MNRAS 000,1–8 (2019) Preprint 27 July 2019 Compiled using MNRAS LATEX style file v3.0
Baby Boomers: An ASAS-SN Search for “Dipper” Stars in the Lupus Star-Forming Region
J. W. Bredall,1 B. J. Shappee,1 T. Jayasinghe,2 E. J. Gaidos,3,4 K. Z. Stanek,2,5 C. S. Kochanek,2,4? J. L. Prieto,6,7 T. W. -S. Holoien†,8 Subo Dong,9 Todd A. Thompson,2,5,10 K. Hart,1 J. Gagn´e11 1Institute for Astronomy, University of Hawai‘i at M¯anoa, 2680 Woodlawn Dr., Honolulu 96822, USA 2Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA 3Department of Earth Sciences, University of Hawai‘i at M¯anoa, Honolulu, HI 96822, USA 4Kavli Institute for Theoretical Physics, University of California at Santa Barbara, Santa Barbara, CA 93106, USA 5Center for Cosmology and Astroparticle Physics, The Ohio State University, 191 W. Woodruff Avenue, Columbus, OH 43210, USA 6N´ucleo de Astronom´ıade la Facultad de Ingenier´ıa y Ciencias, Universidad Diego Portales, Av. Ej´ercito 441, Santiago, Chile 7Millennium Institute of Astrophysics, Santiago, Chile 8Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101, USA 9Kavli Institute for Astronomy and Astrophysics, Peking University, Yi He Yuan Road 5, Hai Dian District, Beijing 100871, China 10Institute for Advanced Study, 1 Einstein Dr., Princeton, NJ 08540, USA 11Carnegie Institution of Washington DTM, 5241 Broad Branch Road NW, Washington, DC 20015, USA
ABSTRACT Examining variable Young Stellar Objects (YSOs), especially “dipper” stars, gives us unique insight into stellar and planetary formation. While there have been several recent high-precision surveys of YSOs in specific star-forming regions, variability can be missed as a result of their short baselines and limited coverage. Here we present a YSO survey of the Lupus star forming region using the All-Sky Automated Survey for Supernovae (ASAS-SN). Despite being home to several well-studied variable YSOs, the Lupus clouds have currently no published cases of dipper stars. With ASAS-SN, we are able to search for variable YSOs in a previously unexplored parameter space. In this search, we have found eight new dippers in Lupus, and present our findings in the context of known dipper populations. Key words: stars: variables: T Tauri, Herbig Ae/Be
1 INTRODUCTION current telescopes (Hedges et al. 2018). Thus, variable YSOs offer us a unique view of stellar and planetary formation. Exoplanetary science has seen much progress in recent years thanks to surveys such as Kepler (Borucki 2016) and TESS Herbst et al.(1994) splits the variability of YSOs (Ricker et al. 2014); however, many of these findings have into three main groups: Types I, II, and III. Each type of made it difficult to reconcile the layout of our own solar variability gives us insight into the different mechanics of system with what we observe in other systems (e.g., Mor- stellar and planetary formation. dasini et al. 2015; Raymond et al. 2018). One of the biggest problems with current models is understanding the struc- Type I variability is characterized by regular, periodic ture of protoplanetary disks and their interactions with the changes in luminosity of up to 0.5 mag, and is caused by magnetic field of the central Young Stellar Object (YSO) the rotation of starspots (Herbst 2012). Measuring the (Morbidelli & Raymond 2016). Recent findings have demon- period and how it changes over time can inform us about strated that observations of variable YSOs allow us to probe the evolution of the system’s angular momentum. Rotation is also an input parameter for stellar magnetodynamic inner (. 1 AU) regions of disks. While Kepler has found sev- eral planets within this region, this scale is not resolvable by models, which can constrain interactions between the disk and stellar magnetic field.
Type II variability is characterized by eruptive brightening ? Radcliffe Fellow events. This is normally observed as flaring, caused by mag- † Carnegie Fellow netic reconnection events. A more extreme case of Type II
© 2019 The Authors 2 Bredall et al.
variability is FU Orionis Stars (FUors), which can brighten work, we use ASAS-SN to perform a detailed search for dip- as much as 1 or 2 mag. This is believed to be caused pers in the Lupus region. by an increase in accretion driven by the magnetic field The Lupus region comprises multiple clouds of low-mass of the star (Herbst Herbst). Observations of these events star formation near the Scorpius Centaurus OB association can inform us of how these flares might affect disk structure. (Comer´on 2008). Lupus 3 is home to many well-studied T Tauri stars such as RU Lup and EX Lup, the prototype Type III variables are characterized by either periodic or EXor variable. While there have been several recent studies aperiodic dimming. UX Orionis Stars (UXors) are a well- on disk structure (e.g., Ansdell et al. 2016b, 2018b) and ac- known example of this phenomenon, caused by occultations cretion (e.g., Nisini et al. 2018) in Lupus YSOs, there are of dust throughout the disk. However, another subset of no published dippers in the Lupus clouds as of the writing Type III variables has emerged in recent years, known as of this paper. We are thus motivated to use our search for “dippers”. Typical dimming for dipper systems is on the dippers as an optical supplement to current spectroscopic, order of 10 − 50%, though in some cases can be up to 5 X-ray, and infrared surveys of the region. mag. Unlike UXors, dippers are believed to be caused by This paper is outlined as follows: Observations and data transiting dust from the inner disk region (Hedges et al. reduction are discussed in §2, and variability selection crite- 2018). Observations of these dippers inform us of inner-disk ria are defined in §3. We find eight dippers in Lupus, which geometry that has otherwise been unavailable to us. are presented and discussed in §4.
There has been a large effort in recent years to observe star-forming regions and classify any variable YSOs therein, 2 OBSERVATIONS with special attention to dipper stars (e.g., Cody et al. 2014; While ASAS-SN has discovered variable YSOs in the past Ansdell et al. 2016a; Rodriguez et al. 2017; Ansdell et al. (e.g., Holoien et al. 2014; Sicilia-Aguilar, A. et al. 2017), 2018a; Cody & Hillenbrand 2018; Hedges et al. 2018). Many the automated data reduction pipeline is best equipped to of these surveys utilize high-cadence, high-precision space detect bright transients such as supernovae. Indeed, smaller- telescopes such as Spitzer (Fazio et al. 2004) or K2 (Howell scale variability detections are intentionally missed by the et al. 2014). These studies have allowed for high-sampling pipeline. Nevertheless, all data is archived and available for of individual transient events; however, they suffer from a more focused investigations. For this work, we use a catalog short baseline of weeks (e.g., Spitzer) to months (e.g., K2 of , YSOs given by Gagn´e(2019), 413 of which are part and TESS). Furthermore, while K2 has been very success- 7 000 of the Lupus Star-Forming Region. ful with finding dippers, its limited viewing window has re- sulted in most surveys being conducted in the Taurus and Upper Sco regions. In addition to space telescopes, ground- 2.1 ASAS-SN Light Curves based observatories such as the Kilodegree Extremely Little Telescope (KELT; Pepper et al. 2007, 2012, 2018) have been The ASAS-SN Network consists of 20 telescopes mounted used for conducting surveys of YSOs. KELT has the advan- on 5 fully-robotic mounts located at the Haleakal¯aObserva- tage of a wider field than K2, but is limited to brighter stars tory, the Cerro Tololo International Observatory, McDonald (8
MNRAS 000,1–8 (2019) Baby Boomers: An ASAS-SN Search for “Dipper” Stars in the Lupus Star-Forming Region 3 motion measured by Gaia for the Lupus YSOs is 11.5447 3.2 Quasi-Periodicity mas/yr, which is well within our tolerance of 200 mas/yr. Now that we have a selection of variable YSOs, we aim To decrease the likelyhood of false positives in our vari- to classify their variability using the statistics Quasi- ability search, the light curves of each star is processed as Periodicity and Flux Asymmetry. Quasi-Periodicity is a follows. First, we remove data points with a Full-Width Half- Q measurement of the periodicity of a variable signal from a Max greater than the 95th percentile for the given detector. source, and is defined as Next, points whose flux detection is consistent with zero is omitted from statistical calculations. Lastly, we apply a ( 2 − 2 ) σresid σinst sigma clipping to the detections based on Chauvenet’s Cri- Q = , (2) (σ2 − σ2 ) terion (Chauvenet 1906). Any source whose light curve has m inst fewer than 2/3 of its original data remaining after these three where σinst is the instrumental error as a function of magni- processes are removed from our sample. This reduced our 2 2 tude, and σresid and σm are the variances of the residual and sample size from 413 to 301 stars. original light curves respectively. For a perfectly periodic sig- There are known systematic offsets between ASAS-SN nal, the variance of the residual is equivalent to instrumental cameras (Jayasinghe et al. 2018). To combine data from dif- noise. As can be seen in eq. (2), a perfectly periodic signal ferent cameras, we use the SciPy interp1d package (Jones would have Q = 0. et al. 01 ) to interpolate the most sampled curve. We then To calculate σresid, we utilize Astropy LombScargle calculated the median difference between this interpolation (VanderPlas et al. 2012; VanderPlas & Ivezi´c 2015) to find and the dataset with the highest number of points taken in the five periods with the highest power. This is compared the same range of Julian Dates. We add this difference to with the top five periods given by the window function; any the second dataset and repeat the process until data from matches within 0.05 days are removed. The curve is then all cameras are aligned. This results in each source having folded to the period with the highest power. We apply a a g-band and V -band light curve comprised of detections boxcar smoothing with a window size of 25% of the period. from all ASAS-SN cameras. This smoothed model is subtracted from the folded curve to Each light curve is then inspected by eye to check for produce a residual, and the standard deviation is calculated. saturation. Stars brighter than 11 mag are at risk of satu- ration in ASAS-SN, which takes on a distinct pattern. Data that demonstrated this pattern are removed from the set. 3.3 Flux Asymmetry This gives us a final sample size of 129. The V -band curves The second metric to classify variability is Flux Asymmetry of the remaining stars are analyzed as outlined in §3. M, given by hm i − m M = 10% med , (3) σ 3 SEARCHING FOR VARIABILITY m where hm i is the mean of the top and bottom 10% of We apply the decontamination of data outline in section 2.2 10% magnitude measurements, m is the median of all mea- to the 413 Lupus YSOs, reducing our sample size to 129. med surements, and σm is the standard deviation of the light We then calculate peak-to-peak variability to remove non- curve. M determines whether variability is predominantly variable stars (see section 3.1), futher reducing our sample brightening (M < 0) or predominantly dimming (M > 0). size to 88. With this remaining data, we calculate Quasi- With both Q and M calculated, we move on to classifying Periodicity and Flux Asymmetry as outlined in Cody Q M variability. et al.(2014). We review these statistics briefly in sections 3.2 and 3.3 respectively.
4 RESULTS AND DISCUSSION 3.1 Peak-to-peak Variability We calculate Q and M for the V -band data of our YSOs to In order to quantify variability, we use the peak-to-peak vari- separate them by variability type. The results are shown in ability metric v from Sokolovsky et al.(2017), defined as Figure 2. The figure is divided into nine regions according to Cody & Hillenbrand(2018) based on different classifications (m − σ ) − (m + σ ) v = i i max i i min , (1) of variability. Stars in the Quasi-periodic Dipping region of ( − σ ) ( σ ) mi i max + mi + i min Figure 2 were examined in detail. In this search, we have where (mi − σi)max and (mi + σi)min are the maximum (min- found eight stars in the Lupus region that show dipper-like imum) values of the difference (sum) of a given magnitude characteristics in their light curves. Figure 3 shows the asso- measurement mi and its associated uncertainty σi, evalu- ciated ASAS-SN light curves. As of the writing of this paper, ated over the entire light curve. We find v for our data set, there have been no published dipper sources in the Lupus as well as for approximately 115,000 random ASAS-SN V - region. band light curves. We calculate the 90th percentile of v as a Also included in Figure 2 are dippers published in Cody function of magnitude to determine differences in instrumen- & Hillenbrand(2018) and Rodriguez et al.(2017). The val- tal error for brighter or dimmer stars. Stars that fall above ues Q and M for the dippers in Cody & Hillenbrand(2018) the 90th percentile line are considered variable and are in- are inconsistent between ASAS-SN and Kepler. We pro- cluded in further calculations. Figure 1 shows our clipping pose that this is the result of Q and M being weighted by for v. Using this method, 88 out of our 129 Lupus YSOs are instrumental error. Because Kepler and other space tele- flagged as variable. scopes have much higher precision than ground telescopes,
MNRAS 000,1–8 (2019) 4 Bredall et al.
0.14 Lupus Dippers Cody 2018 Rodriguez 2017
) 0.12
v Lupus YSOs YSOs 0.10
RY Lup Sz 118 0.08 Sz 98 Sz 96 HW Lup 0.06
Sz 90 Sz 133 0.04 Sz 117 Peak-to-peak variability ( 0.02
0.00
11 12 13 14 15 16 17 18 V (mag)
Figure 1. Peak-to-peak variability v vs ASAS-SN V for our sample of YSOs. Stars flagged as “Dipper” in Cody & Hillenbrand(2018) Table 2 and Rodriguez et al.(2017) Table 3 that were present in our sample are highlighted. The dashed line corresponds to the 90th percentile of v at a given magnitude based on our YSO light curves as well as 115, 000 random ASAS-SN V -band light curves. Notice the saturation effects becoming evident at magnitudes brighter than ∼ 11. YSOs above the dashed line are considered variable, and are included in Figure 2. Out of 129 Lupus YSOs, 88 pass this criterion for variability. The eight Lupus dippers found in §4 are labelled in blue.
Periodic Quasi-Periodic Aperiodic 1.00 − Lupus Dippers Bursting 0.75 Cody 2018 − Rodriguez 2017 Lupus YSOs 0.50 − YSOs
0.25 − Symmetric
0.00
0.25 HW Lup Sz 117
Sz 133 Sz 118 Dipping Flux Asymmetry (M) 0.50 Sz 90 RY Lup 0.75 Sz 96 Sz 98 1.00 0.0 0.2 0.4 0.6 0.8 1.0 Quasi-Periodicity (Q)
Figure 2. Flux Asymmetry vs Quasi-Periodicity for our sample of variable YSOs. Flux Asymmetry M is a measure of variability that is predominantly brightening (M < 0), dimming (M > 0), or symmetric (M = 0). Quasi-Periodicity Q measures if variability is more periodic (Q = 0) or stochastic (Q > 0). The figure is divided into nine distinct regions to classify variability identical to Cody & Hillenbrand (2018). Stars in Lupus that are in the Quasi-Periodic Dipper region are shown in blue. The light curves for these stars were inspected (Figure 3). These proposed dippers are found to exhibit stellar properties consistent with known dipper stars (Figure 4 and Figure 5). The area of each point is equal to the range between the 95th and 5th percentile magnitude of the star’s V -band light curve multiplied by a constant. Stars flagged as “Dipper” in Cody & Hillenbrand(2018) Table 2 and Rodriguez et al.(2017) Table 3 that were present in our sample are also highlighted.
MNRAS 000,1–8 (2019) Baby Boomers: An ASAS-SN Search for “Dipper” Stars in the Lupus Star-Forming Region 5
HW Lup RY Lup 15 16 11.0 16 11 11.5 17 12.0
17 g (mag) g (mag) V (mag) V (mag) 12 18 12.5 18 13.0 7500 8000 8500 7500 8000 8500 JD - 2,450,000 JD - 2,450,000 Sz 90 Sz 96 13 14 14 15
14 15 15 16 g (mag) g (mag) V (mag) V (mag) 15 16 17 16 16 7500 8000 8500 7500 8000 8500 JD - 2,450,000 JD - 2,450,000 Sz 98 Sz 117 13 14.0 13 . 14 14.5 15 5 14 15.0 g (mag) g (mag) V (mag) 15 V (mag) 15 16.0 15.5 16 16 7500 8000 8500 7500 8000 8500 JD - 2,450,000 JD - 2,450,000 Sz 118 Sz 133 15 14 16 16 15 16 17 g (mag) g (mag) V (mag) 16 17 V (mag) 17
17 18 18 7500 8000 8500 7500 8000 8500 JD - 2,450,000 JD - 2,450,000
Figure 3. ASAS-SN light curves for eight dippers in Lupus, in order of ascending RA. Triangles indicated lower limits. The vertical offset between the filters was found by interpolating the V -band data in the overlapping region, finding the mean offset between this interpolation and the g-band data in the overlap, and adding this back to the g-band values. The result was purely visual; indicated g-band magnitudes reflect actual measurements. MNRAS 000,1–8 (2019) 6 Bredall et al.
Lupus Dippers 1.50M 0 Cody 2018 Rodriguez 2017 1.00M YSOs 0.70M
2 0.50M
0.40M
0.35M 4 0.30M
0.25M
6 0.20M
G (mag) 0.15M
Gaia 8 0.10M
10
12
14 0 1 2 3 4 5 Gaia BP-RP (mag)
Figure 4. Gaia DR2 Color-Mag Diagram for our sample of variable YSOs. Stars are colored according to catalog membership. The six Lupus dippers that are in Gaia are highlighted. Stars flagged as “Dipper” in Cody & Hillenbrand(2018) Table 2 and Rodriguez et al. (2017) Table 3 that were present in our sample are aksi highlighted. Evolutionary tracks up to 10 Myr are found using MESA Isochrones & Stellar Tracks (Dotter 2016; Choi et al. 2016; Paxton et al. 2011, 2013, 2015, 2018), with the 1-3 Myr tracks in bold. Note that with one exception, all dippers lie above the main sequence. This is consistent with the theory of dipping being a consequence of star formation. we would expect this type of search in ASAS-SN to be better 5 CONCLUDING REMARKS suited in finding more extreme cases of variability. A more detailed comparison of Q and M in ASAS-SN vs Kepler will be included in future work. We presented initial findings from an ASAS-SN survey of Figure 4 shows a Gaia DR2 HR Diagram of our catalog YSOs in Lupus. We flagged variability based on peak-to- of YSOs. Six of our dippers are in Gaia, and are labelled peak variability v when compared to 115, 000 random V - along with other classified dippers from Cody & Hillenbrand band curves. We then used Quasi-Periodicity and Flux (2018) and Rodriguez et al.(2017). With one exception, all Asymmetry to classify variability type in the V band. In dippers are redder than the main sequence, consistent with doing so, we found eight dippers in the Lupus region, where dippers as very young stars. no dippers have been reported previously. We briefly ex- plored their placement on the Gaia HR Diagram and WISE Color-Color Diagram in the context of known dipper popula- Furthermore, Figure 5 shows a WISE Color-Color di- tions from Cody & Hillenbrand(2018) and Rodriguez et al. agram with new and known dippers labelled. The Lupus (2017). We encourage high-cadence follow up observations dippers have an infrared excess consistent with other known for these objects, as well as searches for more dipper sources dippers, which is to be expected as current models for dip- in the region. Future work will involve a broader analysis of pers require the presence of a disk. the entire Gagn´e(2019) catalog of YSOs.
MNRAS 000,1–8 (2019) Baby Boomers: An ASAS-SN Search for “Dipper” Stars in the Lupus Star-Forming Region 7
1.50 Lupus Dippers Cody 2018 Rodriguez 2017 1.25 YSOs
1.00
0.75
0.50 W1-W2 (mag)
0.25
0.00
0.25 −
2 0 2 4 6 − W3-W4 (mag)
Figure 5. WISE Color-Color Diagram. The Lupus dippers are labelled alongside other known dippers. The infrared excess is consistent with the theory that dipping is caused by occultation of circum-stellar material within the inner disk region.
ACKNOWLEDGEMENTS 11573003 supported by NSFC. TAT acknowledges support from a Simons Foundation Fellowship and from an IBM We thank the Las Cumbres Observatory and its staff for Einstein Fellowship from the Institute for Advanced Study, its continuing support of the ASAS-SN project. ASAS-SN Princeton. is supported by the Gordon and Betty Moore Foundation through grant GBMF5490 to the Ohio State University and NSF grant AST-1515927. Development of ASAS-SN has been supported by NSF grant AST-0908816, the Mt. Cuba John Bredall acknowledges support from Research Ex- Astronomical Foundation, the Center for Cosmology and As- perience for Undergraduate program at the Institute for As- troParticle Physics at the Ohio State University, the Chinese tronomy, University of Hawaii-Manoa funded through NSF Academy of Sciences South America Center for Astronomy grant 6104374. He would also like to thank the Institute for (CASSACA), the Villum Foundation, and George Skestos. Astronomy for their kind hospitality during the course of this project. BJS, KZS, CSK, and TAT are supported by NSF grants AST-1515876, AST-1515927, AST-1814440, and AST-1908952. CSK is also supported by a fellowship from the Radcliffe Institute for Advanced Studies at Harvard Uni- This work is based on observations made by ASAS-SN. versity. Support for JLP is provided in part by FONDECYT We wish to extend our special thanks to those of Hawai- through the grant 1191038 and by the Ministry of Econ- ian ancestry on whose sacred mountain of Haleakal¯a, we are omy, Development, and Tourism’s Millennium Science Ini- privileged to be guests. Without their generous hospitality, tiative through grant IC120009, awarded to The Millennium the observations presented herein would not have been pos- Institute of Astrophysics, MAS. SD acknowledges Project sible.
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