prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the maximum radius of the emitting region for the black body model?

Read the following text and answer briefly.

Context 1: \section{Introduction}

The publicly available XMM-Newton slew data covers to date around 35\%
of the sky  The soft band (0 2$-$2 keV) sensitivity limit of the slews
(6$\times10^{-13}$\,ergs cm$^{-2}$ s$^{-1}$) is close to that of the
ROSAT All-Sky Survey (RASS; Voges et al \ 1999), and in the medium
(2$-$12 keV) band, the slew data goes significantly deeper
(4$\times10^{-12}$\,ergs cm$^{-2}$ s$^{-1}$) than all other previous
large area surveys  Over 7700 individual sources have so far been
detected to a positional accuracy of 8\arcsec   For details on the
the construction and
characteristics of the first released XMM-Newton slew survey
catalogue, see Saxton et al  (2008)  For details of the initial
science results from the slew survey, see Read et al  (2006) 

The comparison of XMM-Newton slew data with the RASS is now giving,
for the first time, the opportunity to find exotic, extreme
high-variability X-ray bursting objects, e g  tidal disruption
candidates (Esquej et al  2007), and also Galactic novae, flare stars,
and flaring white dwarfs, plus eclipsing binaries, AGN and blazars  It
is only with such a large-area survey as the XMM-Newton Slew Survey,
that transient events as these have a chance of being caught 

One such rare event, XMMSL1~J060636 2-694933, which we here show to be
a new Classical Nova, was discovered in an XMM-Newton slew from 18th
July 2006 at a very high count rate of 23 3\,ct s$^{-1}$ (EPIC-pn:
0 2$-$2\,keV)  

Classical novae (see Bode \& Evans 2008 for a review) occur in
interacting binary systems consisting of a white dwarf primary star
and a lower-mass secondary star  The nova itself is a cataclysmic
nuclear explosion caused by the accretion of material (via Roche Lobe
overflow or wind accretion) from the secondary star onto the surface
of the white dwarf; here the pressure and temperature at the base of
the accreted material becomes sufficient to trigger a thermonuclear
runaway  A recent review of the thermonuclear processes powering
classical novae can be found in Starrfield et al \ (2008)   The
accreted material is partially expelled, obscuring the X-ray emission
from the surface of the white dwarf  At later stages, the ejected
material expands further and becomes optically thin, revealing the
nuclear burning on the surface of the white dwarf   This emission
peaks in the soft X-ray regime and it is known as the super-soft
source (SSS) state (Krautter 2008)  Models of the classical nova SSS
state can be found in Tuchman \& Truran (1998) and Sala \& Hernanz
(2005) 

Though many classical novae have been observed in X-rays in their SSS
states (Ness et al \ (2007) for example discuss several examples observed with
Swift), it is in the optical band, early in their outbursts, that
classical novae are almost always discovered  This is because they are
intrinsically optically bright and easily found in inexpensive
wide-area shallow surveys   XMMSL1~J060636 2-694933 is very unusual
therefore in that it has been discovered, as we shall see, later in
its evolution, in the SSS X-ray state 

In this paper we describe the XMM-Newton slew observations
(Section~2), and the follow-up X-ray observations by the Swift XRT
(Section~3) and XMM-Newton (Section~4)  Multiwavelength observations
with Swift-UVOT, Magellan and ASAS are described in Section~5  We then
present a discussion of the results (Section~6), and conclusions 



\begin{table*}[t]
  \caption[]
  {Details of the four XMM-Newton Slew observations and the single (Rev \,1378) 
    dedicated XMM-Newton pointed observation  XMM-Newton revolution, date and observation ID 
    are tabulated, together with the 0 2$-$2 0\,keV X-ray properties of XMMSL1~J060636 2-694933;  
    position, background-subtracted counts, exposure, count-rate, and detection likelihood  For the 
    Rev \,1378 dedicated observation, these properties are given for all the EPIC cameras combined   
    For the slew observations, only the EPIC-pn values are given  In the first two slews the source 
    was not detected, and upper limits are shown in the table }
  \centering
\begin{tabular}{lccccrrrr}
\hline
Rev  & Date & Obs \,ID & RA(J2000)   & Dec(J2000) & Counts         & Exposure & Count rate  & Lik  \\ 
     & (UT) &        &     &            &                & (s) & (s$^{-1}$)  &      \\ \hline 
 351 (slew) & 07/11/01 &  9035100003  &        &               & $<$3 6     & 8 8 & $<$0 41 & $<$$\sim$8      \\
 750 (slew) & 12/01/04 &  9075000003  &        &               & $<$3 2     & 17 3 & $<$0 18 & $<$$\sim$8      \\  
1210 (slew )& 18/07/06 &  9121000003  & 06:06:36 2 & -69:49:33 & 228 8$\pm$14 1 & 9 8 & 23 4$\pm$1 4 & 1777 1   \\ 
1246 (slew) & 28/09/06 &  9121460003  & 06:06:36 5 & -69:49:38 &  12 9$\pm$2 4  & 3 4 &  3 8$\pm$0 7 &   54 7   \\
\vspace{-3 5mm}\\
\hline 
1378 (pointed) & 19/06/07 &  0510010501  & 06:06:36 5 & -69:49:37 & 1511 0$\pm$44 8 & 8940 0 &  0 20$\pm$0 01 & 4630 4          \\
\hline
\end{tabular}
\label{slewtable}
\end{table*}

\section{XMM-Newton slew observations}

XMMSL1~J060636 2-694933 was discovered in XMM-Newton slew 9121000003
from revolution 1210 on 18th July 2006  Details of the standard
XMM-Newton slew data reduction and analysis used, plus the
source-searching and catalogue cross-correlation etc , are presented
in Saxton et al   (2008) 

The source passed through the EPIC-pn detector in 14\,s, at a small
off-axis angle, such that an effective vignetting-corrected soft band
(0 2$-$2\,keV) exposure time of 9 8\,s was achieved   A total of 229
source counts lie within a radius of 20\arcsec, yielding a (EPIC-pn:
0 2$-$2\,keV) count rate of 23 4\,ct s$^{-1}$ 

The source is seen to have no cross-correlation identifications in the
RASS, and no other multiwavelength candidates within 30\arcsec\ in
Simbad\footnote{http://simbad u-strasbg fr/simbad/},
NED\footnote{http://nedwww ipac caltech edu/index html}, and
HEASARC\footnote{http://heasarc gsfc nasa gov/}  The position of the
source in the sky is such that it lies apparently at the outer eastern
edge of the LMC 

XMM-Newton has slewed over this region of sky a number of times, and
though nothing was detected in previous slews from 7th November 2001
and 12th January 2004, the source was seen again on 28th September
2006 (rev \,1246, 72 days after the rev \,1210 discovery), at the same
position, but at a reduced flux level (3 8\,ct s$^{-1}$; EPIC-pn:
0 2$-$2\,keV)   i e  it had reduced in flux by a factor of $\approx$6
in 72 days  XMM-Newton has not slewed over this area of sky since
rev \,1246  Details of the relevant XMM-Newton slews, together with
the (0 2$-$2\,keV) EPIC-pn source position, detected source counts,
count rate and detection likelihood are given in
Table~\ref{slewtable} 

The fact that XMMSL1 J060636 2-694933 is detected in the total-band
(0 2$-$12\,keV) and the soft-band (0 2$-$2\,keV), whilst effectively
zero counts are seen in the hard-band (2$-$12\,keV), is immediately
indicative of the source being very soft  

The moderately high count rate indicates that the spectrum is affected
by pile-up (the on-axis limit is 6\,ct s$^{-1}$ for EPIC-pn full-frame
mode
\footnote{http://xmm esac esa int/external/xmm\_user\_support/documentation
  /uhb\_2 5/index html})  This distorts the spectrum and makes
quantitative spectral analysis of the slew data difficult  We
minimized these effects by following the standard procedure, i e 
ignoring the central part of the Point Spread Function (PSF), and
extracted an event spectrum (containing single and double events) of
the source from within an annulus of 5\arcsec$-$30\arcsec\ radius,
centred on the source position  Unresolved problems associated with
the motion of sources across the detector still exist within slew
data, and approximations currently have to be made when calculating
the associated effective area and detector response matrix files  In
order to perform qualitative spectral analysis, an effective area file
was generated by averaging the individual core-removed effective area
files at 9 different positions along the detector track made by the
source  This accounts for the removal of the piled-up core, and takes
the vignetting and PSF variations into account to a good
approximation   Individual BACKSCAL values have been set by hand, as
have the EXPOSURE values, estimated by calculating the distance
travelled by the source in detector coordinates and finding the time
taken to do this, given a 90\,deg\,hr$^{-1}$ slew speed, then
subtracting the appropriate fractions for chip gaps and bad pixels 
For the response matrix, we used the equivalent canned detector
response matrix for the vignetting-weighted average source position,
for single plus double events and for full-frame mode:
epn\_ff20\_sdY6\_v6 9 rmf  A background spectrum was extracted from a
much larger circular region close to the source and at a similar
off-axis angle 

To fit the slew spectral data, and indeed all the high-energy spectra
in the present paper, the
XSPEC\footnote{http://heasarc gsfc nasa gov/docs/xanadu/xspec/}
spectral fitting package has been used  As $\chi^2$ minimization is
not valid when fitting spectra of low statistical quality, for the
fitting of the slew spectrum (and all the spectral fitting in the
present paper), C-statistics have been used  To take into account the
absorbing column along the line of sight, the {\em wabs} model with
the {\em wilm} cosmic abundance table (Wilms et al \ 2000) has been
used throughout the paper  All the errors quoted in the present paper
are 90\% confidence intervals, unless otherwise stated 

The rev \,1210 slew spectrum shows that the source is very soft, and
appears consistent with a 63$_{-10}^{+12}$\,eV black body, absorbed by
a hydrogen column density of
8 2$_{-4 1}^{+5 4}\times10^{20}$\,cm$^{-2}$  The fit is good, with a
P-statistic value of 0 11, obtained via the XSPEC {\em goodness}
command for this fit, based on 5000 random simulations  The best-fit
hydrogen column is equal to the full Galactic hydrogen column in the
direction of the source (8 0$\pm{1 1}\times10^{20}$\,cm$^{-2}$; Dickey
\& Lockman, 1990, calculated via the FTOOL {\em
  nh}\footnote{http://heasarc gsfc nasa gov/lheasoft/ftools/fhelp/nh txt}) 
The slew spectrum, plus the best fit simple black body model and the
deviations from the model, are shown in Fig \,\ref{slewspec}  The
observed count rate corresponds to a (0 2$-$2\,keV) flux, corrected
for the removal of the saturated PSF core, of
4 8$^{+2 7}_{-1 6}\times10^{-11}$\,ergs cm$^{-2}$ s$^{-1}$ (an
increase in flux over the RASS upper limit, assuming the same spectral
model, by a factor of more than 500) 

Simple power-law, thermal Bremmstrahlung, and other optically thin hot
plasma models are unable to fit the spectrum adequately well   Given
that we later are able to identify the source as a nova (Section~5 2),
then the black-body model will likely be a good approximation 
Furthermore, as we have obtained here a moderate number of slew
counts, the more physically realistic, though more complex atmosphere
model for CO white dwarfs of MacDonald \& Vennes (1991), provided by
K \,Page (private communication), was attempted   This model, used
e g  to model the nova V1974 Cyg (Balman et al \ 1998), yielded a
marginal fit (and not formally a more statistically significant fit;
P-statistic = 0 03, based on 5000 random simulations), with an
effective temperature of 70$^{+8}_{-6}$\,eV, an $N_{\rm H}$ of
3 7$^{+3 2}_{-2 5}$$\times$$10^{20}$\,cm$^{-2}$, and a PSF-corrected
(0 2$-$2\,keV) flux of 4 5$^{+1 3}_{-1 8}\times10^{-11}$\,ergs
cm$^{-2}$ s$^{-1}$  Note that a smaller $N_{\rm H}$ (though perhaps
still consistent with the full Galactic hydrogen column) is now
obtained using the white dwarf atmosphere model  (Note that the
MacDonald \& Vennes (1991) ONe white dwarf atmosphere model was also
attempted, but yielded a marginally worse fit than the CO white dwarf
atmosphere model; only the CO atmosphere model has been used in the
subsequent analysis) 

It is well known (e g  Krautter et al \ 1996) that, because of the
energy-dependent opacity in the white dwarf atmosphere, fits to super
soft source novae spectra with black body models give larger fluxes
and lower temperatures than atmosphere models fit to the same spectra,
and this is seen in the present case  Thus the black body model
requires a larger $N_{\rm H}$ to fit the same data than the atmosphere
model, as is seen  

The model normalizations, corrected for the removal
of the saturated PSF core, can be used to derive an approximate
distance to the source   If we assume a typical emitting region for
the white dwarf atmosphere to be of spherical radius 10$^{9}$\,cm,
then, for the black body model, this distance turns out to be
20$^{+31}_{-10}$\,kpc  The effects discussed above however can lead to
usage of the black body model giving rise to an underestimation of the
distance   For the white dwarf atmosphere model, a larger distance of
71$^{+27}_{-23}$\,kpc is obtained  Both estimates are consistent with
the distance to the LMC ($\sim$50\,kpc, see Section~6), and assuming a
distance of 50\,kpc, the black body derived flux corresponds to a
(pile-up corrected) 0 2$-$2\,keV X-ray luminosity of
1 4$^{+0 8}_{-0 5}\times10^{37}$\,ergs s$^{-1}$ 


\begin{figure}
\centering
\includegraphics[bb=100 20 575 700,clip,width=6 0cm,angle=270]{12082f1 ps}
\caption{XMM-Newton Slew spectrum of XMMSL1 J060636 2-694933 from
  XMM-Newton revolution 1210   The data points (crosses; adjacent data
  bins having been grouped together for the plot to have a significance of at least
  3) have been fitted with a black body model (kT=63\,eV; see text) 
  The solid line shows the best fit to the spectrum  The ratio of the
  data to the best fit model is shown in the lower panel }
\label{slewspec}
\end{figure}


\section{Swift XRT X-ray observations}

We requested and received a prompt observation with Swift of this
source before it moved out of the Swift visibility window in April
2007  We received over 14\,ksec of Swift-XRT time in 7
separate observations and the details of these observations are listed
in Table~\ref{xrttable}  All of the observations were in photon
counting mode and none of the observations showed any times of
significant high-BG flux  In none of the observations did the source
position coincide with any of the dead (micrometeorite-induced)
detector columns  The analysis has been performed using HEASOFT
v6 1 2  The individual XRT observations were astrometrically-corrected
and then stacked to ascertain a best Swift-XRT position $-$ this was
found to be 06 06 37 00 -69 49 33 9 (with a 90\% error radius of
4 0\arcsec)  Source counts were then extracted from each observation
from a circle of radius of 40\arcsec\ at this position  Background
counts were extracted from each observation from large-radius
off-source circles close to the source position  Source counts and
count rates for the individual XRT observations are given in
Table~\ref{xrttable} 


\begin{table}
  \caption[]{Details of the Swift-XRT observations (observation ID, observation date and 
    cleaned exposure time) are tabulated, together with the total (0 2$-$2 0\,keV) background-subtracted 
    counts and count rate from XMMSL1 J060636 2-694933 (see text) }
  \centering
\begin{tabular}{ccrrr}
\hline
ID          & Date     & Exp  & Counts       & Count rate       \\ 
            &   (UT)   &  (s) &              & (s$^{-1}$)        \\ \hline 
00030895001 & 28/02/07 & 1955 & 23 9$\pm$5 1 & 0 0122$\pm$0 0026  \\
00030895002 & 07/03/07 & 1796 & 15 8$\pm$4 2 & 0 0088$\pm$0 0024 \\
00030895003 & 08/03/07 & 1651 & 10 9$\pm$3 6 & 0 0066$\pm$0 0022 \\
00030895004 & 08/03/07 & 2547 & 20 6$\pm$4 8 & 0 0081$\pm$0 0019 \\
00030895005 & 10/03/07 & 2550 & 29 5$\pm$5 7 & 0 0116$\pm$0 0022 \\
00030895006 & 20/03/07 &  552 &  8 6$\pm$3 2 & 0 0156$\pm$0 0057 \\
00030895007 & 22/03/07 & 3391 & 24 4$\pm$5 4 & 0 0072$\pm$0 0016 \\
\hline
\end{tabular}
\label{xrttable}
\end{table}

The observation naturally fell into three time-separated groups, those
of obs \,1, obs \,2-5 and obs \,6-7  A similar analysis applied to
these groups (where the statistics are improved) gives rise to source
counts and count rates of 76 7$\pm$9 3\,counts and
0 0090$\pm$0 0011\,ct~s$^{-1}$ (for obs \,2-5), and
33 0$\pm$6 2\,counts and 0 0084$\pm$0 0016\,ct~s$^{-1}$ (for
obs \,6-7)  (Analysis of all the data together yields
133 6$\pm$12 3\,counts and 0 0092$\pm$0 0009\,ct~s$^{-1}$)  

A spectrum was extracted from all the Swift-XRT data from a 40\arcsec\
radius circle, using grades 0$-$12, centred on the Swift-XRT position 
A background spectrum was extracted again from all the Swift-XRT data,
from large-radius off-source circles close to the source position  An
ARF file was created using {\em xrtmkarf} and the appropriate RMF
(swxpc0to12\_20010101v008 rmf) from the Swift-XRT Calibration Database
was obtained 

Standard spectral models were again fit to the spectral data using
XSPEC  Again, C-statistics were used, as was the {\em wabs} absorption
model with the {\em wilm} cosmic abundance table  It was again 
obvious that only a very soft spectrum would be appropriate for the
data, and the only simple model that was able to fit the data
adequately was a black-body model of temperature
$kT$=$59^{+14}_{-10}$\,eV, with an absorbing hydrogen column of
9 5$^{+5 0}_{-3 9}$$\times$$10^{20}$\,cm$^{-2}$  No sufficiently constrained parameters could
be obtained using the CO white dwarf atmosphere model (MacDonald \&
Vennes 1991)  The Swift-XRT spectrum, together with the best-fit black
body model is shown in Fig \,\ref{xrtspec}  The corresponding
(0 2$-$2 0\,keV) flux is 2 7$^{+0 7}_{-1 2}\times10^{-13}$\,ergs
cm$^{-2}$ s$^{-1}$ (i e  a reduction by more than a factor 100 from
the XMM-Newton slew discovery flux), and the X-ray luminosity, for the
assumed distance of 50\,kpc, is 8 0$^{+2 2}_{-3 5}\times10^{34}$\,ergs
s$^{-1}$ 

\begin{figure}
\centering
\includegraphics[bb=100 15 580 710,clip,width=6 0cm,angle=270]{12082f2 ps}
\caption{Swift-XRT spectrum from XMMSL1 J060636 2-694933  The data
  points (crosses; adjacent data bins having been grouped together for
  the plot to have a significance of at least 3) have been fitted with
  a black body model (kT=59\,eV; see text)  The source has faded by a
  factor of $>100$ since the XMM-Newton revolution 1210 slew
  discovery  The solid line show the best fit to the spectra   The
  ratio of the data to the best fit model is shown in the lower panel 
}
\label{xrtspec}
\end{figure}

A cautious estimate of the size of the emitting region can be obtained
from the model normalization; the assumed distance of 50\,kpc yields a
maximum radius of 4 5$\times$10$^{8}$\,cm (the fit normalization is
essentially unconstrained at the lower bound)   Though great care
should be taken in interpreting this result, as the black body model
is possibly overestimating the luminosity, this obtained radius is
still consistent with that of moderately massive ($>$1 1$M_{\odot}$)
white dwarfs (Hamada \& Salpeter 1961), i e \,the whole white dwarf
surface may still be emitting at 59\,eV 

\section{Dedicated XMM-Newton observations}

We were granted an XMM-Newton Target of Opportunity (ToO) observation,
once the source became again visible to XMM-Newton, and a 10\,ks
XMM-Newton EPIC observation was made on 19th June 2007 (see
Table~\ref{slewtable})  All the XMM-Newton EPIC data, i e   the data
from the two MOS cameras and the single pn camera, were taken in
full-frame mode with the thin filter in place   These data from the
three EPIC instruments have been reprocessed using the standard
procedures in XMM-Newton SAS (Science Analysis System) $-$ v 7 1 0 
Periods of high-background, of which there were very few, were
filtered out of each dataset by creating a high-energy 10$-$15\,keV
lightcurve of single events over the entire field of view, and
selecting times when this lightcurve peaked above 0 75\,ct s$^{-1}$
(for pn) or 0 25\,ct s$^{-1}$ (for MOS)  This resulted in
$\approx$9 4(8 0)\,ks of low-background MOS(pn) data  Details of this dedicated
XMM-Newton observation, together with source position, and
(0 2$-$2\,keV) all-EPIC combined (pn, MOS1, MOS2) detected source
counts, count rate and detection likelihood are given in
Table~\ref{slewtable} 

Source spectra, containing single and double events, were extracted
from the datasets from circles (none of the data were now piled up)
centred on the source position  An extraction radius, estimated from
where the radial surface brightness profile was seen to fall to the
surrounding background level, was set to 30\arcsec  Background spectra
were extracted from each cleaned dataset from a 40\arcsec$-$80\arcsec\
annulus centred on the source position  Point sources seen to
contaminate these larger-area background spectra were removed from the
background spectra to a radius of 60\arcsec  ARF files were created
for the source spectra, and were checked to confirm that the correct
extraction area calculations had been performed  Finally RMF response
files were generated 
 
Standard spectral models were again fit to the spectral data using
XSPEC  Once again it was obvious that only a very soft model would fit the data; the only
simple model that was able to fit the data well (a P-statistic = 0 17,
based on 5000 random simulations) was a black-body model of
temperature $kT$=70$^{+3}_{-4}$\,eV, with an absorbing hydrogen column
of 6 9$^{+1 0}_{-1 6}\times10^{20}$\,cm$^{-2}$  The spectrum, together with this best-fit
model are shown in Fig \,\ref{xmmspec}  The corresponding
(0 2$-$2 0\,keV) flux is only marginally less than the Swift-XRT value
at 2 2$^{+0 8}_{-0 9}\times10^{-13}$\,ergs cm$^{-2}$ s$^{-1}$ and the
X-ray luminosity (for the assumed distance of 50\,kpc) is
6 7$^{+2 5}_{-2 8}\times10^{34}$\,ergs s$^{-1}$ 

\begin{figure}
\centering
\includegraphics[bb=110 15 570 705,clip,width=6 0cm,angle=270]{12082f3 ps}
\caption{XMM-Newton ToO spectrum from XMMSL1 J060636 2-694933  The
  data points (crosses; adjacent data bins having been grouped
  together for the plot to have a significance of at least 3)) have
  been fitted again with a black body model (kT=70\,eV) (see text) 
  EPIC-pn data is shown in black, with EPIC-MOS1 in red and EPIC-MOS2
  in green  The solid lines show the best fit to the spectra   The
  ratios of the data to the best fit model are shown in the lower
  panel }
\label{xmmspec}
\end{figure}

Given that, in this XMM-Newton ToO observation, we had obtained a
larger number of counts ($\raisebox{-1mm}{$\stackrel{>}{\sim}$}$1500 over the 3 EPIC cameras), the
physically more realistic CO white dwarf atmosphere model (MacDonald \&
Vennes 1991) was also attempted  This yielded a marginal fit (and formally
a no more statistically significant fit; P-statistic = 0 04, based on
5000 random simulations), with an effective temperature of
73$^{+3}_{-2}$\,eV, and an $N_{\rm H}$ of
3 4$^{+0 8}_{-0 8}$$\times$$10^{20}$\,cm$^{-2}$  Again, usage of the black body model results
in a larger fitted $N_{\rm H}$ and a lower fitted temperature than
with the atmosphere model  


As before, the model normalization can be used to obtain a cautious
estimate of the size of the emitting region  For the assumed distance
of 50\,kpc, then the black body model returns an emitting region
radius of only 1 3$\pm$0 2$\times$10$^{8}$\,cm   Again care should be
taken, as this may be an overestimation, the black body model having
perhaps overestimated the luminosity  For the white dwarf atmosphere
model, a smaller radius of 0 4$\pm$0 1$\times$10$^{8}$\,cm is
obtained  Note further that the assumption of a larger distance (see
Section~6) would result in a proportionally larger emitting radius 
The range in allowed radius therefore is quite large, and it is not
impossible for for the whole of the white dwarf surface to be emitting
at 70\,eV  If this is the case, then the white dwarf would have to be
at the high end of the mass range ($>$1 2$M_{\odot}$; Hamada \&
Salpeter 1961)  It may be the case then that we are at this point at,
or close to the end of the SSS phase, where the effective temperature
has reached a maximum (Sala \& Hernanz 2005), as is tentatively seen
in the spectral fitting results, and where the photospheric radius has
reached a minimum, close to the white dwarf radius 


\subsection{X-ray variability}

The full (XMM-Newton slew plus Swift-XRT plus XMM-Newton ToO) X-ray
lightcurve of XMMSL1 J060636 2-694933 is shown in
Fig \,\ref{lightcurve}  The calculated (0 2$-$2 0\,keV) flux values
are shown plotted against the number of days since the rev \,1210
XMM-Newton Slew discovery  The first two data points are the
rev \,1210 and the rev \,1246 XMM-Newton Slew observations  Then the
three nested Swift-XRT points are shown and finally the XMM-Newton ToO
observation  The level of RASS upper limit is shown to the bottom
left  The (0 2$-$2 0\,keV) X-ray flux is seen to have dropped by more
than two orders of magnitude in 230 days since the discovery, but is
then seen to have levelled off for the next 120 days, at a level still
$\approx$3 times that of the RASS  Finally, no evidence for any
short-term variability (using time bins down to 100\,s) is seen in the
highest statistic continuous X-ray lightcurve (the $\approx$8 0\,ksec
background-filtered EPIC-pn lightcurve) obtained from the 19/06/07
XMM-Newton observation 

\begin{figure}
\centering
\includegraphics[bb=60 60 550 454,clip,width=8 7cm]{12082f4 ps}
\caption{The full X-ray lightcurve of XMMSL1 J060636 2-694933  Plotted
  are the calculated (0 2$-$2 0\,keV) flux values versus time  The
  first point is the rev \,1210 XMM-Newton Slew observation, then the
  rev \,1246 XMM-Newton Slew observation  The three nested Swift-XRT points
  are shown next and finally the XMM-Newton ToO observation  The RASS upper
  limit is shown bottom left  }
\label{lightcurve}
\end{figure}


\section{Multi-wavelength Follow-up}

\subsection{Swift UVOT}

For the Feb/Mar 2007 Swift observations, we arranged for both the
Swift UVOT-B filter and the UVOT-UVW2 filters to be used in an
approximate exposure time ratio of 1:5, thus ensuring roughly equal
numbers of counts in the two bands (though there is a spectral type
dependency here)  Swift UVOT images in these two filters of the area
of sky around XMMSL1 J060636 2-694933 are shown in Fig \,\ref{uvot} 

Prior to the Swift UVOT observations, a `best-guess' to the possible
candidate optical/IR counterpart would have been the USNO-A2 0 source
0150-04066298 (B~mag: 17 4, R~mag: 16 1), seen 4\arcsec\ south of the
XMM-Newton slew position  The UVOT images however immediately showed
that the optically fainter source at position RA, Dec (J2000) = 06 06
36 4, -69 49 34 3 (error radius: ~0 5\arcsec) was a very strong UVW2
source and very blue, and was very likely the true counterpart to
XMMSL1~J060636 2-694933  (The UVW2 filter spans approximately
800\AA\,, centred at $\approx$1900\AA)

\begin{figure}
\centering
\includegraphics[bb=-82 210 695 585,clip,width=8 7cm]{12082f5 ps}
\caption{Swift UVOT images of the field around XMMSL1 J060636 2-694933 from observation
  00030895002  Left shows the UVOT B-filter and right shows the the
  UVOT UVW2-filter  The large circle is a 20\arcsec\ radius circle around
  the XMM-Newton Slew position  The small circle in the UVW2 image around the
  bright source is reproduced in the B image, indicating that a faint
  optical source is also visible at this position }
\label{uvot}
\end{figure}

The Swift UVOT pipeline processed data were analysed using the UVOT
photometry package {\em uvotsource} released with
FTOOLs\footnote{http://heasarc nasa gov/lheasoft/ftools/ftools\_menu html} 
This package performs aperture photometry on pre-specified source and
background regions, accounting for photometric- (via PSF fitting) and
coincidence loss- effects using the UVOT calibration files  Source
counts were extracted using a 5\arcsec\ radius aperture centred on the
source, while for the background we used a 10\arcsec\ radius aperture
located in a nearby source-free region   We used a larger background
aperture to effectively smooth over the modulo-8 fixed pattern noise
present in UVOT observations and to improve the statistics of the
background counts  Source counts were converted to UVOT UV-magnitudes
using the UVW2 zero-point calibration released with version~2 8 (Build
22) of the CALDB  The source is seen (see Fig \,\ref{uvotlc}) to be
roughly constant over the short duration of the Swift observations,
with a suggestion of a decline towards the end  This is in keeping
with the general form of the X-ray lightcurve (Fig \,\ref{lightcurve})
at this time 

\begin{figure}
\centering
\includegraphics[bb=80 70 535 380,clip,width=8 7cm]{12082f6 ps}
\caption{Variation of the UVW2 magnitude of the bright UV source
  during the Swift observations  The same time axis as
  Fig \,\ref{lightcurve} has been used to aid comparison, and a zoom
  is also shown  The UVW2 filter was only employed during observations
  00030895002, 00030895004, 00030895005, 00030895006 \& 00030895007
  (hence the points span the dates 07/03/07 to 22/03/07)  The errors here are 1-$\sigma$  }
\label{uvotlc}
\end{figure}

It is possible to include the UVOT-detected flux with the XRT spectrum
described in Section~3  UVOT files, created using {\em uvot2pha} for
the five observations (00030895002, 00030895004, 00030895005,
00030895006 \& 00030895007) where the UVW2 filter was employed, were
incorporated into {\em xspec}, along with the appropriate response
file (swuw2\_20041120v104 rsp) from the Swift-XRT Calibration
Database  We attempted to fit a single black-body spectrum to the
Swift-XRT+UV data (again using C-statistics, the {\em wabs} absorption
model and the {\em wilm} cosmic abundance table, plus the inclusion of
the {\em xspec-redden} component to model the absorption in the UV
band)  The best fit however, with a much lower temperature of
$kT$=$36^{+3}_{-4}$\,eV, is a very poor fit to the data; we obtain a
{\em goodness} P-statistic value of 0 00, based on 5000 random
simulations  This notwithstanding, a flux in the UVW2
(1 57$-$7 77\,eV) band of 3 5$\pm{0 2}\times10^{-13}$\,ergs cm$^{-2}$
s$^{-1}$ can be obtained, corresponding to a UVW2 luminosity, for the
assumed distance of 50\,kpc, of 1 0$\pm{0 1}\times10^{35}$\,ergs
s$^{-1}$ 

The very poor single black-body fit above, plus the large change in
fitted temperature is strongly suggestive that a model other than, or
in addition to the XRT-derived kT=59\,eV black body model (Section~3)
should be used to describe the UVW2 data  As we have no UV data other
than in the UVW2 filter, all that can be done is to apply the
XRT-derived black body model to the UVW2+XRT data, and in doing this,
a large flux excess with respect to the XRT-derived black body model
is seen in the UVW2 band  This is shown in Fig \ref{xrtuvotspec}  This
excess in UV emission (most of the $10^{35}$\,ergs s$^{-1}$ discussed
above) is likely due to a combination of residual post-nova nuclear
burning on the surface of the white dwarf, plus accretion in the disk,
including from emission lines  The situation is likely to be rather
complex, depending on the structure of both the ejecta and the
accretion disk, and is beyond the scope of the present work, where we
only have sparse UV data   For a review of the UV emission from
classical novae, see Shore (2008) 


\begin{figure}
\centering
\includegraphics[bb=100 15 580 710,clip,width=6 0cm,angle=270]{12082f7 ps}
\caption{Swift-XRT spectrum (black) from XMMSL1 J060636 2-694933, plus
  the best-fit black-body model to this spectrum (Section~3; Fig \,2),
  but extending into the UV to the Swift-UVOT UVW2 flux points (coloured)
  (see text)  The data points are plotted such that adjacent data
  bins have been grouped together to have a significance of at least
  3  The solid line show the best fit to the Swift-XRT spectrum  The
  ratio of the data to the best fit model is shown in the lower
  panel }
\label{xrtuvotspec}
\end{figure}


\subsection{Magellan optical observations}

On Nov ~13, 14, and 15, 2007, XMMSL1~J060636 2--694933 was observed
with the Low--Dispersion Survey Spectrograph 3 (LDSS3) mounted on the
Magellan Clay telescope  Images were obtained through the Sloan
$g^\prime$, $r^\prime$ and $i^\prime$ filters  On Nov ~15, 2007
conditions were photometric and the Landolt field RU 149A was observed
to flux calibrate the data in the $g^\prime$, $r^\prime$ and
$i^\prime$--bands   The Landolt (1992) magnitudes of the standards
were converted to Sloan magnitudes using the transformations presented
in Smith et al \ (2002)  All the images were debiased and flatfielded
using dome flatfield frames  We applied aperture photometry on each of
the images using DAOPHOT in \textsc{IRAF}\footnote{\textsc {iraf} is
  distributed by the National Optical Astronomy Observatories} to
compute the instrumental magnitudes of the stars  Differential
photometry of the optical counterpart to XMMSL1~J060636 2-694933
(marked by an arrow in Fig ~\ref{magellan}) was performed with respect
to the field star (marked with a `c' in Fig ~\ref{magellan})  This was the
brightest isolated and unsaturated star common to all frames   The
calibrated brightness of this comparison star is $g'= 18 42 \pm 0 04$,
$r'= 17 85 \pm 0 06$ and $i'=17 58 \pm 0 07$ 

\begin{figure}
\centering
\includegraphics[bb=35 215 575 575,clip,width=8 7cm]{12082f8 ps}
\caption{Magellan Clay LDSS3 finder chart  The counterpart to
  XMMSL1~J060636 2-694933 (and the bright Swift-UVOT UVW2-filter
  source; Figs \ref{uvot}\&\ref{uvotlc}) is marked with an arrow  The comparison star is
  shown marked with a 'c' }
\label{magellan}
\end{figure}

In addition to the imaging observations described above, we have
obtained spectroscopic observations on Nov ~13, 14, and 15, 2007 using
the VPH All grism, which has 660 lines per mm, and employing a
1\arcsec\ wide slit  This set-up provides a mean dispersion of 2\AA\,
per pixel  For a slit width of 1 arcsecond and a mean seeing close to
1\arcsec, the mean spectral resolution is $\approx$10\AA  On Nov ~13, 2007
we took 4 exposures of 450\,s each, on Nov ~14, 2007 we took 2
exposures of 900\,s each, and on Nov ~15, 2007 we took one 1200\,s
exposure with the slit at the parallactic angle  The spectra were bias
and flatfield corrected, and extracted in \textsc{IRAF}  The
instrumental response was corrected using the spectrophotometric flux
calibrators LTT 3218 (Nov ~13), H600 (Nov ~14) and LTT 9293 (Nov ~15) 
Significant differences in the flux around H$\alpha$ are apparent with
the flux being 50\% higher during the Nov ~15, 2007 with respect to
the Nov ~13, 2007 observations  Since there is no evidence for
brightening in the $r^\prime$ images we attribute the difference to
the fact that the source was not observed at the parallactic angle on
Nov ~13 and 14, 2007  We exported the one dimensional spectra to the
spectral analysis software package \textsc{molly} for further
analysis 

\begin{figure}
\centering
\includegraphics[bb=70 30 600 800,clip,width=6 8cm,angle=270]{12082f9 ps}
\caption{Magellan Clay averaged optical spectrum of the optical source
  associated with XMMSL1 J060636 2-694933  The flux scaling is
  approximate  The prominent strong emission lines are marked (see
  text)  }
\label{optspec}
\end{figure}

We have averaged all spectra (see Fig ~\ref{optspec})  We find several
strong emission lines  The strongest of these emission lines are best
interpreted as due to [OIII] 4958 9\AA\, and 5006 9\AA\,, He~II at
4685 8\AA\, and a blend of the H$\alpha$ plus the [NII] at 6548 1\AA\,
and 6583 4\AA\,, lines found often in novae (Williams 1992)  In this
case the main [OIII] lines appear redshifted by approximately 2000\,km
s$^{-1}$  We interprete this as due to clumpy outflows in the nova
shell  The integrated light from different outflowing parts can also
explain the substructure that is present in the [OIII] lines  The
outflow velocities that we obtain for the H$\alpha$ and H$\beta$ lines
is $\approx$350\,km s$^{-1}$, hence less than that for the [OIII]
lines   Note that, if XMMSL1~J060636 2-694933 does reside within the
LMC, then the systematic line-of-sight recession velocity of the LMC,
262$\pm$3 4\,km~s$^{-1}$ (van der Marel et al \ 2002), should be taken
into account; i e \,a good fraction of the observed H$\alpha$ and H$\beta$
recession would then be due to the recession of the LMC itself 

\subsection{Long-term Optical light curve}

Analysis of archival robotic optical survey data from 3-minute CCD
exposures (pixel size 14\arcsec 8), obtained with a 70\,mm (200\,mm
focal length) f/2 8 telephoto lens in the course of the All Sky
Automated Survey (ASAS; Pojmanski 2002) show that the visual magnitude
of this source rose from m$_{V}\raisebox{-1mm}{$\stackrel{>}{\sim}$}$14 to m$_{V}$$\approx$12 between
Sep ~18, 2005 and Sep ~30, 2005, and then declined rapidly thereafter (see
Fig \ref{optlc})  ASAS did not detect any significant emission from
the source after around November 2005, the source having dimmed below
the limiting magnitude of ASAS 

The decline from the brightest data point ($\approx$2 2 magnitudes in
10 days, then a further $\sim$1 3 magnitudes in 46 days) suggests that
this is a nova of the 'very fast' speed class (Warner 1995, Downes
et al \ 2001)  We estimate that the time that the light curve takes to
decline 2 magnitudes below maximum observed brightness is
8$\pm$2\,days (see Section~6) 

\begin{figure}
\centering
\includegraphics[bb=30 78 453 549,clip,width=7 8cm,angle=270]{12082f10 ps}
\caption{All Sky Automated Survey V-band magnitudes of the optical counterpart 
to XMMSL1~J060636 2-694933, during outburst (late September 2005) and afterwards }
\label{optlc}
\end{figure}



\section{Discussion}

The optical spectrum, showing lines of [OIII] 4958 9\AA\, and
5006 9\AA\,, He~II at 4685 8\AA\, and a blend of the H$\alpha$ plus
[NII] at 6548 1\AA\, and 6583 4\AA\, suggests that
XMMSL1~J060636 2-694933 was a nova, observed (in Nov 2007) in the late
A$_{0}$ auroral phase  The fact that the observed [OIII] lines are not
in the more usual, optically thin 3:1 ratio, can be explained in terms
of a clumpy outflow scenario, whereby individual clumps of both
rest-frame and redward-shifted material are observed, and the
superposition of these account for the observed [OIII] ratio (note
further that density enhancements can change observed [OIII] ratios to
more like $\sim$1:1)  Clumps of material are often seen in nova ejecta
(e g  Shara et al \ 1997), and outflows of speeds around 2000\,km
s$^{-1}$ are not uncommon in novae (e g  in nova LMC 1991; Schwartz
et al \ 2001) 

XMMSL1~J060636 2-694933 was likely at its onset (in Oct 2005) a very
fast, Fe~{\sc ii} nova (Section~3 and Williams et al \ 1991; Williams
et al \ 1994)  An accurate classification now however is not possible,
so late after maximum brightness  The soft ($kT_{\rm
  eff}$$\approx$60--70\,eV) X-ray spectrum indicates that the nova was
in a super-soft source (SSS) state (Krautter 2008) during its
discovery (in July 2006), and throughout its X-ray decline (by more
than two orders of magnitude) in the observations of Sept 2006, March
2007 and June 2007   Such a state originates from nuclear burning on
the surface of the white dwarf, and measurements of the intensity,
duration, and temperature can be used to estimate the distance to the
nova and the mass of the white dwarf (e g  Balman et al \ 1998; Lanz
et al \ 2005)  Indeed, we believe (Section~4) that the white dwarf
within XMMSL1~J060636 2-694933 may be quite massive
($>$1 2$M_{\odot}$) 

As discussed earlier, classical novae are almost always discovered
optically in the early phases of their outbursts 
XMMSL1~J060636 2-694933 is very unusual therefore in that it has been
discovered first in X-rays  As such, it is useful to compare it with
XMMSL1~J070542 7-381442 (also known as V598 Pup; Read et al \ 2008),
another nova recently discovered (in X-rays) in the XMM-Newton slew
survey  With a peak $m_{V}$ of $\ltsim12$, XMMSL1~J060636 2-694933 is
not a particularly bright nova (c f  V598 Pup, which reached an
m$_{V}$ of $\raisebox{-1mm}{$\stackrel{<}{\sim}$}$4), and so it is not surprising that it went
unnoticed, only being discovered in X-rays during the later (here
291\,days after the outburst), optically thin nebular phase, when
classical novae are typically observed as soft X-ray sources  Though
this delay should be taken as a upper limit, it is long when compared
to V598 Pup ($\raisebox{-1mm}{$\stackrel{<}{\sim}$}$127 days), but may instead be more similar to the
delays of $\sim$200 days seen in V1974 Cyg (Krautter et al \ 1996),
$\sim$6 months of V382 Vel (Orio et al \ 2002), and 6$-$8 months of
V1494 Aql (Drake et al \ 2003)  In their X-ray monitoring of optical
novae in M31, Pietsch et al \ (2007) detect 11 out of 34 novae in
X-rays within a year after their optical outbursts  Seven novae are
seen to be X-ray bright, several (3$-$9) years after outburst, and
three novae showed very short X-ray outbursts, starting within
50\,days of outburst, but lasting only two to three months 
XMMSL1~J060636 2-694933 therefore is not particularly unusual 

A method to estimate the distance to the nova is to use the relation
between the absolute magnitude at maximum brightness and the time that
the light curve takes to decline 2 magnitudes below maximum
brightness, $t_{2}$ (Della Valle \& Livio 1995)  We have no
information over the 12 days between the data point of maximum
brightness and the lower limit prior to this (Fig \,\ref{optlc}), and
therefore we have no exact outburst date, nor exact apparent
magnitude at outburst   Assuming for the moment though that we have
caught the outburst exactly in the Sep ~30, 2005 observation, then we
can estimate (Sect ~5 3) $t_{2}$ to be 8$\pm$2\,days, and using this,
we can estimate (Della Valle \& Livio 1995) the absolute magnitude at
maximum brightness $M_{V}$ to be --8 7$\pm$0 6  An absolute magnitude
of $M_{V}$=--8 7 implies a peak luminosity $\sim$7 times the Eddington
luminosity for a 1\,$M_{\odot}$ white dwarf  This is quite typical of
novae 

With $A_{V}$=0 39$^{+0 05}_{-0 09}$ (90\% error), as derived (Predehl
\& Schmitt 1995) from $N_{\rm
  H}$=6 9$^{+1 0}_{-1 6}\times10^{20}$\,cm$^{-2}$ (from the highest
statistic spectral fit; the XMM-Newton ToO observation), and with
$M_{V}$=--8 7$\pm$0 6, and a peak $m_{V}$ of 12 0, we can derive a
distance to XMMSL1~J060636 2-694933 of 115$^{+43}_{-30}$\,kpc   As
discussed above however, we are unsure as to the exact outburst date
and the maximum brightness at outburst  Our assumed peak $m_{V}$ of
12 0 is almost certainly an underestimation  Although we have no
information in the 12 days prior to Sep ~30, 2005, a simple linear
extrapolation of the early October lightcurve back prior to Sep ~30,
2005 suggests that the actual peak $m_{V}$ was somewhere between 9 and
12  The corresponding distance estimates are then between 29 and
115\,kpc (with a mid-point $m_{V}$=10 5 value yielding a distance
estimate of 58\,kpc)  Many methods have been used to estimate the
distance to the LMC (e g   Kovacs 2000, Nelson et al \ 2000), but a
value of around 50\,kpc appears to be quite robust  Our distance
estimate is certainly consistent with that of the LMC, though the
errors are quite large   It does appear to be the case however, that
our distance estimate places the source far outside of our own Galaxy 
This, together with the source's position on the sky (at the eastern
edge of the LMC) and the sizable ($\sim$Galactic) X-ray hydrogen
column densities obtained from the spectral fits, suggest strongly
that XMMSL1~J060636 2-694933 lies within the LMC itself   Note further
that the (pile-up corrected) spectral model normalizations to the
initial Slew discovery data (Sect ~2) also imply an approximate
distance to XMMSL1~J060636 2-694933 of $\sim$50\,kpc 

The source had, at the time of the slew detection, an absorbed
(0 2$-$2\,keV) X-ray flux of 4 8$^{+2 7}_{-1 6}\times10^{-11}$\,ergs
cm$^{-2}$ s$^{-1}$, corresponding to a 0 2$-$2\,keV X-ray luminosity
(at 50\,kpc) of 1 4$^{+0 8}_{-0 5}\times10^{37}$\,ergs s$^{-1}$ 
Assuming instead for the moment a distance more like 100\,kpc (though
this is thought to be well beyond the LMC, e g  Kovacs 2000), then the
(0 2$-$2\,keV) X-ray luminosity of
5 7$^{+3 0}_{-1 9}\times$$10^{37}$\,erg s$^{-1}$ obtained is at the high end of the X-ray luminosities of
classical SSS-phase novae discussed e g \,in Orio et al \ (2002) and
Ness et al \ (2007)  As discussed though, we have very likely missed
the outburst peak, and as such, our more probable assumed distance of
50\,kpc gives rise to a more typical SSS-phase X-ray luminosity   The
luminosities of 7$-$8$\times$$10^{34}$\,erg s$^{-1}$, obtained during
the Swift and pointed XMM-Newton observations, are more typical of
novae at later times, when the emission can also sometimes be
described by a thermal plasma, rather than a black-body type spectrum,
or a more mixed spectrum, due to the complex structure of the ejecta
and the accretion disk (Krautter 2008, Shore 2008) 


\section{Conclusions}

A bright X-ray source, XMMSL1~J060636 2-694933, was detected in an
XMM-Newton slew on 18 July 2006 at a position where no previous X-ray
source had been seen  The XMM-Newton slew data, plus follow-up dedicated
XMM-Newton and Swift observations, plus optical imaging and
spectroscopic data acquired with the Magellan Clay telescope and 
All-Sky Automated Survey (ASAS) data were used to classify the new object
as a nova, and to examine its properties  The primary conclusions are
as follows:

  \begin{itemize}

  \item The soft X-ray spectrum indicates that the nova was in a
    super-soft source (SSS) state at its discovery in July 2007
    (XMM-Newton slew) and through its X-ray decline (by over two
    orders of magnitude) in September 2006 (XMM-Newton slew), March
    2007 (Swift) and June 2007 (XMM-Newton) 

  \item The Magellan optical spectrum (Nov 2007) of the source
    indicates that it was very likely then a nova in the late
    A$_{0}$ auroral phase 

  \item The very fast optical decline (ASAS) during the nova's onset
    (Oct 2005), indicates that the initial nova was likely of speed class
    'very fast' 

  \item The very fast speed, together with the absolute magnitude at
    maximum brightness and the X-ray absorption, give rise to a
    distance to the source far beyond our own Galaxy  The large
    distance, together with the source's position in the sky, at the
    eastern edge of the LMC, and the spectral information from the
    X-ray data, are very suggestive that the nova is situated within
    the LMC itself 

  \item Analysis of XMM-Newton slew data is continuing to provide a
    powerful means of finding new X-ray transient objects 

\end{itemize}

\begin{acknowledgements}

  The XMM-Newton project is an ESA Science Mission with instruments
  and contributions directly funded by ESA Member States and the USA
  (NASA)  The XMM-Newton project is supported by the Bundesministerium
  f\"ur Wirtschaft und Technologie/Deutsches Zentrum f\"ur Luft- und
  Raumfahrt (BMWI/DLR, FKZ 50 OX 0001), the Max-Planck Society and the
  Heidenhain-Stiftung  AMR and PE acknowledge the support of STFC
  funding, and PGJ of the Netherlands Organisation for Scientific
  Research  The ASAS project is supported by the N2030731/1328 grant
  from the MNiSzW  We thank the referee (G \,Sala) for very useful
  comments and several references that have improved the paper
  notably  We thank Kim Page for providing the white dwarf atmosphere
  model, and we thank her and Graham Wynn for useful discussions  The
  use of the spectral analysis software package \textsc{molly} written
  by Tom Marsh is also acknowledged  MM acknowledges support by a
  Miller Institute Research Fellowship during the time in which part
  of the work was completed 

\end{acknowledgements}


Context 3: Hugh Hilton Goodwin (December 21, 1900 – February 25, 1980) was a decorated officer in the United States Navy with the rank of Vice Admiral  A veteran of both World Wars, he commanded escort carrier  during the Mariana Islands campaign  Goodwin then served consecutively as Chief of Staff, Carrier Strike Group 6 and as Air Officer, Philippine Sea Frontier and participated in the Philippines campaign in the later part of the War 

Following the War, he remained in the Navy and rose to the flag rank and held several important commands including Vice Commander, Military Air Transport Service, Commander, Carrier Division Two and Commander, Naval Air Forces, Continental Air Defense Command 

Early life and career

Hugh H  Goodwin was born on December 21, 1900, in Monroe, Louisiana and attended Monroe High School there (now Neville High School)  Following the United States' entry into World War I in April 1917, Goodwin left the school without receiving the diploma in order to see some combat and enlisted the United States Navy on May 7, 1917  He completed basic training and was assigned to the battleship   Goodwin participated in the training of armed guard crews and engine room personnel as the Atlantic Fleet prepared to go to war and in November 1917, he sailed with the rest of Battleship Division 9, bound for Britain to reinforce the Grand Fleet in the North Sea 

Although he did not complete the last year of high school, Goodwin was able to earn an appointment to the United States Naval Academy at Annapolis, Maryland in June 1918  While at the academy, he earned a nickname "Huge" and among his classmates were several future admirals and generals including: Hyman G  Rickover, Milton E  Miles, Robert E  Blick Jr , Herbert S  Duckworth, Clayton C  Jerome, James P  Riseley, James A  Stuart, Frank Peak Akers, Sherman Clark, Raymond P  Coffman, Delbert S  Cornwell, Frederick J  Eckhoff, Ralph B  DeWitt, John Higgins, Vernon Huber, Albert K  Morehouse, Harold F  Pullen, Michael J  Malanaphy, William S  Parsons, Harold R  Stevens, John P  Whitney, Lyman G  Miller and George J  O'Shea 

Goodwin graduated with Bachelor of Science degree on June 3, 1922, and was commissioned Ensign in the United States Navy  He was subsequently assigned to the battleship  and took part in the voyage to Rio de Janeiro, Brazil, before he was ordered to the Naval Torpedo Station at Newport, Rhode Island for submarine instruction in June 1923  Goodwin completed the training several weeks later and was attached to the submarine   He then continued his further training aboard submarine  and following his promotion to Lieutenant (junior grade) on June 3, 1925, he qualified as submariner 

He then served aboard submarine  off the coast of California, before he was ordered for the recruiting duty to San Francisco in September 1927  While in this capacity, Goodwin applied for naval aviation training which was ultimately approved and he was ordered to the Naval Air Station Pensacola, Florida in August 1928  Toward the end of the training, he was promoted to lieutenant on December 11, 1928, and upon the completion of the training in January 1929, he was designated Naval aviator 

Goodwin was subsequently attached to the Observation Squadron aboard the aircraft carrier  and participated in the Fleet exercises in the Caribbean  He was transferred to the Bureau of Aeronautics in Washington, D C  in August 1931 and served consecutively under the architect of naval aviation William A



ground_truth: 4.5x10^8 cm.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the main methodology used in the research?

Read the following text and answer briefly.

Context 2: Paper Info

Title: On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning
Publish Date: Unkown
Author List: Seth Karten, Siva Kailas, Huao Li, Katia Sycara

Figure

Figure1 By using contrastive learning, our method seeks similar representations between the state-message pair and future states while creating dissimilar representations with random states Thus satisfying the utility objective of the information bottleneck The depicted agents are blind and cannot see other cars 
Figure 2 An example of two possible classes, person and horse, from a single observation in the Pascal VOC game 
Figure 3  Blind Traffic Junction Left: Our method uses compositional complexity and contrastive utility to outperform other baselines in terms of performance and sample complexity The legend provides the mean ± variance of the best performance Right: Top: success, contrastive, and complexity losses for our method Right, Bottom: success, autoencoder loss for ae-comm with supervised pretraining 
Figure 4  Pascal VOC Game Representing compositional concepts from raw pixel data in images to communicate multiple concepts within a single image Our method significantly outperforms ae-comm and no-comm due to our framework being able to learn composable, independent concepts 
Figure 5  Blind Traffic Junction Social shadowing enables significantly lower sample complexity when compared to traditional online MARL 
Beta ablation: Messages are naturally sparse in bits due to the complexity loss Redundancy measures the capacity for a bijection between the size of the set of unique tokens and the enumerated observations and intents Min redundancy is 1 0 (a bijection) Lower is better 

abstract

Explicit communication among humans is key to coordinating and learning  Social learning, which uses cues from experts, can greatly benefit from the usage of explicit communication to align heterogeneous policies, reduce sample complexity, and solve partially observable tasks  Emergent communication, a type of explicit communication, studies the creation of an artificial language to encode a high task-utility message directly from data 
However, in most cases, emergent communication sends insufficiently compressed messages with little or null information, which also may not be understandable to a third-party listener  This paper proposes an unsupervised method based on the information bottleneck to capture both referential complexity and task-specific utility to adequately explore sparse social communication scenarios in multi-agent reinforcement learning (MARL) 
We show that our model is able to i) develop a natural-language-inspired lexicon of messages that is independently composed of a set of emergent concepts, which span the observations and intents with minimal bits, ii) develop communication to align the action policies of heterogeneous agents with dissimilar feature models, and iii) learn a communication policy from watching an expert's action policy, which we term 'social shadowing' 

INTRODUCTION

Social learning agents analyze cues from direct observation of other agents (novice or expert) in the same environment to learn an action policy from others  However, observing expert actions may not be sufficient to coordinate with other agents  Rather, by learning to communicate, agents can better model the intent of other agents, leading to better coordination 
In humans, explicit communication for coordination assumes a common communication substrate to convey abstract concepts and beliefs directly , which may not be available for new partners  To align complex beliefs, heterogeneous agents must learn a message policy that translates from one theory of mind to another to synchronize coordination 
Especially when there is complex information to process and share, new agent partners need to learn to communicate to work with other agents  Emergent communication studies the creation of artificial language  Often phrased as a Lewis game, speakers and listeners learn a set of tokens to communicate complex observations  
However, in multi-agent reinforcement learning (MARL), agents suffer from partial observability and non-stationarity (due to unaligned value functions) , which aims to be solved with decentralized learning through communication  In the MARL setup, agents, as speakers and listeners, learn a set of tokens to communicate observations, intentions, coordination, or other experiences which help facilitate solving tasks  
Agents learn to communicate effectively through a backpropagation signal from their task performance   This has been found useful for applications in human-agent teaming , multirobot navigation , and coordination in complex games such as StarCraft II   Communication quality has been shown to have a strong relationship with task performance , leading to a multitude of work attempting to increase the representational capacity by decreasing the convergence rates  
Yet these methods still create degenerate communication protocols , which are uninterpretable due to joined concepts or null (lack of) information, which causes performance degradation  In this work, we investigate the challenges of learning a arXiv:2302 14276v1 LG] 28 Feb 2023 messaging lexicon to prepare emergent communication for social learning (EC4SL) scenarios 
We study the following hypotheses: H1) EC4SL will learn faster through structured concepts in messages leading to higher-quality solutions, H2) EC4SL aligns the policies of expert heterogeneous agents, and H3) EC4SL enables social shadowing, where an agent learns a communication policy while only observing an expert agent's action policy 
By learning a communication policy, the agent is encouraged to develop a more structured understanding of intent, leading to better coordination  The setting is very realistic among humans and many computer vision and RL frameworks may develop rich feature spaces for a specific solo task, but have not yet interacted with other agents, which may lead to failure without alignment 
We enable a compositional emergent communication paradigm, which exhibits clustering and informativeness properties  We show theoretically and through empirical results that compositional language enables independence properties among tokens with respect to referential information  Additionally, when combined with contrastive learning, our method outperforms competing methods that only ground communication on referential information 
We show that contrastive learning is an optimal critic for communication, reducing sample complexity for the unsupervised emergent communication objective  In addition to the more human-like format, compositional communication is able to create variable-length messages, meaning that we are not limited to sending insufficiently compressed messages with little information, increasing the quality of each communication 
In order to test our hypotheses, we show the utility of our method in multi-agent settings with a focus on teams of agents, high-dimensional pixel data, and expansions to heterogeneous teams of agents of varying skill levels  Social learning requires agents to explore to observe and learn from expert cues 
We interpolate between this form of social learning and imitation learning, which learns action policies directly from examples  We introduce a 'social shadowing' learning approach where we use first-person observations, rather than third-person observations, to encourage the novice to learn latently or conceptually how to communicate and develop an understanding of intent for better coordination 
The social shadowing episodes are alternated with traditional MARL during training  Contrastive learning, which works best with positive examples, is apt for social shadowing  Originally derived to enable lower complexity emergent lexicons, we find that the contrastive learning objective is apt for agents to develop internal models and relationships of the task through social shadowing 
The idea is to enable a shared emergent communication substrate (with minimal bandwidth) to enable future coordi-nation with novel partners  Our contributions are deriving an optimal critic for a communication policy and showing that the information bottleneck helps extend communication to social learning scenarios 
In real-world tasks such as autonomous driving or robotics, humans do not necessarily learn from scratch  Rather they explore with conceptually guided information from expert mentors  In particular, having structured emergent messages reduces sample complexity, and contrastive learning can help novice agents learn from experts 
Emergent communication can also align heterogeneous agents, a social task that has not been previously studied 

Multi-Agent Signaling

Implicit communication conveys information to other agents that is not intentionally communicated   Implicit signaling conveys information to other agents based on one's observable physical position   Implicit signaling may be a form of implicit communication such as through social cues or explicit communication such as encoded into the MDP through "cheap talk"  
Unlike implicit signaling, explicit signaling is a form of positive signaling that seeks to directly influence the behavior of other agents in the hopes that the new information will lead to active listening  Multi-agent emergent communication is a type of explicit signaling which deliberately shares information 
Symbolic communication, a subset of explicit communication, seeks to send a subset of pre-defined messages  However, these symbols must be defined by an expert and do not scale to particularly complex observations and a large number of agents  Emergent communication aims to directly influence other agents with a learned subset of information, which allows for scalability and interpretability by new agents 

Emergent Communication

Several methodologies currently exist to increase the informativeness of emergent communication  With discrete and clustered continuous communication, the number of observed distinct communication tokens is far below the number permissible   As an attempt to increase the emergent "vocabulary" and decrease the data required to converge to an informative communication "language", work has added a bias loss to emit distinct tokens in different situations  
More recent work has found that the sample efficiency can be further improved by grounding communication in observation space with a supervised reconstruction loss   Information-maximizing autoencoders aim to maximize the state reconstruction accuracy for each agent  How-ever, grounding communication in observations has been found to easily satisfy these input-based objectives while still requiring a myriad more samples to explore to find a task-specific communication space  
Thus, it is necessary to use task-specific information to communicate informatively  This will enable learned compression for task completion rather than pure compression for input recovery  Other work aims to use the information bottleneck to decrease the entropy of messages   In our work, we use contrastive learning to increase representation similarity with future goals, which we show optimally optimizes the Q-function for messages 

Natural Language Inspiration

The properties of the tokens in emergent communication directly affect their informative ability  As a baseline, continuous communication tokens can represent maximum information but lack human-interpretable properties  Discrete 1-hot (binary vector) tokens allow for a finite vocabulary, but each token contains the same magnitude of information, with equal orthogonal distance to each other token 
Similar to word embeddings in natural language, discrete prototypes are an effort to cluster similar information together from continuous vectors   Building on the continuous word embedding properties, VQ-VIB , an information-theoretic observation grounding based on VQ-VAE properties , uses variational properties to provide word embedding properties for continuous emergent tokens 
Like discrete prototypes, they exhibit a clustering property based on similar information but are more informative  However, each of these message types determines a single token for communication  Tokens are stringed together to create emergent "sentences" 

Preliminaries

We formulate our setup as a decentralized, partially observable Markov Decision Process with communication (Dec-POMDP-Comm)  Formally, our problem is defined by the tuple, S, A, M, T , R, O, Ω, γ   We define S as the set of states, A i , i ∈ [1, N ] as the set of actions, which includes task-specific actions, and M i as the set of communications for N agents 
T is the transition between states due to the multi-agent joint action space T : S × A 1 ,    , A N → S  Ω defines the set of observations in our partially observable setting  Partial observability requires communication to complete the tasks successfully  O i : M 1 ,    , M N × Ŝ → Ω maps the communications and local state, Ŝ, to a distribution of observations for each agent 
R defines the reward function and γ defines the discount factor 

Architecture

The policy network is defined by three stages: Observation Encoding, Communication, and Action Decoding  The best observation encoding and action decoding architecture is task-dependent, i e , using multi-layer perceptrons (MLPs), CNNs , GRUs , or transformer layers are best suited to different inputs 
The encoder transforms observation and any sequence or memory information into an encoding H  The on-policy reinforcement learning training uses RE-INFORCE or a decentralized version of MAPPO as specified by our experiments  Our work focuses on the communication stage, which can be divided into three substages: message encoding, message passing (often considered sparse communication), and message decoding 
We use the message passing from   For message decoding, we build on a multiheaded attention framework, which allows an agent to learn which messages are most important   Our compositional communication framework defines the message encoding, as described in section 4 

Objective

Mutual information, denoted as I(X; Y ), looks to measure the relationship between random variables, which is often measured through Kullback-Leibler divergence , I(X; Y ) = D KL (p(x, y)||p(x) ⊗ p(y))  The message encoding substage can be defined as an information bottleneck problem, which defines a tradeoff between the complexity of information (compression, I(X, X)) and the preserved relevant information (utility, I( X, Y )) 
The deep variational information bottleneck defines a trade-off between preserving useful information and compression   We assume that our observation and memory/sequence encoder provides an optimal representation H i suitable for sharing relevant observation and intent/coordination information  We hope to recover a representation Y i , which contains the sufficient desired outputs 
In our scenario, the information bottleneck is a trade-off between the complexity of information I(H i ; M i ) (representing the encoded information exactly) and representing the relevant information I(M j =i ; Y i ), which is signaled from our contrastive objective  In our setup, the relevant information flows from other agents through communication, signaling a combination of the information bottleneck and a Lewis game 
We additionally promote complexity through our compositional independence objective, This is formulated by the following Lagrangian, where the bounds on mutual information Î are defined in equations 1, 2, and 10  Overall, our objective is,

Complexity through Compositional Communication

We aim to satisfy the complexity objective, I(H i , M i ), through compositional communication  In order to induce complexity in our communication, we want the messages to be as non-random as possible  That is, informative with respect to the input hidden state h  In addition, we want each token within the message to share as little information as possible with the preceding tokens 
Thus, each additional token adds only informative content  Each token has a fixed length in bits W   The total sequence is limited by a fixed limit, L l W l ≤ S, of S bits and a total of L tokens  We use a variational message generation setup, which maps the encoded hidden state h to a message m; that is, we are modeling the posterior, π i m (m l |h) 
We limit the vocabulary size to K tokens, e j ∈ R D , j ∈ [1, K] ⊂ N, where each token has dimensionality D and l ∈ [1, L] ⊂ N  Each token m l is sampled from a categorical posterior distribution, 0 otherwise such that the message m l is mapped to the nearest neighbor e j   A set of these tokens makes a message m 
To satisfy the complexity objective, we want to use m i to well-represent h i and consist of independently informative m i l  

Independent Information

We derive an upper bound for the interaction information between all tokens  Proposition 4 1  For the interaction information between all tokens, the following upper bound holds: The proof is in Appendix A 1  Since we want the mutual information to be minimized in our objective, we minimize,

Input-Oriented Information

In order to induce complexity in the compositional messages, we additionally want to minimize the mutual information I(H; M ) between the composed message m and the encoded information h  We derive an upper bound on the mutual information that we use as a Lagrangian term to minimize  Proposition 4 2  For the mutual information between the composed message and encoded information, the following upper bound holds:
The proof is in Appendix A 1  Thus, we have our Lagrangian term, Conditioning on the input or observation data is a decentralized training objective 

Sequence Length

Compositional communication necessitates an adaptive limit on the total length of the sequence  Corollary 4 3  Repeat tokens, w, are redundant and can be removed  Suppose one predicts two arbitrary tokens, w k and w l   Given equation 1, it follows that there is low or near-zero mutual information between w k and w l  
A trivial issue is that the message generator will predict every available token as to follow the unique token objective  Since the tokens are imbued with input-oriented information (equation 2), the predicted tokens will be based on relevant referential details  Thus, it follows that tokens containing irrelevant information will not be chosen 
A nice optimization objective that follows from corollary 4 3 is that one can use self-supervised learning with an end-ofsequence (EOS) token to limit the variable total length of compositional message sequences  (3) Algorithm 1 Compositional Message Gen (h t ) m i ∼ N ( ĥ; µ, σ) 9: end for 10: return m

Message Generation Architecture

Now, we can define the pipeline for message generation  The idea is to create an architecture that can generate features to enable independent message tokens  We expand each compressed token into the space of the hidden state h (1-layer linear expansion) since each token has a natural embedding in R |h|  
Then, we perform attention using a softmin to help minimize similarity with previous tokens and sample the new token from a variational distribution  See algorithm 1 for complete details  During execution, we can generate messages directly due to equation 1, resolving any computation time lost from sequential compositional message generation 

Utility through Contrastive Learning

First, note that our Markov Network is as follows: H j → M j → Y i ← H i   Continue to denote i as the agent identification and j as the agent ID such that j = i  We aim to satisfy the utility objective of the information bottleneck, I(M j ; Y i ), through contrastive learning as shown in figure 1  Proposition 5 1 
Utility mutual information is lower bounded by the contrastive NCE-binary objective, The proof is in Appendix A 1  This result shows a need for gradient information to flow backward across agents along communication edge connections 

Experiments and Results

We condition on inputs, especially rich information (such as pixel data), and task-specific information  When evaluating an artificial language in MARL, we are interested in referential tasks, in which communication is required to complete the task  With regard to intent-grounded communication, we study ordinal tasks, which require coordination information between agents to complete successfully 
Thus, we consider tasks with a team of agents to foster messaging that communicates coordination information that also includes their observations  To test H1, structuring emergent messages enables lower complexity, we test our methodology and analyze the input-oriented information and utility capabilities 
Next, we analyze the ability of heterogeneous agents to understand differing communication policies (H2))  Finally, we consider the effect of social shadowing (H3), in which agents solely learn a communication policy from an expert agent's action policy  We additionally analyze the role of offline reinforcement learning for emergent communication in combination with online reinforcement learning to further learn emergent communication alongside an action policy 
We evaluate each scenario over 10 seeds 

Environments

Blind Traffic Junction We consider a benchmark that requires both referential and ordinal capabilities within a team of agents  The blind traffic junction environment requires multiple agents to navigate a junction without any observation of other agents  Rather, they only observe their own state location 
Ten agents must coordinate to traverse through the lanes without colliding into agents within their lane or in the junction  Our training uses REINFORCE   Pascal VOC Game We further evaluate the complexity of compositional communication with a Pascal VOC   This is a two-agent referential game similar to the Cifar game but requires the prediction of multiple classes 
During each episode, each agent observes a random image from the Pascal VOC dataset containing exactly two unique labels  Each agent must encode information given only the raw pixels from the original image such that the other agent can recognize the two class labels in the original image  An agent receives a reward of 0 25 per correctly chosen class label and will receive a total reward of 1 if both agents guess all labels correctly 
See figure 2  Our training uses heterogeneous agents trained with PPO (modified from MAPPO repository)  For simplicity of setup, we consider images with exactly two unique labels from a closed subset of size five labels of the original set of labels from the Pascal VOC data  Furthermore, these images must be of size 375 × 500 pixels 
Thus, the resultant dataset comprised 534 unique images from the Pascal VOC dataset 

Baselines

To evaluate our methodology, we compare our method to the following baselines: (1) no-comm, where agents do not communicate; (2) rl-comm, which uses a baseline communication method learned solely through policy loss ; (3) ae-comm, which uses an autoencoder to ground communication in input observations ; (4) VQ-VIB, which uses a variational autoencoder to ground discrete communication in input observations and a mutual information objective to ensure low entropy communication  
We provide an ablation of the loss parameter β in table 1 in the blind traffic junction scenario  When β = 0, we use our compositional message paradigm without our derived loss terms  We find that higher complexity and independence losses increase sample complexity  When β = 1, the model was unable to converge 
However, when there is no regularization loss, the model performs worse (with no guarantees about referential representation)  We attribute this to the fact that our independence criteria learns a stronger causal relationship  There are fewer spurious features that may cause an agent to take an incorrect action 
In order to understand the effect of the independent concept representation, we analyze the emergent language's capacity for redundancy  A message token m l is redundant if there exists another token m k that represents the same information  With our methodology, the emergent 'language' converges to the exact number of observations and intents required to solve the task 
With a soft discrete threshold, the independent information loss naturally converges to a discrete number of tokens in the vocabulary  Our β ablation in table 1 yields a bijection between each token in the vocabulary and the possible emergent concepts, i e , the enumerated observations and intents  Thus for β = 0 1, there is no redundancy 
Sparse Communication In corollary 4 3, we assume that there is no mutual information between tokens  In practice, the loss may only be near-zero  Our empirical results yield independence loss around 1e − 4  In table 1, the size of the messages is automatically compressed to the smallest size to represent the information 
Despite a trivially small amount of mutual information between tokens, our compositional method is able to reduce the message size in bits by 2 3x using our derived regularization, for a total of an 8x reduction in message size over non-compositional methods such as ae-comm  Since the base unit for the token is a 32-bit float, we note that each token in the message may be further compressed 
We observe that each token uses three significant digits, which may further compress tokens to 10 bits each for a total message length of 20 bits 

Communication Utility Results

Due to coordination in MARL, grounding communication in referential features is not enough  Finding the communication utility requires grounding messages in ordinal information  Overall, figure shows that our compositional, contrastive method outperforms all methods focused on solely input-oriented communication grounding 
In the blind traffic junction, our method yields a higher average task success rate and is able to achieve it with a lower sample complexity  Training with the contrastive update tends to spike to high success but not converge, often many episodes before convergence, which leaves area for training improvement 
That is, the contrastive update begins to find aligned latent spaces early in training, but it cannot adapt the methodology quickly enough to converge  The exploratory randomness of most of the early online data prevents exploitation of the high utility f + examples  This leaves further room for improvement for an adaptive contrastive loss term 
Regularization loss convergence After convergence to high task performance, the autoencoder loss increases in order to represent the coordination information  This follows directly from the information bottleneck, where there exists a tradeoff between utility and complexity  However, communication, especially referential communication, should have an overlap between utility and complexity 
Thus, we should seek to make the complexity loss more convex  Our compositional communication complexity loss does not converge before task performance convergence  While the complexity loss tends to spike in the exploratory phase, the normalized value is very small  Interestingly, the method eventually converges as the complexity loss converges below a normal- ized 0 3 
Additionally, the contrastive loss tends to decrease monotonically and converges after the task performance converges, showing a very smooth decrease  The contrastive f − loss decreases during training, which may account for success spikes prior to convergence  The method is able to converge after only a moderate decrease in the f + loss 
This implies empirical evidence that the contrastive loss is an optimal critic for messaging  See figure 3 

Heterogeneous Alignment Through Communication

In order to test the heterogeneous alignment ability of our methodology to learn higher-order concepts from highdimensional data, we analyze the performance on the Pascal VOC game  We compare our methodology against ae-comm to show that concepts should consist of independent information directly from task signal rather than compression to reconstruct inputs 
That is, we show an empirical result on pixel data to verify the premise of the information bottleneck  Our methodology significantly outperforms the observation-grounded ae-comm baseline, as demonstrated by figure 4  The ae-comm methodology, despite using autoencoders to learn observation-grounded communication, performs only slightly better than no-comm 
On the other hand, our methodology is able to outperform both baselines significantly  It is important to note that based on figure 4, our methodology is able to guess more than two of the four labels correctly across the two agents involved, while the baseline methodologies struggle to guess exactly two of thew four labels consistently 
This can be attributed to our framework being able to learn compositional concepts that are much more easily discriminated due to mutual independence 

Social Shadowing

Critics of emergent communication may point to the increased sample complexity due to the dual communication and action policy learning  In the social shadowing scenario, heterogeneous agents can learn to generate a communication policy without learning the action policy of the watched expert agents  To enable social shadowing, the agent will alternate between a batch of traditional MARL (no expert) and (1st-person) shadowing an expert agent performing the task in its trajectory 
The agent only uses the contrastive objective to update its communication policy during shadowing  In figure , the agent that performs social shadowing is able to learn the action policy with almost half the sample complexity required by the online reinforcement learning agent  Our results show that the structured latent space of the emergent communication learns socially benevolent coordination 
This tests our hypothesis that by learning communication to understand the actions of other agents, one can enable lower sample complexity coordination  Thus, it mitigates the issues of solely observing actions 

Discussion

By using our framework to better understand the intent of others, agents can learn to communicate to align policies and coordinate  Any referential-based setup can be performed with a supervised loss, as indicated by the instant satisfaction of referential objectives  Even in the Pascal VOC game, which appears to be a purely referential objective, our results show that intelligent compression is not the only objective of referential communication 
The emergent communication paradigm must enable an easy-to-discriminate space for the game  In multi-agent settings, the harder challenge is to enable coordination through communication  Using contrastive communication as an optimal critic aims to satisfy this, and has shown solid improvements  Since contrastive learning benefits from good examples, this method is even more powerful when there is access to examples from expert agents 
In this setting, the communication may be bootstrapped, since our optimal critic has examples with strong signals from the 'social shadowing' episodes  Additionally, we show that the minimization of our independence objective enables tokens that contain minimal overlapping information with other tokens 
Preventing trivial communication paradigms enables higher performance  Each of these objectives is complementary, so they are not trivially minimized during training, which is a substantial advantage over comparative baselines  Unlike prior work, this enables the benefits of training with reinforcement learning in multi-agent settings 
In addition to lower sample complexity, the mutual information regularization yields additional benefits, such as small messages, which enables the compression aspect of sparse communication  From a qualitative point of view, the independent information also yields discrete emergent concepts, which can be further made human-interpretable by a post-hoc analysis  
This is a step towards white-box machine learning in multi-agent settings  The interpretability of this learned white-box method could be useful in human-agent teaming as indicated by prior work   The work here will enable further results in decision-making from high-dimensional data with emergent concepts 
The social scenarios described are a step towards enabling a zero-shot communication policy  This work will serve as future inspiration for using emergent communication to enable ad-hoc teaming with both agents and humans 

Appendix

A 1  Proofs Proposition 4 1 For the interaction information between all tokens, the following upper bound holds: Proof  Starting with the independent information objective, we want to minimize the interaction information, which defines the conditional mutual information between each token and, Let π i m (m l |h) be a variational approximation of p(m l |h), which is defined by our message encoder network 
Given that each token should provide unique information, we assume independence between m l   Thus, it follows that our compositional message is a vector, m = [m 1 ,       , m L ], and is jointly Gaussian  Moreover, we can define q( m|h) as a variational approximation to p(m|h) = p(m 1 ;       , m L |h) 
We can model q with a network layer and define its loss as || m − m|| 2   Thus, transforming equation 4 into variational form, we have, it follows that q( m|h) log q( m|h)d m ≥ q( m|h) log Thus, we can bound our interaction information, Proposition 4 2 For the mutual information between the composed message and encoded information, the following upper bound holds:
Proof  By definition of mutual information between the composed messages M and the encoded observations H, we have, Substituting q( m|h) for p( m|h), the same KL Divergence identity, and defining a Gaussian approximation z( m) of the marginal distribution p( m), it follows that, In expectation of equation 1, we have,
This implies that, for m = [m 1 ,       , m L ], there is probabilistic independence between m j , m k , j = k  Thus, expanding, it follows that, where z(m l ) is a standard Gaussian  Proposition 5 1  Utility mutual information is lower bounded by the contrastive NCE-binary objective, Proof  We suppress the reliance on h since this is directly passed through 
By definition of mutual information, we have, Our network model learns π R + (y|m) from rolled-out trajectories, R + , using our policy  The prior of our network state, π R − (y), can be modeled from rolling out a random trajectory, R−  Unfortunately, it is intractable to model π R + (y|m) and π R − (y) directly during iterative learning, but we can sample y + ∼ π R + (y|m) and y − ∼ π R − (y) directly from our network during training 
It has been shown that log p(y|m) provides a bound on mutual information , with the expectation over l p(m l , y l )  However, we need a tractable understanding of the information Y   In the information bottleneck, Y represents the desired outcome  In our setup, y is coordination information that helps create the desired output, such as any action a −  
This implies, y =⇒ a −   Since the transition is known, it follows that a − =⇒ s − f , a random future state  Thus, we have, π This is similar to the proof for lemma A 5, but requires assumptions on messages m from the emergent language  We note that when m is random, the case defaults to lemma A 5  Thus, we assume we have at least input-oriented information in m given sufficiently satisfying equation 2  Given a sufficient emergent language, it follows that y =⇒ a + , where a + is an intention action based on m 
Similarly, since the transition is known, a + =⇒ s + f , a desired goal state along the trajectory  Thus, we have, π R + (y|m) = p(s = s + f |y, m)  Recall the following (as shown in ), which we have adapted to our communication objective, Proposition A 3 (rewards → probabilities)  The Q-function for the goal-conditioned reward function r g (s t , m t ) = (1 − γ)p(s = s g |y t ) is equivalent to the probability of state s g under the discounted state occupancy measure:
and Lemma A 4  The critic function that optimizes equation 8 is a Q-function for the goal-conditioned reward function up to a multiplicative constant 1 The critic function f (s, m, s f ) = y enc(s f ) represents the similarity between the encoding y = enc(s, m) and the encoding of the future rollout s f  
Given lemmas A 5 A 6 A 8 and proposition A 7, it follows that equation 8 is the NCE-binary (InfoMAX ) objective, Î(M j , Y i ) = log σ(f (s, m, s + f )) + log 1 − σ(f (s, m, s − f )) which lower bounds the mutual information, I(M j , Y i ) ≥ Î(M j , Y i )  The critic function is unbounded, so we constrain it to [0, 1] with the sigmoid function, σ( * ) 
We suppress the reliance on h since this is directly passed through  By definition of mutual information, we have, Our network model learns π R + (y|m) from rolled-out trajectories, R + , using our policy  The prior of our network state, π R − (y), can be modeled from rolling out a random trajectory, R− 
Unfortunately, it is intractable to model π R + (y|m) and π R − (y) directly during iterative learning, but we can sample y + ∼ π R + (y|m) and y − ∼ π R − (y) directly from our network during training  It has been shown that log p(y|m) provides a bound on mutual information , with the expectation over l p(m l , y l ) 
However, we need a tractable understanding of the information Y   Lemma A 5  π R − (y) = p(s = s − f |y)  In the information bottleneck, Y represents the desired outcome  In our setup, y is coordination information that helps create the desired output, such as any action a −   This implies, y =⇒ a −   Since the transition is known, it follows that a − =⇒ s − f , a random future state 
Thus, we have, π R − (y) = p(s = s − f |y)  Lemma A 6  π R + (y|m) = p(s = s + f |y, m)  This is similar to the proof for lemma A 5, but requires assumptions on messages m from the emergent language  We note that when m is random, the case defaults to lemma A 5  Thus, we assume we have at least input-oriented information in m given sufficiently satisfying equation 2  Given a sufficient emergent language, it follows that y =⇒ a + , where a + is an intention action based on m 
Similarly, since the transition is known, a + =⇒ s + f , a desired goal state along the trajectory  Thus, we have, π R + (y|m) = p(s = s + f |y, m)  Recall the following (as shown in ), which we have adapted to our communication objective, Proposition A 7 (rewards → probabilities)  The Q-function for the goal-conditioned reward function r g (s t , m t ) = (1 − γ)p(s = s g |y t ) is equivalent to the probability of state s g under the discounted state occupancy measure:
and Lemma A 8  The critic function that optimizes equation 8 is a Q-function for the goal-conditioned reward function up to a multiplicative constant 1 p(s f ) : exp(f * (s, m, s f ) = 1 p(s f ) Q π s f (s, m)  The critic function f (s, m, s f ) = y enc(s f ) represents the similarity between the encoding y = enc(s, m) and the encoding of the future rollout s f  
Given lemmas A 5 A 6 A 8 and proposition A 7, it follows that equation 8 is the NCE-binary (InfoMAX ) objective, which lower bounds the mutual information, I(M j , Y i ) ≥ Î(M j , Y i )  The critic function is unbounded, so we constrain it to [0, 1] with the sigmoid function, σ( * ) 
Context 3:  Moffett and future Chief of Naval Operations Ernest J  King 

In June 1933, Goodwin was ordered to the Naval War College at Newport, Rhode Island, where he completed junior course in May of the following year  He subsequently joined the crew of aircraft carrier  and served under Captain Arthur B  Cook and took part in the Fleet exercises in the Caribbean and off the East Coast of the United States 

He was ordered back to the Naval Air Station Pensacola, Florida in June 1936 and was attached to the staff of the Base Commandant, then-Captain Charles A  Blakely  When Blakely was succeeded by William F  Halsey in June 1937, Goodwin remained in Halsey's staff and was promoted to Lieutenant Commander on December 1, 1937  He also completed correspondence course in International law at the Naval War College 

Goodwin was appointed Commanding officer of the Observation Squadron 1 in June 1938 and attached to the battleship  he took part in the patrolling of the Pacific and 
West Coast of the United States until September 1938, when he assumed command of the Observation Squadron 2 attached to the battleship  

When his old superior from Lexington, now Rear Admiral Arthur B  Cook, was appointed Commander Aircraft, Scouting Force in June 1939, he requested Goodwin as his Aide and Flag Secretary  He became Admiral Cook's protégé and after year and half of service in the Pacific, he continued as his Aide and Flag Secretary, when Cook was appointed Commander Aircraft, Atlantic Fleet in November 1940 

World War II

Following the United States' entry into World War II, Goodwin was promoted to the temporary rank of Commander on January 1, 1942, and assumed duty as advisor to the Argentine Navy  His promotion was made permanent two months later and he returned to the United States in early 1943 for duty as assistant director of Planning in the Bureau of Aeronautics under Rear admiral John S  McCain  While still in Argentina, Goodwin was promoted to the temporary rank of Captain on June 21, 1942 

By the end of December 1943, Goodwin was ordered to Astoria, Oregon, where he assumed command of newly commissioned escort carrier USS Gambier Bay  He was responsible for the initial training of the crew and was known as a strict disciplinarian, but the crew appreciated the skills he taught them that prepared them for combat  Goodwin insisted that everyone aboard has to do every job right every time and made us fight our ship at her best 

During the first half of 1944, Gambier Bay was tasked with ferrying aircraft for repairs and qualified carrier pilots from San Diego to Pearl Harbor, Hawaii, before departed on May 1, 1944, to join Rear admiral Harold B  Sallada's Carrier Support Group 2, staging in the Marshalls for the invasion of the Marianas 

The air unit, VC-10 Squadron, under Goodwin's command gave close air support to the initial landings of Marines on Saipan on June 15, 1944, destroying enemy gun emplacements, troops, tanks, and trucks  On the 17th, her combat air patrol (CAP) shot down or turned back all but a handful of 47 enemy planes headed for her task group and her gunners shot down two of the three planes that did break through to attack her 

Goodwin's carrier continued in providing of close ground support operations at Tinian during the end of July 1944, then turned her attention to Guam, where she gave identical aid to invading troops until mid-August that year  For his service during the Mariana Islands campaign, Goodwin was decorated with Bronze Star Medal with Combat "V" 

He was succeeded by Captain Walter V  R  Vieweg on August 18, 1944, and appointed Chief of Staff, Carrier Division Six under Rear admiral Arthur W  Radford  The Gambier Bay was sunk in the Battle off Samar on October 25, 1944, during the Battle of Leyte Gulf after helping turn back a much larger attacking Japanese surface force 

Goodwin served with Carrier Division Six during the Bonin Islands raids, the naval operations at Palau and took part in the Battle of Leyte Gulf and operations supporting Leyte landings in late 1944  He was later appointed Air Officer of the Philippine Sea Frontier under Rear admiral James L  Kauffman and remained with that command until the end of hostilities  For his service in the later part of World War II, Goodwin was decorated with Legion of Merit with Combat "V"  He was also entitled to wear two Navy Presidential Unit Citations and Navy Unit Commendation 

Postwar service

Following the surrender of Japan, Goodwin assumed command of Light aircraft carrier  on August 24, 1945  The ship was tasked with air missions over Japan became mercy flights over Allied prisoner-of-war camps, dropping food and medicine until the men could be rescued  She was also present at Tokyo Bay for the Japanese surrender on September 2, 1945 

Goodwin returned with San Jacinto to the United States in mid-September 1945 and he was detached in January 1946  He subsequently served in the office of the Chief of Naval Operations until May that year, when he entered the instruction at National War College



ground_truth: An unsupervised method based on the information bottleneck and contrastive learning.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What was Hugh H. Goodwin's rank in the United States Navy?

Read the following text and answer briefly.

Context 3:  Goodwin graduated in June 1947 and served on Secretary's committee for Research on Reorganization  Upon promotion to Rear admiral on April 1, 1949, Goodwin was appointed Chief of Staff and Aide to Commander-in-Chief, Atlantic Fleet under Admiral William H  P  Blandy 

Revolt of the Admirals

In April 1949, the budget's cuts and proposed reorganization of the United States Armed Forces by the Secretary of Defense Louis A  Johnson launched the wave of discontent between senior commanders in the United States Navy  Johnson proposed the merging of the Marine Corps into the Army, and reduce the Navy to a convoy-escort force 

Goodwin's superior officer, Admiral Blandy was call to testify before the House Committee on Armed Services and his harsh statements for the defense of the Navy, costed him his career  Goodwin shared his views and openly criticized Secretary Johnson for having power concentrated in a single civilian executive, who is an appointee of the Government and not an elected representative of the people  He also criticized aspects of defense unification which permitted the Joint Chiefs of Staff to vote on arms policies of individual services, and thus "rob" the branches of autonomy 

The outbreak of the Korean War in summer 1950 proved the proposal of Secretary Johnson as incorrect and he resigned in September that year  Also Secretary of the Navy, Francis P  Matthews resigned one month earlier 

Later service

Due to the Revolts of the admirals, Blandy was forced to retire in February 1950 and Goodwin was ordered to Newport, Rhode Island for temporary duty as Chief of Staff and Aide to the President of the Naval War College under Vice admiral Donald B  Beary in April 1950  Goodwin was detached from that assignment two months and appointed member of the General Board of the Navy  He was shortly thereafter appointed acting Navy Chief of Public Information, as the substitute for Rear Admiral Russell S  Berkey, who was relieved of illness, but returned to the General Board of the Navy in July that year  Goodwin served in that capacity until February 1951, when he relieved his Academy class, Rear admiral John P  Whitney as Vice Commander, Military Air Transport Service (MATS) 

While in this capacity, Goodwin served under Lieutenant general Laurence S  Kuter and was co-responsible for the logistical support of United Nations troops fighting in Korea  The MATS operated from the United States to Japan and Goodwin served in this capacity until August 1953, when he was appointed Commander Carrier Division Two  While in this assignment, he took part in the Operation Mariner, Joint Anglo-American exercise which encountered very heavy seas over a two-week period in fall 1953 

Goodwin was ordered to the Philippines in May 1954 and assumed duty as Commander, U S  Naval Forces in the Philippines with headquarters at Naval Station Sangley Point near Cavite  He held that command in the period of tensions between Taiwan and China and publicly declared shortly after his arrival, that any attack on Taiwan by the Chinese Communists on the mainland would result in US participation in the conflict  The naval fighter planes under his command also provided escort for passing commercial planes  Goodwin worked together with retired Admiral Raymond A  Spruance, then-Ambassador to the Philippines, and accompanied him during the visits to Singapore, Bangkok and Saigon in January 1955 

On December 18, 1955, Goodwin's classmate Rear admiral Albert K  Morehouse, then serving as Commander, Naval Air Forces, Continental Air Defense Command (CONAD), died of heart attack and Goodwin was ordered to CONAD headquarters in Colorado Springs, Colorado to assume Morehouse's position  While in this capacity, he was subordinated to Army General Earle E  Partridge and was responsible for the Naval and Marine Forces allocated to the command designated for the defense of the Continental United States 

Retirement

Goodwin retired on June 1, 1957, after 40 years of active service and was advanced to the rank of Vice admiral on the retired list for having been specially commended in combat  A week later, he was invited back to his Monroe High School (now Neville High School) and handed a diploma showing that he had been graduated with the class of 1918  He then settled in Monterey, California where he taught American history at Stevenson school and was a member of the Naval Order of the United States 

Vice admiral Hugh H  Goodwin died at his home on February 25, 1980, aged 79  He was survived by his wife, Eleanor with whom he had two children, a daughter Sidney and a son Hugh Jr , who graduated from the Naval Academy in June 1948, but died one year later, when the Hellcat fighter he was piloting collided with another over the Gulf of Mexico during training 

Decorations

Here is the ribbon bar of Vice admiral Hugh H  Goodwin:

References

1900 births
1980 deaths
People from Monroe, Louisiana
Military personnel from Louisiana
United States Naval Academy alumni
Naval War College alumni
United States Naval Aviators
United States Navy personnel of World War I
United States Navy World War II admirals
United States Navy vice admirals
United States submarine commanders
Recipients of the Legion of Merit



ground_truth: Vice Admiral.