# NATURE OF W51e2: MASSIVE CORES AT DIFFERENT PHASES OF STAR FORMATION

Hui Shi11affiliation: National Astronomical Observatories, Chinese Academy of
Sciences, Jia20 Datun Road, Chaoyang District, Beijing 100012, China. Email:
shihui, hjl @ nao.cas.cn , Jun-Hui Zhao22affiliation: Harvard-Smithsonian
Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA. Email:
jzhao @ cfa.harvard.edu , J.L. Han11affiliation: National Astronomical
Observatories, Chinese Academy of Sciences, Jia20 Datun Road, Chaoyang
District, Beijing 100012, China. Email: shihui, hjl @ nao.cas.cn

###### Abstract

We present high-resolution continuum images of the W51e2 complex processed
from archival data of the Submillimeter Array (SMA) at 0.85 and 1.3 mm and the
Very Large Array at 7 and 13 mm. We also made line images and profiles of
W51e2 for three hydrogen radio recombination lines (RRLs; H26$\alpha$,
H53$\alpha$, and H66$\alpha$) and absorption of two molecular lines of
HCN(4-3) and CO(2-1). At least four distinct continuum components have been
detected in the 3$\arcsec$ region of W51e2 from the SMA continuum images at
0.85 and 1.3 mm with resolutions of 0.3$\arcsec\times 0.2\arcsec$ and
1.4$\arcsec\times 0.7\arcsec$, respectively. The west component, W51e2-W,
coincides with the ultracompact HII region reported from previous radio
observations. The H26$\alpha$ line observation reveals an unresolved hyper-
compact ionized core ($<0.06\arcsec$ or $<310$ AU) with a high electron
temperature of $1.2\times 10^{4}$ K, with the corresponding emission measure
EM$>7\times 10^{10}{\rm pc~{}cm^{-6}}$ and the electron density $N_{e}>7\times
10^{6}$ cm-3. The inferred Lyman continuum flux implies that the HII region
W51e2-W requires a newly formed massive star, an O8 star or a cluster of
B-type stars, to maintain the ionization. W51e2-E, the brightest component at
0.85 mm, is located 0.9$\arcsec$ east from the hyper-compact ionized core. It
has a total mass of $\sim$140 M☉ according to our spectral energy distribution
analysis and a large infall rate of $>1.3\times 10^{-3}$ M☉yr-1 inferred from
the absorption of HCN. W51e2-E appears to be the accretion center in W51e2.
Given the fact that no free$-$free emission and no RRLs have been detected,
the massive core of W51e2-E appears to host one or more growing massive proto-
stars. Located $2\arcsec$ northwest from W51e2-E, W51e2-NW is detected in the
continuum emission at 0.85 and 1.3 mm. No continuum emission has been detected
at $\lambda\geq$ 7 mm. Along with the maser activities previously observed,
our analysis suggests that W51e2-NW is at an earlier phase of star formation.
W51e2-N is located 2$\arcsec$ north of W51e2-E and has only been detected at
1.3 mm with a lower angular resolution ($\sim 1\arcsec$), suggesting that it
is a primordial, massive gas clump in the W51e2 complex.

HII regions – ISM: individual objects (W51e2) – stars: formation

##  1 INTRODUCTION

Massive stars are formed in dense, massive molecular cores. Detailed physical
processes in star-forming regions have not been well studied until recent
high-resolution observations were available at submillimeter wavelengths. High
angular resolution observations are necessary to unveil the physical environs
and activities of the individual sub-cores as well as their impact on the
overall process of massive star formation.

W51 is a well-known complex of HII regions with about $1\arcdeg$ area in the
Galactic plane (Westerhout, 1958). W51A, also called G49.5-0.4, is the most
luminous region in W51 (Kundu & Velusamy, 1967). Several discrete components
have been found from W51A and referred alphabetically as a$-$i in the right
ascension (R.A.) order (Martin, 1972; Mehringer, 1994). W51e is one of the
brightest regions in W51A. High-resolution centimeter observations have
revealed that W51e consists of several ultracompact (UC) HII regions (Scott,
1978; Gaume et al., 1993), among which the UC HII region of W51e2 appears to
be the brightest. This region is severely obscured by the dust, and no IR
detection has been made at wavelengths shorter than 20 $\mu$m (Genzel et al.,
1982). The inferred distance to W51e2 is $\sim$5.1 kpc (Xu et al., 2009);
thus, 1$\arcsec$ corresponds to a linear scale of about 5100 AU.

Based on the radio continuum observations, spectral energy distribution (SED)
analyses of W51e2 suggest that the emission source from this region consists
of two components, namely, (1) thermal emission from a cold dust component and
(2) free$-$free emission from a hot HII region (Rudolph et al., 1990; Sollins
et al., 2004; Keto et al., 2008).

Observations of hydrogen radio recombination lines (RRLs) were carried out
toward W51e2 (Mehringer, 1994; Keto et al., 2008; Keto & Klaassen, 2008).
Mehringer (1994) failed to detect the H92$\alpha$ line in this region, which
might be attributed to the large optical depth and/or the large pressure
broadening of the UC HII region at 3.6 cm. Based on the observations of
H66$\alpha$, H53$\alpha$, and H30$\alpha$, Keto et al. (2008) argued that the
pressure broadening is responsible for the broad-line widths observed in the
RRLs.

Table 1: Observation parameters for W51e2 Parameters | SMA: 0.85 mm | SMA: 1.3 mm | VLA: 7 mm | VLA: 13 mm
---|---|---|---|---
Observation date | 2007 Jun. 18 | 2005 Sep. 1 | 2004 Feb. 14 | 2003 Sep. 29
Array configuration | Very extended (8 ants) | Extended (6 ants) | AB | BC
Pointing center R.A.(J2000) | 19:23:43.888 | 19:23:43.895 | 19:23:43.918 | 19:23:43.918
Pointing center decl.(J2000) | +14:30:34.798 | +14:30:34.798 | +14:30:28.164 | +14:30:28.164
Frequency(GHz) | 343, 353 | 221, 231 | 43 | 22
Bandwidth | 2.0+2.0 (GHz) | 2.0+2.0 (GHz) | 12.5 (MHz) | 12.5 (MHz)
On-source time (hr) | 2.10 | 4.75 | 5.90 | 3.93
System temperature(K) |  150$\sim$500 |  100$\sim$250 |  |
Bandpass calibrators | J1229+020, J1751+096 | 3C 454.4 | 3C 84 | 3C 84
Phase calibrators |  J1733$-$130, J1743$-$038 | J1751+096, J2025+337 | J1923+210 | J1923+210
| J2015+371 |  |  |
Flux calibrators | Callisto | Ceres | 3C 286 | 3C 286

Observations of molecular lines (e.g. Ho & Young, 1996; Zhang & Ho, 1997;
Zhang et al., 1998; Young et al., 1998; Sollins et al., 2004) showed evidence
for infall (or accretion) gas within 5$\arcsec$ ($<$ 0.2 pc) around W51e2. A
possible rotation was suggested by Zhang & Ho (1997) on the basis of fitting
the position$-$velocity (PV) diagram of the NH3(3,3) absorption line at 13 mm.
A spin-up rotation with an axis of position angle (P.A.) $\approx$20$\arcdeg$
was further suggested based on the velocity gradient of CH3CN(83$-$73) at 2 mm
(Zhang et al., 1998). However, based on the velocity gradient observed from
the H53$\alpha$ line, Keto & Klaassen (2008) interpreted their results as
evidence for rotational ionized accretion flow around the UC HII region and
derived a rotational axis of P.A.$\approx-30\arcdeg$. In addition, a possible
bipolar outflow along the northwest and southeast (NW$-$SE) directions was
suggested based on the observations of the CO(2-1) line emission (Keto &
Klaassen, 2008), which appears to be perpendicular to the axis of the
rotational ionized disk. However, the southwest (SW) elongation of the UC HII
region in Gaume et al.’s (1993) observations was explained as a collimated
ionized outflow. On the other hand, proper-motion measurements of H2O/OH
masers appeared to favor a hypothetical model with an outflow or expanding gas
bubble in W51e2 (Imai et al., 2002; Fish & Reid, 2007).

Thus, improved images and comprehensive analysis of high-resolution
observations at the wavelengths from radio to submillimeter are needed to
reconcile the differences in the interpretation of the results from the
previous observations of W51e2. In this paper, we show the high-resolution
continuum images with the Submillimeter Array (SMA) at 0.85 and 1.3 mm and
with the Very Large Array (VLA) at 7 and 13 mm, as well as line images and
profiles of the hydrogen recombination lines (H26$\alpha$, H53$\alpha$, and
H66$\alpha$) and the molecular lines of HCN(4-3) and CO(2-1). In Section 2, we
describe the details of the data processing and results. In Section 3, we
model the individual components on the basis of the high-resolution
observations of W51e2 with the SMA and VLA. The nature and astrophysical
processes of star formation cores in W51e2 are discussed. A summary and
conclusions are given in Section 4.

##  2 DATA REDUCTION AND RESULTS

We processed the archival data from the SMA and VLA observations of W51e2 and
constructed both the continuum and spectral line images. Details in
calibration and imaging are discussed below along with the relevant parameters
that are summarized in Table 1.

###  2.1 The SMA continuum images at 0.85 and 1.3 mm

The observations of W51e2 at 0.85 mm were carried out with the SMA in the very
extended array (eight antennas) and at 1.3 mm in the extended array (six
antennas). The data were calibrated in Miriad (Sault et al., 1995) following
the reduction instructions for SMA data
111http://www.cfa.harvard.edu/sma/miriad. The system temperature corrections
were applied to the visibility data. The bandpass calibration was made with
strong calibrators for each of the two data sets (Table 1). The flux density
scale was determined using Callisto at 0.85 mm and Ceres at 1.3 mm.

Corrections to the complex gains at 1.3 mm were made by applying the solutions
interpolated from the two nearby QSOs J1751+096 and J2025+337. For the high-
resolution data at 0.85 mm, the visibility data are initially calibrated with
a model of W51e2 derived from observations in a low angular resolution at 0.87
mm. The residual phase errors were further corrected using the self-
calibration technique. The final continuum images were constructed by
combining all the line-free channels in both upper sideband and lower sideband
data.

Figure 1:  High-resolution 0.85 mm continuum image of W51e2 observed with the
SMA. Contours are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2, 3, … and
$\sigma=7.11$ mJy beam-1). The FWHM beam ($0.31\arcsec\times 0.22\arcsec$,
P.A.=$60.6\arcdeg$) is shown at bottom right. Three continuum emission
components e2-NW, e2-E, and e2-W are marked with “+”. The $\lambda 3.6$ cm
position of the UC HII region in W51e2 Gaume et al. (1993) is marked with
“$\times$”.

Figure 2:  (a): 1.3 mm continuum image of W51e2 observed with the SMA.
Contours are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2, 3, … and $\sigma=14.0$
mJy beam-1). The FWHM beam ($1.35\arcsec\times 0.69\arcsec$,
P.A.=$-87.4\arcdeg$) is shown at bottom right. The three components detected
at 0.85 mm together with the additional component e2-N detected at 1.3 mm are
marked with “+”. The position “+” of the submillimeter components and the
peaks of the 3.6 cm continuum (“$\times$”) and H26$\alpha$ (“$\bigstar$”) are
also marked in the rest of the images. (b): 7 mm continuum image of W51e2
observed with the VLA. Contours are $\pm 5\sigma\times 2^{n/2}$ ($n=0$, 1, 2,
3, … and $\sigma=1.12$ mJy beam-1). The FWHM beam ($0.40\arcsec\times
0.15\arcsec$, P.A.=$80.3\arcdeg$) is shown at bottom-right. (c): 13 mm
continuum image of W51e2 observed with VLA. Contours are $\pm 5\sigma\times
2^{n}$ ($n=0$, 1, 2, 3, …, and $\sigma=0.50$ mJy beam-1). The FWHM beam
($0.23\arcsec\times 0.10\arcsec$, P.A.=$82.6\arcdeg$) is shown at bottom
right. (d): Image of the H26$\alpha$ line emission integrated from 25 km s-1
to 84 km s-1 from W51e2. Contours are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2,
3, … and $\sigma=0.146$ Jy beam-1 km s-1). The FWHM beam ($0.25\arcsec$) is
shown at bottom right. All the RRL images are convolved to a common circular
beam ($0.25\arcsec$). The star denotes the peak position of the H26$\alpha$
line and is marked in the rest of the RRL images. (e): Image of the
H53$\alpha$ line emission integrated from 22 km s-1 to 101 km s-1. Contours
are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2, 3, … and $\sigma=9.68\times
10^{-3}$ Jy beam-1 km s-1). (f): Image of the H66$\alpha$ line emission
integrated from 24 km s-1 to 120 km s-1. Contours are $\pm 5\sigma\times
2^{n/2}$ ($n=0$, 1, 2, 3, … and $\sigma=6.14\times 10^{-3}$ Jy beam-1 km s-1).

Figure 1 shows the high-resolution (0.3$\arcsec\times 0.2\arcsec$) continuum
image of W51e2 at 0.85 mm. The complex of W51e2 has been resolved into at
least three bright, compact components (see Figure 1, the symbol “+” marks the
positions of the components). The brightest source, W51e2-E, is located $\sim
0.9\arcsec$ east of the UC HII region which is marked with “$\times$” for the
peak position at 3.6 cm from Gaume et al. (1993). The secondary bright source,
W51e2-W, coincides with the 3.6 cm peak of the UC HII region. The third
component, W51e2-NW, is located about 2$\arcsec$ northwest of W51e2-E. In
addition, these compact emission components appear to be surrounded by an
amorphous halo in the continuum emission at 0.85 mm. The three bright
continuum sources were also detected in the low-resolution continuum image at
1.3 mm (Figure 2(a)). Moreover, a weak continuum component, W51e2-N, is
present $\sim 2\arcsec$ north of W51e2-E.

###  2.2 The VLA continuum images at 7 and 13 mm

The observations of W51e2 at 7 and 13 mm were carried out with the VLA (see
the summarized observing parameters in Table 1). We made the calibrations by
following the standard data-reduction procedure for VLA data with
AIPS222http://www.aips.nrao.edu. Then, the calibrated visibilities were loaded
into the Miriad environment. The imaging and further analysis were carried out
with Miriad.

The dirty images were made using INVERT with robust weighting (robust=0) and
were cleaned with the hybrid deconvolution algorithm. Figures 2(b) and 2(c)
show the continuum images at 7 and 13 mm, respectively. Strong continuum
emission is detected from W51e2-W at both 7 and 13 mm in good agreement with
previous observations at centimeters (Gaume et al., 1993). In addition, a weak
but significant ($>$10$\sigma$) emission feature at W51e2-E has been detected
at only 7 mm. As shown in Figure 2(b), the weak 7 mm continuum source at
W51e2-E has been clearly separated from the UC HII region, W51e2-W.

###  2.3 Hydrogen recombination lines

For the line cubes, the continuum emission was subtracted using the linear
interpolation from the line-free channels with UVLIN. The H26$\alpha$ line
(353.623 GHz) was included in the line cubes made from the SMA data at 0.85
mm. We also made a line image cube to cover the H30$\alpha$ line (231.901 GHz)
from the SMA data at 1.3 mm. Both the H26$\alpha$ and H30$\alpha$ line data
were resampled to 1 km s-1. The rms noises of 49 mJy beam-1 and 81 mJy beam-1
in each of the channel images are inferred for the H26$\alpha$ and H30$\alpha$
line image cubes, respectively. Figure 2(d) shows the integrated line
intensity image of the H26$\alpha$ line made with a 3$\sigma$ cutoff in the
velocity range from 25 to 84 km s-1. The H26$\alpha$ line emission from the UC
HII region (W51e2-W) appears to be very compact and has not been resolved with
the beam of 0.25$\arcsec$, suggesting that the intrinsic source size of the
hyper-compact HII core is $<0.06\arcsec$. The peak position of the integrated
H26$\alpha$ line emission (stars in Figure 2) appears to have a significant
offset of $\sim$0.15$\arcsec$ from the continuum peak at 13 mm (also see the
Gaume’s position in Figure 2(d)). Unlike the other RRLs and continuum emission
at centimeter wavelengths, the line emission at the frequency of the
H30$\alpha$ line (the image is not shown in this paper) shows an extended
distribution covering the entire 3$\arcsec$ region, suggesting that the
H30$\alpha$ line might have been severely contaminated by one or more
molecular lines in W51e2 (e.g., C3H7CN with a rest frequency of 231.9009 GHz).

The line of H66$\alpha$ is broad and weak, which is barely covered by the VLA
correlator band with which we had difficulty determining the line-free
channels. Instead, the continuum levels were determined by averaging all
channels excluding a few relatively strong line emission channels and were
subtracted from the visibility data. The uncertainty is $<5$% in the continuum
levels due to the line contamination. The image cubes of the H53$\alpha$ and
H66$\alpha$ lines were cleaned and convolved to a circular beam of
0.25$\arcsec$, giving the rms noises of 2.4 and 1.5 mJy beam-1 per channel,
respectively. The integrated intensities of H53$\alpha$ (with a 3$\sigma$
cutoff in each channel) and H66$\alpha$ (with a 2$\sigma$ cutoff in each
channel) are shown in Figures 2(e) and 2(f), respectively. Both H53$\alpha$,
and H66$\alpha$ lines are detected from only W51e2-W, the UC HII region. For
the H26$\alpha$, H$53\alpha$, and H66$\alpha$ lines from W51e2-W, we
integrated the line emission region with a size of $0.5\arcsec$ around the
H26$\alpha$ peak position. The profiles of the H26$\alpha$, H$53\alpha$ and
H66$\alpha$ lines are multiplied by the frequency ratios of
$\nu_{H26\alpha}/\nu_{H26\alpha}$, $\nu_{H26\alpha}/\nu_{H53\alpha}$ and
$\nu_{H26\alpha}/\nu_{H66\alpha}$, respectively, as shown in Figure 3. The
measurements of the peak intensity ($I_{peak}$), radial velocity ($V_{LSR}$),
and FWHM line width ($\Delta V$) for the H26$\alpha$, H$53\alpha$, and
H66$\alpha$ lines are given in Table 3.

Figure 3:  Profiles of hydrogen recombination lines, H26$\alpha$, H53$\alpha$,
and H66$\alpha$, integrated from W51e2-W in an area of
0.5$\arcsec\times$0.5$\arcsec$ centered at the H26$\alpha$ line peak. All the
line images have been convolved with the same circular beam of 0.25$\arcsec$.
The spectra are multiplied with the ratios of the rest frequency of the
H26$\alpha$ line to the RRL rest frequencies ($\nu_{H26\alpha}/\nu_{RRL}$).
The vertical dashed line denotes the systematic velocity of 53.9$\pm$1.1 km
s-1 which we determined from the previous measurements of seven different hot
molecular lines (see Section 2.4).

Figure 4:  (a): Image of the HCN(4-3) absorption line integrated from 44 to 62
km s-1. Dashed contours are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2, 3, … and
$\sigma=0.113$ Jy beam-1 km s-1), overlaid on the gray-scaled continuum image
at 0.85 mm. The FWHM beam $0.33\arcsec\times 0.24\arcsec$ (sup=0) is shown at
bottom right. (b): Image of the CO(2-1) absorption line integrated from 43 to
71 km s-1. White dashed contours are $\pm 5\sigma\times 2^{n}$ ($n=0$, 1, 2,
3, … and $\sigma=0.771$ Jy beam-1 km s-1), overlaid on the gray-scaled
continuum image at 0.85 mm. The FWHM beam $1.35\arcsec\times 0.73\arcsec$
(sup=0) is shown at bottom right. (c and d): The spectral profiles of the
HCN(4-3) absorption line toward W51e2-E and W51e2-W, respectively. The
vertical dashed line denotes the systematic velocity of 53.9$\pm$1.1 km s-1.
(e): The spectral profile of the CO(2-1) absorption line toward W51e2-E, with
a vertical dashed line for the systematic velocity.  Table 2: Core fluxes of
W51e2 at Centimeter to Submillimeter bands

parameters | W51e2-W | W51e2-E | W51e2-NW | W51e2-N |  References and Beamsize $\Theta$
---|---|---|---|---|---
R.A.(J2000) | 19:23:43.90 | 19:23:43.96 | 19:23:43.87 | 19:23:43.97 | This paper
Decl.(J2000) | +14:30:34.62 | +14:30:34.56 | +14:30:35.97 | +14:30:36.57 | This paper
Deconvolved size ($\arcsec$)∗ | 0.45 | 0.71 | 0.38 | 1.24 | This paper
S0.85mm(Jy) |  0.35$\pm$0.07 |  3.30$\pm$0.20 |  0.81$\pm$0.34 |  |  This paper: $\Theta=0.4\arcsec$
S1.3mm(Jy) |  |  2.15$\pm$0.12 |  0.62$\pm$0.12 |  0.73$\pm$0.08 |  This paper: $\Theta=1.1\arcsec$
S7mm(Jy) |  0.62$\pm$0.09 |  0.04$\pm$0.01 |  $<$ 0.0052 |  $<$ 0.0052 |  This paper: $\Theta=0.4\arcsec$
S1.3cm(Jy) |  0.43$\pm$0.03 |  $<$ 0.004 |  $<$ 0.004 |  $<$ 0.004 |  This paper: $\Theta=0.4\arcsec$; Gaume et al. (1993): upper limit
S2cm(Jy) |  0.21$\pm$0.05 |  |  |  |  Rudolph et al. (1990): $\Theta=0.4\arcsec$
S3.6cm(Jy) |  0.067$\pm$0.004 |  $<$ 0.001 |  $<$ 0.001 |  $<$ 0.001 |  Gaume et al. (1993): $\Theta=0.21\arcsec$; Gaume et al. (1993): upper limit
S6cm(Jy) |  0.025$\pm$0.003 | – | – | – |  Rudolph et al. (1990): $\Theta=0.6\arcsec$

∗The sizes of e2-W, e2-E, and e2-NW are derived from the 0.85 mm image, and
e2-N is from the 1.3 mm image.
---

###  2.4  Absorption of HCN(4-3) and CO(2-1)

The systematic velocity of W51e2 varies when it is measured using different
hot molecular lines (Sollins et al., 2004; Remijan et al., 2004; Zhang et al.,
1998; Rudolph et al., 1990). In order to minimize the possible effects of the
different distributions with different molecular lines, we averaged these
measurements of molecular lines H13CO+, SO2, SiO, CH3CN, and HCO+ and obtained
a mean value of 53.9$\pm$1.1 km s-1. We adopt this value as the systematic
velocity for W51e2-E hereafter.

The lines of HCN(4-3) at $\nu_{0}=$ 354.505 GHz and CO(2-1) at $\nu_{0}=$
230.538 GHz were covered in the SMA observations at 0.85 mm and 1.3 mm,
respectively. The dirty images were made with natural weighting and
deconvolved with the Hogbom Clark Steer hybrid algorithm. The final rms noises
of $\sigma=$ 65 and 94 mJy beam-1 per channel (1 km s-1 in the channel width)
were achieved for HCN(4-3) and CO(2-1), respectively. Figure 4 shows the
spectra of HCN(4-3) toward W51e2-E and W51e2-W and the spectrum of CO(2-1)
near the peak position of W51e2-E. A significant absorption of the HCN(4-3)
line is detected against the submillimeter core, W51e2-E, and most of which is
redshifted with respect to the systematic velocity of 53.9$\pm$1.1 km s-1 as
indicated by the vertical dashed line in Figure 4(c). Note that the redshifted
absorption of HCN observed with the high angular resolution against the
compact continuum core corresponds to the cold gas in front of the dust core
moving toward it, i.e., the infall. The CO(2-1) spectrum shows a broader
redshifted feature in absorption than that of HCN. Due to the poor angular
resolution in the CO(2-1) observations, the observed broad profile in
absorption is caused not only by the infall but also by the outflow. In
particular, the high redshifted absorption arises very likely from the outflow
gas. In comparison with that of W51e2-E, the spectrum toward W51e2-W only
shows a weak spectral feature (Figure 4(d)) which can be characterized as an
inverse P Cygni profile, suggesting that only little molecular gas (if
present) falls onto the UC HII region.

To show the spatial distribution of the absorption gas, we carried out a
moment analysis. The calculation of the zeroth moment corresponds to the
integrated intensity over the velocity. In order to avoid cancellation between
emission and absorption spectral features in the same beam area, we separate
the absorption and emission in the moment calculation. We found that both the
blueshifted and redshifted emission of the HCN(4-3) line (the images are not
shown in this paper) is extended from northwest to southeast across W51e2-E,
which is, in general, consistent with the molecular outflow direction as
suggested based on the CO(2-1) line observation (Keto & Klaassen, 2008). The
HCN(4-3) absorption, integrated from 44 to 62 km s-1 with a 5$\sigma$ cutoff
in each channel (Figure 4(a)), is found to be mainly concentrated on W51e2-E.
From the lower-resolution observation, the distribution of the CO(2-1)
absorption line integrated from 43 to 71 km s-1 (a 5$\sigma$ cutoff in each
channel, see Figure 4(b)), also shows the absorption peaks at W51e2-E instead
of W51e2-W. We also note that the CO(2-1) absorption shows a relatively
extended feature north of W51e2-E, further indicating that the absorption of
CO(2-1) might indeed be significantly contaminated by the outflow gas.

Along with the absorption spectra, the distribution of the absorbing gas in
W51e2 evidently demonstrates that the submillimeter (dust) core W51e2-E,
instead of W51e2-W (the UC HII region), is the center of accretion for the
majority of the high-density gas from this molecular core.

###  2.5 Spectral Energy Distributions

In lack of high angular resolution observations at millimeter/submillimeter
wavelengths, the flux densities from the entire region of W51e2 were
considered in the previous analyses of the SED (Rudolph et al., 1990; Sollins
et al., 2004; Keto et al., 2008). With the high-resolution observations at the
wavelengths from radio to submillimeter, we are now able to spatially separate
individual emission components in our SED analysis.

Adding the data at 3.6 cm (Gaume et al., 1993) and at 2 and 6 cm (Rudolph et
al., 1990) along with the continuum data at the four wavelengths (13, 7, 1.3,
and 0.8 mm) discussed in this paper, we have seven measurements in flux
densities covering nearly 2 order of magnitude in frequency. For 13, 7, and
0.8 mm data, the flux density measurements were made by fitting a Gaussian to
the individual emission components in the images with the same size of the
convolved FWHM beam (0.4$\arcsec$). For the 1.3 mm data, we determined the
flux density by fitting multiple Gaussian components to the emission features
in a relatively lower resolution (1.1$\arcsec$), which results in relatively
large uncertainties. For the emission cores W51e2-W, W51e2-E, W51e2-NW, and
W51e2-N, the flux densities determined are given in Table 2. The relevant
continuum spectra for the four emission components are shown in Figure 5.

##  3 Nature of the W51e2 complex

Four distinct components have been identified in the 3$\arcsec$ region of
W51e2 from the high-resolution images at wavelengths from centimeter to
submillimeter (Figure 1 and 2). The nature of these components is discussed as
follows.

Figure 5:  The SED for W51e2 cores. (a): SED fitting for W51e2-W with a
free$-$free emission model. Black points denote the measurements from this
work, and the asterisks are from Rudolph et al. (1990) and Gaume et al.
(1993). All flux values are listed in Table 2. (b): The SED fitting for the
W51e2-E core with a thermal dust emission model. The arrows denote the upper
limits from Rudolph et al. (1990) and Gaume et al. (1993). (c): The SED
fitting for the W51e2-NW core. The upper limit of the flux density at 43 GHz
(7 mm) is from our data (3$\sigma$ of the 7 mm image). (d): The SED fitting
for the W51e2-N core.

###  3.1 W51e2-W: UC HII region

W51e2-W is the only core detected at centimeter wavelengths, and its continuum
emission is dominated by the free$-$free emission from the ionized core and
traces the thermal HII region around the central star. Figure 2(b)$-$(f) show
the UC HII region, W51e2-W, including an unresolved core (a possible ionized
disk) and a northeast and southwest (NE$-$SW) extension as a possible outflow.
The NE and SW extensions are best to be seen at 7 and 13 mm in Figure 2(b) and
(c), which is in good agreement with the previous observations at 3.6 and 1.3
cm by Gaume et al. (1993). Gaume et al. (1993) found the spectral indices
$\alpha$ of about 2 (optically thick) and 0.4 for the NE and SW extensions of
W51e2-W, respectively, and suggested that there is a one-side (SW) collimated
ionized outflow from the core.

The H26$\alpha$ line is an excellent tracer for hyper-compact ionized cores
(star in Figure 2(d)), providing a good diagnosis for the presence of an
ionized disk. On the basis of the SMA observations with an angular resolution
of 0.25$\arcsec$, the hyper-compact ionized core which peaked at 59.1 km s-1
in W51e2-W has not been resolved, giving a limit on the intrinsic size of
$\theta_{s}<0.06\arcsec$ and linear size $<310$ AU from a Gaussian fitting.
The elongation shown in the H53$\alpha$ image (Figure 2(e)) agrees with the
radio continuum images observed at 1.3 cm (Figure 2(c) and Gaume et al.,
1993), 3.6 cm (Gaume et al., 1993), and 7 mm (Figure 2(b)), suggesting that
the NE$-$SW extension observed in the H53$\alpha$ line corresponds to the
expansion of ionized gas (or outflow). The H66$\alpha$ line emission also has
a similar elongation (Figure 2(f)), with peak intensity near Gaume’s position
in the NE and an extension structure in the SW. We also found a significant
velocity gradient of the H53$\alpha$ line in the W51e2-W region, in agreement
with what was observed by Keto & Klaassen (2008), redshifted in the SW and
blueshifted in the NE. The broad wings of the recombination lines shown in
Figure 3 (also in Table 3) corresponding to extended, optically thin emission
indicate that it is hard to explain them as an ionized disk. The velocity
gradient observed in the H53$\alpha$ line is more likely produced by an
ionized outflow rather than an ionized accretion disk.

We fitted the SED of W51e2-W with a free$-$free emission model

$\displaystyle S_{\nu}$ $\displaystyle=$
$\displaystyle\Omega_{s}B_{\nu}(T_{e})(1-e^{-\tau_{c}})~{}~{}~{}~{}~{}({\rm
Jy}),$ (1)

where $S_{\nu}$ is the flux density at frequency $\nu$, $\Omega_{s}$ is the
solid angle of the source, $B_{\nu}(T_{e})$ is the Planck function, and
$T_{e}$ is the electron temperature. The continuum optical depth is expressed
by (Mezger & Henderson, 1967) as

$\displaystyle\tau_{c}$ $\displaystyle=$ $\displaystyle
0.0824\left(\frac{T_{e}}{{\rm K}}\right)^{-1.35}\left(\frac{\nu}{{\rm
GHz}}\right)^{-2.1}\left(\frac{\rm EM}{{\rm
pc~{}cm^{-6}}}\right)\alpha(\nu,T_{e}).$ (2)

The emission measure (EM) can be written as

$\displaystyle{\rm EM}=N_{e}^{2}Lf_{V},$ (3)

where $N_{e}$ is the mean electron density, $L$ is the path length, and
$f_{V}$ is the volume filling factor. The dimensionless factor
$\alpha(\nu,T_{e})$ is the order of unity (Mezger & Henderson, 1967). For a
homogeneous ionized core, the model can be expressed with two free parameters,
namely, $T_{e}$ and EM, for the first approximation described above.

The solid curve in Figure 5(a) shows the best fit to the data, suggesting a
turnover frequency of $\nu_{0}\approx 27$ GHz. The derived mean values for the
physical parameters for the overall HII region, $T_{e}$=4900$\pm$320 K and
EM=(1.3$\pm$0.2)$\times 10^{9}$ pc cm-6, are similar to the previous result in
Zhang et al. (1998). Thus, the mean electron density of
$N_{e}\sim$3$\times$105 cm-3 is inferred on the assumption of
$Lf_{V}\sim$0.5$\arcsec\times$5.1 kpc.

Both the Lyman continuum flux ($N_{L}$) and the excitation parameter ($U$) can
be derived (Rubin, 1968; Panagia, 1973; Matsakis et al., 1976) as follows:

$\displaystyle N_{L}$ $\displaystyle\gtrsim$ $\displaystyle 7.5\times
10^{46}{\rm s^{-1}}\left(\frac{S_{\nu}}{\rm Jy}\right)\left(\frac{D}{\rm
kpc}\right)^{2}\left(\frac{\nu}{\rm GHz}\right)^{0.1}\times$ (4)
$\displaystyle\left(\frac{T_{e}}{10^{4}{\rm K}}\right)^{-0.45},$
$\displaystyle U$ $\displaystyle=$ $\displaystyle 3.155\times 10^{-15}{\rm
pc~{}cm^{-2}}\left(\frac{N_{L}}{\rm
s^{-1}}\right)^{\frac{1}{3}}\left(\frac{T_{e}}{10^{4}{\rm
K}}\right)^{\frac{4}{15}},$ (5)

where $D$ is the distance to the source. We assumed $\alpha(\nu,T_{e})\sim 1$.
Based on our model fitting, the continuum emission from e2-W becomes optically
thin at a frequency $>$80 GHz ($\tau<$0.1), and we obtained
$N_{L}$=3.0$\times$1048 s-1 and $U$=37.6 pc cm-2. The inferred Lyman continuum
flux requires a massive star equivalent to a zero-age mean-sequence star of
type O8 located inside the UC HII region W51e2-W (Panagia, 1973), which is
consistent with the result of O7.5 in Rudolph et al. (1990). Alternatively, a
cluster of B-type stars can also be responsible for the ionization. The total
ionized mass within 0.5$\arcsec$ of W51e2-W is $\sim$0.02 M☉, which agrees
with the value derived by Gaume et al. (1993).

For the hyper-compact core ($\theta_{s}<0.06\arcsec$) as observed with the
H26$\alpha$ line, the electron temperature can be estimated from the
H26$\alpha$ line and the continuum flux density at 354 GHz (0.85 mm) on the
assumption of optically thin, LTE condition:

$\displaystyle T_{e}^{*}$ $\displaystyle=$
$\displaystyle\Bigg{[}\left(\frac{6985}{\alpha(\nu,T_{e})}\right)\left(\frac{\nu}{\rm
GHz}\right)^{1.1}\left(\frac{\Delta V_{\rm H26\alpha}}{\rm
km~{}s^{-1}}\right)^{-1}\left(\frac{S_{\rm 0.85\,mm}}{S_{\rm
H26\alpha}}\right)\times$ (6)
$\displaystyle\left(\frac{1}{1+\frac{N(He)}{N(H)}}\right)\Bigg{]}^{0.87}.$

Assuming $N(He)/N(H)=0.096$ (Mehringer, 1994) and $\alpha(\nu,T_{e})=1$, we
have $T_{e}^{*}=12,000\pm 2,000$ K which appears to be considerably higher
than the mean electron temperature of the overall HII region. The EM of the
hyper-compact HII core can be assessed as

$\displaystyle{\rm EM}$ $\displaystyle=$ $\displaystyle 7.1~{}{\rm
pc~{}cm^{-6}}\left(\frac{T_{e}}{\rm K}\right)^{3/2}\left(\frac{\theta_{s}}{\rm
arcsec}\right)^{-2}\left(\frac{\lambda}{\rm mm}\right)\left(\frac{S_{L}}{\rm
Jy}\right)\times$ (7) $\displaystyle\left(\frac{\Delta V}{\rm
km~{}s^{-1}}\right),$

where $T_{e}$ is the electron temperature, $\theta_{s}$ is the intrinsic size
of the HII region, $S_{L}$ is the peak line intensity in Jy, $\Delta V$ is the
FWHM line width in km s-1, and $\lambda$ is the observing wavelength in
millimeters. Based on the measurements of the H26$\alpha$ line from the hyper-
compact ionized core together with the assumption of $T_{e}\approx T_{e}^{*}$,
EM $>7\times 10^{10}$ pc cm-6 is found. The corresponding lower limit of the
volume electron density is $N_{e}>7\times 10^{6}$ cm-3 assuming
$Lf_{V}<0.06\arcsec\times 5.1$ kpc. The inferred high electron temperature and
high electron density suggest that the H26$\alpha$ line arises from a hot,
very compact region ($<0.06\arcsec$) which is probably close to the central
ionizing star(s). With the nature of optically thin and sensitive to the high-
density ionized gas, the H26$\alpha$ line is an excellent tracer of the hot,
high-density ionized region surrounding the ionizing source. In comparison
with the mean electron temperature ($T_{e}$=4900 K) inferred from the
free$-$free emission in a large area (0.5$\arcsec$), the high temperature of
$T_{e}$=12,000 K derived from the H26$\alpha$ in a compact area
($<0.06\arcsec$) suggests that a temperature gradient is present along the
radius of the UC HII region W51e2-W.

Observations of RRLs in a wide range at wavelengths from centimeters to
submillimeters also offer a means to further explore physical conditions of
W51e2-W. Table 3 summarizes the reliable measurements of three RRLs from
W51e2, namely, H26$\alpha$, H53$\alpha$, and H66$\alpha$. We plotted the
spectral profiles of the three RRLs together, and each of the profiles has
been multiplied by a factor of $\nu_{26\alpha}/\nu_{n\alpha}$, the ratio of
the H26$\alpha$ line frequency to that of RRLs (Figure 3). In the case of
optically thin and no-pressure broadening, the three line profiles should
match each other. Comparing H53$\alpha$ with H26$\alpha$, we find that the
peak intensities of the two lines agree with each other, which suggests that,
in general, both the H53$\alpha$ and H26$\alpha$ lines are under an optically
thin, LTE condition. Several velocity peaks in the H26$\alpha$ line indicate
multiple kinematic components in the hyper-compact HII core. The H53$\alpha$
line is characterized by a relatively smooth profile with large velocity
wings. The large velocity wings observed in the H53$\alpha$ line can be
explained by the large velocity gradient in the lower-density electron gas of
the ionized outflow (Gaume et al., 1993). However, the line intensity of
H66$\alpha$ appears to be significantly weaker than the other two high-
frequency RRLs. The peak of the modified line profile of H66$\alpha$ is a
factor of $\sim 5$ less than that of the other two. Considering the fact that
the frequency of H66$\alpha$ is below the turnover frequency of
$\nu_{0}\approx 27$ GHz, we found that the H66$\alpha$ line from the ionized
gas is obscured severely due to the self-absorption process in the hyper-
compact HII core. From Equation (2), a mean optical depth of $\tau_{c}=1.6$ at
$\nu_{H66\alpha}=22.36$ GHz is found. The exponential attenuation of
$exp(-1.6)\approx 0.2$ suggests that the H66$\alpha$ line is attenuated mainly
due to the self-absorption in the ionized core. In addition, the pressure
broadening effect which weakens the lower frequency lines needs to be
assessed.

Table 3: Parameters of RRLs derived from our data∗

| ${\rm H26\alpha}$ | ${\rm H53\alpha}$ | ${\rm H66\alpha}$
---|---|---|---
Rest Frequency (GHz) | 353.623 | 42.952 | 22.364
Measurements |  |  |
$I_{peak}$ (Jy) |  1.29$\pm$0.02 |  0.137$\pm$0.001 |  0.015$\pm$0.003
$V_{LSR}$ (km s-1) |  59.1$\pm$0.2 |  60.4$\pm$0.2 |  62.1$\pm$1.3
$\Delta V$ (km s-1) |  23.1$\pm$0.5 |  32.0$\pm$0.3 |  35.1$\pm$ 8.3
Derived line broadenings |  |  |
$\Delta V_{P}$ (km s-1) | 0.01 | 1.3 | 6.5
$\Delta V_{T}$ (km s-1) | 15.0 | 15.0 | 15.0
$\Delta V_{t}$ (km s-1) | 17.5 | 28.3 | 31.1
$\Delta V_{D}$ (km s-1) | 23.1 | 32.0 | 34.5

∗All line profiles are integrated from the 0.5$\arcsec\times$0.5$\arcsec$
region around the peak position of H26$\alpha$ from maps of the same beamsize
of 0.25$\arcsec$.
---

The total line width of RRLs ($\Delta V$) can be expressed by the Doppler
broadening ($\Delta V_{D}$) and pressure broadening ($\Delta V_{P}$).
According to Brocklehurst & Leeman (1971) and Gordon & Sorochenko (2002), we
have

$\displaystyle\Delta V_{D}$ $\displaystyle=$ $\displaystyle{\rm
km~{}s^{-1}}\sqrt{0.0458\times\left(\frac{T_{e}}{\rm
K}\right)+\left(\frac{\Delta V_{t}}{\rm km~{}s^{-1}}\right)^{2}},$ (8)
$\displaystyle\Delta V_{P}$ $\displaystyle=$ $\displaystyle 3.74\times
10^{-14}~{}{\rm km~{}s^{-1}}~{}n^{4.4}\left(\frac{\lambda}{\rm
mm}\right)\left(\frac{N_{e}}{\rm cm^{-3}}\right)\times$ (9)
$\displaystyle\left(\frac{T_{e}}{\rm K}\right)^{-0.1},$ $\displaystyle\Delta
V$ $\displaystyle=$ $\displaystyle\sqrt{\Delta V_{D}^{2}+\Delta V_{P}^{2}},$
(10)

where $\Delta V_{t}$ is the turbulent broadening of the gas including
broadening due to outflows and disk rotations and $n$ is the principal quantum
number of the transition. We calculated broadenings on the mean electron
temperature ($T_{e}\approx$4900 K) and the mean electron density
($N_{e}\approx$3$\times$105 cm-3) of the UC HII region. The thermal broadening
($\Delta V_{T}$) is 15 km s-1 for $T_{e}$=4900 K. The pressure broadening is a
strong function of the principal quantum number, i.e., $\Delta V_{P}$
increases drastically toward the low-frequency lines. For the high-frequency
line (H26$\alpha$), the pressure broadening ($\Delta V_{P}=0.01$ km s-1) can
be ignored, while for the H66$\alpha$ line, the pressure broadening of $\Delta
V_{P}$=7 km s-1 becomes significant. The pressure broadening is $\Delta
V_{P}$=76 km s-1 for the H92$\alpha$ line, which can actually wash out the
profile of the line emission. Thus, in addition to the opacity effect, the
pressure broadening may also make a considerable contribution to diminish the
H92$\alpha$ line which was not detected in the observations of Mehringer
(1994). The turbulent and/or the dynamical motions of the ionized gas
contribute a total of $\Delta V_{t}\approx$18 km s-1 in the line broadening
for the H26$\alpha$ line and $\Delta V_{t}\approx$28 and 31 km s-1 for the
H53$\alpha$ and H66$\alpha$ lines, respectively. The H26$\alpha$ line traces
the high-density ionized gas in the hyper-compact core or the ionized disk (if
it exists) and is less sensitive to the lower-density outflow which produces
the broad wings in the line profiles. The turbulent and/or dynamical
broadening becomes dominant in the H53$\alpha$ and H66$\alpha$ lines, and
these lower frequency RRLs (H53$\alpha$ and H66$\alpha$) better trace the
lower-density electron gas in the ionized outflow.

In short, W51e2-W consists of a hyper-compact HII core ($<310$ AU) with an
emission measure of EM $>7\times 10^{10}$ pc cm-6 and an ionized outflow. An
early-type star equivalent to an O8 star (or a cluster of B-type stars) is
postulated to have formed within the hyper-compact HII core. The broadening of
the line profiles is dominated by the Doppler broadening including both
thermal and turbulence/dynamical motions. Pressure broadening becomes
significant only for the H66$\alpha$ line and the lines with a larger
principal quantum number.

###  3.2 W51e2-E: A massive proto-stellar core

W51e2-E is located $\sim 0.9\arcsec$ east of the UC HII region W51e2-W. It is
the brightest source in the 0.85 mm continuum image (Figure 1). The flux
density drops drastically at 7 mm (Figure 2(b)). It cannot be detected at
longer wavelengths. The SED of the continuum emission from W51e2-E (Figure
5(b)) appears to arise from a dust core associated with proto-stars.

With the Rayleigh$-$Jeans approximation, the continuum flux density from a
homogeneous, isothermal dust core can be described as (Launhardt & Henning,
1997)

$\displaystyle S_{d}(\nu)$ $\displaystyle=$ $\displaystyle 6.41\times
10^{-6}{\rm Jy}\left(\frac{\kappa_{0}}{\rm
cm^{2}g^{-1}}\right)\left(\frac{\nu}{\nu_{0}}\right)^{\beta}\left(\frac{\nu}{\rm
GHz}\right)^{2}\times$ (11) $\displaystyle\left(\frac{M_{d}}{\rm
M_{\sun}}\right)\left(\frac{T_{d}}{\rm K}\right)\left(\frac{D}{\rm
kpc}\right)^{-2},$

where $\kappa_{0}$ is the dust opacity at frequency $\nu_{0}$, $\beta$ is the
power-law index of the dust emissivity, $M_{d}$ is the mass of dust, $T_{d}$
is the dust temperature in K, and $D$ is the distance to the source. We adopt
the dust opacity value of $\kappa(\lambda=1.3mm)=0.8~{}{\rm cm^{2}g^{-1}}$
(Ossenkopf & Henning, 1994; Launhardt & Henning, 1997) and have three free
parameters for the dust core ($M_{d}$, $T_{d}$, and $\beta$).

The solid line in Figure 5(b) shows the best least-squares fitting to the
observed data. The index of $\beta=0.26\pm 0.08$ derived from our fitting
indicates that emission from the e2-E core might be dominated by large grains
of dust (Miyake & Nakagawa, 1993; Chen et al., 1995; Andrews & Williams,
2007). In addition, because of a lack of flux density measurements at higher
frequencies or shorter wavelengths, the dust mass and temperature are
degenerate in our best fitting, i.e., $M_{d}T_{d}=142\pm 9$ M☉K. Remijan et
al. (2004) derived the kinetic temperature in W51e2 to be $153\pm 21$ K from
the high-resolution observations of CH3CN, which is close to the dust
temperature $T_{d}$=100 K used in Zhang et al. (1998). Adopting that
$T_{d}=$100 K and assuming that the ratio of H2 gas to dust is 100, we found
that the total mass of $\sim$140 M☉ is in the W51e2-E core. Emission from the
W51e2-E region has been slightly resolved, so that the dust core of W51e2-E
might host a number of proto-stellar cores which accrete the surrounding
molecular gas as indicated by both the HCN and CO absorption lines (see
Section 2.4). This proposed scenario for W51e2-E is also consistent with the
hourglass-like magnetic fields inferred from polarization measurements of Tang
et al. (2009) for the W51e2 region. The configuration center of $B$ vectors
coincides with the peak position of the W51e2-E dust core.

From a Gaussian fitting to the absorption spectrum of the HCN(4-3) toward the
center of W51e2-E (Figure 4(c)), we determined the peak line intensity $\Delta
I_{L}=-1.3\pm 0.1$ Jy beam-1, the line center velocity $V_{HCN}$ =$56.4\pm
0.2$ km s-1 and the line width of $\Delta V_{HCN}$=$9.5\pm 0.4$ km s-1. The
infall velocity of the gas is the offset between the center velocity and the
systematic velocity, i.e., $V_{in}=V_{HCN}-V_{sys}\approx 2.5$ km s-1. The
peak continuum intensity of W51e2-E at 0.85 mm is $I_{C}=1.2\pm 0.01$ Jy
beam-1 (corresponding to a brightness temperature of $144\pm 1$ K). The ratio
of $-\Delta I_{L}/I_{C}\sim 1$ suggests that the absorption line is saturated.
Thus, a lower limit on the optical depth (see Qin et al., 2008) is
$\tau_{HCN}>3$. Assuming that the excitation temperature of HCN(4-3) equals
the dust temperature, 100 K, we obtained a lower limit of HCN column density
of $7.9\times 10^{15}$ cm-2. Taking $[H_{2}]/[HCN]\sim 0.5\times 10^{8}$
(Irvine et al., 1987) and the size of the infall region as 0.75$\arcsec$
($\sim 4000$ AU, see Figure 4(a)), we found that the hydrogen volume density
$N_{H_{2}}\sim 6.9\times 10^{6}$ cm-3. The infall rate of the gas can be
estimated by

$\displaystyle{\rm d}M/{\rm d}t$ $\displaystyle=$ $\displaystyle 2.1\times
10^{-5}{\rm M_{\sun}yr^{-1}}\left(\frac{\theta_{in}}{\rm
arcsec}\right)^{2}\left(\frac{D}{\rm kpc}\right)^{2}\left(\frac{V_{in}}{\rm
km~{}s^{-1}}\right)\times$ (12) $\displaystyle\left(\frac{N_{H_{2}}}{\rm
10^{6}cm^{-3}}\right),$

where $2\theta_{in}=0.75\arcsec$ is the diameter of the infalling region, $D$
is the distance to the source, $V_{in}$ is the infall velocity, and the
molecular mass ratio $m/m_{H_{2}}=1.36$ is assumed. With the derived
parameters a lower limit of infall rate $1.3\times 10^{-3}$ M☉ yr-1 is
inferred, suggesting that W51e2-E is the accretion center of the W51e2
complex.

###  3.3 W51e2-NW and W51e2-N

W51e2-NW was clearly detected by Tang et al. (2009) in the continuum emission
at 0.87 mm. They also found a significant concentration of $B$ fields at the
e2-NW. Genzel et al. (1981) and Imai et al. (2002) detected bright and compact
H2O masers near this source. We also detected local enhanced continuum
emission at 0.85 and 1.3 mm. No significant continuum emission has been
detected at 7 and 13 mm (Figure1 and 2), and there are no significant
absorption lines of HCN(4-3) and CO(2-1) in this region. We fitted the SED of
continuum emission with a thermal dust model as shown in Figure 5(c). A lower
limit of $\beta\geq 0.35$ is derived from the fitting, which leads to an upper
limit of $M_{d}T_{d}\leq 39$ M☉K. Assuming the dust temperature in this region
to be $\sim$100 K, we inferred an upper limit of 40 M☉ in the total gas mass
if the ratio of H2 gas to dust is equal to 100\. W51e2-NW appears to be a
massive core at a very early phase of star formation.

Located $\sim 2\arcsec$ north from W51e2-E, W51e2-N is detected in continuum
emission from our lower-resolution (1.4$\arcsec\times 0.7\arcsec$) image at
1.3 mm (Figure 2(a)). This source appears to be resolved out with a high
angular resolution (0.3$\arcsec\times 0.2\arcsec$) observation at 0.85 mm. No
significant continuum emission was detected at the longer wavelengths. The SED
consisting of the flux density at 1.3 mm and the upper limits at 7, 13, and 36
mm is also fitted with a thermal dust model. From the fitted values of the
parameters $\beta\geq 0.75$ and $M_{d}T_{d}\leq 68$ M☉K, we found an upper
limit of $M_{d}\lesssim 70$ M☉ if $T_{d}\sim$100 K and the H2 gas to dust
ratio is 100\. No maser activities have been detected from W51e2-N, suggesting
that W51e2-N is probably a primordial molecular clump in W51e2.

###  3.4 A propagating scenario of star formation in W51e2

Apparently, in the massive molecular core W51e2, the gravitational collapse
occurred first at W51e2-W where an O8 star or a cluster of B-type stars were
formed. Based on the absorption spectrum of the HCN line, there appears to be
little molecular gas present in the immediate environs. The accretion appears
to be paused by the intensive radiation pressure from the central ionized
star(s). Most of the surrounding gas ($\sim$0.02 M⊙) has been ionized. The
thermal pressure in the UC HII region drives an ionized outflow from the
ionized core. The submillimeter observations of dust emission and molecular
line absorption show that the major accretion now has been re-directed to
W51e2-E, 0.9$\arcsec$ ($\sim$4600 AU) east from W51e2-W. W51e2-E becomes the
new dominant gravitational center accreting mass from the surroundings with a
rate of $10^{-3}$ M⊙ yr-1. Star formation activities take place in this
massive core, as shown by maser activities, outflow, and organized $B$-field
structure. Since no free$-$free emission and no RRLs have been detected, the
massive core W51e2-E likely hosts one or more massive proto-stars. The offset
of the radial velocities between the two cores, W51e2-E and W51e2-W, is
$\delta V=59.1-53.9=5.2$ km s-1. If W51e2-W and W51e2-E are gravitationally
bound and W51e2-W is circularly orbiting around W51e2-E, W51e2-E has a
dynamical mass of $>140$ M⊙, in good agreement with the mass derived from the
SED analysis.

A bipolar outflow from W51e2-E has been suggested from the observations of
molecular lines. The impact of the outflow on the medium in its path may
trigger the collapse of the sub-core W51e2-NW and induce further star
formation activities there. W51e2-N represents a gas clump with a considerable
amount of mass for the star formation. This speculative scenario appears to
reasonably explain what we have observed in the W51e2 complex.

##  4 Summary and conclusions

We have presented high-resolution images of the W51e2 region at 0.85, 1.3, 7,
and 13 mm for continuum and hydrogen recombination lines (H26$\alpha$,
H53$\alpha$, and H66$\alpha$) and the molecular lines (HCN(4-3) and CO(2-1)).
The W51e2 complex has been resolved into four distinct components, W51e2-W,
W51e2-E, W51e2-NW, and W51e2-N. We have carried out a comprehensive analysis
of the continuum SED, the RRLs, and the molecular absorption lines and found
that the four cores are at different phases of massive star formation.

1\. W51e2-W, associated with the UC HII region, is the only source from which
the H26$\alpha$, H53$\alpha$, and H66$\alpha$ lines have been detected. The
Lyman continuum flux inferred from the SED analysis suggests that a massive O8
star or a cluster of B-type stars have been formed within the HII region. The
unresolved H26$\alpha$ line emission region suggests the presence of a hot
($T_{e}^{*}=12,000\pm 2,000$ K), hyper-compact ionized core with a velocity of
$59.1\pm 0.2$ km s-1, a linear size of $<310$ AU, and a large emission measure
EM$>7\times 10^{10}$ pc cm-6 (or a high density $N_{e}>7\times 10^{6}$ cm-3).
The H53$\alpha$ and H66$\alpha$ line images show an SW elongation from the
hyper-compact ionized core, suggesting either an expansion or an outflow of
the ionized gas with relatively lower density. No significant detection of the
HCN molecular line in absorption against the compact HII region indicates that
the significant accretion onto this core has been stopped. The line ratios
between the H26$\alpha$, H53$\alpha$, and H66$\alpha$ show that both the
H26$\alpha$ and H53$\alpha$ lines from W51e2-W are in an optically thin, LTE
condition. The emission of the H66$\alpha$ line from the hyper-compact core
appears to be attenuated mainly due to the self-absorption in this HII region.
The line profile of the H26$\alpha$ appears to be dominated by the Doppler
broadening due to the thermal motions of the hot electrons while the line
broadening in both the H53$\alpha$ and H66$\alpha$ is dominated by the Doppler
effect due to the dynamical motions of the ionized outflow. The pressure
broadening in both the H26$\alpha$ and H53$\alpha$ lines is negligible and
becomes significant for the H66$\alpha$ line corresponding to $\Delta
V_{P}\sim$ 7 km s-1.

2\. W51e2-E is a massive ($\sim$140 M☉) dust core as suggested based on the
SED analysis. No hydrogen recombination lines and no radio continuum emission
($\lambda>1$ cm) have been detected from this region, which suggests that
W51e2-E is the major core hosting massive proto-stellar objects. Both the
absorptions of HCN(4-3) and CO(2-1) are redshifted with respect to the
systematic velocity, $53.9\pm 1.1$ km s-1, indicating that a large amount of
molecular gas moves toward the massive core. From the ratios of the absorption
line to continuum, a large infall rate of $>1.3\times 10^{-3}$ M☉yr-1 is
inferred from the HCN absorption line, corresponding to the accretion radius
of 2000 AU.

3\. W51e2-NW appears to be a massive ($\lesssim$ 40 M☉) core at an earlier
phase of star formation. Given its location in the projected path of the
molecular outflow from W51e2-E, the star formation activities of W51e2-NW are
likely to be triggered by the outflow impact on the medium in the W51e2
complex.

4\. W51e2-N appears to be a relatively less dense but massive clump
($\lesssim$ 70 M☉) from which no obvious evidence has been found for star
formation activities.

We thank the referee for helpful comments, Dr. S.-L. Qin for providing help
with the data reduction and analysis, and Professor Y.-F. Wu for helpful
discussions. H. Shi and J.-L. Han are supported by the National Natural
Science Foundation (NNSF) of China (10773016, 10821061, and 10833003) and the
National Key Basic Research Science Foundation of China (2007CB815403).

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# Predicting Directionality in Causal Relations in Text

Pedram Hosseini David A. Broniatowski Mona Diab
The George Washington University
<EMAIL_ADDRESS>
###### Abstract

In this work, we test the performance of two bidirectional transformer-based
language models, BERT and SpanBERT, on predicting directionality in causal
pairs in the textual content. Our preliminary results show that predicting
direction for inter-sentence and implicit causal relations is more
challenging. And, SpanBERT performs better than BERT on causal samples with
longer span length. We also introduce CREST which is a framework for unifying
a collection of scattered datasets of causal relations.

##  1 Introduction

Causal relations play a major role in many tasks in Natural Language
Understanding (NLU) Girju (2003) and discourse analysis Mulder (2008);
Kuperberg et al. (2011). The relation between X and Y is considered causal
when X makes Y happen or exist, or vice versa, where X and Y can be an event,
state, or object. For example, in ”The river had now turned into full flood
after the deluge of rain a few days ago.”, deluge of rain is the cause of
flood which happens when there is an overflow of water.

Automatic extraction of causal relations is a challenging task in Natural
Language Processing (NLP). Previous work on identifying causal relations is
mainly focused on classifying pairs as causal/non-causal without necessarily
considering direction in the pairs. These lines of research either assume a
fixed direction between spans in a pair or do not directly test models in
predicting the direction in a causal pair. For example, Gao et al. (2019a);
Liu et al. (2020) focus only on identifying causal pairs in context and not
specifying the direction in the pairs, namely which entity caused the other.
The knowledge-oriented CNN (K-CNN) model for causal relation extraction model
Li and Mao (2019) attempts at finding sentences that include a causal relation
only by feeding a pair of spans and a sentence as input to the model. On the
other hand, (Dunietz et al., 2018) indirectly addresses the direction
prediction problem by finding cause and effect spans associated with only
explicit causal connectives in context. They adopt a syntactic perspective.

Figure 1: A golden non-causal relation that can be Causal if we change the
direction between spans.

Direction between spans in a relation may play an important role in the
classification of some instances of causal relations. For example, in the non-
causal relation shown in Figure 1, from a commonsense perspective, we know
that milk cannot produce a cow but a cow can produce milk and be the reason
for milk to be produced. Thus, if we treat causal relation classification here
simply as a pair classification task with no direction provided, we will have
a pair that can be marked as both causal and non-causal while the gold label
for the pair is non-causal. We then ask a question here: RQ) how well can a
model tell the difference between cause and effect in a causal relation?

In the rest of the paper, first, we discuss the scatteredness of causal
relation datasets and introduce a simple framework to unify a collection of
well-known datasets of causal relations (§2). Then we explain the
preprocessing steps on datasets we chose from this collection for our
experiments (§3). We report our experiments on predicting direction in causal
relations (§4) along with some preliminary results and qualitative error
analysis (§5). We conclude this paper with reviewing the related work (§6) and
discussing next steps (§7).

##  2 Unifying Causal Relation datasets

Data resources of causal relations are rather scattered and do not often
follow the same schematic or machine-readable format. Such an incompatibility
among resources is not a new problem and it has been slowing down the progress
in the classification of semantic relations including causal relations
Hendrickx et al. (2010). That is why we decided to unify a collection of
widely used resources of causal/non-causal relations to make them easier for
the research community to use.

In this step, we focused on publicly available datasets in which the context
associated with a causal relation is available, and we excluded data resources
in which a relation is only a pair of words/tokens without any given context
such as Luo et al. (2016). The main reason is that causal relation/pair
classification is mainly a context-dependent task. We also excluded data
resources that only contain context and a label (binary or multi-label)
without annotating the spans/arguments of a relation such as Yu et al. (2019).
It is worth pointing out that these excluded resources can fit in the format
we define and they were just not our focus for now. It is also important to
mention that some resources are not publicly available –which itself is
another challenge in addition to the incompatibility of schemes– such as Penn
Discourse Treebank (PDTB) Prasad et al. (2008), and some others such as the
corpus of Temporal-Causal relations Bethard et al. (2008) and BECauSE Dunietz
et al. (2017) only share their annotation. 111E.g. not all the annotated
samples in Bethard et al. (2008) and Dunietz et al. (2017) can be used unless
we have access to Wall Street Journal (WSJ) and The New York Times Annotated
corpora.

In the following subsection, we describe our unifying framework, CREST, in
more detail.

###  2.1 CREST: A Causal Relation Schema222https://github.com/phosseini/CREST

CREST is a schema or more precisely, a unifying framework for storing
causal/non-causal relations. Various formats and schemes are introduced for
annotating causal relations Bethard et al. (2008); Prasad et al. (2008);
O’Gorman et al. (2016); Mostafazadeh et al. (2016). There are two main reasons
for the existence of such a wide range of schematics: 1) some data resources,
Mirza et al. (2014) for instance, are created on top of existing datasets
built for tasks other than causal relation extraction so they have to follow
the same format as the original dataset. And 2) some more recent datasets
include newly defined meta-information for relations that were not annotated
in older datasets. For example, Semeval-2007 task 4 Girju et al. (2007) and
Semeval-2010 task 8 Hendrickx et al. (2010) data do not have annotations for
causal signal or trigger words. But such words are shown to be effective in
causal relation extraction Do et al. (2011) and were later annotated in other
datasets such as BECauSE Dunietz et al. (2017).

By comparing a wide range of well-known and widely-used datasets of causal
relations, we identified the largest common set of features and annotations
among them to define CREST. In Table 1, we listed all these features. We call
the process of converting relations to CREST as CRESTing a dataset.

(Field name: Field value type) | Explanation
---|---
original_id: string |  Identifier of a relation in the original dataset.
span1, span2, signal: list |  These three fields store the arguments of a relation, cause, effect, span (for non-causal relation) and signal/trigger tokens. The value of these fields is a list of token strings.
context: string |  The context in which a relation appears.
idx: dictionary |  Character indices of span1, span2, and signal tokens in the context. Each value is a list of lists where each list in the main list is a pair of start and end indices of a token in context.
label: integer |  Specifies whether there is a causal relation between span1 and span2. 0: non-causal. 1: causal
direction: integer |  Shows the direction of a relation. 0: span1 $\Longrightarrow$ span2 and 1: span1 $\Longleftarrow$ span2
split: integer |  Split to which a relation belongs in the source dataset. (0: train, 1: dev, or 2: test set).
Table 1: List of features in CREST . We use pandas DataFrame data structure
Wes McKinney (2010) to store relations in CREST. All value types are Python
values.

##  3 Data and preprocessing

We chose two datasets that follow the definition of causal relations from a
commonsense perspective to use in our experiments. Compared to other datasets
of causal relations such as Do et al. (2011); Mirza et al. (2014);
Mostafazadeh et al. (2016); Dunietz et al. (2017), these datasets have enough
causal relations to allow us to create fairly balanced train and test splits.
In the following, we briefly introduce each of them:

Penn Discourse Treebank 3.0 (PDTB3) Prasad et al. (2008) contains annotated
samples of discourse connectives, implicit and explicit, and their arguments.
These discourse connectives are taken to be the predicates of binary discourse
relations including causal relations. There are four coarse-grained discourse
relation types at Level-1 including Contingency in PDTB3. And Contingency
relation itself contains the finer-grained Cause relations at Level-2. We use
all Level-3 relations in PDTB3 with Level-2 classes: Cause, Cause+Belief, and
Cause+SpeechAct. We excluded the Level-3 NegResult relations since in these
relations one argument does not cause but prevents the effects mentioned in
the other argument.

EventStoryLine (ESL) v1.5 is created by crowd-sourcing causal relations
between events in news articles Caselli and Inel (2018). EventStoryline’s
crowd-sourcing approaches and experiments follow a commonsense reasoning
perspective of causality. Causality in EventStoryLine refers to the broader
notion of contingent relations rather than a strict causal relation. We
extract all PLOT_LINK tags of the two following classes from EventStoryLine:
1) PRECONDITION, events which enable or cause another event, or 2)
FALLING_ACTION that mark speculations or consequences.

###  3.1 Converting relations to sequences

When we use relation, we mean a pair of span, in a context, where span is a
sequence of one or more tokens and context can be one or more sentences.
Direction of a relation can be either $span1\Rightarrow span2$ or
$span1\Leftarrow span2$. In a causal relation, the starting span is always
cause and the ending span is always effect. $Span1$ and $span2$ in a relation
do not always appear in the same order in context.

As shown in Figure 2, we convert relations to input sequences. For specifying
spans in a sequence, we add the special [unused*] tokens from BERT
vocabulary444For models with a different vocabulary than BERT, the [unused*]
tokens can be replaced by the special tokens in that vocabulary. to the start
and end of each span in the context. It is very important to notice that the
order in which [unused*] tokens appear in a sequence is always the same, no
matter if a relation is cause-effect or effect-cause. As a result, we do not
feed the direction between spans to the models and models will not know the
span type or direction at test time.

Figure 2: Converting an input example to an input sequence with and without
direction, for the BertForSequenceClassification model.

##  4 Experiments

Task: For a causal relation, given a context, two spans of text, namely span1
and span2, this task is a binary classification of the relation between the
two spans into either cause-effect or effect-cause. The goal in this task is
to predict the direction in a causal relation.

We chose two transformer-based language representation models including BERT
Devlin et al. (2018) and SpanBERT Joshi et al. (2020), their base and cased
version. BERT is one of the first transformer-based language models that
achieved a promising performance across a diverse range of NLP tasks and later
inspired the architecture of some other language models Liu et al. (2019); Lan
et al. (2019). SpanBERT is an improved pre-training model, with the same
format as BERT, designed to better predict spans of text. In our experiments,
we use HuggingFace’s BertForSequenceClassification which is a BERT model with
a sequence classification head on top Wolf et al. (2019) implemented in
PyTorch Paszke et al. (2019). In all experiments, we use 4 different random
seeds when fine-tuning our models and report the average result.

###  4.1 Data splits

In all our experiments, we divide our data into train, development (dev), and
test splits with 80:10:10 ratio. Information about data splits is shown in
Table 2.

Data | Source | Train | Dev | Test
---|---|---|---|---
$D_{1}$ | PDTB3 | 5,692 | 727 | 729
$D_{2}$ | ESL | 1,095 | 169 | 224
Table 2: Number of samples in data splits.

Checking context overlaps: There is a possibility that causal relations in a
dataset share the exact or similar context. For example, there are two causal
relations, (cause, effect) pairs, annotated in the following context: ”A major
earthquake struck southern Haiti on Tuesday, knocking down buildings and power
lines”: $("struck","knocking")$ and $("earthquake","knocking")$. Even though
these relations are annotated in the same context, they are stored as two
separate relations. And when splitting the data into train, dev, and test
sets, there is a chance that one of these relations be put in train and
another one in test set, respectively. When creating splits, we check to make
sure there is no such context overlap, full or partial between: 1) train+dev
and test, and 2) train and dev, since we want our models not to be tested on
classifying relations in a context they have already seen and were fine-tuned
on.

##  5 Results

Results of experiments are reported in Table 3. One interesting point to
notice is that on PDTB3 where the length of spans is way longer compared to
ESL, SpanBERT performs better than BERT. And on ESL where the majority of
spans contain one or two tokens, BERT outperforms SpanBERT. Another point to
consider is that even though the average performance of best-performing models
on $D_{2}$ is not too low and is higher than random classification (if we
consider random as baseline here,) not all models could converge well on
$D_{2}$. The small size of training data we use for fine-tuning our models on
$D_{2}$ to a certain degree may explain models’ convergence problem. It is
worth thinking how we can leverage some recent datasets of causal relations
such as GLUCOSE Mostafazadeh et al. (2020) to address this problem or focus on
methods that require a lesser number of golden annotated samples. Our results
here can also be seen as a baseline for future experiments.

###  5.1 Qualitative error analysis

For error analysis and for each language model, we looked at all samples
misclassified by all models we fine-tuned and tested with different random
seeds on each dataset. We observed two main reasons for errors: 1) inter-
sentence relations in which two spans of a relation appear in two different
sentences and far from one another in context, and 2) implicit relations, as
we expected. As mentioned earlier, in PDTB3, spans are relatively longer and
contain more tokens. It will be interesting to see if we can find a better way
to put a model’s attention in an input sequence on the main tokens in a long
span, for example, by adjusting the start and end tokens for each span
([unused*] tokens, in our case) or adding an encoding vector of span
boundaries in input sequences to the models.

Data | Model | P | R | F1
---|---|---|---|---
$D_{1}$ | Random | 0.54 | 0.5 | 0.52
BERT | 0.83 | 0.82 | 0.83
SpanBERT | 0.86 | 0.85 | 0.86
$D_{2}$ | Random | 0.5 | 0.47 | 0.48
BERT | 0.8 | 0.78 | 0.79
SpanBERT | 0.76 | 0.67 | 0.71
Table 3: Precision, Recall, and F1 of evaluating BERT and SpanBERT models on
predicting direction in a causal relation.

##  6 Related work

Work on automatic extraction of causal relations started around the late 80s
and early 90s with a focus on rule-based methods with a substantial amount of
manual work Joskowicz et al. (1989); Kaplan and Berry-Rogghe (1991); Garcia et
al. (1997); Khoo et al. (1998). Starting 2000, a combination of learning-based
and rule-based methods were employed to improve the quality of automatic
causality extraction in text Girju (2003); Chang and Choi (2004, 2006); Blanco
et al. (2008); Do et al. (2011); Hashimoto et al. (2012); Hidey and McKeown
(2016). More recently, word-embedding and language representation models
started to emerge in work around causal relation classification Dunietz et al.
(2018); Pennington et al. (2014); Dasgupta et al. (2018); Gao et al. (2019b).

To the best of our knowledge, the closest study to ours is Bhagat et al.
(2007) where LEDIR is introduced as a method to predict directionality in
inference rules.

##  7 Conclusion

In this work, we evaluated the performance of two bidirectional transformer-
based language models on predicting the direction in causal relations in text.
Our preliminary results show that finding directionality of inter-sentence and
implicit causal pairs is more challenging and SpanBERT performs better than
BERT on classifying causal relations with longer span length. We also
introduced a framework, CREST, for unifying a collection of scattered causal
relation datasets. As our next steps, we will work on different methods of
feeding input sequences to our causal relation classification models such that
boundaries of spans in context are less latent. And, we will continue unifying
new datasets of causal relations and add counterfactual relations to our
unified collection as well.

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##  Appendix A Experiments

Accuracy and evaluation loss plots for our experiments on dev set are shown in
Figures 3, 4, 5, and 6. As can be seen in Figures 5, 6, models fine-tuned on
$D_{2}$ (EventStoryLine v1.5) could not converge well in the majority of cases
which to a certain degree can be explained by the small set of causal
relations we use for fine-tuning our models.

Figure 3: Accuracy and evaluation loss of BERT model on $D_{1}$’s development
set. Figure 4: Accuracy and evaluation loss of SpanBERT model on $D_{1}$’s
development set. Figure 5: Accuracy and evaluation loss of BERT model on
$D_{2}$’s development set. Figure 6: Accuracy and evaluation loss of SpanBERT
model on $D_{2}$’s development set.

We fine-tuned our models with sequence length of 128 and the following
hyperparameter values: Batch size: 16, learning rate: 2e-5, and number of
epochs: 10. All experiments were run on an Amazon AWS p3.2xlarge EC2 instance
with one Tesla V100 GPU.

##  Appendix B CRESTed Datasets

In this section, we briefly introduce datasets we have aggregated and CRESTed
so far. Some statistics related to these datasets are shown in Table 4.

ID | Dataset | Signal | Causal
---|---|---|---
1 | Semeval-2007 task 4 | ✗ | 114
2 | Semeval-2010 task 8 | ✗ | 1,331
3 | EventCausality | ✗ | 485
4 | Causal-TimeBank | ✓ | 318
5 | EventStoryLine v1.5 | ✓ | 2,608
6 | CaTeRS | ✗ | 308
7 | BECauSE v2.1 | ✓ | 554
8 | COPA | ✗ | 1,000
9 | PDTB3 | ✓ | 7,991
Table 4: List of CRESTed datasets. Signal refers to signal words/tokens or
markers annotated for a relation.

Semeval-2007 task 4 contains samples of different semantic relations between
nominals in text Girju et al. (2007). Cause-Effect is one of these semantic
relations. Nominal in this dataset is defined as a noun or base noun phrase
excluding named entities. Samples in Semeval-2007 task 4 are initially
extracted based on predefined patterns and then manually annotated for the
final label. Semeval-2010 task 8 Hendrickx et al. (2010) is very similar to
the Semeval-2007 task 4 dataset and mainly follows the same schema. The main
goal in creating Semeval-2010 task 8 was to have a standard benchmark dataset
for multi-way semantic relation classification in context.

EventCausality is a dataset created for evaluation purposes using news
articles Do et al. (2011). There are two types of relations annotated in this
dataset including causality and relatedness555Since the existence of causality
in Relatedness relations is debatable, we excluded these relations when
converting relations from EventCausality., C and R, respectively. Causality
relations in EventCausality are manually annotated for pairs of events based
on two rules: 1) Cause event should temporally precede the Effect event and 2)
Effect event occurs because the Cause event occurs.

Choice of Plausible Alternatives (COPA) is a tool for evaluating models’
performance in commonsense causal reasoning Roemmele et al. (2011). COPA
consists of 1000 questions, split equally into development and test sets. Each
question is composed of a premise of and two alternatives where premise is
either cause/effect and one of the alternatives more plausibly has a causal
relation with the premise. When CRESTing COPA, we also store pairs of premises
and their less plausible alternatives.

Causal-TimeBank contains explicit causal relations between event pairs in a
sentence Mirza et al. (2014). For annotating causal relations, C-LINKs,
expressions containing affect verbs, link verbs, causative conjunctions, and
prepositions are used in addition to basic construction for CAUSE, ENABLE, and
PREVENT categories of causation and periphrastic causatives. In Causal-
TimeBank, polarity, factuality, and certainty are annotated for events
involved in a causal relation. Signals of causality for CLINKs are annotated
in Causal-TimeBank as well.

CaTeRS has annotations of explicit and implicit causal relations between
events from a collection of stories Mostafazadeh et al. (2016). In CaTeRS,
causal relations between events are annotated more from a commonsense
reasoning perspective than based on the presence of causal markers. Moreover,
annotated relations are either in a sentence or across sentences in a story
which makes CaTeRS a proper dataset especially for evaluating the performance
of models on identifying inter-sentence causal relations.

EventStoryLine v1.5 EventStoryLine is created by crowd-sourcing causal
relations between events in news articles Caselli and Inel (2018).
EventStoryline’s crowd-sourcing approaches and experiments follow a
commonsense reasoning perspective of causality, the approach adopted by CaTeRs
as well. Causality in EventStoryLine refers to the broader notion of
contingent relations rather than a strict causal relation. When converting
EventStoryLine relations to CREST, we extract all PLOT_LINK tags of the two
following classes: 1) PRECONDITION, events which enable or cause another
event, or 2) FALLING_ACTION that mark speculations or consequences.

BECauSE is a bank of effects and causes that are explicitly stated in the
context. BECauSE uses a variety of constructions to express causal relations.
Causal relations in this dataset are not necessarily relations that hold from
a philosophical perspective in the real-world but relations that are expressed
in context as causal Dunietz et al. (2017). Every relation in BECauSE has a
signal word associated with it.

Penn Discourse Treebank (PDTB3) Prasad et al. (2008) contains annotated
samples of discourse connectives, implicit and explicit, and their arguments.
These discourse connectives are taken to be the predicates of binary discourse
relations including causal relations. There are four coarse-grained discourse
relation types at Level-1 including Contingency in PDTB3. And Contingency
relation itself contains the finer-grained Cause relations at Level-2. We use
all Level-3 relations in PDTB3 with Level-2 classes: Cause, Cause+Belief, and
Cause+SpeechAct. We excluded the Level-3 NegResult relations since in these
relations one argument does not cause but prevents the effects mentioned in
the other argument.