: Quanz et al. (2015) confirm a candidate observed in different bands (L′ and M′). A second candidate was found by Currie et al. (2015) using the Gemini Planet Imager (GPI, Macintosh et al. 2006)instrument. Recently, both Rameau et al. (2017) and Sissa et al. (2018) suggest that the candidates are more likely disk features. Mendigutía et al. (2017) found a bar-like structure in the polarized Hα emission around the star. For AK Sco, Janson et al. (2016) found two spiral arms in scattered light, which can be induced by the presence of a companion. For the spiral structures around HD 100453, Dong et al. (2016b) prove, with dynamical simulations, that they are induced by the stellar object detected in NIR light at a projected distance of ~ 119 au. For the stars HD 142527 (Close et al. 2014) and HD 98800 BaBb (Furlan et al. 2007), for which accreting companions are found, we refer the reader to the dedicated Sects. 4.1 and 4.2, respectively.

Some TDs present gaps and rings that can be carved by planets: around V4046 Sgr AB, rings are found at 14 and 29 au (Rapson et al. 2015) as well as 40 au (Avenhaus et al. 2018). The disk around HD 169142 has a gap at 23 au (Honda et al. 2012) and in scattered light at 40 au (Pohl et al. 2017). Around this star, a protoplanet candidate was claimed by Reggiani et al. (2014) and Biller et al. (2014), even if this detection has since been disputed (Ligi et al. 2018). DoAr 44’s disk has a gap from 5 to 32 au (Casassus et al. 2018); in scattered light it resembles a scaled down version of HD 142527 (Avenhaus et al. 2018). The presence of an inner warp in this disk may be caused by a planet (Casassus et al. 2018). The TD around HD 97048 has gaps at 34, 79, 140, and 179 au, and four rings (Ginski et al. 2016). Very recently, Pinte et al. (2019), presented the detection of a doppler kink in the gas flow of the disk. This Keplerian deviation can be explained by the presence of a planet that perturbs the gas flow (Perez et al. 2015). The kink is located exactly in one of the gaps, which is seen in both the ALMA continuum and in the scattered light images. For HD 141569 A, Perrot et al. (2016) found ringlets at 47, 64, and 93 au. The UX Tau A disk has an inner cavity at 56 au (Espaillat et al. 2007). PDS 66 has an inner cavity up to 15 au (Gräfe & Wolf 2013; Wolff et al. 2016).

Table 1

Objects included in the sample with their main characteristics.

2.2 Observations

This survey has been carried out with the instrument SPHERE (Beuzit et al. 2019). SPHERE is a planet finder at the ESO’s VLT that is equipped with an extreme adaptive optics system with a 41 × 41 actuators wavefront control, pupil stabilization, and differential tip-tilt control (Petit et al. 2014). The instrument has three science subsystems: the infrared dual-band imager and spectrograph (IRDIS; Dohlen et al. 2008), an integral field spectrograph (IFS; Claudi et al. 2008), and the Zimpol rapid-switching imaging polarimeter (ZIMPOL; Thalmann et al. 2008; Schmid et al. 2018). The latter, which is the only subsystem in visible light, has been used for the survey. The observing strategy was to take simultaneous images in the Hα narrow band filter (N_Ha; λc = 656.9 nm, Δλ = 1 nm) and in Hα continuum (Cont_Ha; λc = 644.9 nm, Δλ = 4.1 nm). We refer the reader to Schmid et al. (2018) for more details about the filters. All the observations were taken in pupil stabilized mode in order to take advantage of the speckles suppression technique of the angular differential imaging (ADI; Marois et al. 2006).

The observations (ESO programs 099.C-0453, 0100.C-0193, 0101.C-0461, 0102.C-0138, PI: Zurlo) were carried out in service mode, with the exception of the target DoAr 44 for which visitor mode was required during the last four ESO observing periods. To completethe sample for all the TDs that are closer than 200 pc and observable with SPHERE, we included the objects MWC 758, HD 135344B, and HD 142527 (programs 096.C-0267, PI: Huelamo, 096.C-0248, SPHERE GTO). The observations and analysis of the dataset of MWC 758 are presented in Huélamo et al. (2018). For this object, the broad band Hα filter (B_Ha; λc = 655.6 nm, Δλ = 3.35 nm) was used. This same dataset, together with the datasets of the objects HD 135344B and HD 142527 are analysed in Cugno et al. (2019). The different observing dates for each target, together with the total field of view rotation and the mean value of the seeing, are listed in Table 2. In general, the conditions were stable and the seeing was below 0.8′′. The total execution time per target was in between 1h and 1h30min.

Table 2

Summary table of the observations of the survey.

3 Data reduction

The raw data were preprocessed using the ZIMPOL pipeline, an IDL routine developed at ETH Zurich. The pipeline produces bias-subtracted, flat-fielded, remapped images for each of the two filters. We refer the reader to Cugno et al. (2019), for further details. We used a python routine to recenter the images, with a Gaussian two-dimensional fitting and to create two different data cubes, one for the Hα filter and one for the continuum. The python package VIP (Gomez Gonzalez et al. 2017) is used in this analysis. To perform the spectral differential imaging (SDI; Lafrenière et al. 2007) technique each image in the continuum filter was subtracted to the Hα filter after being rescaled and normalized for the central point spread function (PSF).

More specifically, we first rescaled the continuum images. Additionally, in order to normalize the two PSFs, we used a minimum χ-square method, which minimizes the standard deviation inside a circle centered in the center of the image and with a radius of 5 × FWHM, after the subtraction of the two images. In this way, we are able to take the center of the PSF into account, in addition to its wings and the bright central speckles.

We then performed a principal component analysis (PCA) reduction to apply the ADI method with a different number of components,depending on the total number of frames of each observation. In most of the cases, a frame selection rejecting low-quality frames (with a maximum of the 25% of the total number) was applied. We refer to angular spectral differential imaging (ASDI) reduction asthe combination of angular and spectral differential imaging. To align the images, we applied a true north correction of 134 ± 0.5 deg as in Cugno et al. (2019).

A couple of examples of the final ASDI PCA image are shown in Fig. 1 for the object HD 142527 B and Fig. 2 for the quadruple system around HD 98800. The two stellar companions of the spectroscopic binary were resolved at these wavelengths for the first time.

thumbnail Fig. 1

ASDI image of the system HD 142527. The stellar accreting companion is visible in the East. The image shows the very close vicinity of the stellar companion.
thumbnail Fig. 2

ASDI image of the system HD 98800 BaBb. The system is a quadruple: two spectroscopic binaries at the center of the detectorand two stellar companions (one accreting) are visible in the north, which are resolved for the first time in the visible.

4 Analysis and results

No significant detection of point sources has been recovered from the data reduction, apart from the already known stellar companion of HD 142527, which is presented in Sect. 4.1, and the two stellar companions of HD 98800 Ba, in Sect. 4.2. On the other hand, the contrast curves produced reflect the good conditions and exquisite performance of the instrument. We refer the reader to Zurlo et al. (2014, 2016) for a detailed explanation of how we calculated the contrast curves.

In Fig. 3, we present the contrast curves for the target AK Sco in addition to V4046Sgr A (Fig. 4), HD 169142 (Fig. 5), HD 97048 (Fig. 6), HD 100546 (Fig. 7), HD 142527 (Fig. 8), HD 141569A (Fig. 9), HD 135344B (Fig. 10), UX TauA (Fig. 11), PDS 66 (Fig. 12), and HD 100453 (Fig. 13). In the case of multiple datasets for the same object we selected and show here the best one. The reduction was performed for all the datasets. In general, we could reach a very deep contrast of ~ 12 mag at 0.′′ 2 separation from the host star.

Similarly to Cugno et al. (2019), in order to estimate the stellar flux in the continuum filter, we calculated the median of the count rate in an aperture of radius 1.5 arcsec and applied it in Eq. (4) from Schmid et al. (2017) after subtracting the background calculated in a ring at r = 1′′ (see Musso Barcucci et al. 2019). The filter zero point was also taken from Schmid et al. (2017). The calculated flux density was multiplied by the continuum filter effective width of 41.1 Å (see Schmid et al. 2018) in order to obtain the stellar flux. Subsequently, for each object, the contrast at each separation, which was calculated with respect to the PSF in the continuum images, was applied and the flux limit was directly calculated and converted into Hα luminosity. The relationship between Hα luminosity and accretion luminosity, as presented in Rigliaco et al. (2012), was used to obtain the upper limit for the accretion luminosity (as in Cugno et al. 2019). From simulations these formulas were also confirmed for planets (Thanathibodee et al. 2019). In Fig. 14, we show the apparent flux limit for all the targets and the accretion luminosity in Fig. 15. In Fig. 16, we show the mass accretion rate limits if we assume a planet of fixed mass 5 MJup and 1.47 RJup (for the mean age ofour sample), and by exploiting the AMES-COND evolutionary models (Allard et al. 2001) as in Cugno et al. (2019). The same Eq. (4) of Cugno et al. (2019) was applied for the calculation.

In summary:

  • For all the targets of our sample, on average we reach Hα line flux sensitivities,Hα line luminosities and accretion luminosity upper limits of 2 × 10−15 erg s cm−2, 5 × 10−7 L⊙, and 10−6 L⊙ respectively,beyond 0.′′2.

  • We reach an average mass accretion rate sensitivity of 10−9 MJup yr−1 beyond 0.′′2 for planets of 5 MJup.

  • The best sensitivities were obtained for AK Sco, HD 142527, and PDS 66 targets.

  • The worst sensitivity was obtained for DoAr 44 because of the faintness of the central star (V = 12.8), which is located at the limit of the adaptive optics (AO) system.

  • In half of the cases, we reached sufficient sensitivity to redetect PDS 70 b.

thumbnail Fig. 3

Contrast plot for the object AK Sco. In a black solid line, the profile of the PSF is shown, blue represents the Hα continuum filter after being processed with PCA and ADI, and red is the ASDI contrast. The vertical dotted lines represent the extension of the spirals (Janson et al. 2016).
thumbnail Fig. 4

Same as Fig. 3, but for the object V4046 Sgr. The vertical dotted lines represent the locations of the rings detected by Rapson et al. (2015) and Avenhaus et al. (2018).
thumbnail Fig. 5

Same as Fig. 3, but for the object HD 169142. The vertical dotted lines represent the location of the gaps detected by Honda et al. (2012) and Pohl et al. (2017).
thumbnail Fig. 6

Same as Fig. 3, but for the object HD 97048. The vertical dotted lines represent the location of the gaps detected by Ginski et al. (2016).
thumbnail Fig. 7

Same as Fig. 3, but for the object HD 100546. The dotted vertical line is located at the position of the inner gap (Quanz et al. 2015).
thumbnail Fig. 8

Same as Fig. 3, but for the object HD 142527. The red point represents the detected stellar companion.
thumbnail Fig. 9

Same as Fig. 3, but for the object HD 141569A. The vertical dotted lines represent the location of the ringlets detected by Perrot et al. (2016).
thumbnail Fig. 10

Same as Fig. 3, but for the object HD 135344B. The two dotted lines represent the extension of the spiral arms detected by van der Marel et al. (2016) and Hammer et al. (2019).
thumbnail Fig. 11

Same as Fig. 3, but for the object UX TauA. The vertical dotted line represents the location of the gap (Espaillat et al. 2007).
thumbnail Fig. 12

Same as Fig. 3, but for the object PDS 66. The vertical dotted line represents the location of the inner cavity (Gräfe & Wolf 2013; Wolff et al. 2016).
thumbnail Fig. 13

Same as Fig. 3, but for the object HD 100453. The vertical dotted line represents the inner part of the spirals detected by Dong & Fung (2017).
thumbnail Fig. 14

Apparent Hα line flux limits for all the targets of the sample versus angular separation (left) and physical separation (right). The differences between our predictions and other cases from the literature are primarily related to the methods used to extrapolate the accretion luminosity from the Hα line flux; as with SPHERE, the line width cannot be measured.
thumbnail Fig. 15

Accretion luminosity limits for all the targets of the sample versus angular separation (left) and physical separation (right).
thumbnail Fig. 16

Massaccretion rate limits (for a 5 MJup mass planet) for all the targets of the sample versus angular separation (left) and physical separation (right).

4.1 HD 142527

HD 142527 is a Herbig Ae/Be star that harbors a TD (e.g., Canovas et al. 2013; Boehler et al. 2017; Avenhaus et al. 2017) and an M star accreting companion (Biller et al. 2012; Close et al. 2014; Christiaens et al. 2018; Claudi et al. 2019). Price et al. (2018) suggest that the shape of the disk has been carved by the interaction with the binary. Claudi et al. (2019) retrieved the orbit for the companion using all the astrometric data available to date, they found a period of P = 35–137 yr; an inclination of i = 121–130 deg, and two families of values for the eccentricity: 0.2–0.45 and 0.45–0.7. In our data, the astrometry for the stellar companion is as follows: separation of 63.0 ± 1.5 mas and PA 96.4 ± 1 deg, which is consistent with the previous results from Cugno et al. (2019) and Claudi et al. (2019).

4.2 HD 98800

HD 98800 is a quadruple system at a distance of 47 pc (van Leeuwen 2007), which is composed of the following two binaries: HD 98800 BaBb, at a projected separation of 0.′′8 from the spectroscopic binaries, and HD 98800 AaAb, which has a separation of ~ 1 au (Boden et al. 2005). Around the B component, Furlan et al. (2007) found a transition disk composed of an optically thin component with an inner radius of 2 au, then a gap, and an optically thick component with a radius of 5.9 au. The presence of the gap might be explained by the carving of a planet. Kennedy et al. (2019) show ALMA results of the system, which present a very compact dust ring that is 2-au wide at a radius of 3.5 au. Because of a misunderstanding regarding the finding chart, the observationswere carried out with the A component in the center of the image, and they will be performed again. The two stellar companions (HD 98800 BaBb) of the spectroscopic binary were resolved in the visible for the first time.

The two stellar components, which were resolved in the ZIMPOL images, have a separation with respect to the A star of 495.8 ± 1.5 mas and a position angle of 9.0 ± 1.0 deg, and a separation of 507.5 ± 1.5 mas, position angle of 7.5 ± 1.0 deg. They are separated by 17.7 mas. The brightest component (west component) has an Hα flux of 6.5 × 10−16 L⊙, an Hα luminosity of 4.6 × 10−8 L⊙, and an accretion luminosity of 1.1 × 10−8 L⊙, while the faintest is not accreting.

5 Discussion and conclusions

We conducted a survey of nearby (<200 pc) TDs seen in the Hα filter. The purpose was to look for accreting protoplanets in all the disks that present peculiar features that can be induced by the presence of companions. The survey, which was conducted with SPHERE/ZIMPOL, made use of the ASDI technique between the Hα narrow filter and the adjacent continuum. Eleven targets were observed in excellent conditions (seeing <0.′′ 8) and the other three targets were added to the analysis for completeness. We did not detect any previously unknown companions, although the two components of HD 99880 BaBb were resolved for the first time in the visible.

The contrast curves that we obtained for the targets of the survey reflect the exquisite performance of the instrument, and they give strong constraints on the presence of accreting companions around the selected stars. If we compare our detection limits with the predictions from the population synthesis of Mordasini et al. (2017), while also assuming complete cold accretion, we expect to be unable to spot low mass (< 1 MJup) medium accreting (Ṁ = 10−5 ± 1 MJup yr−1) planets or high mass (1–15 MJup) that are not heavily accreting (Ṁ < 10−5.5 MJup yr−1).

The detection limits that we found can help us to investigate some properties of the putative protoplanets; the mass Mp, the planet radius Rp, and the planetary accretion rates Ṁ. Assuming the accretion disk paradigm, it has been theorized that the Hα luminosity, which is produced by shocks at some distance Rs (magnetospheric radius), can be approximated by L Hα =4π R s 2 π B ν (T) ν H α v s /c, \begin{equation*} L_{\textrm{H}\alpha} = 4 \pi R_{\textrm{s}}^2 \pi B_{\nu}(T) \nu_{\textrm{H}_{\alpha}} v_{\textrm{s}}/c,\end{equation*}(1)

where ν H α $\nu_{\textrm{H}_{\alpha}}$ is the Hα line frequency, vs the free-fall velocity of the accreting gas, c the speed of light, Bν(T) the Planck function, and T the temperature of the shock, which is generated by the gravitational conversion between gas accretion to accretion shock luminosity, that is, Lshock = ζGMpṀ∕Rp, and where ζ = 1 − Rp∕RT, where RT is the truncation radius (Zhu 2015b). We used RT ~ 5RP for the truncation radius, as in Cugno et al. (2019).

The shock temperature should be high enough to partially ionize the Hydrogen to be compatible with Hα emission, therefore T ~ 104−108 K (Aoyama et al. 2018). The value of vs is crudely known, but it was estimated to be in the range ~50−100 km s−1 for accreting circumplanetary disks (Zhu 2015b; Aoyama & Ikoma 2019). Also, vs can be approximated by v s = (2G M p / R p ) 1/2 ζ 1/2 , \begin{equation*} v_{\textrm{s}} = (2 G M_{\textrm{p}}/R_{\textrm{p}})^{1/2} \zeta^{1/2},\end{equation*}(2)

where Rp and Mp are the radius and mass of the planet, respectively.

Our detection limits give an upper boundary Hα luminosity of LHα = 5 × 10−7L⊙. In assumingthat the magnetospheric accretion shock occurs at the planet surface, we constrained the maximum planetary-mass able to produce such emission by equating this upper-limit LH α to Eq. (1). For instance, if we assume a shock temperature of T =104 K and vs = 50,  100 km s−1, we obtain from Eq. (1) (assuming RT = 5Rp); Rp = 1.3,  0.9 RJup, respectively. Using the resulting Rp, we derived the following planetary masses from Eq. (2); 1.1,  3.3 MJup, respectively,making the second example with high free-fall velocity less realistic.

It has yet to be determined if some of the nondetections are due to the intrinsic variability of protoplanets, which can be highly variable over time, changing their luminosity on short timescales on the order of hours. That could be a possible explanation for the nondetection of these kinds of objects, despite the very high sensitivity that we can reach with the current instrumentation. If this is the case, in the future, multiepoch opbservations are necessary to unveil the nature of accreting protoplanets in transition disks.

Another possible explanation for the nondetections could be that accreting planets are located within ~200 mas from the host star, where our observation are not sensitive enough due to the bright central speckles. Also, one has to consider that the assumptions that we are making in order to derive accretion luminosities from line luminosities may be inappropriate. For example, the estimate of the PDS 70 b accretion rate, using the different accretion luminosity estimate by Aoyama & Ikoma (2019), was much higher than the one presented in Wagner et al. (2018) for the same line luminosity. This could mean that a given accretion rate would correspond to a lower line luminosity if more realistic conversions are applied.

Aacknowledgements

We thank the anonymous referee for the constructive comments that improved the manuscript. A.Z. acknowledges support from the CONICYT + PAI/ Convocatoria nacional subvención a la instalación en la academia, convocatoria 2017 + Folio PAI77170087. G.C. thanks the Swiss National Foundation for the financial support under the grant number 200 021_169131. M.M. acknowledges financial support from the Chinese Academy of Sciences (CAS) through a CAS-CONICYT Postdoctoral Fellowship administered by the CAS South America Center for Astronomy (CASSACA) in Santiago, Chile, and support from Iniciativa Científica Milenio via the Núcleo Milenio de Formación Planetaria. N.H. has been partially funded by the Spanish State Research Agency (AEI) Project No. ESP2017-87 676-C5-1-R and No. MDM-2017-0737 Unidad de Excelencia María de Maeztu – Centro de Astrobiología (INTA-CSIC).

References

  1. Allard, F., Hauschildt, P. H., Alexander, D. R., Tamanai, A., & Schweitzer, A. 2001, ApJ, 556, 357 [NASA ADS] [CrossRef] [Google Scholar]
  2. Andrews, S. M., Wilner, D. J., Hughes, A. M., Qi, C., & Dullemond, C. P. 2009, ApJ, 700, 1502 [NASA ADS] [CrossRef] [Google Scholar]
  3. Aoyama, Y., & Ikoma, M. 2019, ApJ, 885, L29 [NASA ADS] [CrossRef] [Google Scholar]
  4. Aoyama, Y., Ikoma, M., & Tanigawa, T. 2018, ApJ, 866, 84 [NASA ADS] [CrossRef] [Google Scholar]
  5. Avenhaus, H., Quanz, S. P., Schmid, H. M., et al. 2017, AJ, 154, 33 [NASA ADS] [CrossRef] [Google Scholar]
  6. Avenhaus, H., Quanz, S. P., Garufi, A., et al. 2018, ApJ, 863, 44 [NASA ADS] [CrossRef] [Google Scholar]
  7. Beuzit, J. L., Vigan, A., Mouillet, D., et al. 2019, A&A, 631, A155 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Biller, B., Lacour, S., Juhász, A., et al. 2012, ApJ, 753, L38 [NASA ADS] [CrossRef] [Google Scholar]
  9. Biller, B. A., Males, J., Rodigas, T., et al. 2014, ApJ, 792, L22 [NASA ADS] [CrossRef] [Google Scholar]
  10. Boden, A. F., Sargent, A. I., Akeson, R. L., et al. 2005, ApJ, 635, 442 [NASA ADS] [CrossRef] [Google Scholar]
  11. Boehler, Y., Weaver, E., Isella, A., et al. 2017, ApJ, 840, 60 [NASA ADS] [CrossRef] [Google Scholar]
  12. Canovas, H., Ménard, F., Hales, A., et al. 2013, A&A, 556, A123 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Casassus, S., Avenhaus, H., Pérez, S., et al. 2018, MNRAS, 477, 5104 [NASA ADS] [CrossRef] [Google Scholar]
  14. Christiaens, V., Casassus, S., Absil, O., et al. 2018, A&A, 617, A37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Christiaens, V., Cantalloube, F., Casassus, S., et al. 2019, ApJ, 877, L33 [Google Scholar]
  16. Claudi, R. U., Turatto, M., Gratton, R. G., et al. 2008, SPIE Conf. Ser., 7014, 701418 [Google Scholar]
  17. Claudi, R., Maire, A. L., Mesa, D., et al. 2019, A&A, 622, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Close, L. M., Follette, K. B., Males, J. R., et al. 2014, ApJ, 781, L30 [NASA ADS] [CrossRef] [Google Scholar]
  19. Collins, K. A., Grady, C. A., Hamaguchi, K., et al. 2009, ApJ, 697, 557 [NASA ADS] [CrossRef] [Google Scholar]
  20. Cugno, G., Quanz, S. P., Hunziker, S., et al. 2019, A&A, 622, A156 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Currie, T., Cloutier, R., Brittain, S., et al. 2015, ApJ, 814, L27 [NASA ADS] [CrossRef] [Google Scholar]
  22. Currie, T., Marois, C., Cieza, L., et al. 2019, ApJ, 877, L3 [Google Scholar]
  23. Dodson-Robinson, S. E., & Salyk, C. 2011, ApJ, 738, 131 [CrossRef] [Google Scholar]
  24. Dohlen, K., Langlois, M., Saisse, M., et al. 2008, SPIE Conf. Ser., 7014, 3 [NASA ADS] [Google Scholar]
  25. Dong, R., & Fung, J. 2017, ApJ, 835, 38 [NASA ADS] [CrossRef] [Google Scholar]
  26. Dong, R., Zhu, Z., Rafikov, R. R., & Stone, J. M. 2015, ApJ, 809, L5 [NASA ADS] [CrossRef] [Google Scholar]
  27. Dong, R., Fung, J., & Chiang, E. 2016a, ApJ, 826, 75 [NASA ADS] [CrossRef] [Google Scholar]
  28. Dong, R., Zhu, Z., Fung, J., et al. 2016b, ApJ, 816, L12 [NASA ADS] [CrossRef] [Google Scholar]
  29. D’Orazi, V., Gratton, R., Desidera, S., et al. 2019, Nat. Astron., 3, 167 [NASA ADS] [CrossRef] [Google Scholar]
  30. Ducourant, C., Teixeira, R., Galli, P. A. B., et al. 2014, A&A, 563, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Espaillat, C., Calvet, N., D’Alessio, P., et al. 2007, ApJ, 670, L135 [Google Scholar]
  32. Fairlamb, J. R., Oudmaijer, R. D., Mendigutía, I., Ilee, J. D., & van den Ancker, M. E. 2015, MNRAS, 453, 976 [NASA ADS] [CrossRef] [Google Scholar]
  33. Fortney, J. J., Lodders, K., Marley, M. S., & Freedman, R. S. 2008, ApJ, 678, 1419 [NASA ADS] [CrossRef] [Google Scholar]
  34. Furlan, E., Sargent, B., Calvet, N., et al. 2007, ApJ, 664, 1176 [NASA ADS] [CrossRef] [Google Scholar]
  35. Furlan, E., McClure, M., Calvet, N., et al. 2008, ApJS, 176, 184 [NASA ADS] [CrossRef] [Google Scholar]
  36. Gaia Collaboration 2018, VizieR Online Data Catalog: I/345 [Google Scholar]
  37. Ginski, C., Stolker, T., Pinilla, P., et al. 2016, A&A, 595, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Gomez Gonzalez, C. A., Wertz, O., Absil, O., et al. 2017, AJ, 154, 7 [NASA ADS] [CrossRef] [Google Scholar]
  39. Grady, C. A., Schneider, G., Hamaguchi, K., et al. 2007, ApJ, 665, 1391 [NASA ADS] [CrossRef] [Google Scholar]
  40. Grady, C. A., Schneider, G., Sitko, M. L., et al. 2009, ApJ, 699, 1822 [NASA ADS] [CrossRef] [Google Scholar]
  41. Gräfe, C., & Wolf, S. 2013, A&A, 552, A88 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Gressel, O., Nelson, R. P., Turner, N. J., & Ziegler, U. 2013, ApJ, 779, 59 [NASA ADS] [CrossRef] [Google Scholar]
  43. Haffert, S. Y., Bohn, A. J., de Boer, J., et al. 2019, Nat. Astron., 3, 749 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Hammer, M., Pinilla, P., Kratter, K. M., & Lin, M.-K. 2019, MNRAS, 482, 3609 [NASA ADS] [CrossRef] [Google Scholar]
  45. Honda, M., Maaskant, K., Okamoto, Y. K., et al. 2012, ApJ, 752, 143 [NASA ADS] [CrossRef] [Google Scholar]
  46. Huélamo, N., Chauvin, G., Schmid, H. M., et al. 2018, A&A, 613, L5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Janson, M., Thalmann, C., Boccaletti, A., et al. 2016, ApJ, 816, L1 [Google Scholar]
  48. Kama, M., Folsom, C. P., & Pinilla, P. 2015, A&A, 582, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Kennedy, G. M., Matrà, L., Facchini, S., et al. 2019, Nat. Astron., 3, 278 [NASA ADS] [CrossRef] [Google Scholar]
  50. Keppler, M., Benisty, M., Müller, A., et al. 2018, A&A, 617, A44 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Lafrenière, D., Marois, C., Doyon, R., Nadeau, D., & Artigau, É. 2007, ApJ, 660, 770 [NASA ADS] [CrossRef] [Google Scholar]
  52. Lagage, P.-O., Doucet, C., Pantin, E., et al. 2006, Science, 314, 621 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  53. Ligi, R., Vigan, A., Gratton, R., et al. 2018, MNRAS, 473, 1774 [NASA ADS] [CrossRef] [Google Scholar]
  54. Macintosh, B., Graham, J., Palmer, D., et al. 2006, SPIE Conf. Ser., 6272, 62720 [Google Scholar]
  55. Mamajek, E. E., Meyer, M. R., & Liebert, J. 2002, AJ, 124, 1670 [NASA ADS] [CrossRef] [Google Scholar]
  56. Marois, C., Lafrenière, D., Doyon, R., Macintosh, B., & Nadeau, D. 2006, ApJ, 641, 556 [Google Scholar]
  57. Meeus, G., Montesinos, B., Mendigutía, I., et al. 2012, A&A, 544, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Mendigutía, I., Fairlamb, J., Montesinos, B., et al. 2014, ApJ, 790, 21 [NASA ADS] [CrossRef] [Google Scholar]
  59. Mendigutía, I., Oudmaijer, R. D., Garufi, A., et al. 2017, A&A, 608, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Mendigutía, I., Oudmaijer, R. D., Schneider, P. C., et al. 2018, A&A, 618, L9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Merín, B., Montesinos, B., Eiroa, C., et al. 2004, A&A, 419, 301 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Mesa, D., Keppler, M., Cantalloube, F., et al. 2019, A&A, 632, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Miki, S. 1982, Prog. Theor. Phys., 67, 1053 [NASA ADS] [CrossRef] [Google Scholar]
  64. Montesinos, M., Cuadra, J., Perez, S., Baruteau, C., & Casassus, S. 2015, ApJ, 806, 253 [NASA ADS] [CrossRef] [Google Scholar]
  65. Morbidelli, A., Szulágyi, J., Crida, A., et al. 2014, Icarus, 232, 266 [NASA ADS] [CrossRef] [Google Scholar]
  66. Mordasini, C., Marleau, G. D., & Mollière, P. 2017, A&A, 608, A72 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Müller, A., Keppler, M., Henning, T., et al. 2018, A&A, 617, L2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Musso Barcucci, A., Cugno, G., Launhardt, R., et al. 2019, A&A, 631, A84 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  69. Pecaut, M. J., Mamajek, E. E., & Bubar, E. J. 2012, ApJ, 746, 154 [NASA ADS] [CrossRef] [Google Scholar]
  70. Perez, S., Dunhill, A., Casassus, S., et al. 2015, ApJ, 811, L5 [NASA ADS] [CrossRef] [Google Scholar]
  71. Perrot, C., Boccaletti, A., Pantin, E., et al. 2016, A&A, 590, L7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Petit, C., Sauvage, J.-F., Fusco, T., et al. 2014, Proc. SPIE, 9148, 91480O [Google Scholar]
  73. Pinte, C., van der Plas, G., Ménard, F., et al. 2019, Nat. Astron., 419, 1109 [Google Scholar]
  74. Pohl, A., Benisty, M., Pinilla, P., et al. 2017, ApJ, 850, 52 [NASA ADS] [CrossRef] [Google Scholar]
  75. Price, D. J., Cuello, N., Pinte, C., et al. 2018, MNRAS, 477, 1270 [Google Scholar]
  76. Quanz, S. P., Amara, A., Meyer, M. R., et al. 2015, ApJ, 807, 64 [NASA ADS] [CrossRef] [Google Scholar]
  77. Rameau, J., Follette, K. B., Pueyo, L., et al. 2017, AJ, 153, 244 [NASA ADS] [CrossRef] [Google Scholar]
  78. Rapson, V. A., Kastner, J. H., Andrews, S. M., et al. 2015, ApJ, 803, L10 [NASA ADS] [CrossRef] [Google Scholar]
  79. Reggiani, M., Quanz, S. P., Meyer, M. R., et al. 2014, ApJ, 792, L23 [NASA ADS] [CrossRef] [Google Scholar]
  80. Reggiani, M., Christiaens, V., Absil, O., et al. 2018, A&A, 611, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Rigliaco, E., Natta, A., Testi, L., et al. 2012, A&A, 548, A56 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  82. Sallum, S., Follette, K. B., Eisner, J. A., et al. 2015, Nature, 527, 342 [NASA ADS] [CrossRef] [Google Scholar]
  83. Schmid, H. M., Bazzon, A., Milli, J., et al. 2017, A&A, 602, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Schmid, H. M., Bazzon, A., Roelfsema, R., et al. 2018, A&A, 619, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Sissa, E., Gratton, R., Garufi, A., et al. 2018, A&A, 619, A160 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Song, I., Zuckerman, B., & Bessell, M. S. 2012, AJ, 144, 8 [NASA ADS] [CrossRef] [Google Scholar]
  87. Spiegel, D. S., & Burrows, A. 2012, ApJ, 745, 174 [NASA ADS] [CrossRef] [Google Scholar]
  88. Szulágyi, J., Morbidelli, A., Crida, A., & Masset, F. 2014, ApJ, 782, 65 [NASA ADS] [CrossRef] [Google Scholar]
  89. Tambovtseva, L. V., Grinin, V. P., & Weigelt, G. 2014, A&A, 562, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Tanigawa, T., Ohtsuki, K., & Machida, M. N. 2012, ApJ, 747, 47 [NASA ADS] [CrossRef] [Google Scholar]
  91. Thalmann, C., Schmid, H. M., Boccaletti, A., et al. 2008, SPIE Conf. Ser., 7014, 112 [Google Scholar]
  92. Thalmann, C., Janson, M., Garufi, A., et al. 2016, ApJ, 828, L17 [NASA ADS] [CrossRef] [Google Scholar]
  93. Thanathibodee, T., Calvet, N., Bae, J., Muzerolle, J., & Hernández, R. F. 2019, ApJ, 885, 94 [NASA ADS] [CrossRef] [Google Scholar]
  94. van den Ancker, M. E., de Winter, D., & Tjin A, Djie 1998, A&A, 330, 145 [NASA ADS] [Google Scholar]
  95. van der Marel,N., Cazzoletti, P., Pinilla, P., & Garufi, A. 2016, ApJ, 832, 178 [NASA ADS] [CrossRef] [Google Scholar]
  96. van der Marel,N., Dong, R., di Francesco, J., Williams, J. P., & Tobin, J. 2019, ApJ, 872, 112 [NASA ADS] [CrossRef] [Google Scholar]
  97. van Leeuwen, F. 2007, A&A, 474, 653 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  98. Wagner, K., Follete, K. B., Close, L. M., et al. 2018, ApJ, 863, L8 [NASA ADS] [CrossRef] [Google Scholar]
  99. Wolff, S. G., Perrin, M., Millar-Blanchaer, M. A., et al. 2016, ApJ, 818, L15 [NASA ADS] [CrossRef] [Google Scholar]
  100. Zhu, Z. 2015a, Nature, 527, 310 [NASA ADS] [CrossRef] [Google Scholar]
  101. Zhu, Z. 2015b, ApJ, 799, 16 [NASA ADS] [CrossRef] [Google Scholar]
  102. Zurlo, A., Vigan, A., Mesa, D., et al. 2014, A&A, 572, A85 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  103. Zurlo, A., Vigan, A., Galicher, R., et al. 2016, A&A, 587, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

All Tables

Table 1

Objects included in the sample with their main characteristics.

In the text
Table 2

Summary table of the observations of the survey.

In the text

All Figures

thumbnail Fig. 1

ASDI image of the system HD 142527. The stellar accreting companion is visible in the East. The image shows the very close vicinity of the stellar companion.
In the text
thumbnail Fig. 2

ASDI image of the system HD 98800 BaBb. The system is a quadruple: two spectroscopic binaries at the center of the detectorand two stellar companions (one accreting) are visible in the north, which are resolved for the first time in the visible.
In the text
thumbnail Fig. 3

Contrast plot for the object AK Sco. In a black solid line, the profile of the PSF is shown, blue represents the Hα continuum filter after being processed with PCA and ADI, and red is the ASDI contrast. The vertical dotted lines represent the extension of the spirals (Janson et al. 2016).
In the text
thumbnail Fig. 4

Same as Fig. 3, but for the object V4046 Sgr. The vertical dotted lines represent the locations of the rings detected by Rapson et al. (2015) and Avenhaus et al. (2018).
In the text
thumbnail Fig. 5

Same as Fig. 3, but for the object HD 169142. The vertical dotted lines represent the location of the gaps detected by Honda et al. (2012) and Pohl et al. (2017).
In the text
thumbnail Fig. 6

Same as Fig. 3, but for the object HD 97048. The vertical dotted lines represent the location of the gaps detected by Ginski et al. (2016).
In the text
thumbnail Fig. 7

Same as Fig. 3, but for the object HD 100546. The dotted vertical line is located at the position of the inner gap (Quanz et al. 2015).
In the text
thumbnail Fig. 8

Same as Fig. 3, but for the object HD 142527. The red point represents the detected stellar companion.
In the text
thumbnail Fig. 9

Same as Fig. 3, but for the object HD 141569A. The vertical dotted lines represent the location of the ringlets detected by Perrot et al. (2016).
In the text
thumbnail Fig. 10

Same as Fig. 3, but for the object HD 135344B. The two dotted lines represent the extension of the spiral arms detected by van der Marel et al. (2016) and Hammer et al. (2019).
In the text
thumbnail Fig. 11

Same as Fig. 3, but for the object UX TauA. The vertical dotted line represents the location of the gap (Espaillat et al. 2007).
In the text
thumbnail Fig. 12

Same as Fig. 3, but for the object PDS 66. The vertical dotted line represents the location of the inner cavity (Gräfe & Wolf 2013; Wolff et al. 2016).
In the text
thumbnail Fig. 13

Same as Fig. 3, but for the object HD 100453. The vertical dotted line represents the inner part of the spirals detected by Dong & Fung (2017).
In the text
thumbnail Fig. 14

Apparent Hα line flux limits for all the targets of the sample versus angular separation (left) and physical separation (right). The differences between our predictions and other cases from the literature are primarily related to the methods used to extrapolate the accretion luminosity from the Hα line flux; as with SPHERE, the line width cannot be measured.
In the text
thumbnail Fig. 15

Accretion luminosity limits for all the targets of the sample versus angular separation (left) and physical separation (right).
In the text
thumbnail Fig. 16

Massaccretion rate limits (for a 5 MJup mass planet) for all the targets of the sample versus angular separation (left) and physical separation (right).
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.