11institutetext:  Key Laboratory of Optical Astronomy, National Astronomical
Observatories, Chinese Academy of Sciences, Beijing 100101, China;
<EMAIL_ADDRESS>
22institutetext: Max-Planck Institute for Astronomy, Königstuhl 17, D-69117
Heidelberg, Germany
33institutetext: Department of Astronomy, Beijing Normal University, Beijing
100875, China
44institutetext: School of Physics and Astronomy, University of Birmingham,
Birmingham B15 2TT, UK
55institutetext: Stellar Astrophysics Centre, Department of Physics and
Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus C, Denmark
66institutetext: College of Astronomy and Space Sciences, University of
Chinese Academy of Sciences, Beijing 100049, China
77institutetext: Yunnan Astronomical Observatory, China Academy of Sciences,
Kunming, 650216, China
88institutetext: Key Lab of Space Astronomy and Technology, National
Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
99institutetext: WHU-NAOC Joint Center for Astronomy, Wuhan University, Wuhan,
Hubei 430072, China
Received 20XX Month Day; accepted 20XX Month Day

# LAMOST Time-Domain Survey: First Results of four $K$2 plates

Song Wang 11 Haotong Zhang 11 Zhongrui Bai 11 Hailong Yuan 11 Maosheng Xiang
22 Bo Zhang 33 Wen Hou 11 Fang Zuo 11 Bing Du 11 Tanda Li 445533 Fan Yang
331166 Kaiming Cui 1166 Yilun Wang 1166 Jiao Li 11 Mikhail Kovalev 7722
Chunqian Li 11 Hao Tian 11 Weikai Zong 33 Henggeng Han 1166 Chao Liu 8866 A-Li
Luo 1166 Jianrong Shi 1166 Jian-Ning Fu 33 Shaolan Bi 33 Zhanwen Han 77 Jifeng
Liu 116699

###### Abstract

From Oct. 2019 to Apr. 2020, LAMOST performs a time-domain spectroscopic
survey of four $K$2 plates with both low- and med-resolution observations. The
low-resolution spectroscopic survey gains 282 exposures ($\approx$46.6 hours)
over 25 nights, yielding a total of about 767,000 spectra, and the med-
resolution survey takes 177 exposures ($\approx$49.1 hours) over 27 nights,
collecting about 478,000 spectra. More than 70%/50% of low-resolution/med-
resolution spectra have signal-to-noise ratio higher than 10. We determine
stellar parameters (e.g., $T_{\rm eff}$, log$g$, [Fe/H]) and radial velocity
(RV) with different methods, including LASP, DD-Payne, and SLAM. In general,
these parameter estimations from different methods show good agreement, and
the stellar parameter values are consistent with those of APOGEE. We use the
Gaia DR2 RV data to calculate a median RV zero point (RVZP) for each
spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with
the APOGEE data. The stellar evolutionary and spectroscopic masses are
estimated based on the stellar parameters, multi-band magnitudes, distances
and extinction values. Finally, we construct a binary catalog including about
2700 candidates by analyzing their light curves, fitting the RV data,
calculating the binarity parameters from med-resolution spectra, and cross-
matching the spatially resolved binary catalog from Gaia EDR3. The LAMOST TD
survey is expected to get breakthrough in various scientific topics, such as
binary system, stellar activity, and stellar pulsation, etc.

###### keywords:

astronomical database: miscellaneous — catalogs — stars: fundamental
parameters — binaries: general — binaries: spectroscopic

##  1 Introduction

Time-domain (hereafter TD) exploration of the sky is at the forefront of
modern astronomy. In recent years, the TD astronomy has rapidly advanced
thanks to many wide-field surveys, such as the Palomar Transient Factory (PTF;
Law et al. 2009) and Zwicky Transient Facility (ZTF; Bellm et al. 2019), the
Panoramic Survey Telescope and Rapid Response System (Pan-STARRS; Hodapp et
al. 2004), the SkyMapper (Keller et al. 2007), the Kepler mission (Borucki et
al. 2010), and the Transiting Exoplanet Survey Satellite (TESS; Ricker et al.
2015).

Most of current TD surveys provide imaging data and focus on the
photometrically variable sky, whereas spectroscopic surveys providing multi-
epoch spectra for variable objects are still lacking to date (MacLeod et al.
2018). The SDSS TD spectroscopic survey, a SDSS-IV eBOSS subproject, is
providing repeat observations for about 13000 qusars and 3000 variable stars,
including dwarf carbon stars, white dwarf/M dwarf pairs, hypervariable stars,
and active ultracool (late-M and early-L) dwarfs (MacLeod et al. 2018).
Recently, LAMOST (Large Sky Area Multi-Object fiber Spectroscopic Telescope;
also known as GuoShouJing telescope) started the second 5-year survey program,
LAMOST II, containing both non-TD and TD surveys. In the 5-year survey plan,
about 50% nights (dark/gray nights) are assigned to the low-resolution
spectroscopic (LRS; R $\sim$ 1800) survey, and the other 50% nights
(bright/gray nights) to the medium-resolution spectroscopic (MRS; R $\sim$
7500) survey (see Liu et al. 2020; Zong et al. 2020, for more details).

The LAMOST TD survey will monitor about 200 thousand stars with averagely 60
MRS exposures in five years (Liu et al. 2020), which provides a great
opportunity to get some breakthrough in diverse scientific topics, including
binarity, stellar pulsation, star formation, stellar activity, etc. For
example, many attractive binaries are expected to be discovered during their
last evolutionary stages, such as wide dwarf-main sequence binaries, symbiotic
stars, cataclysmic variables, and even binaries including one neutron star or
black hole. An initial estimation of the precision of the radial velocity (RV)
is close to 1 km/s for the MRS data (Liu et al. 2020), which is about 3–5
times higher than those obtained from the LRS data (Luo et al. 2015). That
means more accurate orbital parameters can be determined for the binaries. We
can also study the variable chromospheric activity of single stars (rotational
modulation) or binaries (orbital modulation), by tracing the behavior of the
CaII H&K and H$\alpha$ lines.

In the past few years, Kepler (Borucki et al. 2010) and K2 missions have
provided precise TD photometric data for hundreds of thousands of stars, which
is a valuable resource for various studies on many topics from exoplanet to
asteroseismology. From 2012 to 2019, LAMOST carried out a LAMOST-Kepler
project, using 14 LAMOST plates to almost fully cover the Kepler field of view
($\sim$ 105 square degrees) (Fu et al. 2020). From 2018, the Phase II of the
LAMOST-Kepler/K2 survey started aiming at collecting MRS data for more than
50,000 stars located in the Kepler field and six K2 plates (Zong et al. 2020).
From 2019 to 2020, LAMOST performed a TD survey of four new K2 plates with
both LRS and MRS observations. In Section 2, we describe this project in
detail, including data reduction and statistics of the observations and
spectra. We describe the stellar parameter determination and comparison with
other databases in Section 3. The mass estimation of the sample stars is given
in Section 4. In Section 5, we present one binary catalog by using different
methods. Finally, we summarize our results and some prospective scientific
goals of this project in Section 6.

##  2 LAMOST OBSERVATION AND DATA REDUCTION

This survey includes four footprints in the K2 campaigns (Figure 1). We used
the Gaia DR2 catalog (Gaia Collaboration et al. 2018) for source selection.
Variable sources recognized by photometric surveys (e.g., ASAS-SN, $K$2) were
preferred. There are totally about 10700 stars in our sample, with magnitudes
ranging from $\approx$10 mag to $\approx$15 mag. Most stars are G- and K-type
stars (Figure 2).

We performed this survey with both the LRS and MRS observations. For LRS
observation, the wavelength coverage is 3650–9000 Å (Luo et al. 2015). For MRS
observation, the blue and red arms cover wavelength ranges from 4950 Å to 5350
Å and from 6300 Å to 6800 Å, respectively (Liu et al. 2020). The LRS survey of
each plate was observed with 3–10 single 600 s exposures in one observation
night; the MRS survey of each plate was observed with 3–8 single 1200 s
exposures. Both the exposure numbers and exposure times may beyond these
ranges depending on the observation condition (e.g., seeing). The fiber
assignment contains target stars, flux standard stars, and sky background
(Table 1).

From Oct. 2019 to Arp. 2020, the LRS survey was totally performed on 26
dark/gray nights, and the MRS survey was taken on 27 bright/gray nights. For
LRS part, we derived 767,158 and 767,150 spectra in the blue and red arms,
respectively, corresponding to a total exposure time of $\approx$46.6 hours.
More than 9000/6800/4100 targets have more than 50/60/70 exposures, and more
than 9000/4100/2800 targets were observed more than 30/40/50 ks. For MRS part,
we gained 478,694 spectra for both the blue and red arms, corresponding to an
exposure time of $\approx$49.1 hours. There are more than 8800/4100/3500
targets with more than 30/40/50 exposures, and more than 8800/4100/3700
targets observed more than 30/40/50 ks. The exposure numbers and exposure
times per source are shown in Figure 3.

The raw CCD data from the LRS and MRS survey were reduced by the LAMOST 2D
pipeline, including bias and dark subtraction, flat filed correction, spectrum
extraction, sky background subtraction, wavelength calibration, etc (see Luo
et al. 2015, for details). The wavelength calibration of the LRS data was
based on the Sr and Th-Ar lamps and night sky lines (Magic et al. 2010),
whereas the wavelength calibration of the MRS data only used the lamps. A
vacuum wavelength scale was applied to the spectra and corrected to the
heliocentric frame at last.

In order to show the spectral quality, we calculated the signal-to-noise ratio
(SNR) of the $g$-band spectrum for the LRS data and the SNR of the whole
spectrum for the MRS data. We derived 538,760 high-quality spectra (SNR $>$
10) in the LRS survey, including 479,996, 276,292, and 103,076 spectra with
SNR above 20, 50, and 100. They corresponded to a fraction of $\sim$ 89.1%,
51.3%, and 19.1% of the high-quality spectra. For the MRS survey, we derived
257,558 spectra with SNR above 10, including 176,603, 62,121, and 16,712 ones
with SNR higher than 20, 50, and 100, corresponding to a fraction of 68.6%,
24.1%, and 6.5% of the high-quality spectra, respectively.

Figure 1: Sky coverage of the four $K$2 plates. The solid line represents the
ecliptic plane.

Figure 2: Left Panel: Histogram of the magnitude distribution of our sample
stars. The truncation around $G\ =$ 15 mag is due to our selection criteria.
Right Panel: Color-magnitude diagram of our sources. The color scale
represents the density of stars. Figure 3: Left Panel: Cumulative histograms
of exposure numbers for the LRS and MRS surveys. Right Panel: Cumulative
histograms of exposure times for the LRS and MRS surveys. Figure 4: Left
Panel: Distribution of SNR for the LRS data. Right Panel: Distribution of SNR
for the MRS data. Table 1: Overview of observations of the four $K$2 plates.

| LRS | MRS
---|---|---
PlanID | R.A. | Dec. | Nstar | NFS | Nsky | Nexp,L | Nnights,L | Nexp,M | Nnights,M
TD035052N235741K01 | 03:50:52.4 | 23:57:41 | 2820 | 78 | 354 | 62 | 8 | 35 | 7
TD064253N231249K01 | 06:42:53.9 | 23:12:49 | 2987 | 79 | 316 | 77 | 11 | 54 | 9
TD084656N120635K01 | 08:46:56.0 | 12:06:35 | 2756 | 80 | 503 | 86 | 10 | 53 | 11
TD104903N100928K01 | 10:49:03.2 | 10:09:28 | 1885 | 77 | 1253 | 57 | 9 | 35 | 8

The columns are: (1) PlanID: the plan name of target field marked with a
string of 18 characters; (2) R.A.: the right ascension of the central star at
epoch J2000; (3) Dec.: the declination of the central star at epoch J2000; (4)
Nstar: the number of input target stars; (5) NFS: the number of flux standard
stars; (6) Nsky: the number of fibers for sky background measurements; (7)
Nexp,L: exposure numbers of the LRS survey; (8) Nnights,L: observed nights of
the LRS survey; (9) Nexp,M: exposure numbers of the MRS survey; (10)
Nnights,M: observed nights of the MRS survey.

##  3 STELLAR PARAMETER DETERMINATION

For the spectra obtained in this project, three groups have been using
independent approaches to characterize the observed stars and derive stellar
parameters.

###  3.1 LASP

For both the LRS and MRS data, the LAMOST Stellar Parameter Pipeline (LASP;
Luo et al. 2015) was used to obtain the atmospheric parameters
($T_{\mathrm{eff}}$, $\log g$, and [Fe/H]) and RV. It consists of two steps:
Correlation Function Initial (CFI) and Ulyss (Wu et al. 2011). The former
method provides initial parameter values for Ulyss to determine accurate
measurements. The basic idea of the CFI algorithm is based on the template
matching method. The synthetic library (from KURUCZ) adopted by the CFI
contains 8903 spectra. In general, five best-matching templates are found with
nonlinear least-squares minimization method for an observed spectrum. We
adopted the linear combination of the stellar parameters of the five templates
as initial guesses (Yee et al. 2017) for Ulyss. This method derived all free
parameters ($T_{\mathrm{eff}}$, $\log g$, [Fe/H], and RV) simultaneously via
minimizing the squared difference between the observed and the template
spectra.

The uncertainties of the parameters can be summarized as 34 K in $T_{\rm eff}$
, 0.06 dex in $\log g$, 0.03 dex in [Fe/H], and 5.7 km/s in RV for the LRS
spectra with SNR $\geq$ 50, and 61 K in $T_{\rm eff}$ , 0.06 dex in $\log g$,
0.04 dex in [Fe/H], and 1.3 km/s in RV for the MRS spectra with SNR $\geq$
50\. For single epoch spectrum, the errors of the atmospheric parameters and
RV were determined by two factors including the SNR and the best-matched
$\chi^{2}$. Here we presented a brief description of the estimation of errors,
and a more detailed description is referred to Du B, et al. (2021, in prep).
Basing on a sample of targets having multiple observations, we obtained the
precision of the parameters using the following estimator:

$\Delta P_{i}=\sqrt{N/(N-1)}(P_{i}-\overline{P})$ (1)

where $i$ ($=$ 1, 2, …, $N$) is one of the individual measurements and $N$ is
the total number of measurements for parameter $P$. Then, we fit both the
precision of the parameter and the best-matched $\chi^{2}$ as functions of the
SNR. Through these two functions, the error of the parameter for single epoch
spectrum can be calculated according to its SNR and the best-matched
$\chi^{2}$.

Besides the RV determined by LASP, we provided four more RV measurements. They
are marked as rv_ku0, rv_71el0, rv_ku1, and rv_71el1, respectively. The first
two RV values were both determined with the cross-correlation method with a
set of synthetic spectra as templates. The only difference is that 483 KURUCZ
model spectra were selected for rv_ku0 and 71 spectra from ELODIE library for
rv_71el0. The latter two values were further calibrated with RV zero point
(RVZP) derived by Th-Ar and Sc arc lamps. A brief description of the cross-
correlation method was presented as follows. First, a rough RV value was
derived by matching an observed spectrum with templates shifted from $-$600
km/s to 600 km/s in step of 40 km/s. Second, matches were carried out between
the observed spectrum and templates shifted from $-$60 km/s to 60 km/s in step
of 1 km/s. Finally, the RV was determined from the highest peak of a group of
correlation functions. More details are referred to Wang et al. (2019). These
RV values are not used in following analysis.

###  3.2 The DD-Payne

For the LRS data, we have also determined the stellar parameters with the DD-
Payne (Ting et al. 2019; Xiang et al. 2019). The DD-Payne derives the stellar
parameters with a hybrid method that combines the data-driven approach with
priors of astrophysical modeling (Ting et al. 2017; Xiang et al. 2019),
utilizing the neural-network spectral interpolating and fitting algorithm of
the Payne (Ting et al. 2019). We inherited the LAMOST DD-Payne model of Xiang
et al. (2019), which constructs a neural-net spectral model using the LAMOST
spectra that have accurate stellar parameters from high-resolution spectra
from GALAH DR2 (Buder et al. 2018) and the value-added stellar parameter
catalog of the APOGEE DR14 derived with the Payne (Ting et al. 2019). The DD-
Payne delivers $T_{\rm eff}$, $\log g$, and elemental abundances for 16
elements, C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Ba, as well
as their error estimates from single-epoch spectra. The error estimates are
obtained by propagating the spectral flux uncertainties in the fitting. To
yield statistically realistic error estimates, Xiang et al. (2019) further
scaled the fitting errors to the dispersion of repeat observations. For a
spectrum with SNR above 50, typical aleatoric uncertainty of the parameter
estimates is 30 K in $T_{\rm eff}$, 0.07 dex in $\log g$, 0.03–0.1 dex in the
elemental abundance [X/H], except for that [Cu/H] and [Ba/H] exhibit larger
uncertainties (0.2–0.3 dex).

The DD-Payne model of Xiang et al. (2019) is built on spectra in rest frame
while itself does not deliver the stellar RV values. We determined RV with a
cross-correlation algorithm, similar to that of the LSP3 (Xiang et al. 2015).
We adopted the PHOENIX synthetic spectra (Husser et al. 2013), after degrading
to the LAMOST line spread function, as the templates of RV determination.
Besides the RV derived from the full LAMOST spectra (3800–9000 Å), we also
delivered the RVb and RVr from the blue- and the red-arm spectra separately,
as it is found that there is considerable systematic offset in the wavelength
calibration between the blue- and red arms of the LAMOST spectrographs (Fig.
5). This systemic offset has been reported by Du et al. (2019) that the RV
value calculated with the $H_{\alpha}$ line in the red arm is higher by $\sim$
7 km/s than those from the blue arm. In following analysis we used the RV
value from the blue arm.

Figure 5: Comparison of the RV values derived with the DD-Payne method from
the blue and red bands of the LRS data.

###  3.3 SLAM

For the MRS data, we also derived the stellar parameters (e.g., $T_{\rm eff}$,
log$g$, and [Fe/H]) with the Stellar LAbel Machine (SLAM) (Zhang et al. 2020a,
b), which is a machine learning method like DD-Payne but based on support
vector regression (SVR). SLAM can generally determine stellar labels over a
wide range of spectral types. It consists of three steps, including data pre-
processing (i.e., spectra normalization and training data standardization),
SVR model training for each wavelength pixel, and stellar label predicting for
observed spectra. Previous tests on the LAMOST MRS data showed that for a
spectrum with $\mathrm{SNR}\approx 50$ the precisions of $T_{\rm eff}$,
log$g$, and [Fe/H] are about 65 K, 0.02 dex, and 0.06 dex, respectively (Zhang
et al. 2020b).

RVs of spectra were first estimated with a cross-correlation function
maximization method111https://github.com/hypergravity/laspec. (Zhang et al.
2021) and were used to shift the normalized spectra to the same scale. Then
SLAM was trained on the synthetic spectral grid based on ATLAS9 model (Allende
Prieto et al. 2018) which is degraded to $R\sim 7500$, and was used to derive
stellar labels including $T_{\mathrm{eff}}$, $\log{g}$, [Fe/H] and
[$\alpha$/Fe].

To efficiently cope with spectroscopic binaries, we estimated a “binarity”
parameter for each spectrum. We generated 100,000 spectra for single stars and
100,000 for binaries basing on stellar evolutionary model (Choi et al. 2016;
Dotter 2016) and the synthetic spectral grid (Allende Prieto et al. 2018),
trained a convolutional neural network (CNN) as a classifier, and finally
predicted the binarity values of observed spectra. This method is initially
described in Jing et al. (2021, in prep) and applied to the LAMOST LRS
spectra. Figure 6 shows the distribution of the binarity parameter for the MRS
spectra with SNR $>$ 10\. The subpopulation with binarity $>$ 0.9 are mostly
double-lined spectroscopic binaries. Manual inspection of those spectra shows
that this classification method is very efficient. Currently this method is
still being improved and tested on more LAMOST MRS spectra (Zhang et al. 2021,
in prep).

Figure 6: Distribution of the binarity parameter for the MRS spectra with SNR
$>$ 10\. The color scale represents the density of stars.

###  3.4 Comparison between different methods

As shown above, we used three independent methods to determine the stellar
parameters. Since the LASP method was used to derive the parameters for both
the LRS and MRS data, we compared their results with those from DD-Payne (for
LRS data) and SLAM (for MRS data). The spectra with SNR $>$ 50 and 4000 K $<$
$T_{\rm eff}$ $<$ 7500 K (LASP results) were used for comparison.

In general, most of the parameters obtained from different methods are in good
agreement (Figure 7). There are some objects showing lower effective
temperatures ($\approx$ 250 K) from LASP results than those from DD-Payne
(Figure 8). These objects are mostly cool dwarfs, which have temperature
estimations ranging from $\sim$4000 K to $\sim$4700 K in LASP results but
ranging from $\sim$4300 K to $\sim$4900 K in DD-Payne results. A group of
objects classified as dwarfs by LASP (log$g$ $\gtrsim$ 3.5) have log$g$
estimations by DD-Payne lower than 3.0. For the MRS data, some hot dwarfs
($T_{\rm eff}\gtrsim$ 6500 K from the LASP results) show higher temperatures
(around 500 K) than those from SLAM (Figure 9). The surface gravity shows
deviation from a symmetric gaussian distribution. Most of these objects are
cool dwarfs ($T_{\rm eff}$ $\lesssim$ 4500 K). This systematic offset is
mainly caused by the different training set: LASP uses the empirical template
library ELODIE, while SLAM uses the synthetic spectral grid from ATLAS9 model.

Figure 7: Top panel: Comparison of the $T_{\mathrm{eff}}$, $\log g$, [Fe/H],
and RV values between LASP and DD-Payne using the LRS data . The black lines
are the best fitting with a single Gaussian distribution to the histograms
(green). Bottom panel: Comparison of the $T_{\mathrm{eff}}$, $\log g$, [Fe/H],
and RV values between LASP and SLAM using the MRS data.

Figure 8: Top Panel: Comparison of $T_{\mathrm{eff}}$ between LASP and DD-
Payne using the LRS data. The colorbar represents $\log g$. Middle Panel:
Comparison of $\log g$ between LASP and DD-Payne. The colorbar represents
$T_{\mathrm{eff}}$. Bottom Panel: Comparison of [Fe/H] between LASP and DD-
Payne. The colorbar represents $\log g$.

Figure 9: Top Panel: Comparison of $T_{\mathrm{eff}}$ between LASP and SLAM
using the MRS data. The colorbar represents $\log g$. Middle Panel: Comparison
of $\log g$ between LASP and SLAM. The colorbar represents $T_{\mathrm{eff}}$.
Bottom Panel: Comparison of [Fe/H] between LASP and SLAM. The colorbar
represents $\log g$.

###  3.5 RV correction

Due to the temporal variation of the zero-points, small systemic offsets exist
in RV measurements (Liu et al. 2019b; Zong et al. 2020; Zhang et al. 2021).
Therefore, the RV value of each spectrum (i.e., each fiber at each exposure)
needs a correction with corresponding zero point. For the MRS data, both the
LAMOST pipeline and Wang et al. (2019) determined a universal RVZP for each
spectrograph by comparing the measured RVs to those of RV standard stars
selected from APOGEE data (Huang et al. 2018). This only corrects the systemic
RVZP offsets between different spectrographs. Liu et al. (2019b) proposed a
method to correct the temporal RVZP variation by using “RV-constant” stars in
each spectrograph. However, we found that there are only few “RV-constant”
stars for some spectrographs in the observations of one field. If the RVZP
varies abruptly in one observation, these “RV-constant” stars will be
excluded, or this observation has to be abandoned.

Here, we used the Gaia DR2 data to determine the RVZPs for each spectrograph
exposure by exposure, and applied them as the common RV shift of the fibers in
the same spectrograph. For each spectrograph, we compared the RVs of the
common objects in each exposure and those from Gaia DR2, and determined a
median offset $\Delta RV$ with two or three iterations. One can determine the
RVZP-corrected RVs by adding the offset $\Delta RV$, and use them to compare
with external RV databases (e.g., APOGEE). As an example, Figure 10 shows the
calculated RVZPs (i.e., $\Delta RV$) of each spectrograph in some exposures of
the TD035052N235741K01 plate.

Figure 10: Top panel: Example of distribution of the RVZPs as a 2D-function of
the spectrograph ID and observed epochs (local MJM) for the TD035052N235741K01
plate by using the LRS data. The numbers mean the RVZP values in km/s, and the
color represents common stars used to calculated the offset values. The
vertical green lines split different nights. Bottom panel: Example of
distribution of the RVZPs as a 2D-function of the spectrograph ID and observed
epochs (local MJM) for the TD035052N235741K01 plate by using the MRS data.

###  3.6 Weighted average values of the stellar parameters

Most of these targets were observed at multiple epochs, which means we can
obtain average values of the stellar parameters and RV for each target. By
using the spectra with SNR above 10, We derived SNR-weighted average values
and corresponding errors for the stellar parameters of each target with the
formulae (Zong et al. 2020):

$\overline{P}=\frac{\sum_{k}w_{k}\cdot P_{k}}{\sum_{k}w_{k}}$ (2)

and

$\sigma_{w}(\overline{P})=\sqrt{\frac{N}{N-1}\frac{\sum_{k}w_{k}\cdot(P_{k}-\overline{P})^{2}}{\sum_{k}w_{k}}}.$
(3)

The index $k$ is the epoch of the measurements of parameter $P$ (i.e., $T_{\rm
eff}$, log$g$, [Fe/H], and RV) for each star, and the weight $w_{k}$ is
estimated with the square of the SNR of each spectrum. Figure 11 shows the
distribution of our samples in the log$g$–$T_{\rm eff}$ diagram.

We used the weighted average values (from LASP estimation) to make a
comparison of the parameters derived from the LRS and MRS data. Generally, the
values of $T_{\mathrm{eff}}$, $\log g$, and [Fe/H] from LRS are in good
agreement with those from MRS (Figure 12). There is a systematic offset
between the LRS and MRS RV measurements ($-5.52\pm 3.30$ km/s). After
correcting the RVZP (Section 3.5), the offset reduces to $-$0.06$\pm$1.94
km/s. The systematic offset nearly disappears, suggesting that our RV
correction method is reasonable and valid.

Figure 11: Hertzsprung-Russel diagram of the sample stars. The color scale
represents the density. Figure 12: Comparison of the $T_{\mathrm{eff}}$, $\log
g$, [Fe/H], and RV values from LRS and MRS data by using the LASP method. The
black lines are the best fitting with a single Gaussian distribution to the
histograms (green) The shaded histogram represent the difference of the RVZP-
corrected RVs from the LRS and MRS data.

###  3.7 Comparison with APOGEE

We cross-matched our sample with the APOGEE DR16 catalog, and there are 1,001
common stars. In general, the values of $T_{\mathrm{eff}}$, $\log g$ and
[Fe/H] from both the LRS and MRS surveys and those from APOGEE are consistent
(Figure 13). It can be seen that the RVZP-corrected RVs show good agreement
with those of APOGEE. As noted in Section 3.2 that the LRS RVs from the blue
and red arms show a systemic offset of $\approx$5–7 km/s, we found the LRS RVs
from red arm agree well with those of APOGEE, with very small offset ($\mu$ =
$-$0.91 km/s; $\sigma$ = 3.48 km/s), although the RVZP-corrected values show
little improvement ($\mu$ = $-$0.76 km/s; $\sigma$ = 3.06 km/s).

There are some outliers showing clear discrepancy of $T_{\mathrm{eff}}$
values. For objects located in the range [4500, 6500] K, their
$T_{\mathrm{eff}}$ values from different methods in this study are consistent
with those from APOGEE (Figure 14). For cooler dwarfs, the LASP returns lower
temperature than APOGEE, while DD-Payne gives higher temperatures. Some of
these sources may be variable stars, since we preferred variable sources to
construct our sample. Inappropriate stellar templates may also result in
inaccurate parameter measurements (Zong et al. 2020).

Although most of the stars in common have consistent metallicities with each
other, we note that some objects show large discrepancy of [Fe/H] values
(Figure 14). Our methods derived much lower metallicity than those of APOGEE.
These sources are cool dwarfs ($T_{\rm eff}\lesssim$ 4000K; log$g$ $\gtrsim$
4.5). Some of these sources are probably variable stars or binaries, and
clearly the parameter estimations of the latter are inaccurate. On the other
hand, it is difficult to determine accurate stellar parameters for very cool
dwarfs.

Figure 13: Comparison of the $T_{\mathrm{eff}}$, $\log g$, [Fe/H], and RV
values from this study and APOGEE data. The method and data from top to bottom
are: LASP using LRS data, DD-Payne using LRS data (the RV determination from
only the blue-arm spectra is adopted), LASP using MRS data, and SLAM using MRS
data. The shaded histogram represent the difference of the RVZP-corrected RVs
and APOGEE.

Figure 14: Top panel: Comparison of $T_{\mathrm{eff}}$ between LRS/LASP,
LRS/DD-Payne, MRS/LASP, MRS/SLAM and APOGEE. The colorbar represents log$g$.
Bottom Panel: Comparison of [Fe/H] between LRS/LASP, LRS/DD-Payne, MRS/LASP,
MRS/SLAM and APOGEE. The colorbar represents log$g$. The size of symbols
represents $T_{\rm eff}$. The black pluses are binary candidates (Section 5).

##  4 Mass Determination

We determined an evolutionary mass by using two methods and a spectroscopic
mass for our sample stars. Since the LASP method was used to derive stellar
parameters for both the LRS and MRS data, we preferred to use their parameter
values, followed by the DD-Payne (for LRS data) and SLAM (for MRS data)
results.

###  4.1 evolutionary mass estimation

We used the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et
al. 2011, 2013, 2015, 2018) (version 12115) to construct a grid of stellar
models. We calculated the initial chemical composition by using the solar
chemical mixture [$(Z/X)_{\odot}$ = 0.0181] (Asplund et al. 2009). The mesa
$\rho-T$ tables based on the 2005 update of the OPAL equation of state tables
(Rogers & Nayfonov 2002) was adopted and we used the OPAL opacities
supplemented by the low-temperature opacities from Ferguson et al. (2005). The
mesa Eddington photosphere was used for the set of boundary conditions for
modelling the atmosphere. The mixing-length theory of convection was
implemented and $\alpha_{\rm MLT}$ refers to the mixing-length parameter. We
also applied the mesa predictive mixing scheme in our model for a smooth
convective boundary. We considered convective overshooting at the core, the
H-burning shell, and the envelope. The exponential scheme by Herwig (2000) was
applied. The overshooting parameter is mass-dependent following a relation as
$f_{\rm{ov}}$ = (0.13$M$ $-$ 0.098)$/$9.0 found by Magic et al. (2010). In
addition, we adopted a fixed $f_{\rm{ov}}$ of 0.018 for models above $M=2.0$
M⊙. The mass-loss rate on the red-giant branch with Reimers prescription was
set as $\eta=0.2$ as constrained by the seismic targets in old open clusters
NGC 6791 and NGC 6819 (Miglio et al. 2012). Our models contain four
independent inputs which are mass (M = 0.76 – 2.2/0.02 $M_{\odot}$), initial
helium fraction ($Y_{\rm init}$ = 0.24 – 0.32/0.02), initial metallicity
([Fe/H]init = $-$0.5 – 0.5/0.1), and the mixing-length parameter ($\alpha_{\rm
MLT}$ = 1.7 $-$ 2.3/0.2). We used the maximum-likelihood estimation to fit to
spectroscopic constraints to determine the stellar masses.

We also applied the “isochrones” Python module (Morton 2015) to estimate
stellar mass, which is an interpolation tool for the fitting of stellar models
to photometric or spectroscopic parameters. By using the trilinear
interpolation in mass–age–[Fe/H] space for any given set of model grids, it is
able to predict physical or photometric properties provided by the models
(Montet et al. 2015). The input of the code includes the measured temperature,
surface gravity, multi-band magnitudes ($G$, $G_{\rm BP}$, $G_{\rm RP}$, $J$,
$H$, and $K_{\rm S}$), Gaia DR2 parallax (Gaia Collaboration et al. 2018) and
reddening $E(B-V)$. The $E(B-V)$ value is calculated with
$E(B-V)=0.884\times{\rm(Bayestar19)}$, with the
latter222http://argonaut.skymaps.info/usage from the Pan-STARRS DR1 (hereafter
PS1) dust map (Green et al. 2015). An example of the fitting results is given
in Figure 15.

We remind that the evolutionary masses were calculated assumed no metal
enrichment. There are about 1200/200 objects with [Fe/H] lower than
$-$0.5/$-$1\. Their masses may be under-estimated if there are significant
$\alpha$-elements enrichment.

Figure 15: Corner plot showing the distribution of physical parameters of
J034004.12+235200.0 as an example, derived from the isochrones code. The
parameters are labeled as, mass ($M$, in M⊙), radius ($R$, in R⊙), age
(log$\tau$, in yr), bolometric magnitude ($M_{\rm bol}$, in mag), and
bolometric luminosity (log$L$, in L⊙).

###  4.2 Spectroscopic mass estimation

The stellar mass can be estimated with the observed spectroscopic and
photometric parameters. First, We calculated an uncertainty-weighted average
bolometric magnitude with Eq. (2) and (3), by using the multi-band magnitudes
($G$, $G_{\rm BP}$, $G_{\rm RP}$, $J$, $H$, and $K_{\rm S}$), the Gaia DR2
distance (Bailer-Jones et al. 2018), the extinction from PS1 dust map, and the
bolometric corrections (Chen et al. 2019). For 2MASS magnitudes, we derived
the attenuation by directly multiplying the extinction coefficients on PS1’s
website 333http://argonaut.skymaps.info/usage by the Bayestar19 value; for
Gaia magnitudes, we calculated the $E(B-V)$ and derived the extinction by
multiplying $E(B-V)$ by the extinction coefficients from Casagrande &
VandenBerg (2018). The bolometric correction is derived from the PARSEC
database 444http://stev.oapd.inaf.it/YBC/, with the input of $T_{\rm eff}$,
log$g$, and [Fe/H] values. Second, the bolometric luminosity was calculated
with the averaged bolometric magnitude and the absolute luminosity and
magnitude of the sun ($L_{\odot}=$ 3.83$\times$ 1033 erg/s; $M_{\odot}=$ 4.74
mag). Finally, we derived the stellar mass with the bolometric luminosity,
effective temperature, and surface gravity following

$M=\frac{L_{\rm bol}}{4\pi~{}G\sigma~{}T_{\rm eff}^{4}}g.\\\ $ (4)

The comparison of mass estimation with MIST grid and isochrones shows good
agreement (Figure 16). However, some targets show higher spectroscopic mass
than the evolutionary mass . There are about 750 sources with $\lvert\Delta
M\rvert/M_{iso}$ $\geq$ 1, and about 270 ones are in our binary catalog
(Section 5). In fact, most of these sources throughout the main sequence are
probably unresolved binaries, since they are clearly brighter than the main-
sequence stars with the same color (Figure 17).

Figure 16: Left Panel: Comparison of the mass values from MISTgrid and
isochrones. Right Panel: Comparison of the mass values from isochrones and
spectroscopic estimation.

Figure 17: Left Panel: Color-magnitude diagram of the sample stars. The larger
(red and blue) dots represent the stars with large mass discrepancy. The red
dots are binary candidates from Section 5. Four isochrones are shown for
comparison, which are at age of 5 billion year with different metallicities
([Fe/H] $=$ $-$2, $-$1, 0, 0.5). Right panel: Four isochrones are drawn, which
are at age of 10 billion year with different metallicities ([Fe/H] $=$ $-$2,
$-$1, 0, 0.5). Table 2: Mass estimations of the sample stars.

Name | $M_{\rm grid}$ | $M_{iso}$ | Dis. | $E(B-V)$ | $M_{\rm bol}$ | $M_{\rm spec}$
---|---|---|---|---|---|---
| (M⊙) | (M⊙) | (kpc) |  | (mag) | (M⊙)
J034004.12+235200.0 | 1.02${}_{-0.04}^{+0.04}$ | 1.18$\pm$0.04 | 279${}_{-3}^{+4}$ | 0.19 | 3.83$\pm$0.04 | 0.94$\pm$0.14
J034007.72+241820.5 | — | 0.99$\pm$0.1 | 903${}_{-32}^{+34}$ | 0.21 | 4.04$\pm$0.01 | 0.7$\pm$0.03
J034008.18+241703.1 | 1.86${}_{-0.24}^{+0.22}$ | 1.33$\pm$0.15 | 1319${}_{-87}^{+100}$ | 0.21 | 0.11$\pm$0.05 | 2.87$\pm$0.13
J034012.25+234313.8 | — | 1.13$\pm$0.34 | 2391${}_{-163}^{+188}$ | 0.23 | 0.41$\pm$0.03 | 0.83$\pm$0.03
J034012.43+233803.1 | 1.24${}_{-0.05}^{+0.04}$ | 1.21$\pm$0.03 | 471${}_{-17}^{+19}$ | 0.19 | 2.57$\pm$0.08 | 2.5$\pm$0.67
J034020.87+234005.1 | 0.96${}_{-0.04}^{+0.06}$ | 1.08$\pm$0.02 | 233${}_{-4}^{+4}$ | 0.22 | 3.89$\pm$0.07 | 1.29$\pm$0.24
J034020.90+242455.5 | — | 0.86$\pm$0.05 | 615${}_{-13}^{+14}$ | 0.19 | 4.89$\pm$0.01 | 0.76$\pm$0.02
J034024.32+242932.1 | — | 0.91$\pm$0.07 | 473${}_{-15}^{+16}$ | 0.21 | 2.08$\pm$0.02 | 1.09$\pm$0.03
J034025.52+241017.1 | — | 2.15$\pm$0.28 | 229${}_{-2}^{+2}$ | 0.29 | 5.99$\pm$0.43 | 0.0$\pm$0.0
J034025.64+244209.5 | 1.24${}_{-0.08}^{+0.08}$ | 1.25$\pm$0.08 | 1030${}_{-32}^{+34}$ | 0.19 | 2.57$\pm$0.07 | 1.82$\pm$0.42
J034025.96+232013.5 | — | 0.96$\pm$0.03 | 1731${}_{-375}^{+569}$ | 0.19 | 1.63$\pm$0.28 | 7.75$\pm$5.95
J034026.48+235823.3 | — | 1.08$\pm$0.21 | 4197${}_{-464}^{+574}$ | 0.28 | -0.47$\pm$0.04 | 1.87$\pm$0.06
J034029.22+234840.1 | 0.92${}_{-0.06}^{+0.06}$ | 0.94$\pm$0.01 | 237${}_{-32}^{+43}$ | 0.22 | 3.51$\pm$0.07 | 5.13$\pm$0.72
J034029.59+233303.9 | — | 0.59$\pm$0.06 | 161${}_{-1}^{+1}$ | 0.21 | 7.01$\pm$0.24 | 0.52$\pm$0.07
J034030.72+242914.2 | — | 0.9$\pm$0.01 | 138${}_{-1}^{+1}$ | 0.03 | 5.68$\pm$0.08 | 1.29$\pm$0.15
J034031.01+242141.0 | — | 0.38$\pm$0.02 | 69${}_{-0}^{+0}$ | 0.2 | 7.68$\pm$0.15 | 0.61$\pm$0.04
J034031.67+234521.9 | 0.92${}_{-0.06}^{+0.06}$ | 0.98$\pm$0.05 | 710${}_{-19}^{+20}$ | 0.22 | 4.49$\pm$0.03 | 0.84$\pm$0.06
J034031.88+243419.1 | 1.3${}_{-0.32}^{+0.36}$ | 1.35$\pm$0.38 | 2101${}_{-138}^{+159}$ | 0.21 | 1.46$\pm$0.04 | 1.57$\pm$0.08
J034034.36+234057.3 | 0.98${}_{-0.02}^{+0.02}$ | 1.05$\pm$0.01 | 134${}_{-1}^{+1}$ | 0.12 | 4.94$\pm$0.08 | 1.15$\pm$0.2
J034034.76+232540.2 | 1.56${}_{-0.14}^{+0.14}$ | 1.74$\pm$0.16 | 1534${}_{-65}^{+71}$ | 0.18 | 1.89$\pm$0.11 | 1.57$\pm$1.44

The columns are: (1) Name; (2) $M_{\rm grid}$: mass estimation from the MIST
grids; (3) $M_{\rm iso}$: mass estimation using the “isochrones” code; (4)
Dis.: distance from Gaia DR2; (5) $E(B-V)$: reddening from PS1 dust map,
calculated as 0.84$\times$Bayesian19; (6) $M_{\rm bol}$: weighted average
value of bolometric magnitude; (7) $M_{\rm spec}$: spectroscopic mass
estimation.
This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.

##  5 Binary sample

We present a binary sample basing on light curve analysis, radial velocity
fitting, the binarity parameter calculated using MRS data (Section 3.3), and
the spatially resolved binary catalog from Gaia EDR3 (El-Badry et al. 2021).
In this study, one star is thought to be a double-lined spectroscopic binary
candidate if the binarity parameters of three more spectra (with SNR above 10)
are larger than 0.9 (Figure 6).

###  5.1 light curve analysis

We first cross-matched our catalog with the $K$2 data, and found more than
3000 stars have light curves, which can be used to detect period signals. The
moving average method was used to smooth the light curve and remove the long-
term trend. We applied the Lomb-Scargle method (Lomb 1976) to determine the
period and classified binaries by analyzing the folded light curves (see more
details in Yang et al. 2020). A brief description was presented as follows.

We used a two-step grid searching method (VanderPlas & Ivezić 2015) to
determine the optimized period. It firstly searches in a broad grid for a
series of period candidates and then zooms in a narrow grid to find the real
peak. The obtained period is regarded as significant only when it is higher
than the false alarm probability. The light curve folded with the significant
period was analyzed by investigating the characteristics. The light curve
templates of variable stars were taken from previous catalogs (e.g. Samus’ et
al. 2017; Kim et al. 2014). The characters of the templates include light
curve period, skewness of the magnitude distribution, median magnitude,
standard deviation of the magnitude, the ratio of magnitudes brighter or
fainter than the average, the ratio between the Fourier components a2 and a4,
10$\%$ and 90$\%$ percentile of slopes of a phase-folded light curve. They
were concluded as identification parameters that trigger the classification
through machine learning method and visual inspection (Paczyński et al. 2006;
Kim & Bailer-Jones 2016; Jayasinghe et al. 2020; Yang et al. 2021, submitted).

We also cross-matched our objects with the variable catalogs of ASAS-SN
555https://asas-sn.osu.edu/variables, Catalina, ZTF (Chen et al. 2020), and
WISE (Chen et al. 2018). Table 3 shows the binaries with different types
(i.e., EA, EB, and EW). Figure 18 shows a binary example of the EW type.

###  5.2 radial velocity fitting

With the radial velocity data from the LAMOST TD survey, we performed a
Keplerian fit using the custom Markov chain Monte Carlo sampler The Joker
(Price-Whelan et al. 2017) for the objects with more than seven exposures. The
Joker works well with non-uniform data and allows to identify circular or
eccentric orbits. We used the RVs of single-exposure spectra to do the
fitting. Four sets of data were used: the LRS RV from LASP, the MRS RV from
LASP, the MRS RV from SLAM, and a joint LRS and MRS RV from LASP. Bad fittings
were removed with visual examination. The fitting with MRS RV data was
preferred, followed by the fitting with the joint data and the LRS RV data.
The derived orbital parameters include period $P$, eccentricity $e$, semi-
amplitude $K$, argument of the periastron $\omega$, mean anomaly at the first
exposure, and systematic RV $\nu$0\. An example of the fitting results is
given in Figure 19. The results are shown in Table 3. For double-lined
spectroscopic binaries, we only used the set of RV with larger semi-amplitude
($K$) to do the fitting.

In addition, for single-line binaries, which are not classified as binaries by
the binarity parameter, we calculated the binary mass function $f(M)$ using
the posterior samples from our RV modeling as follows,

$f(M)=\frac{P\,K_{1}^{3}\,(1-e^{2})^{3/2}}{2\pi
G}=\frac{M_{2}\,\textrm{sin}^{3}i}{(1+q)^{2}},$ (5)

where $K$1 is the semi-amplitude of the primary (i.e., the visible star),
$M_{2}$ is the mass of the secondary, $q=M_{1}/M_{2}$ is the mass ratio, and
$i$ is the system inclination. Combined with the mass estimate of the primary
(Section 4), we estimated a minimum mass of the secondary ($M$2) with an
inclination angle of $i\ =\ $ 90∘.

Figure 18: Left Panel: Part of MRS observations of J065001.65+222127.7 as an
example. The binary feature can be clearly seen from the $H{\alpha}$ line
profiles and motion. Right Panel: The folded light curve using $K$2 data and
RV curve fitted with The Joker. Figure 19: Corner plot of the RV fitting
results of J065001.65+222127.7 as an example, showing distribution of orbital
parameters derived from The Joker. The parameters are labeled as, orbital
period ($P$, in days), eccentricity of the system ($e$), argument of
pericenter ($\omega$, in radians), mean anomaly at reference time ($M$0, in
radians), extra “jitter” added in quadrature to each visit-velocity error
($s$, in km/s), RV semi-amplitude of the star ($K$, in km/s), and the center
of mass velocity ($\nu$0, in km/s).

###  5.3 Spatially resolved binary

By using the Gaia EDR3 database, El-Badry et al. (2021) searched pairs of
stars and estimated the probability that a pair is a chance alignment. They
constructed a catalog of 1.2 million high-confidence, spatially resolved wide
binaries. We cross-matched our sample and their catalog, and found 379 common
sources. Among these objects, 306 ones were classified as main sequence – main
sequence (MSMS) binaries and three ones were distinguished as white dwarf –
main sequence (WDMS) binaries.

To sum up, Table 3 lists 2366 binary candidates, including 148 ones from light
curve analysis, 878 ones from RV fitting, 1534 ones from binarity parameter,
and 379 ones from the spatially resolved catalog of Gaia EDR3.

Table 3: Binary candidates of the four $K$2 plates.

| The Joker | light curve |  binarity $\geq$0.9 | Astrometry
---|---|---|---|---
Name | $P$ | $e$ | $\omega$ | $M_{\rm 0}$ | $K$ | $\nu_{\rm 0}$ |  $f$($M$) |  $M$2min | Type/Survey | $P$ |  | Class | Sep.
| (day) |  |  |  | (km/s) | (km/s) |  M⊙ |  M⊙ |  | (day) |  |  | (AU)
J034012.43+233803.1 | — | — | — | — | — | — | — | — | EA/AAVSO | 17.3679 | — | — | —
J034025.64+244209.5 |  1.4740${}_{-0.0005}^{+0.0004}$ |  0.352${}_{-0.063}^{+0.083}$ |  1.88${}_{-0.15}^{+0.15}$ |  -1.34${}_{-0.27}^{+0.24}$ |  39.7${}_{-1.8}^{+2.5}$ |  -16.5${}_{-2.1}^{+2.6}$ |  0.0078${}_{-0.0008}^{+0.0011}$ |  0.26${}_{-0.02}^{+0.02}$ | — | — | — | — | —
J034031.01+242141.0 | — | — | — | — | — | — | — | — | — | — | 1.00 | — | —
J034051.81+232834.4 |  20.0490${}_{-0.0411}^{+0.0508}$ |  0.514${}_{-0.025}^{+0.028}$ |  -0.10${}_{-0.06}^{+0.05}$ |  0.99${}_{-0.08}^{+0.07}$ |  19.8${}_{-0.7}^{+0.7}$ |  4.8${}_{-0.4}^{+0.4}$ |  0.0102${}_{-0.0010}^{+0.0011}$ |  0.29${}_{-0.01}^{+0.01}$ | — | — | — | — | —
J034100.15+241735.0 | — | — | — | — | — | — | — | — | — | — | — | MSMS | 694
J034108.07+231255.5 |  0.3349${}_{-0.0002}^{+0.0700}$ |  0.370${}_{-0.206}^{+0.230}$ |  4.10${}_{-5.97}^{+0.68}$ |  2.85${}_{-4.76}^{+1.19}$ |  5.9${}_{-1.5}^{+1.6}$ |  18.4${}_{-0.7}^{+0.7}$ |  0.0001${}_{-0.0001}^{+0.0001}$ |  0.01${}_{0.00}^{+0.00}$ | — | — | — | — | —
J034115.98+225250.0 | — | — | — | — | — | — | — | — | — | — | 0.96 | — | —
J034122.94+233730.6 |  2.2546${}_{-0.0792}^{+0.2923}$ |  0.301${}_{-0.166}^{+0.129}$ |  1.69${}_{-0.19}^{+0.59}$ |  0.83${}_{-0.41}^{+1.14}$ |  44.3${}_{-9.1}^{+3.7}$ |  -6.2${}_{-8.1}^{+2.1}$ |  0.0150${}_{-0.0052}^{+0.0109}$ |  0.32${}_{-0.06}^{+0.09}$ | — | — | — | — | —
J034125.62+240919.9 |  7.6155${}_{-0.0224}^{+6.7914}$ |  0.218${}_{-0.131}^{+0.646}$ |  3.83${}_{-6.09}^{+1.15}$ |  0.94${}_{-4.04}^{+2.25}$ |  18.7${}_{-1.5}^{+2.0}$ |  -31.9${}_{-3.9}^{+1.6}$ |  0.0047${}_{-0.0035}^{+0.0015}$ |  0.20${}_{-0.09}^{+0.04}$ | — | — | — | — | —
J034134.72+230542.7 |  1.2253${}_{-0.0005}^{+0.0187}$ |  0.481${}_{-0.050}^{+0.047}$ |  3.85${}_{-0.17}^{+0.15}$ |  -2.15${}_{-0.26}^{+5.93}$ |  9.2${}_{-0.6}^{+1.9}$ |  -40.1${}_{-0.4}^{+0.8}$ |  0.0001${}_{-0.0001}^{+0.0001}$ |  0.04${}_{-0.01}^{+0.01}$ | — | — | — | — | —
J034137.08+230049.9 | — | — | — | — | — | — | — | — | — | — | — | MS?? | 643
J034141.71+241910.1 |  5.2130${}_{-0.7949}^{+0.0109}$ |  0.266${}_{-0.082}^{+0.200}$ |  5.81${}_{-0.49}^{+0.28}$ |  0.99${}_{-0.75}^{+0.40}$ |  21.4${}_{-2.0}^{+3.0}$ |  14.6${}_{-1.4}^{+1.5}$ |  0.0047${}_{-0.0016}^{+0.0019}$ |  0.19${}_{-0.03}^{+0.03}$ | — | — | — | — | —
J034144.50+232159.4 |  4.6605${}_{-0.0080}^{+0.0064}$ |  0.231${}_{-0.059}^{+0.070}$ |  -2.54${}_{-0.31}^{+0.26}$ |  -3.79${}_{-0.40}^{+0.40}$ |  20.7${}_{-1.0}^{+1.0}$ |  -28.3${}_{-0.9}^{+1.0}$ | — | — | — | — | 1.00 | — | —
J034145.06+231235.2 |  0.4943${}_{-0.0001}^{+0.0001}$ |  0.022${}_{-0.017}^{+0.037}$ |  0.67${}_{-1.67}^{+1.90}$ |  0.34${}_{-1.67}^{+1.79}$ |  60.1${}_{-1.2}^{+1.2}$ |  -9.6${}_{-1.9}^{+2.0}$ | — | — | EB/ASASSN | 0.4942 | 1.00 | — | —
J034148.27+224912.0 | — | — | — | — | — | — | — | — | — | — | 1.00 | — | —
J034154.04+224222.6 | — | — | — | — | — | — | — | — | — | — | — | MSMS | 10322
J034205.65+233515.8 |  27.7518${}_{-0.1930}^{+0.1695}$ |  0.360${}_{-0.038}^{+0.040}$ |  2.82${}_{-0.17}^{+0.16}$ |  -0.20${}_{-0.13}^{+0.12}$ |  10.9${}_{-0.3}^{+0.4}$ |  -55.3${}_{-0.4}^{+0.4}$ |  0.0030${}_{-0.0003}^{+0.0004}$ |  0.18${}_{-0.01}^{+0.01}$ | — | — | — | — | —
J034209.14+233004.9 |  12.4971${}_{-0.0076}^{+0.0075}$ |  0.028${}_{-0.016}^{+0.016}$ |  1.45${}_{-0.48}^{+0.58}$ |  -0.09${}_{-0.48}^{+0.59}$ |  52.4${}_{-1.1}^{+1.1}$ |  -12.3${}_{-0.6}^{+0.6}$ | — | — | — | — | 1.00 | — | —
J034210.91+240508.6 |  0.2583${}_{-0.0142}^{+0.0536}$ |  0.316${}_{-0.251}^{+0.353}$ |  1.06${}_{-0.94}^{+3.07}$ |  0.17${}_{-1.01}^{+2.60}$ |  8.9${}_{-6.0}^{+7.0}$ |  7.7${}_{-2.5}^{+4.1}$ |  0.0001${}_{-0.0001}^{+0.0001}$ |  0.01${}_{0.00}^{+0.00}$ | — | — | — | — | —
J034220.18+242806.1 | — | — | — | — | — | — | — | — | — | — | 1.00 | MSMS | 14373

This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.

##  6 Discussion and Summary

With one year of LAMOST observations, our project acquired more than 767,000
low- and 478,000 med-resolution spectra, corresponding to a total exposure
time of $\approx$46.7 and $\approx$49.1 hours, respectively. More than 70%/50%
of low-resolution/med-resolution spectra have SNR above 10.

We determined stellar parameters (e.g., $T_{\rm eff}$, log$g$, [Fe/H]) and RV
by using different methods (i.e., LASP, DD-Payne, and SLAM), and derived SNR-
weighted average values of these parameters for our targets. Generally, these
parameters determined from different methods show good agreement, especially
for late F-, G-, and early K-type stars. The LRS and MRS results show a
discrepancy of the RV measurements ($\approx$5.5 km/s). The comparison of
stellar parameters with APOGEE DR16 show good agreement, but the RV values
from LRS data show a large discrepancy ($\approx$6.5 km/s) with those of
APOGEE. We used the Gaia DR2 RV data to calculate a median RVZP for each
spectrograph exposure by exposure, and the RVZP-corrected RVs agree very well
with those of APOGEE DR16. We derived stellar masses by using different
methods (i.e., MIST grids, isochrones code, and spectroscopic estimation),
with the help of stellar parameters, multi-band magnitudes, distances and
extinction values.

Basing on light curve analysis, radial velocity fitting, the binarity
parameter, and the spatially resolved binary catalog from Gaia EDR3, we
presented a binary catalog including about 2700 candidates. We should remind
that we derived stellar parameters and masses assuming the target is a single
star, which means for the binary candidates, these parameter values may be
unreliable.

Our spectroscopic survey has gained multiple visits (up to 86 LRS visits and
54 MRS visits) for about 10,000 stars, which can effectively leverage sciences
in various research fields, such as

(1) Binary system. The monitoring of RV variation can reveal a large sample of
binaries, especially those double-lined spectroscopic binaries. The time-
series variations of RV, together with the light curves from photometric
surveys, can help to determine the orbital properties, including the period,
eccentricity, inclination angle, etc. The statistical properties of the
binaries (e.g., period, eccentricity, and metallicity) can provide critical
clues on the formation and evolution of the binary systems. In addition,
LAMOST has showed the ability to discover fantastic binaries, such as compact
binaries including a neutron star or black hole (Liu et al. 2019a). Those
binaries are greatly helpful in understanding the late evolution of massive
stars, such as the formation of type Ia supernovae. An analysis of the
binaries in the four plates, including stellar parameter estimation for
individual component, will be presented in a future work (Kovalev et al. 2021,
in prep).

(2) Stellar activity. Many studies have focused on the evolution of stellar
photospheric activity with spots or flares, by using the photometric TD survey
data. In contrast, due to the lack of long-term spectroscopic observation, the
evolution of chromospheric activity was studied for only a few stars. LAMOST
TD survey provides a great opportunity to investigate stellar chromospheric
activity over a large sample of stars with different spectral types, the
variation of chromospheric activity due to rotational modulation of a single
star or orbital modulation of a binary system, and the long-term evolution of
chromospheric activity. All of these are quite helpful to understand the
stellar magnetic activity and dynamo mechanism. An analysis of the stellar
chromospheric activities using Ca II H&K and Balmer lines is under way (Han et
al. 2021, in prep).

(3) Stellar pulsation. Asteroseismology is a unique technique to study the
internal physics of pulsating stars. Precise atmospheric parameters from
LAMOST multiple spectral observations can help to constrain the parameter
space in seismic searches for an optimal model. Periodic variation of
atmospheric parameters and RV due to pulsation provide good opportunity to
probe the dynamical processes of pulsation.

The LAMOST TD data can also be used in many other fields, such as studying the
chemical abundance of special stars (e.g., metal-poor star, Lithium-rich
star), investigating the spatial structure of the Galaxy together with the
Gaia astrometric data, etc.

All the spectra used in this study are now available in the LAMOST DR8. The
observations of the four $K$2 plates will be carried on but with reduced
visiting frequency. At the same time, a similar TD survey of another four $K$2
plates is being carried out.

###### Acknowledgements.

We thank the anonymous referee for helpful comments and suggestions that have
improved the paper. Guoshoujing Telescope (the Large Sky Area Multi-Object
Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project
built by the Chinese Academy of Sciences. Funding for the project has been
provided by the National Development and Reform Commission. LAMOST is operated
and managed by the National Astronomical Observatories, Chinese Academy of
Sciences. This work presents results from the European Space Agency (ESA)
space mission Gaia. Gaia data are being processed by the Gaia Data Processing
and Analysis Consortium (DPAC). Funding for the DPAC is provided by national
institutions, in particular the institutions participating in the Gaia
MultiLateral Agreement (MLA). The Gaia mission website is
https://www.cosmos.esa.int/gaia. The Gaia archive website is
https://archives.esac.esa.int/gaia. In this work, we have made use of SDSS-IV
APOGEE DR16 data. Funding for the Sloan Digital Sky Survey IV has been
provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy
Office of Science, and the Participating Institutions. SDSS-IV acknowledges
support and resources from the Center for High-Performance Computing at the
University of Utah. The SDSS website is www.sdss.org. SDSS is managed by the
Astrophysical Research Consortium for the Participating Institutions of the
SDSS Collaboration which are listed at
www.sdss.org/collaboration/affiliations/. This publication makes use of data
products from the Pan-STARRS1 Surveys (PS1) and the PS1 public science
archive, and the Two Micron All Sky Survey. We acknowledge use of the VizieR
catalogue access tool, operated at CDS, Strasbourg, France, and of Astropy, a
community-developed core Python package for Astronomy (Astropy Collaboration,
2013). This work was supported by the National Science Foundation of China
(NSFC) under grant numbers 11988101, 11933004, and 12003050. It was also
supported by the National Key Research and Development Program of China
(NKRDPC) under grant numbers 2019YFA0405000, 2019YFA0405504, and
2016YFA0400804, and the B-type Strategic Priority Program of the Chinese
Academy of Sciences under grant number XDB41000000. S. W. and H.-L. Y.
acknowledges support from the Youth Innovation Promotion Association of the
CAS (id. 2019057 and 2020060, respectively). T. L. acknowledges the funding
from the European Research Council (ERC) under the European Union Horizon 2020
research and innovation programme (CartographY GA. 804752).

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##  Appendix A Stellar Parameter catalogs

We present the weight-averaged stellar parameters from different methods here.
Table 4, 5, 6, and 7 are from LASP estimation with LRS data, LASP estimation
with MRS data, DD-Payne estimation with LRS data, and SLAM estimation with MRS
data, respectively. Details of the different methods can be found in Section
3.

Table 4: Stellar parameters and RV from LASP estimation with LRS data.

Name | Field | R.A. | Dec. | $T_{\rm eff}$ |  log$g$ | [Fe/H] | RV | corrected RV
---|---|---|---|---|---|---|---|---
|  | (deg) | (deg) | (K) |  |  | (km/s) | (km/s)
J034004.12+235200.0 | TD035052N235741K01 | 55.0172 | 23.86668 |  6492$\pm$24 |  $4.25\pm$0.02 |  $-0.3\pm$0.02 |  $-23.29\pm$4.88 |  $-12.49\pm$3.38
J034007.72+241820.5 | TD035052N235741K01 | 55.03221 | 24.3057 |  5827$\pm$77 |  $4.02\pm$0.13 |  $-0.35\pm$0.18 |  $-2.5\pm$2.24 |  $4.86\pm$2.26
J034008.18+241703.1 | TD035052N235741K01 | 55.03411 | 24.2842 |  4577$\pm$14 |  $2.64\pm$0.04 |  $0.14\pm$0.03 |  $-44.89\pm$1.63 |  $-37.84\pm$1.48
J034012.25+234313.8 | TD035052N235741K01 | 55.05106 | 23.72051 |  4867$\pm$33 |  $2.33\pm$0.09 |  $-0.49\pm$0.03 |  $-34.68\pm$2.25 |  $-24.65\pm$1.54
J034012.43+233803.1 | TD035052N235741K01 | 55.05184 | 23.63421 |  6295$\pm$21 |  $4.12\pm$0.03 |  $-0.22\pm$0.02 |  $1.19\pm$7.95 |  $11.31\pm$6.36
J034020.87+234005.1 | TD035052N235741K01 | 55.087 | 23.66809 |  5909$\pm$23 |  $4.25\pm$0.02 |  $0.05\pm$0.02 |  $40.02\pm$8.11 |  $50.39\pm$9.7
J034020.90+242455.5 | TD035052N235741K01 | 55.08714 | 24.41547 |  5840$\pm$133 |  $4.4\pm$0.18 |  $-0.65\pm$0.07 |  $-15.58\pm$3.19 |  $-8.88\pm$3.15
J034024.32+242932.1 | TD035052N235741K01 | 55.10136 | 24.49227 |  4875$\pm$27 |  $3.12\pm$0.07 |  $-0.55\pm$0.05 |  $-102.41\pm$2.3 |  $-95.37\pm$1.9
J034025.64+244209.5 | TD035052N235741K01 | 55.10687 | 24.70268 |  6367$\pm$86 |  $4.0\pm$0.1 |  $-0.19\pm$0.08 |  $-21.02\pm$22.58 |  $-14.18\pm$22.82
J034025.96+232013.5 | TD035052N235741K01 | 55.10817 | 23.33711 |  6001$\pm$43 |  $4.15\pm$0.06 |  $-0.34\pm$0.04 |  $50.43\pm$2.08 |  $60.99\pm$2.35
J034026.48+235823.3 | TD035052N235741K01 | 55.11036 | 23.97316 |  4538$\pm$42 |  $2.21\pm$0.12 |  $-0.45\pm$0.05 |  $-48.98\pm$2.91 |  $-39.46\pm$2.85
J034029.22+234840.1 | TD035052N235741K01 | 55.1218 | 23.81117 |  5556$\pm$26 |  $4.59\pm$0.02 |  $0.03\pm$0.02 |  $51.51\pm$2.56 |  $62.3\pm$0.88
J034029.59+233303.9 | TD035052N235741K01 | 55.12332 | 23.55111 |  3999$\pm$26 |  $4.43\pm$0.07 |  $-0.24\pm$0.06 |  $-2.23\pm$4.98 |  $7.48\pm$3.01
J034030.72+242914.2 | TD035052N235741K01 | 55.128 | 24.48731 |  5249$\pm$20 |  $4.76\pm$0.03 |  $0.11\pm$0.01 |  $-1.45\pm$1.6 |  $5.76\pm$1.84
J034031.01+242141.0 | TD035052N235741K01 | 55.12922 | 24.3614 |  3789$\pm$8 |  $4.67\pm$0.03 |  $-0.65\pm$0.09 |  $8.5\pm$1.59 |  $15.61\pm$1.18
J034031.67+234521.9 | TD035052N235741K01 | 55.13198 | 23.75611 |  5868$\pm$65 |  $4.29\pm$0.12 |  $-0.12\pm$0.05 |  $-22.59\pm$3.77 |  $-11.75\pm$1.89
J034031.88+243419.1 | TD035052N235741K01 | 55.13285 | 24.57198 |  4876$\pm$96 |  $3.03\pm$0.16 |  $0.0\pm$0.06 |  $46.06\pm$2.5 |  $53.18\pm$2.59
J034034.36+234057.3 | TD035052N235741K01 | 55.1432 | 23.68261 |  5708$\pm$27 |  $4.56\pm$0.02 |  $0.21\pm$0.02 |  $-6.38\pm$1.94 |  $3.53\pm$1.66
J034034.76+232540.2 | TD035052N235741K01 | 55.14486 | 23.42786 |  7897$\pm$225 |  $4.04\pm$0.16 |  $-0.0\pm$0.13 |  $-42.34\pm$8.23 |  $-33.05\pm$7.58
J034034.92+243247.2 | TD035052N235741K01 | 55.14552 | 24.54648 |  6451$\pm$250 |  $4.41\pm$0.21 |  $0.32\pm$0.03 |  $-31.89\pm$8.02 |  $-25.27\pm$7.67

This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.

Table 5: Stellar parameters and RV from LASP estimation with MRS data.

Name | Field | R.A. | Dec. | $T_{\rm eff}$ |  log$g$ | [Fe/H] | RV | corrected RV
---|---|---|---|---|---|---|---|---
|  | (deg) | (deg) | (K) |  |  | (km/s) | (km/s)
J034004.12+235200.0 | TD035052N235741K01 | 55.0172 | 23.86668 |  6405$\pm$50 |  $4.18\pm$0.03 |  $-0.37\pm$0.04 |  $-10.95\pm$0.35 |  $-9.92\pm$1.4
J034007.72+241820.5 | TD035052N235741K01 | 55.03221 | 24.3057 |  5908$\pm$162 |  $4.08\pm$0.23 |  $-0.31\pm$0.08 |  $5.79\pm$0.46 |  $6.24\pm$0.52
J034008.18+241703.1 | TD035052N235741K01 | 55.03411 | 24.2842 |  4549$\pm$20 |  $2.54\pm$0.05 |  $0.09\pm$0.02 |  $-39.6\pm$0.25 |  $-39.2\pm$0.22
J034012.25+234313.8 | TD035052N235741K01 | 55.05106 | 23.72051 |  4894$\pm$28 |  $2.31\pm$0.1 |  $-0.5\pm$0.04 |  $-25.81\pm$0.57 |  $-24.78\pm$0.75
J034012.43+233803.1 | TD035052N235741K01 | 55.05184 | 23.63421 |  6353$\pm$61 |  $4.14\pm$0.05 |  $-0.27\pm$0.03 |  $7.2\pm$2.31 |  $8.72\pm$2.14
J034020.87+234005.1 | TD035052N235741K01 | 55.087 | 23.66809 |  5879$\pm$16 |  $4.2\pm$0.02 |  $-0.02\pm$0.01 |  $40.25\pm$12.9 |  $41.3\pm$12.15
J034024.32+242932.1 | TD035052N235741K01 | 55.10136 | 24.49227 |  4887$\pm$79 |  $3.17\pm$0.08 |  $-0.52\pm$0.04 |  $-95.57\pm$2.04 |  $-94.88\pm$2.04
J034025.96+232013.5 | TD035052N235741K01 | 55.10817 | 23.33711 |  6014$\pm$87 |  $4.13\pm$0.11 |  $-0.42\pm$0.06 |  $60.65\pm$0.79 |  $61.46\pm$1.29
J034029.22+234840.1 | TD035052N235741K01 | 55.1218 | 23.81117 |  5587$\pm$14 |  $4.65\pm$0.02 |  $0.01\pm$0.01 |  $60.77\pm$0.3 |  $61.74\pm$0.71
J034030.72+242914.2 | TD035052N235741K01 | 55.128 | 24.48731 |  5202$\pm$52 |  $4.72\pm$0.06 |  $0.06\pm$0.03 |  $3.66\pm$0.41 |  $4.04\pm$0.38
J034031.01+242141.0 | TD035052N235741K01 | 55.12922 | 24.3614 |  3769$\pm$9 |  $4.64\pm$0.04 |  $-0.82\pm$0.06 |  $14.45\pm$0.22 |  $14.91\pm$0.25
J034031.67+234521.9 | TD035052N235741K01 | 55.13198 | 23.75611 |  5894$\pm$126 |  $4.34\pm$0.14 |  $-0.18\pm$0.09 |  $-14.2\pm$1.09 |  $-13.64\pm$1.13
J034031.88+243419.1 | TD035052N235741K01 | 55.13285 | 24.57198 |  4768$\pm$25 |  $3.06\pm$0.08 |  $-0.11\pm$0.02 |  $53.81\pm$0.35 |  $54.19\pm$0.43
J034034.36+234057.3 | TD035052N235741K01 | 55.1432 | 23.68261 |  5673$\pm$10 |  $4.54\pm$0.02 |  $0.16\pm$0.01 |  $4.73\pm$0.29 |  $5.62\pm$1.09
J034035.40+232248.3 | TD035052N235741K01 | 55.14757 | 23.38009 |  5580$\pm$55 |  $3.79\pm$0.11 |  $0.24\pm$0.06 |  $27.69\pm$1.2 |  $28.98\pm$0.5
J034038.79+242507.8 | TD035052N235741K01 | 55.16163 | 24.41884 |  6345$\pm$52 |  $4.07\pm$0.06 |  $-0.09\pm$0.04 |  $5.3\pm$0.36 |  $5.77\pm$0.45
J034039.96+235046.7 | TD035052N235741K01 | 55.16651 | 23.84634 |  4801$\pm$36 |  $2.65\pm$0.08 |  $-0.25\pm$0.05 |  $32.83\pm$1.16 |  $34.08\pm$0.26
J034041.11+235922.0 | TD035052N235741K01 | 55.1713 | 23.98947 |  4572$\pm$46 |  $1.88\pm$0.12 |  $-0.69\pm$0.06 |  $29.06\pm$1.94 |  $30.65\pm$1.13
J034044.91+243926.0 | TD035052N235741K01 | 55.18717 | 24.65728 |  6845$\pm$32 |  $4.17\pm$0.09 |  $-0.28\pm$0.06 |  $-21.52\pm$0.46 |  $-21.08\pm$0.49
J034046.76+241255.7 | TD035052N235741K01 | 55.19484 | 24.2155 |  6261$\pm$28 |  $4.3\pm$0.03 |  $-0.05\pm$0.02 |  $27.23\pm$0.36 |  $27.65\pm$0.34

This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.

Table 6: Stellar parameters and RV from DD-Payne estimation with LRS data.

Name | Field | R.A. | Dec. | $T_{\rm eff}$ |  log$g$ | [Fe/H] |  RVb | corrected RV
---|---|---|---|---|---|---|---|---
|  | (deg) | (deg) | (K) |  |  | (km/s) | (km/s)
J034004.12+235200.0 | TD035052N235741K01 | 55.0172 | 23.86668 |  6351$\pm$16 |  $4.14\pm$0.06 |  $-0.44\pm$0.03 |  $-29.17\pm$7.35 |  $-17.0\pm$6.24
J034007.72+241820.5 | TD035052N235741K01 | 55.03221 | 24.3057 |  5772$\pm$52 |  $3.75\pm$0.19 |  $-0.41\pm$0.19 |  $-0.59\pm$5.17 |  $4.04\pm$5.02
J034008.18+241703.1 | TD035052N235741K01 | 55.03411 | 24.2842 |  4535$\pm$16 |  $2.26\pm$0.08 |  $0.07\pm$0.02 |  $-44.2\pm$2.6 |  $-40.1\pm$2.04
J034012.25+234313.8 | TD035052N235741K01 | 55.05106 | 23.72051 |  4911$\pm$27 |  $2.47\pm$0.06 |  $-0.46\pm$0.02 |  $-35.17\pm$3.31 |  $-23.52\pm$2.38
J034012.43+233803.1 | TD035052N235741K01 | 55.05184 | 23.63421 |  6177$\pm$12 |  $3.95\pm$0.05 |  $-0.38\pm$0.03 |  $1.7\pm$7.96 |  $13.24\pm$6.32
J034020.87+234005.1 | TD035052N235741K01 | 55.087 | 23.66809 |  5880$\pm$6 |  $4.23\pm$0.02 |  $-0.02\pm$0.01 |  $40.0\pm$8.5 |  $51.95\pm$10.17
J034020.90+242455.5 | TD035052N235741K01 | 55.08714 | 24.41547 |  5912$\pm$40 |  $4.48\pm$0.19 |  $-0.66\pm$0.06 |  $-8.68\pm$5.79 |  $-5.34\pm$5.83
J034024.32+242932.1 | TD035052N235741K01 | 55.10136 | 24.49227 |  4896$\pm$24 |  $2.91\pm$0.05 |  $-0.52\pm$0.03 |  $-98.56\pm$4.27 |  $-94.46\pm$3.58
J034025.64+244209.5 | TD035052N235741K01 | 55.10687 | 24.70268 |  6278$\pm$58 |  $3.74\pm$0.13 |  $-0.39\pm$0.05 |  $-14.98\pm$23.51 |  $-11.41\pm$24.06
J034025.96+232013.5 | TD035052N235741K01 | 55.10817 | 23.33711 |  5907$\pm$31 |  $4.0\pm$0.05 |  $-0.52\pm$0.03 |  $51.55\pm$6.72 |  $63.68\pm$6.86
J034026.48+235823.3 | TD035052N235741K01 | 55.11036 | 23.97316 |  4583$\pm$49 |  $2.27\pm$0.09 |  $-0.54\pm$0.55 |  $-51.4\pm$24.51 |  $-40.39\pm$24.31
J034029.22+234840.1 | TD035052N235741K01 | 55.1218 | 23.81117 |  5491$\pm$10 |  $4.46\pm$0.03 |  $-0.08\pm$0.02 |  $49.64\pm$2.82 |  $61.99\pm$1.31
J034029.59+233303.9 | TD035052N235741K01 | 55.12332 | 23.55111 |  4396$\pm$41 |  $4.5\pm$0.08 |  $-0.43\pm$0.07 |  $-6.31\pm$9.38 |  $4.89\pm$9.01
J034030.72+242914.2 | TD035052N235741K01 | 55.128 | 24.48731 |  5137$\pm$7 |  $4.53\pm$0.04 |  $-0.07\pm$0.02 |  $0.1\pm$2.26 |  $5.08\pm$2.97
J034031.01+242141.0 | TD035052N235741K01 | 55.12922 | 24.3614 |  4260$\pm$26 |  $4.33\pm$0.1 |  $-0.92\pm$0.05 |  $0.58\pm$8.8 |  $4.7\pm$8.27
J034031.67+234521.9 | TD035052N235741K01 | 55.13198 | 23.75611 |  5836$\pm$32 |  $4.23\pm$0.12 |  $-0.23\pm$0.06 |  $-25.68\pm$9.39 |  $-13.17\pm$8.51
J034031.88+243419.1 | TD035052N235741K01 | 55.13285 | 24.57198 |  4791$\pm$48 |  $2.76\pm$0.07 |  $-0.06\pm$0.04 |  $49.55\pm$6.33 |  $53.71\pm$6.57
J034034.36+234057.3 | TD035052N235741K01 | 55.1432 | 23.68261 |  5586$\pm$8 |  $4.46\pm$0.02 |  $0.04\pm$0.02 |  $-8.51\pm$2.12 |  $2.99\pm$1.84
J034034.76+232540.2 | TD035052N235741K01 | 55.14486 | 23.42786 |  7667$\pm$85 |  $4.06\pm$0.24 |  $-0.3\pm$0.13 |  $-45.06\pm$9.66 |  $-34.17\pm$9.36
J034034.92+243247.2 | TD035052N235741K01 | 55.14552 | 24.54648 |  6446$\pm$190 |  $4.34\pm$0.29 |  $0.08\pm$0.12 |  $13.76\pm$40.23 |  $17.07\pm$39.8

This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.

Table 7: Stellar parameters and RV from SLAM estimation with MRS data.

Name | Field | R.A. | Dec. | $T_{\rm eff}$ |  log$g$ | [Fe/H] | RV | corrected RV
---|---|---|---|---|---|---|---|---
|  | (deg) | (deg) | (K) |  |  | (km/s) | (km/s)
J034004.12+235200.0 | TD035052N235741K01 | 55.0172 | 23.86668 |  6323$\pm$103 |  $4.16\pm$0.14 |  $-0.48\pm$0.05 |  $-10.39\pm$0.28 |  $-10.07\pm$0.99
J034007.72+241820.5 | TD035052N235741K01 | 55.03221 | 24.3057 |  5811$\pm$156 |  $4.11\pm$0.18 |  $-0.23\pm$0.09 |  $6.41\pm$0.42 |  $6.22\pm$0.41
J034008.18+241703.1 | TD035052N235741K01 | 55.03411 | 24.2842 |  4551$\pm$70 |  $1.99\pm$0.11 |  $0.11\pm$0.05 |  $-38.57\pm$0.23 |  $-38.78\pm$0.18
J034012.25+234313.8 | TD035052N235741K01 | 55.05106 | 23.72051 |  4924$\pm$74 |  $2.16\pm$0.15 |  $-0.51\pm$0.06 |  $-25.57\pm$0.3 |  $-25.31\pm$0.71
J034012.43+233803.1 | TD035052N235741K01 | 55.05184 | 23.63421 |  6092$\pm$89 |  $3.93\pm$0.09 |  $-0.38\pm$0.06 |  $7.82\pm$2.31 |  $8.57\pm$2.16
J034020.87+234005.1 | TD035052N235741K01 | 55.087 | 23.66809 |  5874$\pm$37 |  $4.15\pm$0.04 |  $-0.05\pm$0.03 |  $40.95\pm$12.68 |  $41.27\pm$12.07
J034024.32+242932.1 | TD035052N235741K01 | 55.10136 | 24.49227 |  4812$\pm$162 |  $2.73\pm$0.49 |  $-0.65\pm$0.15 |  $-94.08\pm$0.5 |  $-94.15\pm$0.42
J034025.96+232013.5 | TD035052N235741K01 | 55.10817 | 23.33711 |  5865$\pm$90 |  $4.15\pm$0.12 |  $-0.47\pm$0.08 |  $61.19\pm$0.69 |  $61.29\pm$1.12
J034029.22+234840.1 | TD035052N235741K01 | 55.1218 | 23.81117 |  5444$\pm$46 |  $4.55\pm$0.05 |  $-0.18\pm$0.03 |  $61.44\pm$0.29 |  $61.7\pm$0.61
J034030.72+242914.2 | TD035052N235741K01 | 55.128 | 24.48731 |  5076$\pm$75 |  $4.38\pm$0.12 |  $-0.14\pm$0.03 |  $4.39\pm$0.42 |  $4.16\pm$0.4
J034031.01+242141.0 | TD035052N235741K01 | 55.12922 | 24.3614 |  3337$\pm$98 |  $2.72\pm$0.23 |  $-0.89\pm$0.12 |  $15.99\pm$0.3 |  $15.8\pm$0.31
J034031.67+234521.9 | TD035052N235741K01 | 55.13198 | 23.75611 |  5882$\pm$39 |  $4.28\pm$0.15 |  $-0.37\pm$0.11 |  $-13.31\pm$0.68 |  $-13.55\pm$0.68
J034031.88+243419.1 | TD035052N235741K01 | 55.13285 | 24.57198 |  4691$\pm$136 |  $2.65\pm$0.23 |  $-0.14\pm$0.05 |  $54.29\pm$0.41 |  $54.09\pm$0.44
J034034.36+234057.3 | TD035052N235741K01 | 55.1432 | 23.68261 |  5471$\pm$40 |  $4.4\pm$0.05 |  $0.06\pm$0.03 |  $5.43\pm$0.34 |  $5.62\pm$0.93
J034035.40+232248.3 | TD035052N235741K01 | 55.14757 | 23.38009 |  5510$\pm$96 |  $3.7\pm$0.21 |  $0.2\pm$0.1 |  $28.7\pm$1.21 |  $29.18\pm$0.49
J034038.79+242507.8 | TD035052N235741K01 | 55.16163 | 24.41884 |  6200$\pm$75 |  $3.83\pm$0.08 |  $-0.16\pm$0.07 |  $5.98\pm$0.3 |  $5.82\pm$0.34
J034039.96+235046.7 | TD035052N235741K01 | 55.16651 | 23.84634 |  4835$\pm$76 |  $2.4\pm$0.12 |  $-0.26\pm$0.06 |  $33.93\pm$0.95 |  $34.41\pm$0.23
J034041.11+235922.0 | TD035052N235741K01 | 55.1713 | 23.98947 |  4719$\pm$81 |  $1.31\pm$0.14 |  $-0.79\pm$0.08 |  $29.64\pm$0.79 |  $30.46\pm$0.16
J034044.91+243926.0 | TD035052N235741K01 | 55.18717 | 24.65728 |  6309$\pm$254 |  $3.9\pm$0.35 |  $-0.48\pm$0.1 |  $-21.46\pm$0.72 |  $-21.63\pm$0.69
J034046.76+241255.7 | TD035052N235741K01 | 55.19484 | 24.2155 |  6128$\pm$46 |  $4.19\pm$0.05 |  $-0.09\pm$0.03 |  $27.72\pm$0.3 |  $27.51\pm$0.26

This table is available in its entirety in machine-readable and Virtual
Observatory (VO) forms in the online journal. A portion is shown here for
guidance regarding its form and content.