# Low-Resolution Near-infrared Stellar Spectra Observed by the Cosmic Infrared
Background Experiment (CIBER)

Min Gyu Kim11affiliation: Dept. of Physics and Astronomy, Seoul National
University, Seoul 08826, Korea  22affiliation: Korea Astronomy and Space
Science Institute (KASI), Daejeon 34055, Korea  , Hyung Mok Lee11affiliation:
Dept. of Physics and Astronomy, Seoul National University, Seoul 08826, Korea
, Toshiaki Arai33affiliation: Department of Space Astronomy and Astrophysics,
Institute of Space and Astronautical Science (ISAS), Japan Aerospace
Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa
252-5210, Japan  , James Bock44affiliation: Department of Astronomy,
California Institute of Technology, Pasadena, CA 91125, USA  55affiliation:
Jet Propulsion Laboratory (JPL), 4800 Oak Grove Dr., Pasadena, CA 91109, USA
, Asantha Cooray66affiliation: Center for Cosmology, University of California,
Irvine, Irvine, CA 92697, USA  , Woong-Seob Jeong22affiliation: Korea
Astronomy and Space Science Institute (KASI), Daejeon 34055, Korea  , Seong
Jin Kim22affiliation: Korea Astronomy and Space Science Institute (KASI),
Daejeon 34055, Korea  , Phillip Korngut44affiliation: Department of Astronomy,
California Institute of Technology, Pasadena, CA 91125, USA  55affiliation:
Jet Propulsion Laboratory (JPL), 4800 Oak Grove Dr., Pasadena, CA 91109, USA
, Alicia Lanz44affiliation: Department of Astronomy, California Institute of
Technology, Pasadena, CA 91125, USA  , Dae Hee Lee22affiliation: Korea
Astronomy and Space Science Institute (KASI), Daejeon 34055, Korea  , Myung
Gyoon Lee11affiliation: Dept. of Physics and Astronomy, Seoul National
University, Seoul 08826, Korea  , Toshio Matsumoto33affiliation: Department of
Space Astronomy and Astrophysics, Institute of Space and Astronautical Science
(ISAS), Japan Aerospace Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku,
Sagamihara, Kanagawa 252-5210, Japan  , Shuji Matsuura33affiliation:
Department of Space Astronomy and Astrophysics, Institute of Space and
Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA), 3-1-1
Yoshinodai, Chuo-ku, Sagamihara, Kanagawa 252-5210, Japan  77affiliation:
Department of Physics, Kwansei Gakuin University, Hyogo 669-1337, Japan  , Uk
Won Nam22affiliation: Korea Astronomy and Space Science Institute (KASI),
Daejeon 34055, Korea  , Yosuke Onishi33affiliation: Department of Space
Astronomy and Astrophysics, Institute of Space and Astronautical Science
(ISAS), Japan Aerospace Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku,
Sagamihara, Kanagawa 252-5210, Japan  88affiliation: Department of Physics,
Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550,
Japan  , Mai Shirahata33affiliation: Department of Space Astronomy and
Astrophysics, Institute of Space and Astronautical Science (ISAS), Japan
Aerospace Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku, Sagamihara,
Kanagawa 252-5210, Japan  , Joseph Smidt66affiliation: Center for Cosmology,
University of California, Irvine, Irvine, CA 92697, USA  99affiliation:
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545,
USA  , Kohji Tsumura1010affiliation: Frontier Research Institute for
Interdisciplinary Science, Tohoku University, Sendai 980-8578, Japan  , Issei
Yamamura33affiliation: Department of Space Astronomy and Astrophysics,
Institute of Space and Astronautical Science (ISAS), Japan Aerospace
Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa
252-5210, Japan  , and Michael Zemcov1111affiliation: Center for Detectors,
School of Physics and Astronomy, Rochester Institute of Technology, Rochester
NY 14623, USA  55affiliation: Jet Propulsion Laboratory (JPL), 4800 Oak Grove
Dr., Pasadena, CA 91109, USA<EMAIL_ADDRESS>
###### Abstract

We present near-infrared (0.8-1.8 $\micron$) spectra of 105 bright (${m_{J}}$
$<$ 10) stars observed with the low resolution spectrometer on the rocket-
borne Cosmic Infrared Background Experiment (CIBER). As our observations are
performed above the earth’s atmosphere, our spectra are free from telluric
contamination, which makes them a unique resource for near-infrared spectral
calibration. Two-Micron All Sky Survey (2MASS) photometry information is used
to identify cross-matched stars after reduction and extraction of the spectra.
We identify the spectral types of the observed stars by comparing them with
spectral templates from the Infrared Telescope Facility (IRTF) library. All
the observed spectra are consistent with late F to M stellar spectral types,
and we identify various infrared absorption lines.

catalogs — infrared: stars — stars: general — techniques: spectroscopic

##  1  Introduction

Precise ground-based measurements of stellar spectra are challenging in the
near-infrared (IR) because of the contaminating effects of telluric lines from
species like water, oxygen, and hydroxyl in the earth’s atmosphere. Telluric
correction using standard stars is generally used to overcome this problem,
but these corrections are problematic in wavelength regions marked by strong
line contamination, such as from water and hydroxyl. In contrast, space-based
spectroscopy in the near-IR does not require telluric correction, so can
provide new insights into stellar atmospheres (e.g. Matsuura et al. 1999;
Tsuji et al. 2001), especially near $1\micron$ where starlight is not
reprocessed by dust in the circumstellar environment (Meyer et al., 1998). In
particular, near-IR spectra can be used to study the age and mass of very
young stars (Joyce et al., 1998; Peterson et al., 2008), and the physical
properties of very cool stars (Sorahana & Yamamura, 2014).

Of particular interest in the study of the atmospheres of cool stars is water.
According to early models of stellar photospheres (Russell, 1934), H2O existed
only in later than M6 type stars, and until recently observations have
supported this. In 1963, the balloon-borne telescope Stratoscope II observed
H2O in two early M2-M4 giant stars (Woolf et al., 1964) at 1.4 and
$1.9\,\micron$. Several decades later, Tsuji et al. (1997) measured H2O
absorption in an M2.5 giant star using the Infrared Space Observatory (Kessler
et al. 1996), and Matsuura et al. (1999) observed water at 1.4, 1.9, 2.7, and
$6.2\micron$ for 67 stars with the Infrared Telescope in Space (Murakami et
al. 1996; Matsumoto et al. 2005). Surprisingly, Tsuji et al. (2001) discovered
water features in late K-type stars. These results required a new stellar
photosphere model to explain the existence of H2O features in hotter than M6
type stars (Tsuji et al., 2015).

The low resolution spectrometer (LRS; Tsumura et al. 2013) on the Cosmic
Infrared Background Experiment (CIBER; Bock et al. 2006; Zemcov et al. 2013)
observed the diffuse infrared background from 0.7 to 2.0 $\micron$ during four
flights above the Earth atmosphere. The LRS was designed to observe the near-
IR background (Hauser & Dwek, 2001; Madau & Pozzetti, 2000), and as a result
finds excess extragalactic background light above all known foregrounds
(Matsuura et al. 2016, ApJ, submitted, 2016). Furthermore, we precisely
measure astrophysical components contributing to the diffuse sky brightness
(see Leinert et al. 1998 for a review). For example, Tsumura et al. (2010)
observed a component of the zodiacal light absorbed by silicates in a broad
band near $800\,$nm. By correlating the LRS with a 100 $\micron$ dust map
(Schlegel, 1998), Arai et al. (2015) measured smooth diffuse galactic light
spectrum from the optical band to the near-IR and constrained the size
distribution of interstellar dust, which was dominated by small particles
(half-mass radius $\sim$0.06 $\micron$).

The LRS also observed many bright galactic stars, enabling us to study their
near-IR SEDs. In this paper, we present flux-calibrated near-IR spectra of 105
stars from $0.8\leq\lambda\leq 1.8\,\micron$ with spectral resolution
$15\leq\lambda/\Delta\lambda\leq 30$ over the range. The paper is organized as
follows. In Section 2, the observations and instrumentation are introduced. We
describe the data reduction, calibration, astrometry, and extraction of the
stellar spectra in Section 3. In Section 4, the spectral typing and features
are discussed. Finally, a summary and discussion are given in Section 5.

##  2 Instrument

The LRS is one of the four optical instruments of the CIBER payload (Zemcov et
al., 2013); the others are a narrowband spectrometer (Korngut et al. 2013) and
two wide-field imagers (Bock et al., 2013). The LRS (Tsumura et al., 2013) is
a prism-dispersed spectrometer with five rectangular $5.35^{\circ}\times
2.8\arcmin$ slits imaging a 5.8 ∘ $\times$ 5.8 ∘ field of view. The detector
has $256\times 256$ pixels at a pixel scale of $1.36\arcmin\times
1.36\arcmin$. CIBER has flown four times (2009 February, 2010 July, 2012
March, and 2013 June) with apogees and total exposure times of over 325 km and
$\sim$ 240 s, respectively, in the first three flights and of 550 km and 335 s
in the final, non-recovered flight. Due to spurious signal contamination from
thermal emission from the shock-heated rocket skin, we do not use the first
flight data in this work (Zemcov et al., 2013). Eleven target fields were
observed during the three subsequent flights, as listed in Table 1. Details of
the field selection are described in Matsuura et al. 2016, ApJ, submitted
(2016).

During the observations, the detector array is read nondestructively at $\sim
4\,$Hz frame-1. Each field is observed for many tens or hundreds of frames,
and an image for each field is obtained by computing the slope of the
accumulated values for each pixel (Garnett & Forrest, 1993). Figure 1 shows an
example image of the North Ecliptic Pole (NEP) region obtained during the
second flight. More than 20 bright stars ($m_{J}$ $<$ 11) are observed. The
stellar spectra are characterized by a small amount of field distortion as
well as an arc-shaped variation in constant-wavelength lines along the slit
direction. The latter is known as a “smile” and is a known feature of prism
spectrometers (Fischer et al., 1998). Details of the treatment of these
distortions are described in Section 3.3 and 3.4.

##  3 Data Analysis

In this section, we describe how we perform background subtraction,
calibration, photometric estimation, astrometric registration, and spectral
extraction from the LRS-observed images.

###  3.1 Pixel response correction

We measure the relative pixel response (flat field) in the laboratory before
each flight (Arai et al., 2015). The second- and the third-flight data are
normally corrected with these laboratory flats. However, for the fourth flight
from the laboratory calibrations do not extend to the longest wavelengths
($\lambda\geq 1.4\micron$) because the slit mask shifted its position with
respect to the detector during the flight. We therefore use the second-flight
flat field to correct the relative response for the fourth-flight data, as
this measurement covers $\lambda>1.6\micron$. To apply this flat field, we
need to assume that the intrinsic relative pixel response does not vary
significantly over the flights. To check the validity of this assumption, we
subtract the second flat image to the fourth flat image for overlapped pixels
and calculate the pixel response difference. We find that only 0.3 % of pixels
with response measured in both are different by $2\sigma$, where $\sigma$ is
the standard deviation of the pixel response. Finally, we mask 0.06 % of the
array detectors to remove those pixels with known responsivity pathologies and
those prone to transient electronic events (Lee et al., 2010).

###  3.2 Calibration

For each flight, the absolute brightness and wavelength irradiance
calibrations have been measured in the laboratory in collaboration with the
National Institute of Standards and Technology. The details of these
calibrations can be found in Tsumura et al. (2013). The total photometric
uncertainty of the LRS brightness calibration is estimated to be $\pm 3$%
(Tsumura et al., 2013; Arai et al., 2015).

###  3.3 Background Removal

The raw image contains not only spectrally dispersed images of stars but also
the combined emission from zodiacal light $\lambda I_{\lambda}^{\rm ZL}$,
diffuse galactic light $\lambda I_{\lambda}^{\rm DGL}$, the extragalactic
background $\lambda I_{\lambda}^{\rm EBL}$, and instrumental effects $\lambda
I_{\lambda}^{\rm inst}$ (Leinert et al., 1998). The measured signal $\lambda
I_{\lambda}^{\rm meas}$ can be expressed as

$\lambda I_{\lambda}^{\rm meas}=\lambda I_{\lambda}^{\ast}+\lambda
I_{\lambda}^{\rm ZL}+\lambda I_{\lambda}^{\rm ISL}+\lambda I_{\lambda}^{\rm
DGL}+\lambda I_{\lambda}^{\rm EBL}+\lambda I_{\lambda}^{\rm inst},$ (1)

where we have decomposed the intensity from stars into a resolved component
$\lambda I_{\lambda}^{\ast}$ and an unresolved component arising from the
integrated light of stars below the sensitivity of the LRS $\lambda
I_{\lambda}^{\rm ISL}$. It is important to subtract the sum of all components
except $\lambda I_{\lambda}^{\ast}$ from the measured brightness to isolate
the emission from detected stars. At this point in the processing, we have
corrected for multiplicative terms affecting $\lambda I_{\lambda}^{\rm meas}$.
Dark current, which is the detector photocurrent measured in the absence of
incident flux, is an additional contribution to $\lambda I_{\lambda}^{\rm
inst}$. The stability of the dark current in the LRS has been shown to be 0.7
nW m-2 sr-1 over each flight, which is a negligible variation from the typical
dark current (i.e., 20 nW m-2 sr-1; (Arai et al., 2015)). As a result, we
subtract the dark current as part of the background estimate formed below.

The relative brightnesses of the remaining background components are
wavelength-dependent, so an estimate for their mean must be computed along
constant-wavelength regions, corresponding to the vertical columns in Figure
1. Furthermore, because of the LRS’s large spatial PSF, star images can extend
over several pixels in the imaging direction and even overlap one another.
This complicates background estimation in pixels containing star images and
reduces the number of pixels available to estimate the emission from the
background components.

To estimate the background in those pixels containing star images, we compute
the average value of pixels with no star images along each column, as
summarized in Figure 2. We remove bright pixels that may contain star images,
as described in Arai et al. (2015). The spectral smile effect shown in Figure
1 introduces spectral curvature along a column. We estimate it causes an error
of magnitude $\delta\lambda/\lambda<10^{-2}$, which is small compared to the
spectral width of a pixel. Approximately half of the rows remain after this
clipping process; the fraction ranges from 45 % to 62 % depending on the
stellar density in each field. This procedure removes all stars with $J>13$,
and has a decreasing completeness above this magnitude (Arai et al., 2015).

To generate an interpolated background map, each candidate star pixel is
replaced by the average of nearby pixels calculated along the imaging
direction from the $\pm 10$ pixels on either side of the star image. We again
do not explicitly account for the spectral smile. This interpolated background
image is subtracted from the measured image, resulting in an image containing
only bright stellar emission. The emission from faint stars and bright stars
that inefficiently illuminate a grating slit that contributes to
$I_{\lambda}^{\rm ISL}$ is naturally removed in this process.

###  3.4 Star Selection

The bright lines dispersed in the spectral direction in the background-
subtracted images are candidate star spectra. To calculate the spectrum of
candidate sources, we simply isolate individual lines of emission and map the
pixel values onto the wavelength using the ground calibration. However, this
procedure is complicated both by the extended spatial PSF of the LRS and by
source confusion.

To account for the size of the LRS spatial PSF (FWHM $\sim$1.2 pixels) as well
as optical distortion from the prism that spreads the star images slightly
into the imaging direction, we sum five rows of pixels in the imaging
direction for each candidate star. Since the background emission has already
been accounted for, this sum converges to the total flux as the number of
summed rows is increased. By summing five rows, we capture $>99.9$% of a
candidate star’s flux. The wavelengths of the spectral bins are calculated
from the corresponding wavelength calibration map in the same way.

From these spectra, we can compute synthetic magnitudes in the $J$\- and
$H$-bands, which facilitate comparison to Two-Micron All-Sky Survey (2MASS)
measurements. We first convert surface brightness in nW m-2 sr-1 to flux in nW
m-2 Hz-1, and then integrate the monochromatic intensity over the 2MASS band,
applying the filter transmissivity of the $J$\- and $H$-bands (Cohen et al.,
2003). To determine the appropriate zero magnitude, we integrate the $J$\- and
$H$-band intensity of Vega’s spectrum (Bohlin & Gilliland, 2004) with the same
filter response. The $J$\- and $H$-band magnitudes of each source are then
calculated, allowing both flux and color comparisons between our data and the
2MASS catalog.

Candidate star spectra may be comprised of the blended emission from two or
more stars, and these must be rejected from the catalog. Such blends fall into
one of two categories: (i) stars that are visually separate but are close
enough to share flux in a 5 pixel-wide photometric aperture, and (ii) stars
that are close enough that their images overlap so as to be indistinguishable.
We isolate instances of case (i) by comparing the fluxes calculated by summing
both three and five rows along the imaging direction for each source. If the
magnitude or $J-H$ color difference between the two apertures is larger than
the statistical uncertainty (described in Section 3.6), we remove those
spectra from the catalog. To find instances of case (ii), we use the 2MASS
star catalog registered to our images using the procedure described in Section
3.5. Candidate sources that do not meet the criteria presented below are
rejected.

To ensure the catalog spectra are for isolated stars rather than for
indistinguishable blends, we impose the following requirements on candidate
star spectra: (i) each candidate must have $J<11$; (ii) the $J$-band magnitude
difference between the LRS candidate and the matched 2MASS counterpart must be
$<1.5$; (iii) the $J-H$ color difference between the LRS candidate star and
the matched 2MASS counterpart must be $<0.3$; and (iv) among the candidate
2MASS counterparts within the 500$\arcsec$ ($=6$ pixel) radius of a given LRS
star, the second-brightest 2MASS star must be fainter than the brightest one
by more than 2 mag at the J band. Criterion (i) excludes faint stars that may
be strongly affected by residual backgrounds, slit mask apodization, or source
confusion. The second and third criteria mitigate mismatching by placing
requirements on the magnitude and color of each star. In particular, the $J-H$
color of a source does not depend on the slit apodization or the position in
image space (see Figure 3), so any significant change in $J-H$ color as the
photometric aperture is varied suggests that more than a single star could be
contributing to the measured brightness. Finally, it is possible that two
stars with similar $J-H$ colors lie close to each other, so the last criterion
is applied to remove stars for which equal-brightness blending is an issue.
Approximately one in three candidate stars fails criterion (iv). The number of
candidate stars rejected at each criterion is described in Table 2.

In addition, three of LRS candidate stars are identified as variables in the
SIMBAD database 111http://simbad.u-strasbg.fr/simbad/. We also identify two
stars as binary and multiple-star systems as well as four high proper motion
stars. Through these stringent selection requirements, we conservatively
include only the spectra of bright, isolated stars in our catalog. Finally,
105 star spectra survive all the cuts, and the corresponding stars are
selected as catalog members.

###  3.5 Astrometry

We match the synthesized LRS $J$, $H$, and $J-H$ information with the 2MASS
point source catalog (Skrutskie et al., 2006) to compute an astrometric
solution for the LRS pointing in each sky image. This is performed in a
stepwise fashion by using initial estimates for the LRS’s pointing to solve
for image registration on a fine scale.

As a rough guess at the LRS pointing, we use information provided by the
rocket’s attitude control system (ACS), which controls the pointing of the
telescopes (Zemcov et al., 2013). This provides an estimated pointing solution
that is accurate within 15 $\arcmin$ of the requested coordinates. However,
since the ACS and the LRS are not explicitly aligned to one another, a finer
astrometric registration is required to capture the pointing of the LRS to
single-pixel accuracy.

To build a finer astrometric solution, we simulate images of each field in the
2MASS J-band using the positional information from the ACS, spatially
convolved to the LRS PSF size. Next, we apodize these simulated 2MASS images
with the LRS slit mask, compute the slit-masked magnitudes of three reference
stars, and calculate the $\chi^{2}$ statistic using

$\chi^{2}{{}_{p,q}}=\sum_{i}\left(\frac{F_{LRS,i}-F_{2MASS,i}}{\sigma_{LRS,i}}\right)^{2},$
(2)

where index i represents each reference star and subscripts p and q index the
horizontal and vertical positions of the slit mask, respectively. $F_{LRS,i}$
and $F_{2MASS,i}$ are the fluxes in the LRS and 2MASS $J$-band, and
$\sigma_{LRS,i}$ is the statistical error of the LRS star (see Section 3.6).
The minimum $\chi^{2}$ gives the most likely astrometric position of the slit
mask. Since, on average, there are around five bright stars with $J<9$ per
field, spurious solutions are exceedingly unlikely, and all fields give a
unique solution.

Using this astrometric solution, we can assign coordinates to the rest of the
detected LRS stars. We estimate that the overall astrometric error is
120$\arcsec$ by computing the mean distance between the LRS and 2MASS
coordinates of all matched stars. The error corresponds to 1.5 times the pixel
scale. We check the validity of the astrometric solutions by comparing the
colors and fluxes between the LRS and matched 2MASS stars. In Figures 3 and 4,
we show the comparison of the $J-H$ colors and fluxes of the cross-matched
stars in each field. Here, we multiply the LRS fluxes at the J- and H-band by
2.22 and 2.17, respectively, to correct for the slit apodization. The
derivation of correction factors is described in Section 5. On the whole, they
match well within the error range.

###  3.6 Spectral Error Estimation

Even following careful selection, the star spectra are subject to various
kinds of uncertainties and errors, including statistical uncertainties, errors
in the relative pixel response, absolute calibration errors, wavelength
calibration errors, and background subtraction errors.

Statistical uncertainties in the spectra can be estimated directly from the
flight data. We calculate the $1\sigma$ slope error from the line fit (see
Section 2) as we generate the flight images; this error constitutes the
estimate for the statistical photometric uncertainty for each pixel. In this
statistical error, we include contributions from the statistical error in the
background estimate and the relative pixel response. The error in the
background signal estimate is formed by computing the standard deviation of
the $\pm$10 pixels along the constant-$\lambda$ direction for each pixel to
match the background estimate region. This procedure captures the local
structure in the background image, which is a reasonable measure of the
variation we might expect over a photometric aperture. Neighboring pixels in
the wavelength direction have extremely covariant error estimates in this
formulation, which are acceptable since the flux measurements are also
covariant in this direction. A statistical error from the relative pixel
response correction is applied by multiplying 3% of the relative response by
the measured flux in each field (Arai et al., 2015). To compute the total
statistical error, each constituent error is summed in quadrature for each
pixel.

Several instrumental systematic errors are present in these measurements,
including those from wavelength calibration, absolute calibration, and
relative response correction. In this work, we do not explicitly account for
errors in the wavelength calibration, as the variation is $\pm$ 1 nm over 10
constant-wavelength pixels, which is $<0.1R$. In all flights, $<$ 3 % absolute
calibration error is applied (Arai et al., 2015). For the longest-wavelength
regions ($\lambda$ $>$ 1.6 $\micron$) of the fourth-flight data that are not
measured even in the second-flight flat, we could not perform flat correction.
Instead, we apply a systematic error amounting to 5.3 % of the measured sky
brightness. The error is estimated from pixels in the short-wavelength regions
($\lambda$ $<$ 1.4 $\micron$) of the fourth-flight flat. We calculate
deviations from unity for those pixels and take a mean of 5.3 %. The linear
sum of systematic errors is then combined with statistical error in
quadrature.

##  4 The Spectra

The 105 stellar spectra that result from this processing can be used to test
spectral type determination algorithms and study near-IR features that are
invisible from the ground. Despite the relatively low spectral resolution of
our stellar spectra, we identify several molecular bands, particularly for the
late-type stars. We present the $J-$band-normalized LRS spectra for each of
the catalog stars in Figure 5.

General information for each spectrum is summarized in Table 3 with the
corresponding star ID. All spectra are publicly available in electronic form
222http://astro.snu.ac.kr/$\sim$mgkim/. The spectra are presented without the
application of interstellar extinction corrections, since extinction
correction assumes both a color index and the integrated Galactic extinction
along the line of sight. Therefore, without knowing the stars’ distances, it
is difficult to make progress. For CIBER fields, typical extinction ranges
from 0.005 to 0.036 mag at the J-band if we assume extinction coefficients
R(J) with 0.72 (Yuan et al., 2013)

###  4.1 Spectral type determination

The star spectral types are determined by fitting known spectral templates to
the measured LRS spectra. We use the Infrared Telescope Facility (IRTF) and
Pickles (Pickles, 1998) templates for the SED fitting. The SpeX instrument
installed on the IRTF observed stars using a medium-resolution spectrograph (R
$=$ 2000). The template library contains spectra for 210 cool stars (F to M
type) with wavelength coverage from 0.8 to 2.5 $\micron$ (Cushing, 2005;
Rayner, 2009). The Pickles library is a synthetic spectral library that
combines spectral data from various observations to achieve wavelength
coverage from the UV (0.115 $\micron$) to the near-IR (2.5 $\micron$). It
contains 131 spectral templates for all star types (i.e., O to M type) with a
uniform sampling interval of 5 $\AA$.

To perform the SED fit, we degrade the template spectra to the LRS spectral
resolution using a mean box-car smoothing kernel corresponding to the slit
function of the LRS. Both the measured and template spectra are normalized to
the $J$-band flux. We calculate the flux differences between the LRS and
template spectra using

$\chi^{2}=\sum_{\lambda}\left(\frac{F_{LRS,\lambda}-F_{ref,\lambda}}{\sigma_{LRS,\lambda}}\right)^{2},$
(3)

where $F_{LRS,\lambda}$ and $F_{ref,\lambda}$ are the fluxes of the observed
and template spectra at wavelength $\lambda$ normalized at $J$-band and
$\sigma_{LRS,\lambda}$ is the statistical error of the observed spectrum. The
best-fitting spectral type is determined by finding the minimum $\chi^{2}$.

No early-type (i.e., O, B, A) stars are found in our sample; all stars have
characteristics consistent with those of late-type stars (F and later).
Because the IRTF library has about twice the spectral type resolution of the
Pickles library, we provide the spectral type determined from the IRTF
template in Table 3. Since the IRTF library does not include a continuous set
of spectral templates, we observe discrepancies between the LRS and best-fit
IRTF templates, even though the $J-H$ colors are consistent between 2MASS and
the LRS within the uncertainties. The Pickles and IRTF fits are consistent
within the uncertainty in the classification ($\sim$ 0.42 spectral subtypes).

A color-color diagram for the star sample is shown in Figure 6. Although the
color-color diagram does not allow us to clearly discriminate between spectral
types, qualitatively earlier-type stars are located in the bluer region, while
later-type stars are located in the redder region, consistent with
expectations. LRS stars well follow the color-color distributions of typical
2MASS stars in LRS fields, as indicated by the gray dots.

To estimate the error in our spectral type determination, we compare our
identifications with the SIMBAD database (Wenger et al., 2000), where 63 of
the 105 stars have prior spectral type determinations. Figure 7 shows the
spectral types determined from the IRTF fit versus those from the SIMBAD
database. The 1$\sigma$ error of type difference is estimated to be 0.59
spectral subtypes, which is comparable with those in other published works
(Gliese, 1971; Jaschek & Jaschek, 1973; Jaschek, M., 1978; Roeser, 1988; Houk
et al., 1999). The error can be explained with two factors: (i) the low
spectral resolution of the LRS and (ii) the SED template libraries, which do
not represent all star types.

Five stars are observed twice in different flights (BA2_5 and BB4_6, N2_6 and
N3_5, BA2_1 and BA3_4, BB2_1 and BB3_1, and BB2_4 and BB3_4; see Figure 8),
enabling us to investigate the interflight stability of the spectra. For BA2_5
and BB4_6, the spectral type is known to be F8, while our procedure yields F7V
and F1II from the second- and fourth-flight data, respectively. For N2_6 and
N3_5, the known type is K5 while we determine M0.5V for both flights. For
BA2_1 and BA3_4, the known type is F5 while we determine F7III and F2III-IV in
the second and third flights. For BB2_1 and BB3_1, the fitted types are
G8IIIFe5 and K4V for a K1 type star, and the type of BB2_4 and BB3_4 are not
known but are fitted to F9V for both flights. The determined spectra are
consistent within an acceptable error window, though the longer-wavelength
data exhibit large differences, which can be attributed to calibration error.
We present the spectra of each star from both flights in Table 3. This
duplication results in our reporting of 110 spectra in the catalog, even
through only 105 individual stars are observed.

##  5 Discussion

We determined the spectral type of 105 stars as well as the associated typing
error (0.59 spectral subtypes) assessed by comparing the type against a set of
63 previously determined spectral types. Representative examples of the
measured spectra for different spectral types are shown in Figure 9. Molecular
absorption lines are evident in these spectra, including the CaII triplet and
various CN bands.

Since we observed stars above the earth’s atmosphere, observations of the H2O
molecular band are possible. However, they are not able to distinguish between
CN and H2O at 1.4 $\micron$ since both have the same bandhead and appear in
late-type stars (Wing & Spinrad, 1970). For example, the spectral features of
M2-M4 (super)giant stars observed by Stratoscope II, previously identified as
CN, were identified as H2O (Tsuji et al., 2000). Several subsequent
observations show clear evidence that water features exist even in K type
stars, requiring modifications of present stellar photosphere models (Tsuji et
al., 2000).

In our spectral catalog, most K and M type stars exhibit a broad absorption
band around 1.4 $\micron$. Although it is not possible to identify specific
molecular bands with our data, we cannot exclude the presence of H2O in the
spectra of these stars. Future mid-IR measurements at $6.3\micron$ would help
disentangle the source of the spectral features by removing the spectral
degeneracies between CN and H2O (Tsuji et al., 2001).

As these spectra are free from telluric contamination and the LRS is
calibrated against absolute irradiance standards (Arai et al., 2015), in
principle these measurements could be used as near-IR spectral standards.
However, our lack of knowledge of the instrument response function (IRF) on
the spectral plane complicates the use of these measurements for the absolute
photometric calibration of stars. Specifically, the LRS’s IRF depends on the
end-to-end optical properties of the instrument. Because we use a slit mask at
the focus of an optical coupler (Tsumura et al., 2013), the full IRF knowledge
of the focusing element of the optical coupler is difficult to disentangle
from other effects. As a result, we would need to know the precise IRF to
assign an absolute error estimate to an absolute calibration of the star
images. This response function was not characterized during ground testing.

Nevertheless, we consider it instructive to check the validity of photometric
results whether or not the estimated magnitudes of the LRS stars are
reasonable compared to previous measurements. We perform an empirical
simulation as follows. For each LRS star, we generate a point source image
with the flux of the 2MASS counterpart convolved to the LRS PSF. Instrumental
noise and source confusion from faint stars ($J>13$) based on the 2MASS stars
around a target star are also added. We measure the photometric flux of the
simulated star image in the same way as for the LRS stars as described in this
paper. An aperture correction is applied to the LRS stars, since stars that
are clipped by the slit mask will appear to have a reduced flux measurement.
Figures 10 and 11 show the ratios of the band-synthesized flux of each LRS
star to the flux of the corresponding 2MASS star with statistical errors. The
range explained by our simulations is illustrated as a color-shaded area. The
LRS stars fall within the expected flux range. Also, the flux ratios of the
stars between flights well agree, validating the stability of the photometric
calibrations for the three CIBER flights. The large scatter at faint stars is
caused by background noise, including adjacent faint stars and the instrument.
The statistical J- and H-band flux errors are 3.89 % and 4.51 %, with
systematic errors of 2.98 % and 3.82 %. We conclude that the achievable
uncertainties on the absolute photometric amplitudes of these spectra are not
competitive with other measurements (e.g. the existing 2MASS J and H-band flux
errors are 1.57 % and 2.36 %, respectively).

The slit mask apodization correction ultimately limits the accuracy of our
absolute calibration measurement and can lead to subtle biases. However, by
connecting them with precise spectral measurements, we can improve the
accuracy of LRS stellar spectra. The European Space Agency’s Gaia (Perryman et
al., 2001; Jordi et al., 2010) mission is a scanning all-sky survey that uses
a blue photometer (0.33$\micron$ $<$ $\lambda$ $<$ 0.68$\micron$) and a red
(0.64$\micron$ $<$ $\lambda$ $<$ 1.05$\micron$) one to cover 0.33$\micron$ to
1.05$\micron$ with spectral resolution similar to that of the LRS. Because the
Gaia photometers spectrally overlap with the LRS, we expect to eventually be
able to unambiguously correct for the slit mask apodization and achieve an
absolute flux calibration with less than 2 % accuracy over the full range
$0.4\leq\lambda\leq 1.6\,\mu$m for our 105 stars.

In addition, the data reduction procedure described here may be a useful guide
for the Gaia analysis. Since Gaia uses a prism-based photometer source
detection, the data will show a nonlinear spatial variation of constant-
wavelength bands and flux losses by a finite window size, as in our
measurements. The background estimation will also require careful treatment
with precise estimation of the end-to-end Gaia PSF.

This work was supported by NASA APRA research grants NNX07AI54G, NNG05WC18G,
NNX07AG43G, NNX07AJ24G, and NNX10AE12G. Initial support was provided by an
award to J.B. from the Jet Propulsion Laboratory’s Director’s Research and
Development Fund. Japanese participation in CIBER was supported by KAKENHI
(20·34, 18204018, 19540250, 21340047, 21111004, and 26800112) from Japan
Society for the Promotion of Science (JSPS) and the Ministry of Education,
Culture, Sports, Science, and Technology. Korean participation in CIBER was
supported by the Pioneer Project from the Korea Astronomy and Space Science
Institute. M.G.K. acknowledges support from the Global PhD Fellowship Program
through the NRF, funded by the Ministry of Education (2011-0007760). H.M.L.
and M.G.L. were supported by NRF grant 2012R1A4A1028713. M.Z. and P.K.
acknowledge support from NASA postdoctoral program fellowships, and A.C.
acknowledges support from NSF CAREER awards AST-0645427 and NSF AST-1313319.
We thank the dedicated efforts of the sounding rocket staff at the NASA
Wallops Flight Facility and White Sands Missile Range and also thank Dr. Allan
Smith, Dr. Keith Lykke, and Dr. Steven Brown (NIST) for the laboratory
calibration of the LRS. This publication makes use of data products from the
2MASS, which is a joint project of the University of Massachusetts and the
Infrared Processing and Analysis Center/California Institute of Technology,
funded by the NASA and the NSF. This research has made use of the SIMBAD
database, operated at CDS, Strasbourg, France, and the SpeX library .

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Figure 1: An example CIBER-LRS image toward the NEP field. The five
illuminated columns are dispersed spectra from the five slits of the LRS, and
the bright horizontal lines in each column are images of individual stars. As
an example, we highlight a single horizontal light trail by a red box; this is
the light from a single star dispersed from 0.7 to 2.0 $\micron$. The bright
dots are pixels hit by cosmic rays. The yellow boxes highlight representative
examples of stellar spectra disturbed by the prism. Note that the distortion
direction is different between the upper and lower parts of the image, and the
distortion becomes negligible at the center line of the image.

Figure 2: Flow chart of the background image construction. (a) Same as Figure
1. The red box indicates the set of rows to be averaged. (b) Histogram of
averaged values for each row. This average values for each slit are drawn with
different color. (c) Image after iterative sigma clipping of bright rows from
(b). The red box indicates the size of $\pm$ 10 pixels that are averaged. (d)
Reconstructed background image including all instrumental noise and undetected
faint stars.

Figure 3: LRS J-H color comparison with cross-matched 2MASS J-H color. Each
color corresponds to a different flight. The dashed line shows a linear fit,
exhibiting a slight systematic offset from unity. The J-H colors of LRS stars
are conserved regardless of the slit apodization effect.

Figure 4: The 2MASS J- and H-band fluxes are shown as a function of the LRS J-
and H-band. Each color represents the data obtained on a different flight.
Slit apodization effect is corrected for all LRS stars. Correction factors are
derived based on the slit simulation for magnitude ranges covered by the LRS
stars, as shown in Figure 10 and 11.

Figure 5: LRS spectra of stars identified in this survey. The blue curve
represents the IRTF template degraded to fit the observed LRS spectrum,
indicated by a red curve. All spectra are normalized at the J-band. The
original template (gray color) is superimposed for comparison. The LRS ID and
best-fit IRTF type are indicated on the upper right at each panel. (b)-(f) LRS
spectra identified in this work. The color code is the same as that in Figure
5. Figure 5 (Contnued): Continued. Figure 5 (Contnued): Continued. Figure 5
(Contnued): Continued. Figure 5 (Contnued): Continued. Figure 5 (Contnued):
Continued. Figure 6: Color-color diagram for all identified stars. The J-H and
K-H color information is from 2MASS, and the type information is from the IRTF
fit. The background gray dots indicate stars drawn from the 2MASS catalog of
each CIBER field. The colors represent different stellar types. The scatter of
types over the J-H color can be explained either by the noncontinuous IRTF
library or by uncertainties in spectral subclass.

Figure 7: Type comparison determined from the IRTF fit and the literature for
63 stars whose types are already known. The dashed and dotted lines represent
the 1$\sigma$ error and $\pm$1 spectral type, respectively. The colors
represent the different flights’ data. Two A-type stars, indicated by an
arrow, are fitted to F-type stars. Fit types based on the Pickles library also
give the same results.

Figure 8: Five stars are serendipitously observed in two independent flights.
Each panel shows two spectra extracted from each flight. Top left panel: 2nd
flight (BA2_5), 4th flight (BB4_6). Top right panel: 2nd flight (N2_6), 3rd
flight (N3_5). Middle left panel: 2nd flight (BA2_1), 3rd flight (BA3_4).
Middle right panel: 2nd flight (BB2_1), 3rd flight (BB3_1). Bottom left panel:
2nd flight (BB2_4), 3rd flight (BB3_4). The large discrepancies arise from
calibration error above 1.6 $\micron$ but show consistency of in-flight
calibration below 1.6 $\micron$.

Figure 9: Representative examples of LRS spectra from this work. The color
code is the same as that in Figure 5. F, G, K, and M stellar types are shown
in each panel. Compared to other types, a typical F-type spectrum (top left
panel) does not show any obvious absorption features across the wavelength
range. We identified several features in our LRS spectra that correspond to
typical absorption lines in the near-IR (i.e., CaII with bandhead at 0.85
$\micron$, CN with bandhead at 0.95, 1.15, and 1.5 $\micron$). The strongest
feature in the F-type stars (top left) is the CaII triplet line, indicated
with an arrow at 0.85 $\micron$. From types later than G (top right), CN bands
appear with bandheads at 1.1, 0.91, 0.94, and 1.4$\micron$. We also identified
M-type stars, as indicated in the bottom right panel. Since M-type stars have
dominant molecular bands in their spectra, the identified lines are blended
with other strong molecular bands, such as TiO (bandhead at 0.82$\micron$),
ZrO (bandhead at 0.93$\micron$), FeH (bandhead at 0.99$\micron$), and H2O
(bandhead at 1.4$\micron$). The strength of each line depends on the spectral
type.

Figure 10: Flux ratios of all LRS stars to the matched 2MASS stars in the
J-band. Each color represents the stars observed from each flight. Since the
LRS flux is apodized by the slit mask, an aperture correction has been made to
yield ratio unity in the ideal case (dotted line). The averaged original flux
ratio is drawn as a dashed line, and its reciprocal is used for aperture
correction. The color-shaded area shows the range of relation we expect from
an instrument simulation, representing the upper and lower bounds of the
absolute calibrations of the LRS.

Figure 11: Same as Figure 10 but for the H-band.

Table 1:  Rocket-Commanded Coordinates for the observed field. Arabic numbers after the Hyphen for the Elat fields indicate the flight number  Field | R.A. | Decl.
---|---|---
Elat10-2 | 15:07:60.0 | -2:00:00
Elat30-2 | 14:44:00 | 20:00:00
Elat30-3 | 15:48:00 | 9:30:00
Elat10-4 | 12:44:00 | 8:00:00
Elat30-4 | 12:52:00 | 27:00:00
NEP | 18:00:00 | 66:20:23.987
SWIRE | 16:11:00 | 55:00:00
BootesA | 14:33:54.719 | 34:53:2.396
BootesB | 14:29:17.761 | 34:53:2.396
Lockman | 10:45:12.0 | 58:00:00
DGL | 16:47:60.0 | 69:00:00

Table 2:  Number of stars rejected at each criterion  Flight | Total Candidates | Crit.(i) | Crit.(ii) | Crit.(iii) | Crit.(iv) | Total in Final Catalog
---|---|---|---|---|---|---
2nd flight | 198 | 15 | 43 | 8 | 145 | 38
3rd flight | 177 | 14 | 41 | 6 | 127 | 30
4th flight | 171 | 23 | 43 | 5 | 117 | 42

Table 3:  Star catalog

Flight | Field | ID | Name | R.A.aafootnotemark:  | Decl.bbfootnotemark:  | LRS Jccfootnotemark:  | LRS Hddfootnotemark:  | 2MASS Jeefootnotemark:  | 2MASS Hfffootnotemark:  | SIMBAD typeggSpectral type given from SIMBAD database. | Best-fit IRTF Type | $\chi^{2}$ | Note
---|---|---|---|---|---|---|---|---|---|---|---|---|---
| Elat10 | E102_1 | TYC5000-614-1 | 15:06:50.134 | -00:02:47.746 | 9.020 | 8.283 | 8.283 | 7.608 | K2 | K3III | 0.720 | …
| Elat10 | E102_2 | … | 14:59:05.568 | -01:08:23.294 | 9.095 | 8.279 | 8.350 | 7.484 | … | M0IIIb | 4.582 | …
| Elat10 | E102_3 | HD131553 | 14:54:20.898 | -01:52:19.938 | 9.576 | 9.241 | 8.673 | 8.472 | F0V | G0Ib-II | 0.522 | …
| Elat10 | E102_4 | HD134456 | 15:09:58.320 | -00:52:47.269 | 7.872 | 7.754 | 6.982 | 6.854 | F2III | F2III-IV | 0.076 | …
| Elat10 | E102_5 | TYC5001-847-1 | 15:14:43.328 | -01:31:43.763 | 9.940 | 9.633 | 9.226 | 8.898 | … | F8Ib | 0.416 | …
| Elat10 | E102_6 | BD-01-3038 | 15:14:15.481 | -01:37:09.268 | 8.273 | 7.633 | 7.477 | 6.862 | K0 | M0.5V | 0.462 | …
| Elat10 | E102_7 | HD133213 | 15:03:28.468 | -03:10:05.732 | 8.802 | 8.751 | 8.066 | 8.030 | A2III | F5II-III | 0.086 | …
| Elat30 | E302_1 | BD+22-2745 | 14:46:03.405 | 22:04:37.528 | 8.065 | 7.499 | 7.158 | 6.664 | G5 | K7V | 0.304 | …
| Elat30 | E302_2 | HD127666 | 14:32:02.149 | 22:04:47.600 | 8.645 | 8.396 | 7.866 | 7.676 | G5 | F8V | 0.045 | …
| Elat30 | E302_3 | HD131132 | 14:51:16.019 | 18:38:59.284 | 6.648 | 6.111 | 5.803 | 5.334 | K0 | G8IIIFe5 | 0.260 | …
| Elat30 | E302_4 | BD+19-2867 | 14:49:56.793 | 18:37:29.741 | 10.875 | 10.668 | 10.195 | 9.928 | G5 | G1II-IIIFe-1CH0.5 | 0.705 | …
| Elat30 | E302_5 | BD+19-2857 | 14:45:32.922 | 18:40:20.255 | 7.342 | 6.643 | 6.466 | 5.815 | K2 | M0V | 0.234 | …
| Elat30 | E302_6 | TYC1481-620-1 | 14:46:48.921 | 17:30:12.359 | 10.208 | 9.644 | 9.620 | 9.138 | … | K4V | 0.551 | …
| Elat30 | E302_7 | BD+18-2928 | 14:45:45.544 | 17:30:17.950 | 6.555 | 5.752 | 5.752 | 5.050 | M0 | K3IIIFe-0.5 | 1.488 | …
2nd | NEP | N2_1 | BD+68-954 | 17:43:43.944 | 68:24:26.593 | 10.067 | 9.742 | 9.394 | 9.168 | F5 | F0II | 0.064 | …
NEP | N2_2 | … | 17:38:56.867 | 66:22:12.587 | 10.726 | 10.216 | 10.440 | 9.937 | … | G5IIIa | 0.240 | …
NEP | N2_3 | BD+67-1039A | 17:52:45.953 | 67:00:12.935 | 8.925 | 8.587 | 8.571 | 8.130 | … | F8Ib | 0.045 | …
| NEP | N2_4 | TYC4208-116-1 | 17:49:23.407 | 65:28:22.606 | 7.646 | 6.837 | 6.840 | 6.047 | … | K4III | 0.807 | …
| NEP | N2_5 | BD+67-1067 | 18:20:50.229 | 67:55:01.776 | 8.199 | 7.694 | 7.430 | 6.939 | K0 | K3V | 0.119 | …
| NEP | N2_6iifootnotemark:  | HD166779 | 18:07:35.504 | 63:54:12.298 | 6.544 | 5.874 | 5.706 | 5.078 | K5 | M0.5V | 0.221 | …
| SWIRE | S2_1 | HD144245 | 16:01:58.920 | 56:36:03.496 | 6.921 | 6.238 | 6.173 | 5.505 | K5 | K3III | 0.201 | …
| SWIRE | S2_2 | HD144082 | 16:01:09.819 | 56:26:23.172 | 7.929 | 7.644 | 7.135 | 6.944 | F5 | G1VFe-0.5 | 0.051 | …
| SWIRE | S2_3 | HD147733 | 16:20:51.242 | 54:23:10.320 | 8.172 | 8.125 | 7.414 | 7.351 | A3 | F8IV | 0.059 | …
| SWIRE | S2_4 | HD234317 | 16:32:27.630 | 54:20:14.320 | 8.713 | 8.283 | 7.999 | 7.564 | G5 | K1V | 0.081 | …
| SWIRE | S2_5 | HD146736 | 16:15:15.896 | 52:01:48.338 | 8.929 | 8.618 | 8.140 | 7.884 | G5 | F9IIIa | 0.060 | …
| BootesA | BA2_1jjfootnotemark:  | HD126878 | 14:27:13.534 | 34:43:19.996 | 8.631 | 8.385 | 7.783 | 7.640 | F5 | F7III | 0.046 | …
| BootesA | BA2_2 | TYC2557-719-1 | 14:41:46.727 | 33:34:23.452 | 10.800 | 10.557 | 10.045 | 9.783 | … | F2III-IV | 0.331 | …
| BootesA | BA2_3 | TYC2556-652-1 | 14:33:46.073 | 33:34:53.886 | 10.341 | 9.620 | 9.352 | 8.717 | K9V | M1.5V | 1.125 | high-proper-motion
| BootesA | BA2_4 | BD+34-2527 | 14:25:57.827 | 33:34:32.984 | 9.846 | 9.426 | 9.250 | 8.973 | G5III | G5V | 0.120 | …
| BootesA | BA2_5hhfootnotemark:  | HD126210 | 14:23:24.060 | 33:34:19.099 | 8.480 | 8.274 | 7.653 | 7.492 | F8 | F7V | 0.039 | …
| BootesA | BA2_6 | BD+34-2522 | 14:21:54.490 | 33:34:35.580 | 7.311 | 6.514 | 6.307 | 5.545 | K5 | K3IIIFe-0.5 | 0.584 | …
| BootesA | BA2_7 | … | 14:41:50.085 | 32:24:33.790 | 10.848 | 10.330 | 10.178 | 9.587 | … | M2V | 1.521 | …
| BootesA | BA2_8 | TYC2553-127-1 | 14:29:10.917 | 32:27:40.871 | 10.252 | 9.490 | 9.130 | 8.483 | … | K2III | 1.255 | …
| BootesB | BB2_1kkfootnotemark:  | TYC2560-1157-1 | 14:38:39.909 | 35:31:13.224 | 9.347 | 8.799 | 8.611 | 8.100 | K1 | G8IIIFe5 | 0.143 | …
| BootesB | BB2_2 | BD+36-2489 | 14:24:52.634 | 35:32:12.714 | 9.026 | 8.530 | 8.773 | 8.484 | G5 | G7IV | 0.107 | …
| BootesB | BB2_3 | BD+32-2490 | 14:34:03.366 | 32:06:02.588 | 9.640 | 9.089 | 8.835 | 8.414 | K0 | G8IIIFe1 | 0.127 | …
| BootesB | BB2_4llfootnotemark:  | BD+31-2630 | 14:33:01.264 | 30:56:33.554 | 10.240 | 9.793 | 9.504 | 9.246 | … | F9V | 0.336 | …
| BootesB | BB2_5 | TYC2553-961-1 | 14:24:21.497 | 30:58:03.684 | 10.323 | 9.713 | 9.351 | 8.864 | … | G8IIIFe1 | 0.580 | …
| Elat30 | E303_1 | BD+11-2874 | 15:52:08.230 | 10:52:28.103 | 7.882 | 7.169 | 6.692 | 6.012 | K5V | M0.5V | 0.330 | spectroscopic binary
| Elat30 | E303_2 | HD141631 | 15:49:47.057 | 10:48:24.520 | 8.251 | 7.922 | 7.555 | 7.096 | K2 | G4O-Ia | 0.206 | …
| Elat30 | E303_3 | TYC947-300-1 | 15:50:53.577 | 09:41:15.828 | 10.379 | 9.841 | 9.861 | 9.310 | … | K1IIIFe-0.5 | 0.595 | …
| Elat30 | E303_4 | HD141531 | 15:49:16.496 | 09:36:42.408 | 7.718 | 7.052 | 6.971 | 6.337 | K | M1V | 0.089 | …
| NEP | N3_1 | HD164781 | 17:57:03.647 | 68:49:19.744 | 8.948 | 8.601 | 7.733 | 7.423 | K0 | G8V | 0.076 | …
| NEP | N3_2 | TYC4428-1122-1 | 17:54:46.231 | 68:06:42.016 | 9.753 | 9.250 | 9.009 | 8.353 | … | K1IIIbCN1.5Ca1 | 0.629 | …
| NEP | N3_3 | BD+67-1050 | 18:06:45.898 | 67:50:40.686 | 8.273 | 7.722 | 7.485 | 6.976 | K2 | K1IIIbCN1.5Ca1 | 0.134 | …
| NEP | N3_4 | BD+65-1248 | 18:12:21.398 | 65:36:17.381 | 7.214 | 6.492 | 6.359 | 5.635 | K5 | K5III | 0.919 | …
| NEP | N3_5iifootnotemark:  | HD166779 | 18:07:35.504 | 63:54:12.298 | 6.711 | 6.077 | 5.706 | 5.078 | K5 | M0.5V | 0.455 | …
| NEP | N3_6 | TYC4226-812-1 | 18:25:26.020 | 66:00:38.783 | 9.655 | 9.417 | 8.924 | 8.714 | … | F8Ia | 0.293 | …
| SWIRE | S3_1 | BD+55-1802 | 16:01:45.359 | 54:48:40.882 | 10.325 | 10.033 | 9.570 | 9.330 | G0 | G2IV | 0.392 | …
| SWIRE | S3_2 | TYC3870-1085-1 | 15:54:21.929 | 53:36:47.786 | 10.417 | 10.198 | 9.554 | 9.300 | … | G2II-III | 0.871 | …
3rd | SWIRE | S3_3 | TYC3870-366-1 | 15:53:29.099 | 53:28:36.008 | 8.669 | 8.062 | 7.928 | 7.281 | … | M1V | 0.285 | …
SWIRE | S3_4 | TYC3877-704-1 | 16:10:22.667 | 54:28:38.784 | 9.017 | 8.472 | 8.258 | 7.715 | … | K1IIIbCN1.5Ca1 | 0.239 | …
SWIRE | S3_5 | TYC3877-1592-1 | 16:01:43.031 | 53:06:25.855 | 10.233 | 9.746 | 9.566 | 9.077 | … | G9III | 0.136 | …
| SWIRE | S3_6 | TYC3878-216-1 | 16:25:31.829 | 53:25:25.453 | 9.065 | 8.709 | 8.364 | 8.020 | … | G1IIICH1 | 0.214 | …
| Lockman | L3_1 | V*DM-UMa | 10:55:43.521 | 60:28:09.613 | 7.975 | 7.476 | 7.194 | 6.621 | K0III | G2Ib | 0.233 | …
| Lockman | L3_2 | HD94880 | 10:58:21.518 | 59:16:53.422 | 7.787 | 7.482 | 6.900 | 6.629 | G0 | G0Ib-II | 0.115 | …
| Lockman | L3_3 | HD92320 | 10:40:56.905 | 59:20:33.065 | 7.947 | 7.662 | 7.148 | 6.852 | G0 | F2-F5Ib | 0.109 | high-proper-motion
| Lockman | L3_4 | HD237955 | 10:57:44.114 | 58:10:01.103 | 9.799 | 9.619 | 8.705 | 8.508 | G0 | F5III | 0.038 | …
| Lockman | L3_5 | TYC3827-847-1 | 11:01:59.570 | 56:58:11.510 | 9.498 | 9.094 | 8.816 | 8.279 | … | M2V | 0.479 | …
| Lockman | L3_6 | HD237961 | 11:00:12.007 | 56:59:49.481 | 9.267 | 9.049 | 8.495 | 8.271 | G0 | G1VFe-0.5 | 0.304 | …
| BootesA | BA3_1 | BD+362491 | 14:26:05.241 | 35:50:00.776 | 8.897 | 8.498 | 8.095 | 7.676 | K0 | G3II | 0.515 | …
| BootesA | BA3_2 | HD128368 | 14:35:32.053 | 34:41:11.540 | 7.436 | 6.789 | 6.530 | 5.942 | K0 | M0.5V | 0.215 | …
| BootesA | BA3_3 | BD+35-2576 | 14:32:31.567 | 34:42:09.493 | 9.291 | 8.834 | 9.058 | 8.737 | K0 | F5Ib-G1Ib | 0.143 | …
| BootesA | BA3_4jjfootnotemark:  | HD126878 | 14:27:13.534 | 34:43:19.996 | 9.190 | 9.091 | 7.783 | 7.640 | F5 | F2III-IV | 0.060 | …
| BootesB | BB3_1kkfootnotemark:  | TYC2560-1157-1 | 14:38:39.909 | 35:31:13.224 | 9.416 | 8.918 | 8.611 | 8.100 | K1 | K4V | 0.124 | …
| BootesB | BB3_2 | BD+32-2503 | 14:41:07.455 | 32:04:45.095 | 9.628 | 9.449 | 8.853 | 8.624 | … | F8Ib | 0.198 | …
| BootesB | BB3_3 | BD+32-2456 | 14:18:52.718 | 32:06:31.003 | 9.191 | 8.531 | 7.992 | 7.444 | K2III | K0.5IIICN1 | 0.534 | …
| BootesB | BB3_4llfootnotemark:  | BD+31-2630 | 14:33:01.264 | 30:56:33.554 | 10.170 | 9.940 | 9.504 | 9.246 | … | F9V | 0.438 | …
| Elat10 | E104_1 | HD111645 | 12:50:42.449 | 08:52:30.238 | 8.908 | 8.691 | 8.124 | 7.920 | F8 | F7III | 0.041 | …
| Elat10 | E104_2 | BD+11-2491 | 12:46:07.870 | 11:09:25.744 | 10.229 | 9.992 | 9.486 | 9.201 | F8 | F2-F5Ib | 0.162 | …
| Elat10 | E104_3 | … | 12:41:28.720 | 10:52:57.907 | 10.959 | 10.368 | 10.599 | 10.096 | … | K5V | 0.702 | …
| Elat10 | E104_4 | HD110777 | 12:44:20.102 | 06:51:16.916 | 8.442 | 8.212 | 7.663 | 7.418 | G0 | F8Ia | 0.148 | …
| Elat10 | E104_5 | BD+10-2440 | 12:33:51.920 | 09:31:54.156 | 8.139 | 7.372 | 6.662 | 5.860 | … | K3II-III | 1.012 | …
| Elat10 | E104_6 | HD109824 | 12:37:48.044 | 04:59:07.195 | 6.860 | 6.296 | 6.092 | 5.542 | K0 | K0.5IIb | 0.570 | …
| Elat30 | E304_1 | … | 13:02:54.144 | 26:23:27.762 | 8.966 | 8.441 | 8.267 | 7.756 | … | K1IIIbCN1.5Ca1 | 0.478 | …
| Elat30 | E304_2 | BD+27-2207 | 13:02:50.671 | 26:50:00.402 | 10.924 | 10.630 | 10.141 | 9.899 | F8 | F8Ib | 0.262 | …
| Elat30 | E304_3 | TYC1995-264-1 | 13:02:50.439 | 27:29:22.283 | 10.212 | 10.004 | 9.586 | 9.251 | … | G1VFe-0.5 | 0.121 | …
| Elat30 | E304_4 | BD+27-2197 | 12:57:45.577 | 27:01:51.600 | 10.562 | 10.374 | 9.873 | 9.672 | F5 | F2Ib | 0.098 | …
| Elat30 | E304_5 | TYC1995-1123-1 | 12:57:25.736 | 28:18:25.992 | 9.837 | 9.006 | 8.997 | 8.229 | … | M1.5V | 0.608 | …
| Elat30 | E304_6 | LP322-154 | 12:57:04.818 | 29:30:36.860 | 10.454 | 9.808 | 9.740 | 9.096 | K5V | M0.5V | 1.460 | high-proper-motion
| Elat30 | E304_7 | TYC2532-820-1 | 12:56:45.236 | 30:44:22.556 | 10.678 | 10.006 | 9.838 | 9.324 | K1V | M1V | 0.344 | …
| NEP | N4_1 | BD+68-951 | 17:38:51.760 | 68:13:16.536 | 9.137 | 8.449 | 7.942 | 7.438 | K0 | K1.5IIIFe-0.5 | 0.273 | multiple-star
| NEP | N4_2 | HD161500 | 17:41:10.318 | 65:13:10.301 | 7.442 | 6.860 | 6.633 | 6.119 | K2 | K1IIIbCN1.5Ca1 | 0.312 | …
| NEP | N4_3 | G227-20 | 17:52:11.850 | 64:46:08.720 | 9.077 | 8.391 | 8.249 | 7.615 | M0.5V | M1.5V | 0.449 | high-proper-motion
4th | NEP | N4_4 | TYC4208-1599-1 | 17:52:05.421 | 64:37:15.827 | 10.278 | 9.725 | 9.929 | 9.259 | … | M2V | 0.486 | …
NEP | N4_5 | BD+64-1227A | 17:52:17.178 | 64:14:16.411 | 8.816 | 8.500 | 8.400 | 8.125 | … | F8Ib | 0.046 | …
NEP | N4_6 | TYC4213-161-1 | 18:03:24.923 | 67:12:41.681 | 10.171 | 9.868 | 9.327 | 9.115 | … | F7III | 0.109 | …
| NEP | N4_7 | BD+66-1074 | 18:03:15.008 | 66:20:29.069 | 7.609 | 6.866 | 6.739 | 6.046 | K5 | K3II-III | 1.262 | …
| NEP | N4_8 | HD170592 | 18:25:24.759 | 65:45:34.470 | 7.474 | 7.143 | 6.722 | 6.409 | K0 | G5V | 0.148 | …
| SWIRE | S4_1 | TYC3870-1026-1 | 15:55:16.319 | 54:45:12.510 | 10.127 | 9.564 | 9.332 | 8.829 | … | K3V | 0.261 | …
| SWIRE | S4_2 | TYC3496-1361-1 | 15:56:04.610 | 52:13:29.543 | 8.240 | 7.566 | 7.519 | 6.825 | … | K3III | 0.421 | …
| SWIRE | S4_3 | TYC3880-1133-1 | 16:03:15.627 | 56:02:35.210 | 8.711 | 7.821 | 7.791 | 6.995 | … | M2.5IIIBa0.5 | 2.347 | …
| SWIRE | S4_4 | TYC3877-484-1 | 16:03:12.065 | 54:44:27.658 | 9.047 | 8.361 | 7.846 | 7.288 | … | K2IIIFe-1 | 0.147 | …
| SWIRE | S4_5 | HD234308 | 16:26:05.554 | 52:18:08.266 | 8.652 | 8.101 | 7.932 | 7.407 | K0 | K1IIIFe-0.5 | 0.237 | …
| DGL | D4_1 | TYC4419-1623-1 | 16:14:22.875 | 69:55:54.455 | 10.093 | 9.624 | 9.419 | 8.810 | … | M2V | 0.373 | …
| DGL | D4_2 | TYC4419-1631-1 | 16:18:10.929 | 69:16:36.761 | 9.923 | 9.466 | 9.229 | 8.916 | … | K1V | 0.124 | …
| DGL | D4_3 | BD+67-943 | 16:29:52.210 | 66:47:45.154 | 9.390 | 9.120 | 8.606 | 8.417 | F8 | F8Ia | 0.110 | …
| DGL | D4_4 | TYC4196-2280-1 | 16:34:34.354 | 65:36:05.818 | 10.424 | 9.946 | 9.783 | 9.339 | … | G4V | 0.232 | …
| DGL | D4_5 | HD151286 | 16:40:37.776 | 70:34:14.772 | 7.110 | 6.668 | 6.237 | 5.794 | … | G3II | 0.070 | …
| DGL | D4_6 | BD+69-873 | 16:47:31.365 | 68:51:02.603 | 8.338 | 7.820 | 7.495 | 7.010 | K0 | G7.5IIIa | 0.111 | …
| DGL | D4_7 | HD154273 | 16:58:40.137 | 69:38:05.431 | 7.022 | 6.508 | 6.197 | 5.746 | K0 | G7.5IIIa | 0.106 | …
| DGL | D4_8 | TYC4424-1380-1 | 17:08:33.058 | 71:00:28.044 | 9.242 | 8.911 | 9.008 | 8.727 | … | G2IV | 0.109 | …
| DGL | D4_9 | TYC4421-2278-1 | 17:16:54.688 | 67:38:26.279 | 8.993 | 8.460 | 8.269 | 7.792 | … | K1IIIFe-0.5 | 0.174 | …
| BootesB | BB4_1 | TYC2557-870-1 | 14:40:08.540 | 34:40:29.669 | 10.107 | 9.545 | 9.249 | 8.768 | … | M2V | 0.331 | …
| BootesB | BB4_2 | HD128094 | 14:34:10.846 | 30:59:10.356 | 7.857 | 7.240 | 6.963 | 6.405 | K0 | K2III | 0.226 | …
| BootesB | BB4_3 | TYC2559-388-1 | 14:34:47.808 | 35:34:09.419 | 9.761 | 9.346 | 9.011 | 8.550 | G8V | G6III | 0.184 | …
4th | BootesB | BB4_4 | TYC2553-947-1 | 14:28:52.868 | 31:30:30.316 | 8.505 | 7.763 | 7.642 | 6.917 | … | K2III | 0.170 | …
BootesB | BB4_5 | V*KT-Boo | 14:29:02.513 | 33:50:38.929 | 8.699 | 8.271 | 7.846 | 7.465 | G | G0Ib-II | 0.074 | …
BootesB | BB4_6hhfootnotemark:  | HD126210 | 14:23:24.060 | 33:34:19.099 | 8.764 | 8.749 | 7.653 | 7.492 | F8 | F1II | 0.194 | …
| BootesB | BB4_7 | TYC2549-413-1 | 14:23:23.452 | 34:33:24.854 | 9.399 | 8.885 | 8.510 | 7.947 | … | K1IIIbCN1.5Ca1 | 0.269 | …

a,ba,bfootnotetext: The J2000.0 right ascension (RA) and the declination (Dec)
of a star in a sexagesimal from 2MASS data.

c,dc,dfootnotetext: Vega magnitude of the LRS.

e,fe,ffootnotetext: Vega magnitude of the matched 2MASS point source catalog.

h,i,j,k,lh,i,j,k,lfootnotetext: A star that observed from two independent
flights.


# Response-conditioned Turn-taking Prediction

Bing’er Jiang  Erik Ekstedt  Gabriel Skantze
Division of Speech, Music and Hearing, KTH Royal Institute of Technology
{binger, erikekst<EMAIL_ADDRESS>
###### Abstract

Previous approaches to turn-taking and response generation in conversational
systems have treated it as a two-stage process: First, the end of a turn is
detected (based on conversation history), then the system generates an
appropriate response. Humans, however, do not take the turn just because it is
likely, but also consider whether what they want to say fits the position. In
this paper, we present a model (an extension of TurnGPT) that conditions the
end-of-turn prediction on both conversation history and what the next speaker
wants to say. We found that our model consistently outperforms the baseline
model in a variety of metrics. The improvement is most prominent in two
scenarios where turn predictions can be ambiguous solely from the conversation
history: 1) when the current utterance contains a statement followed by a
question; 2) when the end of the current utterance semantically matches the
response. Treating the turn-prediction and response-ranking as a one-stage
process, our findings suggest that our model can be used as an incremental
response ranker, which can be applied in various settings.

##  1 Introduction

A fundamental component of spoken dialog system (SDS) is turn-taking, i.e.,
the decision of when to take turns at appropriate places, without causing long
response delays or interrupting the user. In other words, the system must be
able to correctly identify when the user is yielding the turn, and it is
appropriate to make a response, and when the user is simply making a mid-
utterance pause (Skantze, 2021). Traditionally, this has been done using a
simple silence threshold. However, silence is not a very good indicator of
turn-shifts and more modern approaches instead use various cues known to be
important in human-human turn-taking, such as lexico-syntactic cues, prosody,
or gaze (Gravano and Hirschberg, 2011; Ishii et al., 2016; Lala et al., 2019;
Ekstedt and Skantze, 2022).

Ekstedt and Skantze (2020) proposed TurnGPT, a transformer-based language
model that incrementally processes words in the user’s utterance and predicts
the probability of a turn-shift after each word. This is similar to the notion
of syntactic or pragmatic completion points that have been identified in
conversation analysis (Ford and Thompson, 1996). In their analysis of TurnGPT,
Ekstedt and Skantze (2020) found that the 20% of the model’s attention is
directed towards utterances earlier than the current one, indicating that it
is sensitive to pragmatic aspects of dialogue.

While such models are indeed a step forward, there is a still an important
component missing that we will address in this paper. When humans make a
decision to take the turn, it is not just based on whether there are enough
turn-yielding cues in the interlocutor’s utterance. Sacks et al. (1974) use
the notion of transition-relevant places, or TRP, for places where a
transition could potentially take place (but does not have to). Thus, many
places for turn-shifts are highly optional. To partly address this problem,
Ishii et al. (2022) annotated the willingness of the next speaker to take the
turn, and built a model that could predict this willingness based on
multimodal cues.

Whether a turn-shift takes place or not also depends on the intention of the
next speaker, and what they want to say. For dialogue systems, this means that
the system should not automatically take the turn once the transition-
probability passes a certain threshold, and only then decide what it should
respond. Instead, the system should take the potential response into account
when deciding whether it is appropriate to take the turn or not.

We call this response-conditioned turn-taking prediction, which is illustrated
in Figure 1. In this paper, we investigate to what extent and under what
scenarios such response-conditioning would help to predict turn-shifts. We
present a model called RC-TurnGPT, which is an extension of TurnGPT.

Figure 1: Response-conditioned turn-taking prediction.

Note that the current study does not intend to address how and when the next
speaker comes up with what they would like to say. This depends of course on
the exact implementation of the dialogue system, which could for example be
response-ranking (Gao et al., 2020) or an intent-based planning approach (FAIR
et al., 2022). Regardless of this, the model proposed here could be used to
incrementally rank or score potential responses to see whether they fit well
from a turn-taking perspective.

##  2 Methods

TurnGPT is a unidirectional transformer-based language model (LM) optimized
through cross-entropy to predict the next token in a sequence. It is a pre-
trained GPT-2 (base) model (Radford et al., 2019), finetuned on unpunctuated
dialog corpora, with a special turn-shift token (TS) that delimits consecutive
turns. RC-TurnGPT is an extension of this model, by also conditioning the
prediction on the response.

While the RC-TurnGPT model is architecturally equivalent to TurnGPT, it
differs in the training objective through a simple data transformation. This
transformation permutes the ordering of turns in a similar approach as the FIM
pre-training objective of Bavarian et al. (2022). We consider turn-based
dialog sequences to consist of three parts: the context/history (H), the
current utterance (CU) and the next response (R). The task is to correctly
predict the location of the turn-shift token in the current utterance,
$CU_{i}$, given the history, $H_{i}$, and the next response, $R_{i}$, over all
samples $i$ in the dataset, $D$. The samples $i\in D_{I}$ are extracted by
applying a turn-based sliding window approach with a step size of 1 and a
window size of 3 turns.

However, instead of the uniform left-to-right next token prediction task of
regular LMs, the RC-TurnGPT model train on ordered sequences of {R, H, CU},
masking the loss over R and H to solely learn over the CU turns. This enables
the model to use information of both H and R while keeping the original left-
to-right next token prediction setup.

Finally, the TurnGPT model utilized three special tokens in addition to the
original GPT-2 vocabulary, the aforementioned TS token and two speaker tokens.
The speaker tokens are similar to positional embeddings and are added to the
word embeddings to encode the speaker identity over each word. Because of the
permuted ordering of the RC-TurnGPT setup we also include a fourth special
response-token that are added to the words of the response to distinguish them
from the actual context. Both the base model and the datasets were implemented
using Huggingface Wolf et al. (2020); Lhoest et al. (2021).

###  2.1 Data

We train RC-TurnGPT and the baseline TurnGPT on two types of data sets based
on Ekstedt and Skantze (2020): Assistant and Written Social. The former
constitutes of three task-oriented dialog corpora: Taskmaster Byrne et al.
(2019), MetaLWOZ Lee et al. (2019), and MultiWoz Zang et al. (2020). The
latter includes two corpora constructed by human-human written dialogs:
CuriosityDialogs Rodriguez et al. (2020) and DailyDialog Li et al. (2017). All
datasets are written dialogs with clearly defined turns. The resulting full
dataset contains 106,830 dialogs for training, 9,362 for validation, and 7,897
for test, with an average number of turns being 13.69.

###  2.2 Evaluation

To evaluate the models, we propose five turn-level based metrics that measures
the turn-shift performance in various ways. The models are considered to make
a turn-shift prediction when the probability exceeds a certain threshold
optimized for performance over the validation split, for each model
independently.

First, we define turn-level accuracy (TL-Acc) to be the percentage of turns
where the turn-shift probability exceeds the threshold at, and only at, the
ground-truth end of turn. Second, the no response rate (NRR) is the percentage
of turns where the threshold is never exceeded and the model fails to make a
response. The third metric is defined to measure the barge-in rate (BR), the
percentage of turns where the models would make a turn-shift prediction before
the actual turn-shift.

We also investigate instances where the two models make different turn-taking
decisions to see how well the response would fit, using perplexity as a
measure. We use the TurnGPT model to calculate the average perplexity over the
response (R-PPL).

Lastly, we define the ordinal spike rate (OSR) to be the percentage of turns
where the probability is the greatest at the end of the turn. This metric does
not consider a threshold but simply measures how many times the highest
probability is located at the correct turn-shift location.

##  3 Results

###  3.1 Aggregate results

Table 1 shows that RC-TurnGPT performs better in all evaluations metrics,
although the improvement is not large overall. While 55.77% turn-level
accuracy may not seem very high, it should be noted that even predictions
different from ground-truth turn-shift can also be valid in everyday
conversations, especially in long utterances where several completion points
are likely. While the threshold-based binary metric is low, the probability-
based OSR is much higher, indicating that the model is indeed able to detect
end of turn reflected by assigning the highest probability. Furthermore, the
perplexity of the response also decreases, showing that when one or both of
the two models make a mistake, the response fits better with the context for
the turn-shifts RC-TurnGPT takes.

Metric | Turn-GPT | RC-TurnGPT
---|---|---
TL-Acc $\uparrow$ | 53.93% | 55.77%
NRR $\downarrow$ | 20.90% | 19.23%
BR $\downarrow$ | 25.17% |  24.75%
R-PPL $\downarrow$ | 1.923 | 1.918
OSR $\uparrow$ | 88.57% |  89.17%
Table 1: The turn-level accuracy (TL-Acc), no response rate (NRR), barge-in
rate (BR), response perplexity (R-PPL) and the ordinal spike rate (OSR)
performance for TurnGPT and RC-TurnGPT. Best performance are bold.

###  3.2 Model analysis

In order to better understand when conditioning on the response helps turn-
shift prediction and when it does not, we proceed to analyse cases where only
RC-TurnGPT makes the correct prediction, and where both models are successful.

We extract all turns in the test set where TurnGPT makes a pre-mature turn-
shift prediction but RC-TurnGPT correctly predicts the end of the turn. We
sort the turns by the difference in probability assigned by the two models at
the TurnGPT-predicted turn-shift. We then investigate the difference between
the top and bottom 1000 cases. By comparing these two subsets, we can better
understand when conditioning on the response makes the biggest difference. We
identified two scenarios which we hypothesized would be important: 1)
statement to question; 2) semantic matching.

#### Statement to question

refers to cases where the current utterance consists of at least one statement
and ends with a question. As there are more than one natural completion point,
TurnGPT will be greedy while RC-TurnGPT will take the response into
consideration and choose a later completion point as turn shift. Consider the
following dialogue in Figure 2 (Current Utterance plotted, Response in
caption):

Figure 2: Different turn-taking predictions: TurnGPT predicts the turn-shift
at the end of a statement; RC-TurnGPT predicts the end of a question.
Response: sure first of all it’s very important for you not to be late

Figure 2 shows that without conditioning on the response, TurnGPT spikes at an
early completion point interrupting the current speaker. However, as the
response clearly corresponds to an answer to a request, RC-TurnGPT waits until
the speaker finishes their request.

In order to quantify this effect, we use punctuations to calculate how often
TurnGPT makes a mistake by missing a question. We use the top/bottom subsets
and ask GPT3111Model version: “text-curie-001” Brown et al. (2020) to insert
punctuation over the ground truth turns (advice in this example) and the
incomplete TurnGPT predicted turns (week in this example). We then calculate
the ratio of cases where the former ends with a question mark while the latter
does not. The top cases contain 36.3% statements to questions and the bottom
11.7%. The higher ratio in the top cases indicates that the RC-TurnGPT model
recognizes this pattern and uses the response conditioning to wait for the
appropriate moment to take the turn.

#### Semantic matching

refers to cases where the response semantically corresponds to the
specification made in the later parts of the current utterance. Consider the
dialogue in Figure 3:

Figure 3: Different turn-taking predictions: RC-TurnGPT’s prediction allows
closer semantic matching between current utterance and response. Response:
sure vietnam achieved an 8% gdp growth between 1990 and 1997

As the response clearly addresses the topic of economy, Figure 3 shows that
RC-TurnGPT would spike only after economy is specified, whereas TurnGPT has
two spikes at both places and would predict the turn shift after v-iet-nam. It
is important to note that while the response has no lexical overlap, the model
still manages to find the semantic correlation.

In order to investigate whether RC-TurnGPT consitently recognizes such
pattern, we use Sentence-Bert Reimers and Gurevych (2019) to measure the
Semantic Textual Similarity between the Response and the last part of the
actual turns missed by TurnGPT (here, ’s economy). The average cosine distance
for the top and bottom subsets are 0.293 and 0.209 respectively. This
indicates that where RC-TurnGPT outperforms TurnGPT, it does consider the
semantic content of the response and delays predicting a turn-shift until the
relevant semantic information has been stated.

#### Non-ambiguous turn-completions.

In addition, there are also a large number of cases where the current
utterance has a fairly simple structure and hence it is not ambiguous where to
take the turn.In those cases, conditioning on the next response obviously
makes a very small difference. As illustrated in Figure 4, given that there is
only one completion point, both models predict the turn shift correctly. This
also explains why there are no drastic improvements for RC-TurnGPT when
looking at aggregate results on the whole test set, as most of the task-
oriented dialogues contain such simple utterances, which TurnGPT can perform
well on.

Figure 4: Similar turn-taking predictions for a simple utterance. Response: it
is the capital of france

##  4 Discussion and conclusion

In this study, we examined how turn-taking prediction can be improved when
conditioned on the response. We found that the response conditioning is
particularly helpful under two circumstances, mainly by preventing greedy
turn-taking at earlier completion point: 1) when the current utterance
contains statements followed by questions; 2) when the end of the current
utterance semantically matches the response. However, for simple utterances
with fewer completion points, TurnGPT is already capable of predicting the
correct turn shift, and there is no additional help from conditioning on the
response.

We should again stress that this paper does not address the question of how
and when the system comes up with a potential response. However, this analysis
shows that it is indeed possible to find a more suitable transition-point,
when conditioning on the response. As we have suggested, the decision what to
say and when it say it should be considered as a joint decision rather than a
two-step process. In this regard, the RC-TurnGPT model could be used as an
incremental response ranker, which does not only consider different responses
at each step, but which can also decide not to respond and wait for more
input. For instance, it can be applied in an interview setting where the model
(interviewer) asks questions (ranking from a list of interview questions) and
take the turn at appropriate places. For future work, it would also be
interesting to involve the utility of the candidate responses (from the
system’s perspective). In the interview scenario, this could for example mean
that the system can find moments where certain important questions can be
asked, and which also fit well from a turn-taking perspective.

## Limitations

As mentioned above, the current study is limited to the question of whether
(and when) conditioning turn-taking prediction on the response improves the
performance. It does not yet show how the model could be incorporated in a
spoken dialogue system. Moreover, this study focuses only on written
conversations without incorporating spoken dialogues. Thus, the
interpretations can be limited to dialogues that are relatively ‘formal’
without hesitations, repetitions, etc. Note also that we only analyse lexical
cues to turn-taking (just like with TurnGPT), and leave out other modalities
for future work.

## Ethics Statement

The current study does not involve any human subjects and we do not foresee
any ethical consequences.

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