# Near-IR Variability in young stars in Cygnus OB7

Thomas S. Rice11affiliation: Department of Astronomy, Harvard University, 60
Garden Street, Cambridge, MA 02138. , Scott J. Wolk22affiliation: Harvard-
Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138. ,
Colin Aspin33affiliation: Institute for Astronomy, University of Hawaii at
Manoa, 640 N Aohoku Pl, Hilo, HI 96720.

###### Abstract

We present the first results from a 124 night $J$, $H$, $K$ near-infrared
monitoring campaign of the dark cloud L 1003 in Cygnus OB7, an active star-
forming region. Using 3 seasons of UKIRT observations spanning 1.5 years, we
obtained high-quality photometry on 9,200 stars down to $J$=17 mag, with
photometric uncertainty better than 0.04 mag. On the basis of near-infrared
excesses from disks, we identify 30 pre-main sequence stars, including 24
which are newly discovered. We analyze those stars and find the NIR excesses
are significantly variable.

All 9,200 stars were monitored for photometric variability; among the field
star population, $\sim$160 exhibited near-infrared variability (1.7% of the
sample). Of the 30 YSOs (young stellar objects), 28 of them (93%) are variable
at a significant level. 25 of the 30 YSOs have near-infrared excess consistent
with simple disk-plus-star classical T Tauri models. Nine of these (36%) drift
in color space over the course of these observations and/or since 2MASS
observations such that they cross the boundary defining the NIR excess
criteria; effectively, they have a transient near-infrared excess. Thus, time-
series $JHK$ observations can be used to obtain a more complete sample of
disk-bearing stars than single-epoch $JHK$ observations. About half of the
YSOs have color-space variations parallel to either the classical T Tauri star
locus (Meyer et al., 1997), or a hybrid track which includes the dust
reddening trajectory. This indicates that the NIR variability in YSOs that
possess accretion disks arises from a combination of variable extinction and
changes in the inner accretion disk: either in accretion rate, central hole
size and/or the inclination of the inner disk. While some variability may be
due to stellar rotation, the level of variability on the individual stars can
exceed a magnitude. This is a strong empirical suggestion that protoplanetary
disks are quite dynamic and exhibit more complex activity on short timescales
than is attributable to rotation alone or captured in static disk models.

accretion, accretion disks – stars: formation – stars: pre-main sequence –
stars: variables – infrared: stars

##  1 Introduction

Near-infrared studies of young stars allow for the direct detection of
optically thick disks around these stars via excess $K$-band flux (Lada &
Adams, 1992; Lada et al., 2000). The intrinsic colors of these disk-bearing
young stars occupy a well-defined locus in $(J-H)$ vs. $(H-K)$ two-color space
(henceforth called $JHK$ space), and their position along this locus is
determined by physical parameters such as their inclination angle, disk inner
hole size, and accretion rate (Meyer et al., 1997; Robitaille et al., 2006).
This technique of identifying young stellar objects (YSOs) using their near-
infrared colors has been used extensively to characterize populations of young
stars associated with star-forming regions in Orion, Taurus, Ophiuchus,
Chamaeleon, and others for nearly two decades (e.g. Strom et al., 1993, 1995;
Itoh et al., 1996; Hillenbrand et al., 1998; Oasa et al., 1999; Haisch et al.,
2000; Robberto et al., 2010).

Disk-bearing young stars, also known as classical T Tauri stars (cTTs), have
long been identified as optically variable (Joy, 1945; Herbig, 1962), with
this variability. At a minimum the variability is due to a combination of (I)
cold starspots, (II) hot accretion spots, and (III) circumstellar dust
occultations (Herbst et al., 1994). Optical variability can be used to
effectively identify young low-mass stars, even in regions lacking other
tracers of star formation such as molecular clouds (Briceño et al., 2005).

The variability of T Tauri stars in the near-infrared has been less thoroughly
studied. One of the first projects was carried out by Skrutskie et al. (1996)
who sampled bright T-Tauri stars, mostly members of the Taurus cloud, over the
course of a few nights. They found nearly all the stars varied significantly
and the amplitude of K-band source variability was weakly correlated with
(K$-$L) excess – a reliable disk diagnostic. From a multi-epoch study of the
Serpens cloud core, Kaas (1999) found the IR-variability a strong indicator of
youth. Variability across 14 epochs was used to reveal new YSOs in the $\rho$
Oph cluster (Alves de Oliveira & Casali, 2008). In the early part of the last
decade, Carpenter et al. (2001) used 16 observations of a $0\fdg 84\times
6\arcdeg$ region over 19 days to study NIR variability toward the Orion Nebula
Cluster, and Carpenter et al. (2002) used 15 observations of a similarly sized
field in the Chamaeleon I star-forming region over 4 months; the typical peak-
to-peak amplitude of variability seen was around 0.2 magnitudes in each band.
These studies found near-IR (NIR) variability most commonly arose from cold
starspots, hot accretion spots, and variable extinction. However, among stars
with a near-infrared excess ($\sim 25\%$ of the variable stars possessed NIR
excess), changes in the accretion disk were required to explain the observed
variability. Eiroa et al. (2002) combined optical and NIR data of 18 bright
stars and found 12 had correlated optical and NIR variability trends,
suggestive of a common physical origin such as spots and/or variable
extinction. The lack of correlation in the other objects was taken as a sign
that in those stars, distinct processes wereAn investigation of long-term NIR
variability in a large sample of T Tauri stars using 2-3 epochs covering
baselines of 6-8 years was carried out by Scholz et al. (2009) (see also
Scholz, 2012). They find the fraction of large-amplitude variables increases
for progressively longer baselines. They derived a 2500-year upper limit on
the duty cycle for large-scale episodic accretion events. The YSOVAR survey of
an 0.9 $\textrm{deg}^{2}$ region of the Orion Nebula Cluster (Morales-Calderón
et al., 2011) obtained 81 epochs of mid-IR Spitzer photometry over 40
consecutive days, in conjunction with 32 epochs of $J$-band images from UKIRT
and 11 epochs of $K_{s}$ photometry from CFHT. In that study of disk-bearing
young stars, various phenomena were observed, including periodic variability
and disk occultation events.

In this paper, we present a survey in which the photometric variability of
objects in the Braid Nebula star-forming region within Cygnus OB7 was
monitored for nearly two years. Our goal is to detect high-confidence YSO
candidates with precision photometry, study their variability, and analyze the
stability of the near-infrared disk diagnostic.

The constellation Cygnus contains several rich and complex star-forming
regions including Cygnus X as well as the North American and Pelican nebulae
(Reipurth & Schneider, 2008). Nine OB associations have been found in Cygnus,
with Cygnus OB7 the nearest at a distance of around 800 pc (Aspin et al.,
2009, distance modulus $\mu=9.5$). Within Cygnus OB7 lies a complex of several
dark clouds collectively known as Kh 141 (Khavtasi, 1955) that have been
individually identified in the Lynds catalog (Lynds, 1962). The dark cloud LDN
1003 in Cyg OB7 has been identified as a site of active star formation, having
first been studied in the optical by Cohen (1980) who found a diffuse red
nebula he named RNO 127. This nebula was later determined to be a bright
Herbig-Haro (HH) object by Melikian & Karapetian (2001, 2003, HH 428). Further
study in the optical and near-infrared identified a number of Herbig-Haro
objects (Devine et al., 1997; Movsessian et al., 2003) and multiple IRAS
sources (Dobashi et al., 1996) that reveal the presence of a young stellar
population and significant star formation activity.

The presence of two FUors in this region (Reipurth & Aspin, 1997; Greene et
al., 2008; Aspin et al., 2011) that has come to be known as the Braid Nebula
region (Movsessian et al., 2006) led to a focused, multi-wavelength effort to
study the young stars in this dark cloud, which is currently ongoing. A
narrowband optical and near-infrared survey of HH outflows observed using the
Subaru 8 m telescope (Magakian et al., 2010) found 12 outflows that have
identifiable originating/exciting sources and many more nebulous objects not
yet associated with identified sources. Aspin et al. (2009) carried out a
near-infrared integral field spectroscopic survey to identify actively
accreting sources such as T Tauri stars, which studied 16 sources in the field
and identified 12 young stars among them. Using the Caltech Submillimeter
Observatory, a 1.1 mm map of cold gas and dust associated with these young
stars was obtained (Aspin et al., 2011), identifying 55 cold dust clumps, 11
of which were associated with IRAS sources. The wide range of evolutionary
states encountered in this region, from starless clumps to optically visible T
Tauri stars, suggests that star formation here is an ongoing process rather
than a one-time occurrence.

In this paper we describe a $JHK$ monitoring survey of the Braid Nebula star-
forming region in Cygnus OB7. We plan to present first results from this
survey in two papers; this is paper 1 of 2. Our goal in Paper I is to identify
disk-bearing young stars, to broadly characterize the variability properties
of these stars using sensitive, long-baseline, nightly-cadence time-series
photometry to investigate the reliability of the near-infrared excess
criterion itself with respect to time. In the second paper (Wolk et al. 2012,
in prep, hereafter known as Paper II) we will describe the specific phenomena
seen in the lightcurves, carefully analyze the variability and periodic nature
of specific stars and present variability statistics for the field star
population.

In §2 we outline the UKIRT observations and data reduction procedures. In §3
we describe our method of identifying stars with near-infrared excesses and
studying their variability. In §4 our results are presented, and in §5 we
discuss the broad implication of these results on infrared studies of young
stars.

##  2 Data

###  2.1 Observations and Data Processing

The $J$, $H$, and $K$ observations of a $0.9^{\circ}\times 0.9^{\circ}$ region
in Cygnus OB7 were obtained using the Wide Field Camera (WFCAM) instrument on
the United Kingdom InfraRed Telescope (UKIRT), an infrared-optimized 3.8 m
telescope atop Mauna Kea, Hawaii at 13,800 feet elevation. These data consist
of WFCAM observations taken from 26-April-2008 to 11-October-2009 (Figure 1)
in three observing seasons as part of a special observation program described
in Aspin et al. 2012 (in preparation). The first season was in spring of 2008
and covered 26 nights. The second season was during fall 2008 and lasted 71
days. The third season was approximately one year later and lasted 75 days.
During the monitoring runs, data were taken once per night on a total of 124
nights in all three bands. Atmospheric seeing was between $0\farcs 5$ and
$1\farcs 6$ on any given night. The $J$, $H$, $K$ filter bands on WFCAM comply
with the $JHK$ broadband filters from the Mauna Kea Observatories Near-
Infrared filter set (Casali et al., 2007; Tokunaga et al., 2002); WFCAM’s
photometric system is described in Hewett et al. (2006).

Figure 1: Schematic representation of when data were taken for this study.
Nights with high errors have been removed (See §2.2).

Detailed specifications for the WFCAM instrument are given in Casali et al.
(2007). The instrument has four 2048 $\times$ 2048 Rockwell Hawaii-II PACE
arrays with a scale of 0.4 arcseconds per pixel, giving a combined solid angle
of 0.21 square degrees per exposure. The four detectors are widely spaced, at
94% of one detector’s width apart (see Fig. 2). We used a standard effective
integration time of 40 seconds per pointing. In a stepping pattern, WFCAM can
scan a nearly-complete square degree of the sky in four pointings (A, B, C, D
in Fig. 2). The pointings have a minor overlap on their edges such that stars
in the overlapping region were observed twice per night.

Data from the survey were pipeline-reduced and processed by the WFCAM Science
Archive System (Irwin, 2008; Hambly et al., 2008), which is also used for the
UKIDSS survey described in Lawrence et al. (2007). Near-infrared observations
are calibrated against 2MASS sources in the field which have extinction-
corrected color $0.0\leq J-K\leq 1.0$ and 2MASS signal-to-noise ratio $>10$ in
each filter. For the target stars, total uncertainties in photometry are
typically 2% down to $J$=16.5, $H$=16, and $K$=15, and errors are less than 4%
at $J$=17 (Hodgkin et al., 2009). In processing such a wide field of view, a
large number of data quality issues arise and are typically dealt with by the
pipeline by assigning photometric error flags for issues such as bad pixels,
deblending, saturation, and other effects.

Figure 2:  The footprint of WFCAM consists of four detectors spaced by (94%)
of their width, covering a non-contiguous 0.21 square degrees; four pointings
are required to fill the observing field. In this figure we show how the 16
tiles in this observing field are imaged: the 4 tiles marked “A”, called
“footprint A”, are observed in a simultaneous imaging, then the telescope is
slewed south by $15\arcmin$ to observe the “B” tiles, west by $15\arcmin$ to
observe “C” tiles, and finally back north $15\arcmin$ to observe the “D”
tiles. Underlaid is a map of star counts across the field, showing clearly the
structure of the dark cloud L 1003, which causes the mean extinction in each
tile to vary.

###  2.2 Data Retrieval & Cleaning

We retrieved the processed photometry data from the WFCAM Science Archive
website via an SQL interface. All 124 nights of data were retrieved, cross-
matched, and merged together into a single catalog containing columns for
object ID, observation date, sky coordinates, $JHK$ photometry, and various
photometric processing flags. Our initial query was for data that satisfied
the criteria $J$, $H$, $K<18$; $J$, $H$, $K>9$; and photometric uncertainty
$\sigma_{(J,H,K)}<0.1$, in order to include all possibly relevant data on
young stars in this region (see §3.2 on magnitude cuts) while keeping the
downloaded catalog file at a manageable size.

Hodgkin et al. (2009) present an empirically derived correction from the
pipeline-estimated photometric error to the true, measured error:

$M^{2}=cE^{2}+s^{2}$ (1)

where $M$ is the measured total error, $E$ is the estimated photometric
uncertainty given by the pipeline, the constant of proportionality $c=1.082$,
and the systematic component $s=0.021$. We applied this update to the
estimated photometric uncertainties after retrieving the data and confirming
that night-to-night variations at the 2% level were typical even for high
signal-to-noise stars.

To assess the photometric integrity of this dataset, we calculated the mean
$JHK$ colors for each pointing on each night, averaged over all stars. We
excluded observations where the mean colors showed significant deviations. For
each night, we computed the mean $J-H$ and $H-K$ color of all reliable stars
within each detector footprint (here, “reliable” denotes stars with
photometric uncertainties less than 0.1 magnitude in each band and no
processing error flags, while avoiding bright stars). In practice, this
translated to stars between $13<J<18$, $12<H<17$, and $11.5<K<16.5$.
Typically, 25,000 stars met this criterion every night. We find that
systematic night-to-night color deviations of the ensemble on each footprint
are indeed about two percent (as expected), but a significant minority ($\sim
15\%$) of nights exhibit large offsets in color space (see Fig. 3), likely due
to non-uniform extinction from thin clouds. We applied a form of iterative
outlier clipping to select and remove anomalous nights from our analysis,
leaving only the nights whose mean colors lay within $3\sigma$ of the outlier-
clipped, time-averaged global mean color.

We also note that the mean color in each footprint is significantly different.
This is because each footprint samples regions of different visual extinction,
as seen in Fig. 2. Instrumental effects are not expected to cause this, as all
4 detectors are included in each footprint.

Out of the 124 nights in the original survey, 24 nights were rejected due to
significant deviations in the mean color, leaving 100 nights for our analysis
(Fig. 1).

Figure 3:  An illustration of our procedure to reject suspicious observations.
On each night, we calculated the mean color of a large sample of stars in each
footprint. Each nightly mean color is plotted as one point for footprints A,
B, C, and D (see Fig. 2). The dashed blue ellipses enclose the nightly mean
colors for observations considered “reliable”. Nights with mean colors outside
that region were rejected.

##  3 Disk Identification and Analysis

Our scientific goal in this project is to detect young stars that possess
disks by their $K$-band excess, to briefly characterize the variability of
these disked stars, and to investigate the stability of these $K$-band
excesses with respect to time.

###  3.1 The Near-Infrared Excess

To detect optically thick disks around young stars, we use the near-infrared
excess criterion developed by Lada & Adams (1992) and the classical T Tauri
star (CTTS) locus reported by Meyer et al. (1997). We consider stars to have a
near-infrared excess consistent with an optically thick disk at $K$-band
(hereafter referred to simply as a “$K$-band excess”) if their colors fall
significantly to the right of $JHK$ space demarcated by the main sequence
reddening band and within the CTTS locus. To ensure the disk signatures are
significant, we require the sources to be 4$\sigma$ red–ward of the reddening
vector associated with the reddest, non-disk bearing stars (Lada & Adams,
1992):

$(J-H)\leq 1.714\times(H-K)$ (2)

and not more than 4 $\sigma$ below an empirically derived locus of the de-
reddened location of about 30 CTTS on the near IR color-color diagram (Meyer
et al., 1997):

$(J-H)\geq 0.58\times(H-K)+0.52$ (3)

Figure 4:  A JHK color-color diagram showing the mean colors of the 9200 stars
included in our analysis plotted as black points. The meaning of regions “P”,
“D”, and “E” are explained in the text at §3.1. The thirty stars that we
identify as disked are plotted as red circles; three stars (10%) lie, on
average, in region “P”, but are considered disk-bearing due to their observed
variability that moves them into region “D” (see §3.4). The solid line is the
locus of main-sequence stars (Koornneef, 1983) and the CTTS locus (Meyer et
al., 1997). Dashed lines are parallel to the reddening vector (Rieke &
Lebofsky, 1985), and a reference reddening vector corresponding to 5
magnitudes of visual extinction ($A_{V}=5$) is shown as a solid arrow.

We show the distribution of stars within $JHK$ space in Figure 4. Underplotted
is the locus of main-sequence star intrinsic colors (Koornneef, 1983) as a
solid curved line, and the classical T Tauri star locus (Meyer et al., 1997)
as a solid straight line that terminates near (1.0, 1.0) in $JHK$ space,
corresponding to the highest accretion rates and smallest disk hole sizes
found by Meyer et al. (1997). Reddening vectors using the extinction law
presented in Rieke & Lebofsky (1985) are plotted out from the tip of the CTTS
locus and the main-sequence curve. Loosely following Itoh et al. (1996), we
partition the inhabited areas of $JHK$ space into 3 regions: “P”, “D”, and
“E”, meaning “photosphere”, “disk”, and “extreme” respectively, demarcated by
these reddening vectors.

Region “P” is inhabited by stars whose NIR emission is dominated by their
photosphere. This includes main sequence stars, giants, and some pre-main
sequence stars with a small or negligible $K$-band excess, including CTTS with
small $K$-band excess, as well as weak T Tauri stars (wTTs). Single-epoch or
time-averaged near-infrared colors cannot distinguish between main sequence
stars and YSOs that lie in this region.

Region “D” is occupied by stars whose NIR emission originates from both a
photosphere and a disk, and is consistent with simple models of an accreting,
optically thick disk at $K$-band (Meyer et al., 1997). All stars in “D” are
definite disk-bearing young stars, but disked stars can also occupy Regions
“P” or “E”, so stars in Region “D” are not a complete sample of disk-bearing
stars.

Region “E” contains stars with more excess at $K$-band than can be accounted
for by an accreting, geometrically flat disk. These stars will be hereafter
referred to as “extreme $K$-excess stars”, and are expected to be less-
evolved; their redder colors may be due to emission from a circumstellar
envelope. Class I protostars have been found to inhabit this region due to
their redder colors (Lada & Adams, 1992; Robitaille et al., 2006).

###  3.2 Study Depth

Our goal is to search for high-confidence pre-main sequence stars in Cygnus
OB7. We chose a J=17 brightness cut. This limits errors in $J$ to about 4%
with similar errors in $H$ and $K$ for typical stellar colors. This reduced
our input catalog to 9,200 stars. At the published distance of Cyg OB7 (800
pc; distance modulus $\mu=9.5$), we can estimate to what stellar mass depth
this survey reaches by using pre-main sequence isochrones calculated from
Siess et al. (2000). For these isochrones we assume a typical YSO age of
$10^{6}$ yr. The most extinguished YSO in this sample is seen through about
11.5 magnitudes of visual extinction, as estimated by tracing its $JHK$ color
back to the CTTS locus and measuring the resulting color offset in units of
$A_{V}$. Assuming a maximum extinction of $A_{V}=12$, this survey is reaches a
nominal depth of 0.3 $M_{\sun}$, and in less extinguished regions where
$A_{V}<7$ we should reach down to the hydrogen-burning limit ($\sim
0.1M_{\sun}$). However, since the deepest part of the clouds have not been
penetrated by the survey we have no knowledge of the maximum extinction, nor
any depth to which we can be assured we are complete.

###  3.3 Variability

We identify a star as “variable” if it is seen to change at a level greater
than its photometric noise. To quantitatively select stars that are variable
in this dataset, we use the Stetson variability index $S$ (Stetson, 1996;
Carpenter et al., 2001). The Stetson index is useful for multi-wavelength
simultaneous observations, as it assumes that true variability will cause
observations at different wavelengths to rise or fall in unison; its
usefulness as a criterion for variability has been established by multiple
time-series studies (e.g. Carpenter et al., 2001, 2002; Plavchan et al., 2008;
Morales-Calderón et al., 2011). The Stetson index identifies variables even
among stars whose variability is comparable to photometric noise without any
assumptions about the type of variability seen, except that true variability
should cause all channels to vary.

The Stetson index is computed by the following equation:

$S={\sum_{i=1}^{p}\textrm{sgn}\left(P_{i}\right)\sqrt{\left|P_{i}\right|}}$
(4)

where $p$ is the number of pairs of simultaneous observations of a star.
$P_{i}=\delta_{j(i)}\delta_{k(i)}$ is the product of the relative error of two
observations.The relative error is defined as:

$\delta_{i}=\sqrt{\frac{n}{n-1}}\frac{m_{i}-\bar{m}}{\sigma_{i}}$ (5)

for a given band. The size of the bias is $\sqrt{{(n-1)}/{n}}$ where $n$ is
the total number of observations contributing to the mean. The second term is
the standard error term, where $m_{i}$ is the measure magnitude, $\bar{m}$ the
mean magnitude and $\sigma_{i}$ the intrinsic error of the individual
measurement.

Formally, the Stetson index is designed to identify stars as variable when
$S>1$ if photometric uncertainties are properly estimated. After applying the
error correction described in §2.2 and calculating $S$ for all 9,200 stars, we
find the outlier-clipped mean $S$ value to be 0.2, with the outlier-clipped
distribution having a standard deviation of 0.16. Therefore, stars with $S\geq
1$ can be considered $5\sigma$ variables, and we use $S\geq 1$ as our
criterion for variability (Fig 5).

All stars brighter than $J$=17 with no photometric processing error flags were
analyzed for variability. Among these 9,200 field stars, we recover $\sim$160
that are variable according to the Stetson index $S>1$. The positions of these
160 stars are plotted in Fig. 6 as blue squares. In this paper, we focus on
the identification and variability characteristics of the disked population;
the variability characteristics seen in the non-disked stars will be discussed
in Paper II.

Figure 5: The value of the Stetson index for all 9200 stars. As a function of
H magnitude the distribution is flat with a typical value of about 0.2. The
threshold of 1 is about a 4 $\sigma$ deviation and is exceeded by $\sim$160
sources.  Figure 6:  The spatial distribution of disked and variable stars
detected in our analysis. Disked stars are plotted as red circles; variable
stars that lack $K$-band excess are plotted as blue squares. Most (90%) disked
stars lie within the boundaries of the dark cloud, while variables are found
uniformly in the field.

###  3.4 Transient Excesses

If a star exhibits a $K$-band excess in only a fraction of its observations,
we consider its $K$-band excess to be transient. We do not expect the
circumstellar disks of such stars to actually disappear and reappear; rather,
the disks in such systems are likely undergoing physical changes that cause
their $H-K$ colors to vary back and forth across the line demarcating
unambiguous disked stars (region “D”) from ambiguous main sequence stars
(region “P”). Such a change could feasibly be induced by, star spots (hot or
cool), impulsive heating events such as stellar flares, changes in (inner)
disk inclination, local extinction, central hole size, or a varying accretion
rate (Bouvier & Bertout, 1989; Meyer et al., 1997; Scholz, 2012).

To identify and characterize stars with transient $K$-band excess, all data
satisfying our quality filter were evaluated against Equations 2 and 3 (see
§3.1). Stars that showed a $K$-band excess according to these criteria were
tallied, producing a table of stars with $K$-band excess in at least one
observation, along with the number of times that star was observed and the
fraction of nights that the star displayed a $K$-band excess.

We find 528 stars that show a $K$-band excess on at least one night. Given our
$4\sigma$ cutoff, we expect a substantial number of single-night false
positives due to photometric noise assuming Gaussian statistics in $\sim
920,000$ individual observations. We filter most of these false positives by
removing all stars that show a near-infrared excess in fewer than 15% of
nights or those that met our measurement criteria on 25 or fewer nights. (See
Figure 7, inset.) This cut makes us insensitive to any YSOs who genuinely
possess a disk that contributes to a significant $K$-band excess in less than
15% of observations, but it filters out virtually all false positives while
allowing us to remain sensitive to stars with small and moderate, but stable,
$K$-band excesses.

These criteria identify 42 disked candidates out of the original 9,200 stars.
We individually inspected the remaining lightcurves. If a star was selected by
these criteria but (a) had no photometric variability greater than noise (i.e.
$S<1$), (b) had a $JHK$ color trajectory consistent with Gaussian noise around
a mean value, and (c) on average, lay on or to the left of the boundary
between region “P” and “D” in $JHK$ color space (see Fig. 4), then we
concluded it was not clearly a CTTS that possessed a $K$-band excess, and
removed it from our analysis. 12 stars were removed this way.

Figure 7:  A histogram showing how steady the near-infrared excess was in each
of the 30 YSOs. Inset is the raw sample showing the 528 stars with a $K$-band
excess on at least one night. All stars with $K$ excess on less than 15% of
nights were rejected as false positives (dotted line). Most of the confirmed
YSOs show a consistent NIR excess on every night or nearly every night, but a
significant minority of the YSO sample (seven stars) exhibit a transient
$K$-band excess.

##  4 Results

After applying these criteria we recover 30 pre-main sequence stars, whose
properties we present in Tables 1 and 2. We designate them RWA 1–30.

###  4.1 Identification of pre-main sequence stars

Based on the method presented in §3, we report the identification of 30 young
stellar objects that possess a near-infrared excess consistent with an
optically thick disk at $K$-band (a “$K$-band excess”). Of these, 5 were
previously reported as actively accreting YSOs by Aspin et al. (2009) based on
Br$\gamma$ emission and other spectral signatures, and one was reported as a
possible but unconfirmed YSO; the remaining 24 are new discoveries.

The positions of stars identified with $K$-band excess were checked against
the IPAC database; all had a 2MASS counterpart within 0.2 arcsec, except for
RWA 28 which had no counterpart within 2″. Two stars were also found as IRAS
sources, and seven are AKARI sources. Six stars have been discussed by Aspin
et al. (2009). 2MASS photometry corroborate the presence of a $K$-band excess
at a significant level for 15 stars. In 10 more, the colors are within 2.5
$\sigma$ of the line separating regions “P” and “D”, so would be considered
ambiguous. Four stars have 2MASS colors indicating a significant _lack_ of
$K$-band excess. The YSOs CN 3S (RWA 5) and CN 7 (RWA 13) did not show NIR
excess at 2MASS epoch but were identified as $K$-band excess sources in these
observations; the classification of CN 3S was inconclusive based on its
spectrum at $1.4-2.5\mu$m, but our identification of it as a variable star
that possesses a $K$-band excess in these 2008-2009 observations, supports its
status as a YSO.

With the exception of source CN 3N, we recovered all of the YSOs identified in
Aspin et al. (2009) that our search was sensitive to – the only other stars
that we missed were either too bright or too faint for our search, or did not
show a NIR excess at the time of the 2MASS observations presented in Aspin et
al. (2009). The recovery of spectroscopically confirmed young stars in our
analysis provides a useful indication that our search is finding real YSOs.
However, this is not expected to be a complete sample of all of the young
stars in the field for three reasons. First, not all stars that possess an
accreting circumstellar disk (Class II stars) are identifiable in a $JHK$
color-color diagram, especially those seen at unfavorable inclination angles,
low accretion rates, and/or large inner disk holes (Meyer et al., 1997). Many
of these disked stars can be recovered using longer-wavelength observations
(Haisch et al., 2000; Lada et al., 2000). Second, the brightness cutoffs used
in this study to guarantee reliable photometry exclude the brighter PMS stars
and, if they exist, fainter or more substantially extincted ($A_{V}>7$) low-
mass stars. Three stars in this field (CN 3N, Cyg 19, IRAS 15N) have been
confirmed as YSOs based on spectroscopic and 2MASS observations (Aspin et al.,
2009), but are saturated in the WFCAM images; two confirmed PMS stars (the
Braid Star and IRAS 14) are likewise fainter than our cutoff. Finally, in this
analysis we removed all stars that showed any photometric error flags, such as
from deblending or bad pixels, that may in fact have useful photometry
sufficient to identify a $K$-band excess. Nonetheless, our goal in this paper
was not a complete determination of the YSO population, but rather a high-
confidence sample of $K$-excess stars whose NIR variability properties could
be reliably studied.

###  4.2 Variability of pre-main sequence stars

Of the 30 YSOs, 28 (93%) are variable at a significant level. Values of $S$
among these stars range from $S=2$ to $S=60$. Variable stars typically vary in
all 3 bands, with most stars also showing color variations. $J$-band RMS
variability in these stars ranged widely from 0.02 mag to 0.70 mag, peak-to-
trough variability ranging from near the photometric noise limit to greater
than two magnitudes at $J$ (Table 3). The median $J$-band RMS on these
variable YSOs was $\sim$0.1 mag, corresponding to a median variability index
$S\sim 10$. YSOs varied significantly on all timescales studied. Many varied
noticeably from one night to the next. However, the manner of the variations
differed among the stars with some showing slow and steady changes, while
others were more abrupt. Figure 8 shows the $K$-band lightcurves from season 2
for two “typical” stars, RWA 15 and RWA 17. In the case of RWA 15, the global
range is about 0.75 mag with night to night changes of nearly 30%. The data
also seem to have a pattern of peaks and troughs separated by about 10 days
(these will be discussed further in paper II). RWA 17 is a little chaotic in
the beginning, but in general, it shows a slow steady increase of 25% over the
course of a month, followed by a decline.

Figure 8: K band light curves for 2 sample stars RWA15 and RWA 17. Data are
from season 2. The $Stetson$ index for each star is given for season 2.

The only two $K$-excess sources which are not identified as variable, RWA 28
and 30, are both at the faintest end of our search near $J=17$ with the
largest photometric uncertainties, typically around 4% at $J=17$; hence the 2%
variability noted in some brighter stars could go undetected here. RWA 30 is
plausibly variable under its photometric noise: its $S$ value is 0.76,
$3.5\sigma$ higher than the mean $S=0.2$ value for non-variable stars, and its
observed $J$-band RMS value (while dominated by the photometric uncertainty)
is higher than four disked stars identified as variable. RWA 28, on the other
hand, shows no indications of any true variability: its variability index
$S=0.18$ is consistent with stars whose observed variations arise purely from
photometric noise. RWA 28’s photometric noise causes it to drift near the
border between “P” and “D” (in similar fashion to the 12 stars rejected from
our source list as described in §3.4). It was not excluded from our source
list because unlike the 12 excluded stars, RWA 28’s mean $JHK$ colors lie
squarely in region “D”. No other stars in our source list are suspicious in
this way. Overall, over 90% of the YSOs we identify are variable at a
significant level.

###  4.3 Extreme NIR excess.

Twenty-five stars lie in region “D” for the color-color diagram for at least
part of the observations, The remaining 5 stars, which lie in “E”, have more
excess at $K$-band than can be accounted for by accreting T-Tauri stars, like
those in Taurus which were used by Meyer et al. (1997) to derive the cTTSs
locus. We refer to these as “extreme $K$-excess stars”. Four of the five
extreme $K$-excess stars (RWA 2, 15, 19, and 26) exhibit extreme variability
as well, with variability index $S>15$. These stars may be younger, more
active counterparts to the relatively quiet classical T Tauri stars that
inhabit region “D”; the extra $K$-band excess may arise from warm, infalling
circumstellar material that is not in a disk. Indeed, three of these stars are
detected as AKARI ($9-200\mu$m) sources, and the brighter two are also IRAS
($25-100\mu$m) sources. Spectral energy distribution fits of these mid and far
IR data following Robitaille et al. (2006) support the interpretation that
they are less-evolved “Class I” protostars. Two of these stars also give
indications of being eruptive variables (Wolk et al. 2012, in prep).

###  4.4 Transient NIR excesses.

Of the 25 simple $K$-excess stars, seven vary in color space such that they
spend more than 15% of their time in region “P”, and would not be detected by
near-infrared excess criterion at these epochs (see Fig. 7). Figure 9 shows
examples of two such stars. Further, 3 of these 7 stars have mean colors that
lay in region “P”; these YSOs would be undetected in a search of time-averaged
$JHK$ color. Finally, comparison with 2MASS data show two stars that possess a
$K$-band excess in all of these UKIRT observations but show no significant
$K_{s}$-band excess at the 2MASS epoch. These nine stars (nearly 1/3 of our
sample), have been identified as exhibiting a transient $K$-band excess.

Among the variable CTTS candidates, many show $JHK$ color-color variability
parallel to either the CTTS locus (Meyer et al., 1997) or to a combination of
the CTTS locus plus extinction; the two remaining show chaotic behavior in
$JHK$ space. In Figure 10 we show the trajectories of the thirty YSOs. The
upper-left panel shows the simple disked variables which show small ($<$ 0.5
mag in color) variations which, for the most part, appear to move the star
parallel to the main sequence track or directly along the CTTS locus. The
lower-left panel shows the extreme variables, plus a few stars which move
parallel to the main sequence. The upper-right panel shows eight trajectories
which appear to be indicative of systematic changes in the disk structure.
Theoretical models of the CTTS locus (Meyer et al., 1997) derive its slope as
owing to different accretion rates, disk hole sizes, and inclination angles.
Among the extreme $K$-excess stars, color-space variability is largely
chaotic, but in two cases seems to roughly follow the same pattern of positive
color slope that seems to contain contributions from the CTTS track and from
the dust reddening track. Of course, there are more than just 3 parameters
(accretion rates, disk hole size, and inclination angle) which determine the
final location. By varying 14 parameters in their radiative transfer–based
models, Robitaille et al. (2006) calculate 200,000 model SEDs in evolutionary
stages.111 Robitaille et al. (2006) use “stages” as a theoretical equivalent
to the observational “classes” but the mapping is not exact since stages 0-III
cover Classes 0-II. The Class of an object can depend both on Stage and, for
example, viewing angle. Additional model parameters that appear susceptible to
short timescale variations include the effective stellar temperature, which
can change due to flares or spots, as well as parameters relating to the disk
structure such as the scale height of the inner disk.

While we see stars regularly cross between “P” and “D”, no stars cross between
“D” and “E”. Our sample is very small and not cleanly defined in terms of
Class. thus, the results are more open to speculation than interpretation. The
YSOs in this sample seem to separate into simple-disked Class II stars that
inhabit regions “P” and “D”, and more extreme sources that inhabit region “E”.
Models describe all but one of the stars in the “E” region as stage I
(Robitaille et al., 2006). There is a paucity of stage I models which occupy
the “D” region. This supports speculation that these are Class I sources and
we infer from Robitaille et al. (2006) that changes in the various accretion
parameters in these stars lead to changes in $J-H$ and $H-K$ color which are
mediated by an envelope which is more complex than the thin disk surrounding
Class II (Stage III) objects.

Figure 9:  $JHK$ color trajectories for RWA 4 and 23, two of the nine YSOs
identified in this analysis as having a transient near-infrared excess. RWA 4
has a significant near-infrared excess in only 41% of observations, and its
time-averaged mean $JHK$ colors lie in region “P”. RWA 23 exhibits a
significant NIR excess in 59% of observations. Colored circles indicate the
progression of time from early 2008 (dark blue) to late 2009 (dark red). Solid
line: CTTS locus. Dashed line: reddening vector. The plus (+) in the bottom-
right corner illustrates the typical uncertainty on each individual $JHK$
measurement.  Figure 10:  The color trajectories of 30 YSOs over the course of
the observations. Each star s trajectory is plotted in a different color; some
colors are repeated. Color trajectories can be broadly divided into three
groups: small systematic variations (upper-left), large systematic variation
(upper-right), and others – including large stochastic variables and a few
stars that parallel the cool main sequence (lower-left). All are overlaid in
the lower-right. Nine YSOs drift between regions P (photosphere) and D (disk)
and could be missed by single-epoch observations. A few lie, on average, in
region P and would likely be invisible to a search of time-averaged JHK color.
The small plus (+) in the bottom-right of each panel illustrates the typical
uncertainty on each individual measurement.

##  5  Discussion

###  5.1 YSOs are variable in the near-infrared

As found in §4.2, virtually all of the detected YSOs showing a $K$-band excess
also exhibit near-infrared variability. Importantly, our search did not
include variability as a selection criterion except to disambiguate close
cases. As noted in §3.2, our study is not complete. For example, only 6 of the
12 YSOs discussed in Aspin et al. (2009) were recovered by our study. Further,
the stars in our sample were subject to both brightness and faintness cuts to
ensure sensitivity to photometric variations on the order of $\sim$2%.
Nonetheless, it is clear that near-infrared variability is a behavior common
to all disked pre-main sequence stars bright enough to be measured at $>$2%
accuracy and possess a $K$-band excess. This is consistent with previous NIR
variability studies of young stars (Carpenter et al., 2001, 2002) and also
consistent with optical studies (Herbst et al., 1994; Briceño et al., 2005).

It seems likely that NIR variability could be used alone to identify young
stars, as seen in the optical (e.g. Briceño et al., 2005; Parihar et al.,
2009). Parihar et al. (2009) noted that long term monitoring increased the
variability detection rate in the optical by about 50% for periodic variables.
We do not find the effect of extended monitoring as pronounced. Twenty-six of
the 30 stars were found to be variable via the Stetson index in the 26 night,
first observing season. For the 70+ nights of seasons two and three, the
results were 25/30 and 27/30 respectively. Even the inclusion of all three
seasons only lead to the detection of 28/30 as variables. Because it was
consistently the same stars which were detected as non-variable (RWA 6, 8, 11,
28 and 30), it appears the detection of variability on the longer datasets was
not an effect of long term periodicity, but rather the increase in signal to
noise enabled by the additional data. We suspect that using the specific
trajectories in $JHK$ color space seen in these 30 stars (§4.4) as an
additional selection criterion could be useful in detecting disked stars
within the “P” portion of the reddening band. This would also be consistent
with variability seen in Carpenter et al. (2001) where stars that lack near-
infrared excess, but that are associated with the Orion A molecular cloud, are
seen to vary at a level significantly higher than field stars. This means that
Class III stars should be detected as NIR variables (e.g. Parihar et al.,
2009; Wolk et al., $in~{}prep.$).

###  5.2 On the variability of the NIR disk diagnostic

While other studies have used time-series $JHK$ photometry to investigate
young stars with disks, the use of time-averaged NIR colors to identify disked
stars (e.g. Carpenter et al., 2002) will still miss some YSOs. In this study,
we found three stars whose time-averaged colors showed no infrared excess, but
whose variability carried them into region “D” of $JHK$ space in 20%-50% of
observations, revealing the presence of a circumstellar disk around these
stars. Therefore, simply searching through time-averaged colors is not a
sufficient YSO detection technique in time-series observations.

$JHK$ observations are known not to be sensitive to 100% of disks around young
stars. The CTTS locus is partially degenerate with reddened main sequence
colors in $JHK$ space (Meyer et al., 1997), and previous infrared studies of
young stellar populations show $L$-band (3.5 $\mu$m) observations can detect
disks around $\sim 85\%$ of young stars at age $\sim 0.3-1$ Myr, while
$JHK$-only single-epoch surveys see disks around only $\sim 50-60\%$ of the
same sample of stars (Haisch et al., 2000; Lada et al., 2000). We summed up
the probability of seeing a disk around each of the RWA stars on a given
night. The probabilities range from $\sim$ 18% through 80% with many stars
that always showed disks (100%). We then calculated an expectation value of
how many disked stars we expect to see on a single night. For our data this
came out to about 25. So on an average night we expect to see 25 of the 30 RWA
stars in the “D” region of the diagram. Multiple observations gave us 30
stars, i.e. an $\sim$20% increase. If our results are typical, then a direct
consequence of this study is that 20$\pm$ 8% more disked stars may be found by
using multiple $JHK$ observations spread out over about a month, increasing
$JHK$ disk sensitivity to roughly $60\%-70\%$. In situations where it is
significantly more practical to obtain multiple $JHK$ observations than to
acquire $L$-band imaging or to carry out a spectroscopic survey to investigate
accretion, this approach could prove a useful way to simultaneously increase
the number of identified circumstellar disks and study variability of young
stars.

###  5.3 On the underlying cause for NIR variability in YSOs

Of the 30 YSOs, 28 are variable at a significant level. As seen in the upper
portion of Figure 10, about half of these vary along linear tracks. Some YSO’s
parallel the CTTS locus of Meyer et al. (1997), others seem follow a somewhat
steeper slope. As presented in §4.4, the aggregate color-domain variability
behavior is consistent with changes in mass accretion rate, inner hole size,
and inclination angle, in some cases combined with changes in extinction or
starspot coverage. Other variability mechanisms exist. These were summarized
recently by Scholz et al. (2009). The dominant process can be indicated by the
range of magnitude and color changes exhibited by the stars (summarized in
Table 3). Among these mechanisms are rotationally modulated changes due to
cool spots or hot spots on the stellar surface, extinction changes, and
changes in the inner disk. Our goal in this section is to discuss possible
factors that may induce the observed variability, not to distinguish among
them.

Changes in the overall extinction may be the simplest to imagine. Perhaps
induced by the disk, extinction can cause unlimited changes in the apparent
flux of the stars. However, such changes should move the star in the direction
of the reddening vector. Figure 10 shows no pure examples of this. However,
there are many cases where the data appear to move predominantly in this
direction (see Figure 10 upper-right). RWA 17 and RWA 26 show some of the
clearest examples of changes in reddening (Fig. 11). However, it is clear from
the color–magnitude plots that the observed changes are not due to reddening
alone.

Cool spots, like those on the Sun, were first identified as a contributor to
the variability of PMS stars in the 1980s (Vrba et al., 1985). Even static
stellar spots induce variability because of the rotation of the star.
Starspots have been used regularly as a method of measuring stellar periods
(e.g. Attridge & Herbst, 1992). But there is a limit to the variability cool
spots can induce, since the spot is typically only 1000-1500K cooler than the
nominal photosphere. In the I band, the luminosity change is typically $<$ 15%
(Cohen, Herbst, & Williams, 2004). The implied color change due to a lower
effective temperature is $<$ 5%. All the stars in our sample exceed a color
range of 9% in $J-K$ (Table 3).

Hot spots, thought to arise from accretion, can cause a larger signal than
cool spots since the temperature difference is typically larger (a factor of 2
or 3 hotter than the surrounding photosphere). These can induce signals as
high at 1 magnitude at $J$ and color changes of 40% in $J-K$, even with a
filling factor as small as 1% (Scholz et al., 2009). However, over 1/3 of our
sample exceeds this color range, so hot spots alone cannot account for this
variability. Of the remaining 20 stars, half of them have color changes in
excess of of 25% in $J-K$, indicative of very active hot spots or a
combination of variable hot spot and other effects.

Figure 11: Season 2 color data for RWA 17 (left) and RWA 26 (right). While the
data generally track the reddening vectors, the significant width of the
tracks indicates a secondary cause of the variations. “Time” refers to days
since the first observation – April 26, 2008

The CTTS locus is derived from models of T Tauri stars with accretion rates
spanning two orders of magnitude, disk hole sizes spanning $1-10R_{\star}$,
and a full range of observable inclination angles (see Meyer et al., 1997;
Robitaille et al., 2006, esp. Fig. 3 and Fig. 18 respectively). That most of
the $JHK$ variability we see in CTTS candidates is focused along this track is
evidence that changes in the overall accretion structure – disk inclination,
hole size and accretion rate (the size of the hot spots) – are the primary
cause for $JHK$ variability in about half of the stars. This is especially
true for the subset of stars in Figure 10 upper-left which move right along
this track and those in Figure 10 upper-right which appear to follow a hybrid
of this track plus reddening.

Because of the degeneracy between the 3 parameters (disk inclination, hole
size and accretion rate), it is not possible to easily disentangle the
contributions from each of these 3 parameters. That said, it is easy to
imagine ways that they might co-vary. For example, a decreasing (or
increasing) inner disk hole size might naturally be simultaneous with an
increasing (or decreasing) accretion rate. The line-of-sight inclination of
the innermost edge of the disk – not the inclination of the entire disk –
might reasonably vary due to warping in response to a strong, misaligned
stellar magnetic field and the rotation of the star. Observational and
theoretical evidence for warped accretion disks has been provided by Bouvier
et al. (2003) and Espaillat et al. (2011). For the data presented here, we do
not attempt to model the individual stars to identify the specific mechanisms
of variability.

###  5.4 Individual variability

In addition to the aggregate color-domain variability just analyzed, we have
identified a number of striking pattens of variability in individual stars’
lightcurves. Periodic, quasi-periodic and eruptive variability is seen among
the identified YSOs, mirroring previously studied classes of variable YSOs
such as the periodic disk eclipses of AA Tau (Bouvier et al., 2003), and the
eruptive, large-scale accretion events of EX Lup and V1118 Ori (Aspin et al.,
2010; Audard et al., 2010). Many classes of variability including eclipsing
and contact binaries, and “long-period” ($P\sim$ weeks) pulsating stars are
seen among the $\sim 160$ variable field stars. In one case one of the disked
stars appears to be part of an eclipsing system. A detailed investigation of
these variable stars will appear in Paper II.

##  6 Summary

We observed a star-forming region in the dark cloud L 1003 in Cyg OB7 on more
than 100 nights spanning 1.5 years using NIR wide-band photometry. Using the
$K$-band excess diagnostic, we found 30 candidate PMS stars, including 25
disked objects (CTTS candidates) and 5 young stars with extreme $K$-band
excess (Class I candidates). Among the 25 CTTS candidates, nine (36%) cross
the main sequence reddening band cutoff, indicating that single-epoch
observations are insufficient to identify all YSOs that show $K$-band excess.
Even time-series observations may miss some stars if they only select using
time-averaged $JHK$ colors. Additionally, the pattern of variability in color
space seen in the variable CTTS candidates is a strong indication that NIR
variability in young stars arises from a combination of variable extinction
and changes in the inner accretion disk. While some of the variability may be
due to rotationally modulated starspots other possibilities include changes in
accretion rate, inner hole size, and/or disk inclination. None of the extreme
$K$-band excess stars are seen to cross into the “disked” region of the NIR
color-color space.

To summarize our results:
(1) The 30 pre-main sequence stars discussed in this paper include 24 newly
identified YSOs.
(2) Overall, $>$90% of the young stars with disks are variable. Over 80% are
variable on a time scale of about 1 month.
(3) YSOs can be separated into “simple-disk” or “extreme” classes based on
degree of $K$-band excess.
(4) 36% of “simple-disk” $K$-band excess sources have a transient $K$-band
excess.
(5) The color behavior of many of the “simple-disk” YSOs is consistent with
changes in disk geometry and/or accretion rate.

In this paper we have presented an analysis of a unique dataset: containing
multi–season NIR monitoring for variability of young stars. A follow-up paper
(Wolk et al., $in~{}prep.$) will discuss the variability of field stars, and a
phenomenological categorization of NIR variability seen in YSOs. Further
observations, at both IR and X-ray wavelengths, are planned to better
characterize the overall pre-main sequence population in this field.

##  7 Acknowledgements

Thanks to Joseph Hora and David Charbonneau for useful comments as this
research project was being developed. Thanks also to Mike Read and Nicholas
Cross for assistance with the data retrieval. Thanks to Bo Reipurth for
stimulating discussions.

S.J.W. is supported by NASA contract NAS8-03060 (Chandra). T.S.R. was
supported by Grant #1348190 from the Spitzer Science Center. Thanks also to
the NSF REU program for funding part of this research via NSF REU site grant
#0757887. This research has made use of the NASA/ IPAC Infrared Science
Archive, which is operated by the Jet Propulsion Laboratory, California
Institute of Technology, under contract with the National Aeronautics and
Space Administration. The United Kingdom Infrared Telescope is operated by the
Joint Astronomy Centre on behalf of the Science and Technology Facilities
Council of the U.K. We thank A. Nord, L. Rizzi, and T. Carroll for assistance
in obtaining these observations. We also thank the University of Hawaii Time
Allocation Committee for allocating the nights during which these observations
were made. The authors wish to recognize and acknowledge the very significant
cultural role and reverence that the summit of Mauna Kea has always had within
the indigenous Hawaiian community. We are most fortunate to have the
opportunity to conduct observations from this sacred mountain.

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Table 1: Object Coordinates, Identification, and Median Photometry

|  |  |  | Median WFCAM Photometric Values |
---|---|---|---|---|---
Object ID | R.A. (J2000) | Decl. (J2000) | 2MASS ID | $J$ | $H$ | $K$ | Other ID
RWA 1 | 21:00:16.570 | +52:26:23.105 | 21001656+5226230 | 12.886$\pm$0.021 | 11.796$\pm$0.021 | 10.982$\pm$0.021 | CN 2aaFrom Aspin et al. 2009.
RWA 2 | 21:02:05.456 | +52:28:54.477 | 21020547+5228544 | 16.487$\pm$0.028 | 14.155$\pm$0.022 | 11.851$\pm$0.021 | 21005+5217bbAKARI ID
|  |  |  |  |  |  | 2102055+522854ccIRAS ID
RWA 3 | 20:59:04.194 | +52:21:44.936 | 20590419+5221448 | 14.404$\pm$0.022 | 13.098$\pm$0.021 | 12.208$\pm$0.021 |
RWA 4 | 20:58:59.790 | +52:22:18.340 | 20585978+5222182 | 12.403$\pm$0.021 | 11.420$\pm$0.021 | 10.823$\pm$0.021 |
RWA 5 | 21:00:05.086 | +52:34:04.916 | 21000508+5234049 | 12.875$\pm$0.021 | 11.601$\pm$0.021 | 10.778$\pm$0.021 | CN 3SaaFrom Aspin et al. 2009.
RWA 6 | 21:02:43.791 | +52:23:48.278 | 21024378+5223484 | 14.865$\pm$0.022 | 13.605$\pm$0.021 | 12.741$\pm$0.021 |
RWA 7 | 21:00:02.165 | +52:35:16.205 | 21000217+5235160 | 15.361$\pm$0.023 | 13.872$\pm$0.021 | 12.646$\pm$0.021 | 2100019+523515ccIRAS ID
RWA 8 | 21:02:14.992 | +52:32:40.380 | 21021498+5232405 | 16.114$\pm$0.025 | 15.107$\pm$0.023 | 14.400$\pm$0.023 |
RWA 9 | 21:02:38.301 | +52:17:40.841 | 21023831+5217408 | 14.758$\pm$0.022 | 13.620$\pm$0.021 | 12.872$\pm$0.021 |
RWA 10 | 21:00:55.768 | +52:25:22.066 | 21005576+5225221 | 14.783$\pm$0.022 | 13.833$\pm$0.021 | 13.188$\pm$0.021 |
RWA 11 | 20:59:46.192 | +52:33:21.470 | 20594619+5233216 | 13.159$\pm$0.021 | 11.881$\pm$0.021 | 10.961$\pm$0.021 |
RWA 12 | 20:59:51.844 | +52:40:20.613 | 20595184+5240205 | 14.190$\pm$0.021 | 12.132$\pm$0.021 | 10.739$\pm$0.021 | 2059518+524020ccIRAS ID
RWA 13 | 21:00:17.257 | +52:28:25.488 | 21001725+5228253 | 12.100$\pm$0.021 | 11.199$\pm$0.021 | 10.632$\pm$0.021 | CN 7aaFrom Aspin et al. 2009.
RWA 14 | 21:02:01.476 | +52:54:35.758 | 21020145+5254357 | 15.476$\pm$0.023 | 14.632$\pm$0.022 | 14.087$\pm$0.022 |
RWA 15 | 21:00:35.175 | +52:33:24.410 | 21003517+5233244 | 15.128$\pm$0.022 | 13.883$\pm$0.021 | 12.432$\pm$0.021 | CN 1aaFrom Aspin et al. 2009., 20590+5221bbAKARI ID
|  |  |  |  |  |  | 2100352+523324ccIRAS ID
RWA 16 | 20:58:49.664 | +52:19:46.513 | 20584965+5219465 | 12.749$\pm$0.021 | 11.862$\pm$0.021 | 11.249$\pm$0.021 |
RWA 17 | 20:58:50.100 | +52:32:54.427 | 20585009+5232543 | 14.221$\pm$0.021 | 11.957$\pm$0.021 | 10.383$\pm$0.021 | 2058500+523255
RWA 18 | 20:59:45.016 | +52:17:49.147 | 20594500+5217492 | 12.756$\pm$0.021 | 11.832$\pm$0.021 | 11.115$\pm$0.021 |
RWA 19 | 20:59:40.710 | +52:34:13.470 | 20594071+5234135 | 14.679$\pm$0.022 | 12.460$\pm$0.021 | 10.655$\pm$0.021 | CN 6aaFrom Aspin et al. 2009.
|  |  |  |  |  |  | 2059408+523414ccIRAS ID
RWA 20 | 21:00:19.042 | +52:27:28.307 | 21001903+5227281 | 11.622$\pm$0.021 | 10.631$\pm$0.021 | 9.794$\pm$0.021 | CN 8aaFrom Aspin et al. 2009.
|  |  |  |  |  |  | 2100191+522728ccIRAS ID
RWA 21 | 21:00:36.443 | +52:12:03.049 | 21003643+5212030 | 12.748$\pm$0.021 | 11.807$\pm$0.021 | 11.164$\pm$0.021 |
RWA 22 | 20:59:08.952 | +52:22:39.320 | 20590895+5222392 | 13.197$\pm$0.021 | 12.066$\pm$0.021 | 11.244$\pm$0.021 |
RWA 23 | 20:57:43.462 | +52:51:00.401 | 20574345+5251004 | 14.892$\pm$0.022 | 14.040$\pm$0.021 | 13.476$\pm$0.021 |
RWA 24 | 20:59:19.282 | +52:25:43.857 | 20591928+5225437 | 13.657$\pm$0.021 | 12.077$\pm$0.021 | 11.008$\pm$0.021 |
RWA 25 | 21:01:02.586 | +52:27:08.640 | 21010258+5227086 | 11.859$\pm$0.021 | 11.085$\pm$0.021 | 10.610$\pm$0.021 |
RWA 26 | 21:01:02.575 | +52:24:00.053 | 21010256+5223599 | 16.913$\pm$0.036 | 15.199$\pm$0.023 | 13.707$\pm$0.022 |
RWA 27 | 21:01:16.921 | +52:28:32.587 | 21011691+5228325 | 14.704$\pm$0.022 | 13.514$\pm$0.021 | 12.832$\pm$0.021 |
RWA 28 | 20:59:51.642 | +52:41:32.479 | not detected | 16.922$\pm$0.037 | 15.837$\pm$0.028 | 15.080$\pm$0.026 |
RWA 29 | 20:59:10.396 | +52:07:44.327 | 20591038+5207442 | 16.115$\pm$0.025 | 15.124$\pm$0.023 | 14.258$\pm$0.022 |
RWA 30 | 21:01:01.073 | +52:10:42.787 | 21010106+5210427 | 17.049$\pm$0.039 | 15.275$\pm$0.024 | 13.775$\pm$0.022 |

Note. — Median photometric values were extracted from 100 $JHK$ observations
of each star.

Table 2: Variability Characteristics

| Observed RMS | Color RMS | Stetson index | P/D/E | Transient excess?
---|---|---|---|---|---
Object ID | $J$ | $H$ | $K$ | $J-H$ | $H-K$ | $S$ | (on average) |
RWA 1 | 0.473 | 0.379 | 0.278 | 0.099 | 0.103 | 42.96 | D | no
RWA 2 | 0.698 | 0.746 | 0.493 | 0.191 | 0.361 | 59.95 | E | no
RWA 3 | 0.022 | 0.022 | 0.019 | 0.021 | 0.019 | 3.78 | D | no
RWA 4 | 0.086 | 0.085 | 0.087 | 0.028 | 0.041 | 8.59 | P | yes
RWA 5 | 0.064 | 0.051 | 0.054 | 0.023 | 0.028 | 7.72 | D | yesaaTransient $K$-excess classification based on 2MASS data
RWA 6 | 0.020 | 0.023 | 0.031 | 0.013 | 0.013 | 2.23 | D | yesaaTransient $K$-excess classification based on 2MASS data
RWA 7 | 0.241 | 0.276 | 0.295 | 0.059 | 0.068 | 30.81 | D | no
RWA 8 | 0.101 | 0.056 | 0.067 | 0.050 | 0.068 | 2.77 | D | yes
RWA 9 | 0.066 | 0.078 | 0.094 | 0.017 | 0.021 | 8.65 | D | no
RWA 10 | 0.028 | 0.032 | 0.043 | 0.016 | 0.021 | 3.33 | D | no
RWA 11 | 0.029 | 0.027 | 0.041 | 0.014 | 0.016 | 3.40 | D | no
RWA 12 | 0.154 | 0.133 | 0.134 | 0.037 | 0.042 | 14.45 | D | no
RWA 13 | 0.093 | 0.087 | 0.091 | 0.030 | 0.042 | 7.95 | D | yes
RWA 14 | 0.265 | 0.201 | 0.184 | 0.050 | 0.072 | 25.05 | P | yes
RWA 15 | 0.248 | 0.199 | 0.184 | 0.104 | 0.110 | 16.58 | E | no
RWA 16 | 0.127 | 0.112 | 0.091 | 0.028 | 0.048 | 11.50 | D | no
RWA 17 | 0.287 | 0.194 | 0.137 | 0.098 | 0.070 | 24.21 | D | no
RWA 18 | 0.135 | 0.115 | 0.112 | 0.037 | 0.053 | 11.53 | D | no
RWA 19 | 0.135 | 0.182 | 0.145 | 0.067 | 0.057 | 16.15 | E | no
RWA 20 | 0.128 | 0.126 | 0.144 | 0.030 | 0.032 | 10.80 | D | no
RWA 21 | 0.245 | 0.190 | 0.136 | 0.062 | 0.068 | 19.91 | D | no
RWA 22 | 0.062 | 0.067 | 0.093 | 0.019 | 0.034 | 7.59 | D | no
RWA 23 | 0.054 | 0.056 | 0.073 | 0.022 | 0.043 | 6.74 | D | yes
RWA 24 | 0.052 | 0.048 | 0.061 | 0.014 | 0.023 | 5.51 | D | no
RWA 25 | 0.075 | 0.055 | 0.067 | 0.032 | 0.042 | 6.50 | D | yes
RWA 26 | 0.270 | 0.244 | 0.205 | 0.097 | 0.078 | 15.87 | E | no
RWA 27 | 0.247 | 0.181 | 0.132 | 0.070 | 0.058 | 34.58 | P | yes
RWA 28 | 0.035 | 0.019 | 0.015 | 0.036 | 0.025 | 0.18 | D | no
RWA 29 | 0.037 | 0.042 | 0.052 | 0.022 | 0.020 | 3.61 | D | no
RWA 30 | 0.033 | 0.020 | 0.022 | 0.032 | 0.019 | 0.76 | E | no

Note. — Typical photometric errors are $\sim 2\%$ Refer to Table 1 for more
details.

Table 3: Variability extrema  Object ID | Median $K$ | $\Delta J$ | $\Delta K$ | $\Delta J-H$ | $\Delta H-K$ | $\Delta J-K$
---|---|---|---|---|---|---
RWA 1 | 10.98 | 1.85 | 1.13 | 0.45 | 0.50 | 0.93
RWA 2 | 11.90 | 2.74 | 1.78 | 1.19 | 1.36 | 1.64
RWA 3 | 12.21 | 0.35 | 0.30 | 0.13 | 0.13 | 0.12
RWA 4 | 10.82 | 1.23 | 0.55 | 1.10 | 0.51 | 0.84
RWA 5 | 10.78 | 0.67 | 0.48 | 0.19 | 0.22 | 0.40
RWA 6 | 12.74 | 0.09 | 0.11 | 0.07 | 0.06 | 0.09
RWA 7 | 12.69 | 0.81 | 0.93 | 0.20 | 0.25 | 0.39
RWA 8 | 14.40 | 0.43 | 0.32 | 0.47 | 0.44 | 0.52
RWA 9 | 12.87 | 0.26 | 0.36 | 0.10 | 0.10 | 0.14
RWA 10 | 13.19 | 0.15 | 0.31 | 0.09 | 0.11 | 0.19
RWA 11 | 10.96 | 0.14 | 0.18 | 0.07 | 0.12 | 0.12
RWA 12 | 10.74 | 0.73 | 0.57 | 0.28 | 0.24 | 0.35
RWA 13 | 10.63 | 0.58 | 0.43 | 0.20 | 0.25 | 0.44
RWA 14 | 14.07 | 1.49 | 0.73 | 0.42 | 0.70 | 0.99
RWA 15 | 12.43 | 1.27 | 0.89 | 0.47 | 0.59 | 1.02
RWA 16 | 11.25 | 0.61 | 0.41 | 0.18 | 0.22 | 0.39
RWA 17 | 10.38 | 0.94 | 0.51 | 0.37 | 0.25 | 0.59
RWA 18 | 11.12 | 0.66 | 0.61 | 0.18 | 0.22 | 0.40
RWA 19 | 10.61 | 0.71 | 0.60 | 0.26 | 0.40 | 0.40
RWA 20 | 9.76 | 0.87 | 0.89 | 0.17 | 0.15 | 0.20
RWA 21 | 11.17 | 1.20 | 0.67 | 0.28 | 0.32 | 0.60
RWA 22 | 11.24 | 0.73 | 0.45 | 0.51 | 0.14 | 0.52
RWA 23 | 13.48 | 0.48 | 0.35 | 0.12 | 0.25 | 0.34
RWA 24 | 11.01 | 0.28 | 0.29 | 0.07 | 0.12 | 0.12
RWA 25 | 10.61 | 0.49 | 0.32 | 0.22 | 0.22 | 0.31
RWA 26 | 13.71 | 1.33 | 1.05 | 0.49 | 0.35 | 0.71
RWA 27 | 12.83 | 1.99 | 1.37 | 0.33 | 0.50 | 0.79
RWA 28 | 15.08 | 0.17 | 0.10 | 0.22 | 0.13 | 0.19
RWA 28 | 14.24 | 0.17 | 0.23 | 0.16 | 0.10 | 0.25
RWA 30 | 13.78 | 0.22 | 0.11 | 0.22 | 0.11 | 0.20
Median |  | 0.66 | 0.46 | 0.22 | 0.23 | 0.40
Maximum |  | 2.74 | 1.78 | 1.19 | 1.36 | 1.64
Minimun |  | 0.09 | 0.10 | 0.07 | 0.06 | 0.09