# Detection and Characterization of Oscillating Red Giants: First Results from the TESS Satellite

Victor Silva Aguirre, Dennis Stello, Amalie Stokholm, Jakob R. Mosumgaard, Warrick H. Ball, Sarbani Basu, Diego Bossini, Lisa Bugnet, Derek Buzasi, Tiago L. Campante, et al.

► To cite this version:

Victor Silva Aguirre, Dennis Stello, Amalie Stokholm, Jakob R. Mosumgaard, Warrick H. Ball, et al. Detection and Characterization of Oscillating Red Giants: First Results from the TESS Satellite. The Astrophysical Journal Letters, 2020, 889 (2), pp.L34. 10.3847/2041-8213/ab6443. hal-02475599

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# Detection and Characterization of Oscillating Red Giants: First Results from the TESS Satellite

Víctor Silva Aguirre¹, Dennis Stello¹,²,³, Amalie Stokholm¹, Jakob R. Mosumgaard¹, Warrick H. Ball¹,⁴,⁵, Sarbani Basu⁵, Diego Bossini⁶, Lisa Bugnet⁷,⁸, Derek Buzasi⁹, Tiago L. Campante⁶,¹⁰, Lindsey Carboneau⁹, William J. Chaplin¹,⁴, Enrico Corsaro¹¹, Guy R. Davies¹,⁴, Yvonne Elsworth¹,⁴, Rafael A. García⁷,⁸, Patrick Gaulme¹²,¹³, Oliver J. Hall¹,⁴, Rasmus Handberg¹, Marc Hon², Thomas Kallinger¹⁴, Liu Kang¹⁵, Mikkel N. Lund¹, Savita Mathur¹⁶,¹⁷, Alexey Mints¹⁸, Benoit Mosser¹⁹, Zeynep Çelik Orhan²⁰, Thaíse S. Rodrigues²¹, Mathieu Vrard²,²², Mutlu Yıldız²⁰, Joel C. Zinn²,²²,²³, Sibel Örtel²⁰, Paul G. Beck¹⁶,¹⁷,²⁴,²⁵, Keaton J. Bell²⁵,⁶², Zhao Guo²⁶, Chen Jiang²⁷, James S. Kuszlewicz¹², Charles A. Kuehn²⁸, Tanda Li¹,³,⁴, Mia S. Lundkvist¹, Marc Pinsonneault²², Jamie Tayar²⁹,⁶³, Margarida S. Cunha⁴,⁶, Saskia Hekker¹,¹², Daniel Huber²⁹, Andrea Miglio¹,⁴, Mario J. P. F. G. Monteiro⁶,¹⁰, Ditte Slumstrup¹,³⁰, Mark L. Winther¹, George Angelou³¹, Othman Benomar³²,³³, Attila Bódi³⁴,³⁵, Bruno L. De Moura³⁶, Sébastien Deheuvels³⁷, Aliz Derekas³⁴,³⁸,³⁹, Maria Pia Di Mauro⁴⁰, Marc-Antoine Dupret⁴¹, Antonio Jiménez¹⁶,¹⁷, Yveline Lebreton¹⁹,⁴², Jaymie Matthews⁴³, Nicolas Nardetto⁴⁴, Jose D. do Nascimento, Jr.⁴⁵,⁴⁶, Filipe Pereira⁶,¹⁰, Luisa F. Rodríguez Díaz¹, Aldo M. Serenelli⁴⁷,⁴⁸, Emanuele Spitoni¹, Edita Stonkute⁴⁹, Juan Carlos Suárez⁵⁰,⁵¹, Robert Szabó³⁴,³⁵, Vincent Van Eylen⁵², Rita Ventura¹¹, Kuldip Verma¹, Achim Weiss³¹, Tao Wu⁵³,⁵⁴,⁵⁵, Thomas Barclay⁵⁶,⁵⁷, Jørgen Christensen-Dalsgaard¹, Jon M. Jenkins⁵⁸, Hans Kjeldsen¹, George R. Ricker⁵⁹, Sara Seager⁵⁹,⁶⁰,⁶¹, and Roland Vanderspek⁵⁹

¹ Stellar Astrophysics Centre (SAC), Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus C, Denmark victor@phys.au.dk

² School of Physics, The University of New South Wales, Sydney, NSW 2052, Australia
³ Sydney Institute for Astronomy (SIfA), School of Physics, University of Sydney, Sydney, NSW 2006, Australia
⁴ School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, UK
⁵ Department of Astronomy, Yale University, New Haven, CT 06520, USA
⁶ Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal
⁷ IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
⁸ AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, F-91191 Gif-sur-Yvette, France
⁹ Dept. of Chemistry and Physics, Florida Gulf Coast University, 10501 FGCU Boulevard South, Fort Myers, FL 33965, USA
¹⁰ Departamento de Física e Astronomia, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
¹¹ INAF—Osservatorio Astrofísico di Catania, Via S. Sofia, 78, I-95123 Catania, Italy
¹² Max Planck Institute for Solar System Research, Justus-von-Liebig Weg 3, D-37077 Göttingen, Germany
¹³ Department of Astronomy, New Mexico State University, P.O. Box 30001, MSC 4500, Las Cruces, NM 88003-8001, USA
¹⁴ Institut für Astrophysik, Universität Wien, Türkenschanzstrasse 17, A-1180 Vienna, Austria
¹⁵ Department of Astronomy, Beijing Normal University, 100875 Beijing, People's Republic of China
¹⁶ Instituto de Astrofísica de Canarias, E-38200 La Laguna, Tenerife, Spain
¹⁷ Departamento de Astrofísica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain
¹⁸ Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany
¹⁹ LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-92195 Meudon, France
²⁰ Department of Astronomy and Space Sciences, Science Faculty, Ege University, 35100, Bornova, İzmir, Turkey
²¹ Osservatorio Astronomico di Padova—INAF, Vicolo dell'Osservatorio 5, I-35122 Padova, Italy
²² Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA
²³ Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
²⁴ Institute of Physics, Karl-Franzens University of Graz, NAWI Graz, Universitätsplatz 5/II, A-8010 Graz, Austria
²⁵ DIRAC Institute, Department of Astronomy, University of Washington, Seattle, WA 98195-1580, USA
²⁶ Center for Exoplanets and Habitable Worlds, Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA16802, USA
²⁷ School of Physics and Astronomy, Sun Yat-Sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, People's Republic of China
²⁸ Department of Physics and Astronomy, University of Northern Colorado, Greeley, CO 80639, USA
²⁹ Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA
³⁰ European Southern Observatory, Alonso de Córdova 3107, Vitacura, Santiago, Chile
³¹ Max-Planck-Institut für Astrophysics, Karl Schwarzschild Strasse 1, D-85748, Garching, Germany
³² Solar Science Observatory, NAOJ, Mitaka, Japan
³³ Center for Space Science, New York University Abu Dhabi, Abu Dhabi, UAE
³⁴ Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, H-1121 Budapest, Konkoly Thege M. út 15-17, Hungary
³⁵ MTA CSFK Lendület Near-Field Cosmology Research Group, Hungary
³⁶ Instituto Federal do R.G. do Norte - IFRN, Brazil
³⁷ IRAP, Université de Toulouse, CNRS, CNES, UPS, Toulouse, France
³⁸ ELTE Eötvös Loránd University, Gothard Astrophysical Observatory, Szombathely, Hungary
³⁹ MTA-ELTE Exoplanet Research Group, 9700 Szombathely, Szent Imre h.u. 112, Hungary
⁴⁰ INAF-IAPS, Istituto di Astrofisica e Planetologia Spaziali, Via del Fosso del Cavaliere 100, I-00133 Roma, Italy
⁴¹ STAR Institute, University of Liège, 19C Allée du 6 Août, B-4000 Liège, Belgium
⁴² Univ Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251, F-35000 Rennes, France
⁴³ Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
⁴⁴ Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, Laboratoire Lagrange, France
⁴⁵ Universidade Federal do Rio Grande do Norte, UFRN, Departamento de Física, 59078-970, Natal, RN, Brazil
⁴⁶ Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
⁴⁷ Instituto de Ciências del Espacio (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193 Cerdanyola del Valles, Spain
⁴⁸ Institut d'Estudis Espacials de Catalunya (IEEC), Gran Capita 4, E-08034, Barcelona, Spain
## Abstract

Since the onset of the “space revolution” of high-precision high-cadence photometry, asteroseismology has been demonstrated as a powerful tool for informing Galactic archeology investigations. The launch of the NASA *Transiting Exoplanet Survey Satellite (TESS)* mission has enabled seismic-based inferences to go full sky—providing a clear advantage for large ensemble studies of the different Milky Way components. Here we demonstrate its potential for investigating the Galaxy by carrying out the first asteroseismic ensemble study of red giant stars observed by *TESS*. We use a sample of 25 stars for which we measure their global asteroseimic observables and estimate their fundamental stellar properties, such as radius, mass, and age. Significant improvements are seen in the uncertainties of our estimates when combining seismic observables from *TESS* with astrometric measurements from the *Gaia* mission compared to when the seismology and astrometry are applied separately. Specifically, when combined we show that stellar radii can be determined to a precision of a few percent, masses to 5%–10%, and ages to the 20% level. This is comparable to the precision typically obtained using end-of-mission *Kepler* data.

*Unified Astronomy Thesaurus concepts: Asteroseismology (73); Fundamental parameters of stars (555); Red giant stars (1372)*

*Supporting material: machine-readable table*

## 1. Introduction

Asteroseismology of red giant stars has been one of the major successes of the *CoRoT* and *Kepler* missions. The unambiguous detection of non-radial oscillations has fundamentally widened our understanding of the inner workings of red giants, including the conditions in their core (e.g., Bedding et al. 2011). The observed frequency spectra have allowed the determination of the physical properties of thousands of red giants to an unprecedented level of precision (e.g., Miglio et al. 2013), paving the way for the emergence of asteroseismology as a powerful tool for Milky Way studies and Galactic archeology (e.g., Miglio et al. 2009; Casagrande et al. 2016; Anders et al. 2017; Silva Aguirre et al. 2018; Sharma et al. 2019). The *Transiting Exoplanet Survey Satellite (TESS)* Ricker et al. 2015) is on the path of continuing this legacy with its all-sky survey that is expected to increase the number of detected oscillating red giants by an order of magnitude compared to the tens of thousands reported by its predecessors *CoRoT* and *Kepler*.

In the nominal *TESS* mission, the ecliptic northern and southern hemispheres are each observed during thirteen 27 day-long sectors, and most (92%) of the surveyed sky will be monitored for just 1–2 sectors. Except for the 20,000 targets pre-selected in each sector for 2 minutes cadence observations, all stars are observed as part of the full-frame images obtained in 30 minutes cadence, similar to the long-cadence sampling of the *Kepler* satellite. The length of the observations sets the

lower limit on the oscillation frequencies one can resolve, and the sampling sets the upper frequency limit. We know from previous *Kepler* observations that one month of 30 minutes cadence data should be well suited to detect oscillations in the low red-giant branch and sufficient to measure the global oscillation properties characterizing the frequency spectrum, in particular, its frequency of maximum power, $\nu_{max}$, and the frequency separation between overtone modes, $\Delta\nu$ (Bedding et al. 2010). These in turn can be used in combination with complementary data such as the effective temperature, $T_{eff}$, the relative iron abundance, [Fe/H], and parallax, to obtain precise stellar properties (including ages) when applying asteroseismic-based grid modeling approaches (see, e.g., Rodrigues et al. 2017; Pinsonneault et al. 2018).

Due to the large sky coverage, approximately 97% of asteroseismic detections in red giants from the *TESS* nominal mission data are expected to come from stars observed for only one or two sectors.<fnref n="64" /> Here we set out to explore the capability of *TESS* to detect the oscillations in giants ranging from the base of the red giant branch to the red clump, determine their stellar properties, and use that to assess the prospects for Galactic archeology studies using one to two sectors of *TESS* data.

## 2. Target Selection

Our goal is to have a representative sample of giants including the types of stars in which we can expect to detect

<footnote n="49"><sup>49</sup> Institute of Theoretical Physics and Astronomy, Vilnius University, Saulėtekio al. 3, 10257 Vilnius, Lithuania</footnote>

<footnote n="50"><sup>50</sup> Dept. Física Teórica y del Cosmos, Universidad de Granada, E-18006 Granada, Spain</footnote>

<footnote n="51"><sup>51</sup> Instituto de Astrofísicaicaśica de Andalucía (CSIC), Glorieta de la Astronomía s/n, E-18008 Granada, Spain</footnote>

<footnote n="52"><sup>52</sup> Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking RH5 6NT, UK</footnote>

<footnote n="53"><sup>53</sup> Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming, 650216, People's Republic of China</footnote>

<footnote n="54"><sup>54</sup> Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming, 650216, People's Republic of China</footnote>

<footnote n="55"><sup>55</sup> Center for Astronomical Mega-Science, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing, 100012, People's Republic of China</footnote>

<footnote n="56"><sup>56</sup> NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA</footnote>

<footnote n="57"><sup>57</sup> University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA</footnote>

<footnote n="58"><sup>58</sup> NASA Ames Research Center, Moffett Field, CA 94035, USA</footnote>

<footnote n="59"><sup>59</sup> Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA</footnote>

<footnote n="60"><sup>60</sup> Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA</footnote>

<footnote n="61"><sup>61</sup> Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA</footnote>

<footnote>Received 2019 November 13; revised 2019 December 9; accepted 2019 December 14; published 2020 January 29</footnote>

<footnote n="62">⁶² NSF Astronomy and Astrophysics Postdoctoral Fellow and DIRAC Fellow.</footnote>

<footnote n="63">⁶³ Hubble Fellow.</footnote>

<footnote n="64">⁶⁴ Based on a preliminary simulation of the full *TESS* sky (TESS GI Proposal No. G011188).</footnote>
oscillations from one sector 30 minutes cadence TESS data. We selected red-giant candidates observed during sectors 1 and/or 2 that were deemed viable for asteroseismic detections according to their predicted properties based on the Hipparcos catalog (Van Leeuwen 2007). We first estimated the stellar T<sub>eff</sub> and luminosity using B-V color, V-band, and Hipparcos parallax, and the color-temperature and bolometric correction relations of Flower (1996). We then obtained a prediction of ν<sub>max</sub> (∝T<sub>eff</sub><sup>3.5</sup> M/L; solar scaled, e.g., Yu et al. 2018) for each star assuming a mass of 1.2 M<sub>☉</sub>, which is representative of a typical red giant as observed by Kepler (and unlikely to be more than a factor of two from the true value of each star, e.g., Yu et al. 2018). We note that one of our targets (TIC 129649472) is a known exoplanet host star recently analyzed by Campante et al. (2019).

To ensure that the selected targets were amenable to asteroseismic detection from one sector of 30 minutes cadence data, we required that they would have an expected $v_{max}$ in the range 30–220 µHz and $T_{eff}$ in the typical range of red giants of 4500–5200 K. In addition, we applied a narrower $T_{eff}$ range of 4500–4700 K for the stars with $v_{max}$ between 30 and 70 µHz, to avoid having red clump stars dominating our sample. The resulting sample of stars span evolutionary phases from the base of the red giant branch to the red giant branch bump, as well as some clump stars.

From this sample, we selected the 25 brightest targets for light curve extraction and asteroseismic analysis. The faintest stars in our sample turned out to be ~6–7th magnitude in the V-band (see Table 1). Under the assumption that the photometric performance of TESS is similar to Kepler's, apart from its smaller aperture, this magnitude limit is equivalent to 11–12th magnitude for Kepler. Because single-quarter observations from Kepler's second life, K2, showed no oscillation detection bias for red giants brighter than around twelfth magnitude (Stello et al. 2017), we would expect to detect oscillations in all 25 giants with TESS.

Figure 1 illustrates the location of the selected stars in the Hertzsprung–Russell (HR) diagram and the applied selection criteria. We confirmed that the stars were in sectors 1–2 using the Web TESS Viewing Tool (WTV).<sup>65</sup>

### 3. Data Processing and Asteroseismic Analysis

The stars selected were included in an early release of processed data from the TASOC pipeline.<sup>66</sup> The calibrated full-frame images were produced by the TESS Science Processing Operations Center (SPOC) at NASA Ames Research Center (Jenkins et al. 2016), and processed by combining the methodology from the K2P2 pipeline (Lund et al. 2015) for extracting the flux from target pixel data with the KASOC filter for systematics correction (Handberg & Lund 2014). The resulting TASOC light curves were high-pass filtered using a filter width of 4 days, corresponding to a cut-off frequency of approximately 3 µHz, and 4σ outliers were removed. Finally, we used linear interpolation to fill gaps that lasted up to three consecutive cadences and derived the Fourier transforms (power frequency spectra) of each light curve.

The light curves for the seven stars observed in both sectors were merged. To follow the approach anticipated for the

millions of light curves from the TESS full-frame images in the future, we first applied the neural-network-based detection algorithm by Hon et al. (2018) resulting in detection of oscillations in the power spectra of all stars except one. The non-detection (TIC 204314449) is listed as an A2 dwarf and a “Visual Double” in the University of Michigan Catalog of two-dimensional spectral types for the HD stars (Houk 1994), and hence possibly too hot to show solar-like oscillations, or potentially contaminated. For the current test case, the number of stars was small enough that we visually checked the results, which confirmed all detections and the non-detection. The power spectra of a representative sample of the stars are shown in Figure 2, showing clear oscillation excess power and the frequency pattern required to measure both $v_{max}$ and $\Delta v$.

The neural network also supplies a rough estimate for $v_{max}$, which we provided as a prior to 13 independent groups analyzing the power spectra to extract high-precision values of both $v_{max}$, $\Delta v$, and their respective uncertainties using their preferred method. These methods have been thoroughly tested and described in the literature (see, e.g., Gaulme et al. 2009; Huber et al. 2009; Hekker et al. 2010; Mathur et al. 2010; Mosser et al. 2011; Kallinger et al. 2012; Corsaro & De Ridder 2014; Davies et al. 2016; Campante et al. 2017; Zinn et al. 2019).

From the 13 independent determinations of the global asteroseismic parameters we adopted as central reference value for $\Delta v$ and $v_{max}$ the results from the pipeline by Gaulme et al. (2009), as this method was on average closest to the ensemble mean after applying a 2σ outlier rejection. Uncertainties in the global asteroseismic parameters obtained by the selected pipeline are at the 1.9% and 2.4% level for $\Delta v$ and $v_{max}$, respectively. These uncertainties are of comparable magnitude to those obtained from a single campaign with the K2 mission (see the Appendix in Stello et al. 2017) and about twice as large as those extracted from 50 days of Kepler observations (see Figures 3 and 4 in Hekker et al. 2012). We report the central values and statistical uncertainties in $\Delta v$ and $v_{max}$ from the selected pipeline for all targets in Table 1.

For each star, we take into account the scatter across the different methods by adding in quadrature the standard deviation among the central values retained after the 2σ outlier rejection procedure to the formal uncertainty reported by the selected reference method. This consolidation process yields median uncertainties of 3.9% in $\Delta v$ and 2.6% in $v_{max}$, where the individual contribution arising from this systematic component to the total uncertainty is listed in Table 1. We note that we could decrease the level of uncertainties resulting from our “blind” statistical consolidation approach by for example checking the $\Delta v$ and $v_{max}$ results against the power spectra and/or échelle diagrams (see Figure 5 in Stello et al. 2011). However, we want to draw a realistic picture of the uncertainties one can expect when dealing with large ensembles of stars (as expected from TESS) where detailed “boutique” analysis/checking on a star-by-star basis is not practically feasible. Hence, our quoted uncertainties are conservative, but representative for analysis of TESS red giants where several pipelines are involved.

### 4. Derived Stellar Properties

We have determined stellar properties for a subsample of 17 stars that had spectroscopic measurements of effective temperature and chemical composition available in the literature. As

<footnote n="65"><sup>65</sup> https://heasarc.gsfc.nasa.gov/cgi-bin/tess/webtess/wtv.py</footnote>

<footnote n="66"><sup>66</sup>T'DA Data Release Notes—Data Release 3 for TESS Sectors 1+2 (https://doi.org/10.5281/zenodo.2510028).</footnote>
<table><caption>**Table 1**

Measured and Derived Stellar Properties of Our Targets</caption><thead><tr><th>TIC</th><th>HIP</th><th>ν<sub>max</sub><br>(μHz)</th><th>Δν<br>(μHz)</th><th>V<br>(mag)</th><th>T<sub>eff</sub><br>(K)</th><th>[Fe/H]<br>(dex)</th><th>R<br>(R<sub>☉</sub>)</th><th>M<br>(M<sub>☉</sub>)</th><th>Age<br>(Gyr)</th><th>Atmospheric Properties</th></tr></thead><tbody><tr><td>13097379</td><td>114842</td><td>59.10 ± 1.50 ± 1.01</td><td>6.02 ± 0.03 ± 0.24</td><td>6.646 ± 0.010</td><td>4634 ± 80</td><td>0.04 ± 0.08</td><td>8.49 ± 0.28 ± 0.17</td><td>1.22 ± 0.08 ± 0.06</td><td>6.10 ± 1.06 ± 0.97</td><td>Luck (2015)</td></tr><tr><td>38574220</td><td>19805</td><td>29.40 ± 0.90 ± 0.72</td><td>4.06 ± 0.20 ± 0.26</td><td>5.577 ± 0.009</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>38828538</td><td>21253</td><td>189.90 ± 1.60 ± 0.42</td><td>14.90 ± 0.10 ± 0.13</td><td>5.896 ± 0.009</td><td>4828 ± 80</td><td>0.11 ± 0.08</td><td>4.66 ± 0.10 ± 0.06</td><td>1.21 ± 0.05 ± 0.03</td><td>6.20 ± 0.50 ± 1.02</td><td>Alves et al. (2015)</td></tr><tr><td>39082723</td><td>4293</td><td>49.30 ± 2.10 ± 1.99</td><td>5.20 ± 0.10 ± 0.06</td><td>5.574 ± 0.009</td><td>4706 ± 80</td><td>-0.05 ± 0.08</td><td>9.30 ± 0.27 ± 0.17</td><td>1.19 ± 0.09 ± 0.07</td><td>5.90 ± 1.20 ± 1.37</td><td>Alves et al. (2015)</td></tr><tr><td>47424090</td><td>112612</td><td>28.30 ± 1.80 ± 1.90</td><td>3.40 ± 0.10 ± 0.29</td><td>6.930 ± 0.010</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>70797228</td><td>655</td><td>31.80 ± 1.50 ± 0.75</td><td>4.37 ± 0.20 ± 0.37</td><td>5.787 ± 0.009</td><td>4750 ± 80</td><td>0.12 ± 0.08</td><td>11.27 ± 0.61 ± 0.47</td><td>1.19 ± 0.13 ± 0.11</td><td>6.80 ± 2.20 ± 2.20</td><td>Jones et al. (2011)</td></tr><tr><td>77116701</td><td>103071</td><td>48.30 ± 7.60 ± 29.85</td><td>5.64 ± 0.20 ± 3.37</td><td>8.568 ± 0.018</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>111750740</td><td>113148</td><td>142.60 ± 2.70 ± 1.11</td><td>11.80 ± 0.10 ± 0.23</td><td>5.658 ± 0.009</td><td>4688 ± 80</td><td>0.16 ± 0.08</td><td>5.11 ± 0.16 ± 0.09</td><td>1.06 ± 0.07 ± 0.05</td><td>10.80 ± 1.78 ± 1.71</td><td>Wittenmyer et al. (2016)</td></tr><tr><td>115011683</td><td>103836</td><td>58.80 ± 1.20 ± 0.97</td><td>6.10 ± 0.10 ± 0.01</td><td>6.057 ± 0.010</td><td>4590 ± 80</td><td>-0.13 ± 0.08</td><td>7.92 ± 0.21 ± 0.22</td><td>1.04 ± 0.07 ± 0.07</td><td>9.90 ± 1.63 ± 2.01</td><td>Wittenmyer et al. (2016)</td></tr><tr><td>129649472</td><td>105854</td><td>31.80 ± 1.20 ± 1.39</td><td>4.20 ± 0.20 ± 0.11</td><td>5.755 ± 0.009</td><td>4748 ± 80</td><td>0.28 ± 0.08</td><td>10.85 ± 0.60 ± 0.24</td><td>1.13 ± 0.12 ± 0.06</td><td>8.50 ± 2.76 ± 1.87</td><td>Jofré et al. (2015)</td></tr><tr><td>139756492</td><td>106566</td><td>27.60 ± 0.90 ± 0.35</td><td>4.16 ± 0.20 ± 0.65</td><td>6.819 ± 0.010</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>141280255</td><td>25918</td><td>150.40 ± 1.00 ± 0.57</td><td>12.52 ± 0.02 ± 0.10</td><td>5.307 ± 0.009</td><td>4630 ± 80</td><td>0.33 ± 0.08</td><td>4.98 ± 0.12 ± 0.05</td><td>1.07 ± 0.06 ± 0.03</td><td>11.70 ± 2.62 ± 2.39</td><td>Meléndez et al. (2008)</td></tr><tr><td>144335025</td><td>117075</td><td>68.50 ± 1.60 ± 0.64</td><td>7.35 ± 0.20 ± 0.39</td><td>6.194 ± 0.010</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>149347992</td><td>26190</td><td>165.80 ± 4.10 ± 16.12</td><td>11.10 ± 0.40 ± 0.60</td><td>6.405 ± 0.010</td><td>5132 ± 80</td><td>-0.17 ± 0.08</td><td>7.20 ± 0.38 ± 0.20</td><td>2.17 ± 0.22 ± 0.06</td><td>0.80 ± 0.30 ± 0.10</td><td>Randich et al. (1999)</td></tr><tr><td>155940286</td><td>1766</td><td>73.20 ± 1.30 ± 0.32</td><td>7.40 ± 0.02 ± 0.11</td><td>6.810 ± 0.010</td><td>4630 ± 80</td><td>0.03 ± 0.08</td><td>6.95 ± 0.18 ± 0.14</td><td>1.01 ± 0.06 ± 0.04</td><td>12.00 ± 1.78 ± 36.07</td><td>Wittenmyer et al. (2016)</td></tr><tr><td>175375523</td><td>114775</td><td>60.00 ± 1.10 ± 0.30</td><td>5.80 ± 0.10 ± 0.14</td><td>5.899 ± 0.009</td><td>4660 ± 80</td><td>0.26 ± 0.08</td><td>9.00 ± 0.30 ± 0.58</td><td>1.38 ± 0.09 ± 0.21</td><td>4.50 ± 0.76 ± 1.35</td><td>Jones et al. (2011)</td></tr><tr><td>183537408</td><td>117659</td><td>57.90 ± 1.10 ± 0.80</td><td>6.20 ± 0.20 ± 0.42</td><td>6.781 ± 0.010</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>204313960</td><td>113801</td><td>106.00 ± 3.30 ± 1.47</td><td>9.40 ± 0.50 ± 0.43</td><td>6.083 ± 0.010</td><td>4897 ± 80</td><td>-0.20 ± 0.08</td><td>6.50 ± 0.18 ± 0.13</td><td>1.32 ± 0.07 ± 0.05</td><td>3.80 ± 0.57 ± 0.57</td><td>Randich et al. (1999)</td></tr><tr><td>220517490</td><td>12871</td><td>117.30 ± 1.20 ± 0.60</td><td>10.87 ± 0.02 ± 0.15</td><td>5.846 ± 0.009</td><td>4961 ± 80</td><td>-0.26 ± 0.08</td><td>5.61 ± 0.11 ± 0.10</td><td>1.10 ± 0.04 ± 0.04</td><td>6.10 ± 0.50 ± 1.18</td><td>Alves et al. (2015)</td></tr><tr><td>237914586</td><td>17440</td><td>47.00 ± 1.40 ± 1.33</td><td>5.74 ± 0.20 ± 0.08</td><td>3.959 ± 0.009</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>270245797</td><td>109584</td><td>72.20 ± 1.70 ± 0.64</td><td>6.60 ± 0.10 ± 0.27</td><td>6.239 ± 0.009</td><td>4824 ± 80</td><td>-0.10 ± 0.08</td><td>8.73 ± 0.23 ± 0.39</td><td>1.60 ± 0.09 ± 0.13</td><td>2.10 ± 0.22 ± 0.64</td><td>Alves et al. (2015)</td></tr><tr><td>281597433</td><td>2789</td><td>73.30 ± 1.00 ± 1.37</td><td>7.20 ± 0.03 ± 0.19</td><td>6.163 ± 0.010</td><td>4700 ± 80</td><td>-0.41 ± 0.08</td><td>6.77 ± 0.15 ± 0.24</td><td>0.95 ± 0.05 ± 0.08</td><td>11.00 ± 1.35 ± 2.73</td><td>Randich et al. (1999)</td></tr><tr><td>439399563</td><td>343</td><td>44.30 ± 1.40 ± 0.66</td><td>4.54 ± 0.06 ± 0.11</td><td>5.892 ± 0.009</td><td>4778 ± 80</td><td>0.11 ± 0.08</td><td>10.69 ± 0.34 ± 0.40</td><td>1.44 ± 0.11 ± 0.14</td><td>3.50 ± 0.71 ± 0.78</td><td>da Silva et al. (2015)</td></tr><tr><td>441387330</td><td>102014</td><td>46.60 ± 0.80 ± 0.86</td><td>5.25 ± 0.10 ± 0.25</td><td>5.592 ± 0.009</td><td>4710 ± 80</td><td>-0.02 ± 0.08</td><td>10.18 ± 0.34 ± 0.68</td><td>1.40 ± 0.09 ± 0.20</td><td>3.40 ± 0.57 ± 1.19</td><td>Jones et al. (2011)</td></tr></tbody></table>

Note. Observed V-magnitudes are extracted from the Tycho-2 catalog. The global asteroseismic quantities and stellar properties include a statistical and systematic component derived as described in Sections 3 and 4, respectively. We report them here as *value* ± *σ*<sub>sta</sub> ± *σ*<sub>sys</sub>. The last column gives the reference from which we retrieved the central values of *T*<sub>eff</sub> and [Fe/H] used for the grid-based modeling. Their uncertainties have been homogenized to 80 K and 0.08 dex, respectively (see Section 4).

(This table is available in its entirety in machine-readable form.)
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Asteroseismic HR diagram showing (predicted) $v_{max}$ instead of luminosity. Red dots show the selected targets inside the black selection box. For reference, the Sun is shown as well as all *Hipparcos* stars brighter than sixth magnitude (gray dots). Solar metallicity MESA tracks from Stello et al. (2013) are shown to guide the eye with masses in solar units indicated (pre- and post- helium core-ignition phases are shown separately).</figcaption></figure>

one of our goals is to follow the same analysis procedure expected for large ensembles of stars, we assumed fixed uncertainties in $T_{eff}$ and [Fe/H] of 80 K and 0.08 dex, which are at the level of those provided by current large-scale spectroscopic surveys. To extract the physical properties of our sample, the atmospheric information was complemented with the asteroseismic scaling relations:

$$ \left( \frac{\Delta \nu}{\Delta \nu_{\odot}} \right)^2 \approx \frac{\bar{\rho}}{\bar{\rho}_{\odot}} \qquad (1) $$

$$ \left( \frac{\nu_{\max}}{\nu_{\max, \odot}} \right) \approx \frac{M}{M_{\odot}} \left( \frac{R}{R_{\odot}} \right)^{-2} \left( \frac{T_{\text{eff}}}{T_{\text{eff}, \odot}} \right)^{1/2}, \qquad (2) $$

where we adopted $\Delta\nu_{\odot} = 135.5$ (µHz) and $\nu_{\max, \odot} = 3140$ (µHz) as obtained by our reference pipeline from the analysis of solar data.

Seven teams independently applied grid-based modeling pipelines based on stellar evolution models or isochrones to determine the main physical properties of the targets (see Basu et al. 2012; Silva Aguirre et al. 2015; Rodrigues et al. 2017; Mints & Hekker 2018; Yıldız et al. 2019, and references therein). When matching the models to the atmospheric properties and the global asteroseismic parameters $\Delta\nu$ and $\nu_{max}$ the pipelines yielded median uncertainties of ~6% in radius, ~14% in mass, and ~50% in age. These statistical uncertainties are of the same magnitude to those obtained with the K2 mission (Sharma et al. 2019), as expected from the similar resulting errors in the global seismic parameters described in Section 3, and about a factor of two larger than what can be achieved with the full duration of the *Kepler* observations (Pinsonneault et al. 2018).

In addition to the asteroseismic information, five of the pipelines can include parallaxes from Gaia DR2 (Gaia Collaboration et al. 2018) coupled with Tycho-2 (Høg et al. 2000) observed V-magnitudes in their fitting algorithm to

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Power spectra sample of our targets representative of the $v_{max}$ range that they cover from around the red clump (top) to the low luminosity red giant branch (bottom). Left panels: spectra shown in log-log space (smoothed in red) showing the location of the oscillation power excess, $v_{max}$, indicated by red arrows on top of a frequency-dependent granulation background and flat white noise component. Right panels: close-up of spectra showing locations of the roughly equally spaced radial modes (using red equally spaced vertical lines to guide the eye) and their average separation, $\Delta\nu$ (red horizontal arrows). In the three bottom rows multiple dipole ($l=1$) mixed modes are resolved in between consecutive radial modes as indicated by the black brackets.</figcaption></figure>

further constrain the stellar properties. As a consequence of having the additional constraint on stellar radius from the astrometry, the resulting uncertainties decrease to a level of ~3% in radius, ~6% in mass, and ~20% in age. This level of precision resembles that obtained with the use of the full length of asteroseismic observations from the nominal *Kepler* mission, and emphasizes the potential of TESS for Galactic studies using
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Comparison of stellar radii obtained with BASTA when fitting different combinations of input parameters: Gaia DR2 parallax and V-band magnitude (ω), global asteroseismic parameters (Δν, νmax), and all combined (ω, Δν, νmax). Effective temperature and composition are also fitted in all cases. See the text for details.</figcaption></figure>

red giants given its larger sky coverage, simple and reproducible selection function, and one order of magnitude higher expected yield of asteroseismic detections than any other previous mission.

To illustrate the differences in the obtained stellar properties arising from the selection of fitted observables, Figure 3 shows the stellar radius obtained with one of the pipelines (BASTA; Silva Aguirre et al. 2015) when fitting different combinations of input parameters. The figure uses as the reference value the case when, in addition to the atmospheric properties, only the Gaia DR2 parallax and observed V-band magnitude are included in the fit. For the majority of the targets the results are consistent across the three sets within their formal statistical uncertainties. A summary of the measured and derived stellar properties for our targets can be found in Table 1, where we have listed the central values and statistical uncertainties obtained with the BASTA pipeline, and determined the systematic contribution as the standard deviation across the results reported by all pipelines.

Two targets (TIC 141280255 and TIC 149347992) present a larger disagreement between the radii obtained with parallax and the seismic set (Δν, νmax). We investigated whether these discrepancies were due to the quality of the astrometric data by computing the re-normalized unit weight error (RUWE)<fnref n="67" /> for our sample of stars. In the case of TIC 141280255 we obtained a RUWE = 1.98, which is above the value recommended by the Gaia team as a criterion for a good astrometric solution (RUWE ≤ 1.4). Therefore, we adopt for this star the stellar properties obtained from fitting the asteroseismic input only (Δν, νmax).

In the case of TIC 149347992 the discrepancy is the result of predicted evolutionary phases: while the parallax-only solution suggests that the star in the clump phase, the asteroseismic fit favors a star in the red giant branch. The combined fit therefore presents a bimodal distribution that encompasses these two families of solutions. A similar situation occurs in the fit of TIC 175375523, which shows agreement in the radius

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Distribution of fractional age uncertainties for our sample of stars determined by the BASTA pipeline fitting different combinations of available observables. The points indicate the individual values used to construct the Gaussian kernel density estimation. For better visualization we have excluded TIC 175375523 from the figure. See the text for details.</figcaption></figure>

determined from different sets of input but has a fractional age uncertainty above unity when only (Δν, νmax) are included in the fit. Its resulting age distribution is bimodal in this set as both red giant branch and clump models can reproduce the observations, but the inclusion of parallax information favors the red giant branch solution and accounts for the ~17% statistical uncertainty reported in Table 1. The availability of evolutionary classifications from deep neural networks trained on short *Kepler* data (Hon et al. 2018) would further decrease the obtained uncertainties by clearly disentangling these two scenarios.

In Figure 4 we plot the distribution of fractional age uncertainties obtained with BASTA for the three considered cases of input, showing the clear improvement in precision when asteroseismic information and parallax are simultaneously included in the fit. For visualization purposes we have excluded the target TIC 175375523 from the figure. Our stellar ages at the 20% level are significantly more precise than what is obtained by data-driven and neural-network methods trained using asteroseismic ages from *Kepler* (above the 30% level, see e.g., Mackereth et al. 2019). As a final remark, we note that asteroseismically derived properties of red giants are accurate to at least a similar level than our statistical uncertainties (below ~5% and ~10% for radii and masses, respectively; see the discussion in e.g., Pinsonneault et al. 2018, and references therein). We have made emphasis on our achieved precision instead of accuracy as our results could still be affected by a systematic component arising from uncertainties in evolutionary calculations, although recent investigations quantifying these effects at solar metallicity suggest that they are smaller than our statistical uncertainties (Silva Aguirre et al. 2019).

## 5. Conclusions

We presented the first ensemble analysis of red giant stars observed with the TESS mission. We selected a sample of 25 stars where we expected to detect oscillations based on their magnitude and parallax value, and analyzed the extracted light curves in search for asteroseismic signatures in the power spectra. Our main findings can be summarized as follows.

<footnote n="67">⁶⁷ See Gaia technical note GAIA-C3-TN-LU-LL-124-01 (https://www.cosmos.esa.int/web/gaia/dr2-known-issues).</footnote>
1. We detected oscillations in all the stars (except one that was likely incorrectly listed as a red giant). Despite the modest number of stars in our sample, our detection yield supports that the *TESS* photometric performance is similar to that of *Kepler* and K2 except shifted by about 5 magnitudes toward brighter stars due to its smaller aperture.

2. Individual pipelines retrieve the global asteroseismic parameters with uncertainties at the ~2% level in $\Delta\nu$ and ~2.5% in $\nu_{max}$, which, respectively, increase to ~4% and ~3.5% when we take into account the scatter across results. We consider these uncertainties to be representative for the forthcoming ensemble analysis of *TESS* targets observed in 1–2 sectors, as individual validation of the results will not be feasible due to the large number of targets observed.

3. Grid-based modeling techniques applying asteroseismic scaling relations were used to retrieve stellar properties for the 17 targets with spectroscopic information. Radii, masses, and ages were obtained with uncertainties at the 6%, 14%, and 50% level, and decrease to 3%, 6%, and 20% when parallax information from *Gaia* DR2 is included.

The expected number of red giants with detected oscillations by *TESS* (~500,000⁶⁸) greatly surpasses the final yield of *Kepler* (~20,000). In this respect, the combination of *TESS* observations, *Gaia* astrometry, and large-scale spectroscopic surveys holds a great potential for studies of Galactic structure where precise stellar properties (particularly ages) are of key importance. We note that the recently approved extended *TESS* mission will change the 30 minutes cadence to 10 minutes, making it possible to detect oscillations of stars of smaller radii using the full-frame images. This will enable more rigorous investigations of the asteroseismic mass scale for giants when anchored to empirical mass determinations (e.g., from eclipsing binaries) of turn-off and subgiant stars.

This Letter includes data collected by the *TESS* mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the *TESS* mission is provided by NASA's Science Mission directorate. Funding for the *TESS* Asteroseismic Science Operations Centre is provided by the Danish National Research Foundation (grant agreement No. DNRF106), ESA PRODEX (PEA 4000119301), and Stellar Astrophysics Centre (SAC) at Aarhus University. V.S. A. acknowledges support from the Independent Research Fund Denmark (Research grant 7027-00096B). D.B. is supported in the form of work contract FCT/MCTES through national funds and by FEDER through COMPETE2020 in connection to these grants: UID/FIS/04434/2019; PTDC/FIS-AST/30389/2017 & POCI-01-0145-FEDER-030389. L.B., R.A.G., and B.M. acknowledge the support from the CNES/PLATO grant. D.B. acknowledges NASA grant NNX16AB76G. T.L.C. acknowledges support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 792848 (PULSATION). This work was supported by FCT/MCTES through national funds (UID/FIS/04434/2019). E.C. is funded by the European Union's Horizon 2020 research and innovation program under

the Marie Skłodowska-Curie grant agreement No. 664931. R.H. and M.N.L. acknowledge the support of the ESA PRODEX programme. T.S.R. acknowledges financial support from Premiale 2015 MITiC (PI B. Garilli). K.J.B. is supported by the National Science Foundation under Award AST-1903828. M. S.L. is supported by the Carlsberg Foundation (grant agreement No. CF17-0760). M.C. is funded by FCT/MCTES through national funds and by FEDER through COMPETE2020 through these grants: UID/FIS/04434/2019, PTDC/FIS-AST/30389/2017 & POCI-01-0145-FEDER-030389, CEECIND/02619/2017. The research leading to the presented results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 338251 (StellarAges). A.M. acknowledges support from the European Research Council Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, grant agreement No. 772293, http://www.asterochronometry.eu). A.M.S. is partially supported by MINECO grant ESP2017-82674-R. J.C.S. acknowledges funding support from Spanish public funds for research under projects ESP2017-87676-2-2, and from project RYC-2012-09913 under the 'Ramón y Cajal' program of the Spanish Ministry of Science and Education. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center for the production of the SPOC data products.

## ORCID iDs

Víctor Silva Aguirre https://orcid.org/0000-0002-6137-903X

Dennis Stello https://orcid.org/0000-0002-4879-3519

Amalie Stokholm https://orcid.org/0000-0002-5496-365X

Jakob R. Mosumgaard https://orcid.org/0000-0001-9234-430X

Warrick H. Ball https://orcid.org/0000-0002-4773-1017

Sarbani Basu https://orcid.org/0000-0002-6163-3472

Diego Bossini https://orcid.org/0000-0002-9480-8400

Lisa Bugnet https://orcid.org/0000-0003-0142-4000

Derek Buzasi https://orcid.org/0000-0002-1988-143X

Tiago L. Campante https://orcid.org/0000-0002-4588-5389

Lindsey Carboneau https://orcid.org/0000-0003-1001-5137

William J. Chaplin https://orcid.org/0000-0002-5714-8618

Enrico Corsaro https://orcid.org/0000-0001-8835-2075

Guy R. Davies https://orcid.org/0000-0002-4290-7351

Rafael A. García https://orcid.org/0000-0002-8854-3776

Patrick Gaulme https://orcid.org/0000-0001-8330-5464

Oliver J. Hall https://orcid.org/0000-0002-0468-4775

Rasmus Handberg https://orcid.org/0000-0001-8725-4502

Marc Hon https://orcid.org/0000-0003-2400-6960

Thomas Kallinger https://orcid.org/0000-0003-3627-2561

Liu Kang https://orcid.org/0000-0001-5219-7894

Mikkel N. Lund https://orcid.org/0000-0001-9214-5642

Savita Mathur https://orcid.org/0000-0002-0129-0316

Alexey Mints https://orcid.org/0000-0002-8440-1455

Benoit Mosser https://orcid.org/0000-0002-7547-1208

Zeynep Çelik Orhan https://orcid.org/0000-0002-9424-2339

Thaïse S. Rodrigues https://orcid.org/0000-0002-9414-339X

Mutlu Yıldız https://orcid.org/0000-0002-7772-7641

Joel C. Zinn https://orcid.org/0000-0002-7550-7151

Sibel Örtel https://orcid.org/0000-0001-5759-7790

<footnote n="68">⁶⁸ Based on a preliminary simulation of the full *TESS* sky (*TESS* GI Proposal No. G011188).</footnote>
Paul G. Beck https://orcid.org/0000-0003-4745-2242

Keaton J. Bell https://orcid.org/0000-0002-0656-032X

Zhao Guo https://orcid.org/0000-0002-0951-2171

Chen Jiang https://orcid.org/0000-0002-7614-1665

James S. Kuszlewicz https://orcid.org/0000-0002-3322-5279

Marc Pinsonneault https://orcid.org/0000-0002-7549-7766

Jamie Tayar https://orcid.org/0000-0002-4818-7885

Margarida S. Cunha https://orcid.org/0000-0001-8237-7343

Saskia Hekker https://orcid.org/0000-0002-1463-726X

Andrea Miglio https://orcid.org/0000-0001-5998-8533

Mario J. P. F. G. Monteiro https://orcid.org/0000-0003-0513-8116

Ditte Slumstrup https://orcid.org/0000-0003-4538-9518

George Angelou https://orcid.org/0000-0003-4463-1907

Othman Benomar https://orcid.org/0000-0001-9405-5552

Aliz Derekas https://orcid.org/0000-0002-6526-9444

Maria Pia Di Mauro https://orcid.org/0000-0001-7801-7484

Yveline Lebreton https://orcid.org/0000-0002-4834-2144

Jaymie Matthews https://orcid.org/0000-0002-4461-080X

Nicolas Nardetto https://orcid.org/0000-0002-7399-0231

Jose D. do Nascimento, Jr. https://orcid.org/0000-0001-7804-2145

Filipe Pereira https://orcid.org/0000-0002-2157-7146

Luisa F. Rodríguez Díaz https://orcid.org/0000-0002-0588-1375

Emanuele Spitoni https://orcid.org/0000-0001-9715-5727

Vincent Van Eylen https://orcid.org/0000-0001-5542-8870

Rita Ventura https://orcid.org/0000-0002-5152-0482

Kuldeep Verma https://orcid.org/0000-0003-0970-6440

Jayme Weiss https://orcid.org/0000-0002-3843-1653

Tao Wu https://orcid.org/0000-0001-6832-4325

Thomas Barclay https://orcid.org/0000-0001-7139-2724

Jørgen Christensen-Dalsgaard https://orcid.org/0000-0001-5137-0966

Jon M. Jenkins https://orcid.org/0000-0002-4715-9460

Sara Seager https://orcid.org/0000-0002-6892-6948

Roland Vanderspek https://orcid.org/0000-0001-6763-6562

## References

Alves, S., Benamati, L., Santos, N. C., et al. 2015, MNRAS, 448, 2749

Anders, F., Chiappini, C., Rodrigues, T. S., et al. 2017, A&A, 597, A30

Basu, S., Verner, G. A., Chaplin, W. J., & Elsworth, Y. 2012, ApJ, 746, 76

Bedding, T. R., Huber, D., Stello, D., et al. 2010, ApJL, 713, L176

Bedding, T. R., Mosser, B., Huber, D., et al. 2011, Natur, 471, 608

Campante, T. L., Corsaro, E., Lund, M. N., et al. 2019, ApJ, 885, 31

Campante, T. L., Veras, D., North, T. S. H., et al. 2017, MNRAS, 469, 1360

Casagrande, L., Silva Aguirre, V., Schlesinger, K. J., et al. 2016, MNRAS, 455, 987

Corsaro, E., & De Ridder, J. 2014, A&A, 571, A71

da Silva, R., Milone, A. d. C., & Rocha-Pinto, H. J. 2015, A&A, 580, A24

Davies, G. R., Silva Aguirre, V., Bedding, T. R., et al. 2016, MNRAS, 456, 2183

Flower, P. J. 1996, ApJ, 469, 355

Gaia Collaboration, Katz, D., Antoja, T., et al. 2018, A&A, 616, A11

Gaulme, P., Appourchaux, T., & Boumier, P. 2009, A&A, 506, 7

Handberg, R., & Lund, M. N. 2014, MNRAS, 445, 2698

Hekker, S., Broomhall, A.-M., Chaplin, W. J., et al. 2010, MNRAS, 402, 2049

Hekker, S., Elsworth, Y., Mosser, B., et al. 2012, A&A, 544, A90

Hög, E., Fabricius, C., Makarov, V. V., et al. 2000, A&A, 355, L27

Hon, M., Stello, D., & Yu, J. 2018, MNRAS, 476, 3233

Houk, N. 1994, in ASP Conf. Ser. 60, The MK Process at 50 years: A Powerful Tool for Astrophysical Insight, ed. C. Corbally, R. O. Gray, & R. F. Garrison (San Francisco, CA: ASP), 285

Huber, D., Stello, D., Bedding, T. R., et al. 2009, CoAst, 160, 74

Jenkins, J. M., Twicken, J. D., McCauliff, S., et al. 2016, Proc. SPIE, 9913, 99133E

Jofré, E., Petrucci, R., Saffe, C., et al. 2015, A&A, 574, A50

Jones, M. I., Jenkins, J. S., Rojo, P., & Melo, C. H. F. 2011, A&A, 536, A71

Kallinger, T., Hekker, S., Mosser, B., et al. 2012, A&A, 541, 51

Luck, R. E. 2015, AJ, 150, 88

Lund, M. N., Handberg, R., Davies, G. R., Chaplin, W. J., & Jones, C. D. 2015, ApJ, 806, 30

Mackereth, J. T., Bovy, J., Leung, H. W., et al. 2019, MNRAS, 489, 176

Mathur, S., García, R. A., Régulo, C., et al. 2010, A&A, 511, 46

Meléndez, J., Asplund, M., Alves-Brito, A., et al. 2008, A&A, 484, L21

Miglio, A., Chiappini, C., Morel, T., et al. 2013, MNRAS, 429, 423

Miglio, A., Montalbán, J., Baudin, F., et al. 2009, A&A, 503, L21

Mints, A., & Hekker, S. 2018, A&A, 618, A54

Mosser, B., Elsworth, Y., Hekker, S., et al. 2011, A&A, 537, A30

Pinsonneault, M. H., Elsworth, Y. P., Tayar, J., et al. 2018, ApJS, 239, 32

Randich, S., Gratton, R., Pallavicini, R., Pasquini, L., & Carretta, E. 1999, A&A, 348, 487

Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, JATIS, 1, 014003

Rodrigues, T. S., Bossini, D., Miglio, A., et al. 2017, MNRAS, 467, 1433

Sharma, S., Stello, D., Bland-Hawthorn, J., et al. 2019, MNRAS, 490, 5335

Silva Aguirre, V., Bojsen-Hansen, M., Slumstrup, D., et al. 2018, MNRAS, 475, 5487

Silva Aguirre, V., Christensen-Dalsgaard, J., Cassisi, S., et al. 2019, arXiv:1912.04909

Silva Aguirre, V., Davies, G. R., Basu, S., et al. 2015, MNRAS, 452, 2127

Stello, D., Huber, D., Bedding, T. R., et al. 2013, ApJL, 765, L41

Stello, D., Meibom, S., Gilliland, R. L., et al. 2011, ApJ, 739, 13

Stello, D., Zinn, J., Elsworth, Y., et al. 2017, ApJ, 835, 83

Van Leeuwen, F. 2007, A&A, 474, 653

Wittenmyer, R. A., Liu, F., Wang, L., et al. 2016, AJ, 152, 19

Yıldız, M., Çelik Orhan, Z., & Kayhan, C. 2019, MNRAS, 489, 1753

Yu, J., Huber, D., Bedding, T. R., et al. 2018, ApJS, 236, 42

Zinn, J. C., Stello, D., Huber, D., & Sharma, S. 2019, ApJ, 884, 107