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 future direction mentioned in the conclusion?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
Publish Date: 10 Mar 2023
Author List: Mamoru Komachi (from Tokyo Metropolitan University), Takashi Okumura (from Kitami Institute of Technology), Hiromasa Horiguchi (from National Hospital Organization), Yuji Matsumoto

Figure

Fig  1 Example of part of a discharge summary which is a dummy we created 
Fig  2  Overview of our proposed method A new feature embedding layer encoding hospital, physician, disease, and length of stay is added to the standard transformer architecture The figure shows an example of hospital embedding 
Statistics of our data for experiment 
of summarization models with different meta-information The best results are highlighted in bold Each score is the average of three models with different seeds The BS and BR indicate BERTScore and BLEURT, respectively 
Statistics on the number of cases handled by physicians C/P denotes Cases/Physician, which indicates how many cases an individual physician has Method of Grouping Physician IDs A most naive method of mapping physician IDs to features is without any grouping process The data contains 4,846 physicians, so |M | was set to 4,846 However it caused our model's training to be unstable This might be due to the many physician IDs appearing for the first time in the test time Table

abstract

During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called "discharge summary" when the patient is discharged  Automated generation of discharge summary can greatly relieve the physicians' burden, and has been addressed recently in the research community 
Most previous studies of discharge summary generation using the sequenceto-sequence architecture focus on only inpatient notes for input  However, electric health records (EHR) also have rich structured metadata (e g , hospital, physician, disease, length of stay, etc ) that might be useful  This paper investigates the effectiveness of medical meta-information for summarization tasks 
We obtain four types of meta-information from the EHR systems and encode each meta-information into a sequence-to-sequence model  Using Japanese EHRs, meta-information encoded models increased ROUGE-1 by up to 4 45 points and BERTScore by 3 77 points over the vanilla Longformer  Also, we found that the encoded meta-information improves the precisions of its related terms in the outputs 
Our results showed the benefit of the use of medical meta-information 

INTRODUCTION

Clinical notes are written daily by physicians from their consults and are used for their own decision-making or coordination of treatment  They contain a large amount of important data for machine learning, such as conditions, laboratory tests, diagnoses, procedures, and treatments  While invaluable to physicians and researchers, the paperwork is burdensome for physicians ,  
Discharge summaries, a subset of these, also play a crucial role in patient care, and are used to share information between hospitals and physicians (see an example in Figure )  It is created by the physician as a summary of notes during hospitalization at the time of the patient's discharge, which is known to be very time-consuming 
Researchers have begun to apply automatic summarization techniques to address this problem -   Previous studies used extractive or abstractive summarization methods, but most of them focused on only progress notes for inputs  Properly summarizing an admission of a patient is a quite complex task, and requires various meta-information such as the patient's age, gender, vital signs, laboratory values and background to specific diseases 
Therefore, discharge summary generation needs more medical meta-information, than similar but narrower tasks such as radiology report generation  However, what kind of meta-information is important for summarization has not been investigated, even though it is critical not only for future research on medical summarization but also for the policy of data collection infrastructure 
In this paper, we first reveal the effects of meta-information on neural abstractive summarization on admissions  Our model is based on an encoder-decoder transformer with an additional feature embedding layer in the encoder (Figure )  Hospital, physician, disease, and length of stay are used as meta-information, and each feature is embedded in the vector space 
For experiments, we collect progress notes, discharge summaries and coded information from the electronic health record system, which are managed by a largest multi-hospital organization in Japan  Our main contributions are as follows: • We found that a transformer encoding meta-information generates higher quality summaries than the vanilla one, and clarified the benefit of using meta-information for medical summarization tasks 
• We found that a model encoding disease information can produce proper disease and symptom words following the source  In addition, we found that the model using physician and hospital information can generate symbols that are commonly written in the summary  • We are the first to apply the abstractive summarization method to generate Japanese discharge summaries 
In the studies of summarization of medical documents, it is common to retrieve key information such as disease, examination result, or medication from EHRs -   Other researchs more similar to our study targeted to help physicians get the point of medical documents quickly by generating a few key sentences -  
Studies generating contextualized summaries can be categorized by the type of model inputs and architectures  Some studies produced a whole discharge summary using structured data for input - The sensitivity of the gram stain for bacterial meningitis is about 60%, and the sensitivity of the culture is not high either 
Also, the glucose in the cerebrospinal fluid would have been slightly lower  Although no definitive diagnosis could be made, bacterial meningitis was the most suspicious disease  The causative organism was assumed to be MRSA, and vancomycin and meropenem (meningitis dose) were used to cover a wide range of enteric bacteria 
a whole discharge summary from free-form inpatient records -   The free-form data is more challenging since it is noisier than structured data  In inputting of the free-form data, extractive summarization methods, which extract sentences from the source, are commonly used , -   On the other hands, an encoder-decoder model was used for abstractive summarization , , with a limited number of studies 
The various issues in the abstractive generation of discharge summary would be studied in the future  Studies using medical meta-information have long been conducted on a lot of tasks -   In abstractive summarization on discharge summary,  developed a model incorporating similarity of progress notes and information of the record author 
They presented an idea of integrating meta-information into the abstractive summarization model on medical documents, but did not reveal how meta-information would affect the quality of the summaries  Our method is based on the encoder-decoder transformer model  The transformer model is known for its high performance and has been widely used in recent studies, thus it is suitable for our purpose 
As shown in Figure , the standard input to a transformer's encoder is created by a token sequence T = [t 0 , t 1 ,    , t i ] and position sequence P = [p 0 , p 1 ,    , p i ], where i is the maximum input length  The token and position sequences are converted into token embeddings E T and positional embeddings E P by looking up the vocabulary tables 
The sum of E T and E P is input into the model  In this paper, we attempt to encode meta-information to feature embeddings  We follow the segment embeddings of BERT and the language embeddings of XLM , which provide additional information to the model  It is not a new idea but is suitable for our validation 
Our method is formulated as follows: Let M be feature type, M ∈ {Vanilla, Hospital, Physician, Disease, Length of stay}, since we set five types of features  Feature embeddings E M is created by looking up the feature table where m j is featue value (e g , pysician ID, disease code, etc ) and |M | is the maximum number of differences in a feature 
In our study, |M | is set to four different values depending on features  Specifically, they are as follows  a) Hospital: As shown in Table , the data includes five hospital records  They were obtained mechanically from the EHR system  b) Physician: Physicians are also managed by IDs in the EHR systems  We hashed the physician IDs into 485 groups containing 10 people each 
Specifically, as a naive strategy, we shuffled and listed the cases within each hospital, and hashed them into groups in the order of appearance of the physician IDs  So each group has the information about the relevance of the hospitals  The reason for employing a grouping strategy is described in Appendix A 
c) Disease: Two types of disease information exist in our EHRs: disease names and disease codes called ICD-10   We did not use any disease names in the inputs for our experiment  Instead, we encoded diseases with the first three letters of the ICD-10 code, because they represent well the higher level concept 
The initial three letters of the ICD-10 codes are arranged in the order of an alphabetic letter, a digit, and a digit, so there are a total of 2,600 ways to encode a disease  In our data, some ICD-10 codes were missing, although all disease names were systematically obtained from the EHR system  For such cases, we converted the disease names into ICD-10 codes using MeCab with the J-MeDic (MANBYO 201905) dictionary 
Also, diseases can be divided into primary and secondary diseases, but we only deal with the primary diseases  d) Length of stay: The length of stay can be obtained mechanically from the EHR system and the maximum value was set to 1,000 days  We set |M | for vanilla, hospital, physician, disease, and length of stay to 1, 5, 485, 2,600, and 1,000, respectively  
The vanilla embedding is prepared for the baseline in our experiment and to equalize the total number of parameters with the other models  The input to our model is the sum of E T , E P and E M   We also prepare an extra model with all features for our experiments  This takes all four feature embeddings (hospital, physician, disease, and length of stay) added to the encoder 

Datasets and Metrics

We evaluated our proposed method on a subset of data from National Hospital Organization (NHO), the largest multiinstitutional organization in Japan  The statistics of our data are shown in Table  , which includes 24,630 cases collected from five hospitals  Each case includes a discharge summary and progress notes for the days of stay 
The data are randomly split into 22,630, 1,000, and 1,000 for train, validation, and test, respectively  Summarization performances are reported in ROUGE-1, ROUGE-2, ROUGE-L and BERTScore in terms of F1  In addition, we also employed BLEURT , which models human judgment 

Architectures and Hyperparameters

Due to our hardware constraints we need a model that is computationally efficient, so we employed the Longformer instead of the conventional transformer  Longformer can    In our model, number of layers, window size, dilation, input sequence length, output sequence length, batch size, learning rate and number of warmup steps are 8, 256, 1, 1024, 256, 4, 3e-5 and 1K, respectively 
Other hyperparameters are the same as in the original Longformer, except for the maximum number of epochs is not fixed and the best epoch  It is selected for each training using the validation data based on ROUGE-1  Also, the original Longformer imports pretrained-BART parameters to initial values, but we do not use pre-trained Japanese BART in this study 
We used three GeForce RTX 2080 TI for our experiments  Our vocabulary for preparing input to Longformer is taken from UTH-BERT , which is pre-trained on the Japanese clinical records  Since the vocabulary of UTH-BERT is trained by WordPiece , we also tokenize our data with WordPiece  However, the vocabulary does not include white space and line breaks, which cannot be handled, so we add those two tokens to the vocabulary, resulting in a total size of 25,002 
The vocabulary has all tokens in full characters, so we normalized full-wdith characters by converting all alphanumeric and symbolic characters to half-width for byte fallback  , we found that all the models with encoded medical meta-information perform better in ROUGE-1, ROUGE-L and BLEURT than the vanilla Longformer 
However, in BERTScore, only hospital and disease models outperform the vanilla  Specifically, disease information is most effective, improving ROUGE-1, ROUGE-2, ROUGE-L, BERTScore and BLEURT by 4 45, 0 73, 3 12, 3 77 and 0 21 points over the vanilla model, respectively  This seems to be because disease information and the ICD-10 ontology efficiently cluster groups with similar representations 
In contrast, in ROUGE-2 and ROUGE-L, the model with physician embedding is inferior to the vanilla model  This seems to be a negative effect of grouping physicians without any consideration of their relevance  It would be better to cluster them by department, physician attributes, similarity of progress notes, etc  Regarding low ROUGE-2 scores in all models, a previous study using the English data set also reported a low ROUGE-2 score of about 5%, which may indicate an inherent difficulty in discharge summary generation 
In BERTScore, the models with the physician and the length of stay did not reach the performance of the vanilla model, suggesting that the system's outputs are semantically inferior  The model with all features performed the lowest of all models in BERTScore  The reason for the low score of the model with all features seems to be that its number of parameters in feature embedding was four times larger than that of the model with the individual feature, and the amount of training data was insufficient 
In BLEURT, all models with meta-information outperform vanilla, which suggests that they are more natural to humans  To analyze the influence of encoded meta-information on the outputs, we evaluate the precisions of the generated text  Specifically, we measure the probability that the generated words are included in the gold summary to investigate if the proper words are generated 
Some previous studies on faithfulness, which also analyze the output of summarization, have employed words or entities -   In this study, we focused on words, not entities, because we wanted to visualize expressions that are not only nouns  The words were segmented by MeCab with the J-MeDic  For each segmented word, the numeral and symbol labels were assigned as parts of speech by MeCab, the morphological analyzer, while the disease and symptom were assigned by the J-Medic dictionary 
The results, shown in Figure , indicate that the encoded disease information leads to generate more proper disease and symptom words  This indicates that the meta-information successfully learns disease-related expressions  The encoded hospital or physician information also improved the precision of symbols generation 
This suggests that different hospitals and physicians have different description habits (e g , bullet points such as "•", "*" and "-", punctuation such as "。" and " ", etc ), which can be grouped by meta-information  In this paper, we conducted a discharge summary generation experiment by adding four types of information to Longformer and verified the impact of the meta-information 
The results showed that all four types of information exceeded the performance of the vanilla Longformer model, with the highest performance achieved by encoding disease information  We found that meta-information is useful for abstractive summarization on discharge summaries  Our limitations are that we used Japanese EHR, the limited number of tested features and not performing human evaluations 
As for the efficacy of the meta-information, we believe that our results are applicable to non-Japanese, but it is left as Fig    The precisions of words in the generated summaries  The vertical axis shows the probability that the words exist in the gold summary  a future work  Other meta-information may be worth verifying such as the patient's gender, age, race, religion and used EHR system, etc 
It is hard to collect a large amount of medical information and process it into meta-information, so we may need to develop a robust and flexible research infrastructure to conduct a more large scale cross-sectional study in the future  In the discharge summary generation task, which demands a high level of expertise, the human evaluation requires a lot of physicians' efforts and it is a very high cost which is unrealistic 
This is a general issue in tasks dealing with medical documents, and this study also could not perform human evaluations  On this research, informed consent and patient privacy are ensured in the following manner  Notices about their policy and the EHR data usage are posted at the hospitals  The patients who disagree with the policies can request opt-out and are excluded from the archive 
In case of minors and their parents, followed the same manner  In the case of minors and their parents are same  To conduct a research on the archive, researchers must submit their research proposals to the institutional review board  After the proposal is approved, the data is anonymized to build a dataset for analysis 
The data is accessible only in a secured room at the NHO headquarters, and only statistics are brought out of the secured room, for protection of patients' privacy  In the present research, the analysis was conducted under the IRB approval (IRB Approval No : Wako3 2019-22) of the Institute of Physical and Chemical Research (RIKEN), Japan, which has a collaboration agreement with the National Hospital Organization 
This data is not publicly available due to privacy restrictions  shows the detailed number of cases handled by physicians  In all hospitals, there is a large difference between the median and the maximum of cases/physician  This indicates that a few physicians handle a large number of cases and many physicians handle fewer cases 
It is impossible to avoid physician IDs first seen at test time without some process that averages the number of cases a physician holds 
Context 2: KSTP (1500 AM; SKOR North) is a commercial AM radio station licensed to Saint Paul, Minnesota  It is the flagship AM radio station of Hubbard Broadcasting, which also owns several other television and radio stations across the United States   KSTP has a sports radio format and is the ESPN Radio Network affiliate for Minneapolis-St  Paul   The radio studios are  on University Avenue in Minneapolis, shared with sister stations KSTP-FM, KSTP-TV, KTMY, and KSTC-TV  On weekdays, KSTP airs local sports shows from 9 a m  to 9 p m  and carries ESPN programming weekday mornings, late nights and weekends  Some KSTP shows are simulcast on other sports radio stations in the region 

KSTP runs the maximum power for AM stations, 50,000 watts   It shares clear-channel, Class A status on 1500 AM with WFED in Washington, D C   KSTP broadcasts a directional signal at night, using a three-tower array, with its transmitter on U S  Route 61 at Beam Avenue in Maplewood   Programming is also heard on 250 watt FM translator K235BP at 94 9 MHz in Bemidji 

History

WAMD and KFOY
KSTP's start in 1928 was the product of a merger between two pioneering Twin Cities stations: WAMD ("Where All Minneapolis Dances") in Minneapolis, first licensed on February 16, 1925 to Stanley E  Hubbard, and KFOY in St  Paul, first licensed on March 12, 1924 to the Beacon Radio Service in St  Paul 

Following a few test transmissions, WAMD made its formal debut broadcast on February 22, 1925  (In later interviews Stanley Hubbard traced WAMD's start to April 1924 ) It was located at the Marigold Dance Garden, and featured nightly "Midnight Frolics" broadcasts by the ballroom's orchestra  It is claimed that WAMD was the first radio station to be completely supported by running paid advertisements  Effective June 15, 1927, WAMD was assigned to 1330 kHz 

On November 11, 1927 WAMD's transmitter site at Oxboro Heath on Lyndale Avenue South burned down, two weeks after the station had been sold to the National Battery Company  An initial arrangement was made to carry WAMD's programs over WRHM (now WWTC), transmitting on WAMD's 1330 kHz frequency  Beginning on November 24, 1927 the WAMD broadcasts, still on 1330 kHz, were shifted to KFOY's facility in St  Paul  (At this time KFOY was assigned to 1050 kHz)  The next day it was announced that National Battery had purchased KFOY, and as of December 1, 1927 both KFOY and WAMD were reassigned to 1350 kHz  WAMD continued making regular broadcasts until the end of March 1928, while KFOY, although it continued to be licensed for a few more months on a time-sharing basis with WAMD, ceased operations at this point 

National Battery Company
In mid-December 1927, the National Battery Company announced it had received permission from the Federal Radio Commission (FRC) to build a new station, with the call letters KSTP, operating from a transmitter site to be constructed three miles south of Wescott  The next month it was reported that the new station, still under construction, had been assigned to 1360 kHz  KSTP made its debut broadcast on March 29, 1928  Although technically it was a separate station from WAMD and KFOY, both of which were formally deleted on April 30, 1928, overall KSTP was treated as the direct successor to a consolidated WAMD and KFOY 

Hubbard became the merged station's general manager, acquiring controlling interest in 1941  A month after the merger, KSTP became an affiliate for the NBC Red Network   It remained with NBC for 46 years  On November 11, 1928, under the provisions of the FRC's General Order 40, KSTP was assigned to a "high-powered regional" frequency of 1460 kHz  The only other station assigned to this frequency was WTFF in Mount Vernon Hills, Virginia (later WJSV, now WFED, Washington, D C )  On February 7, 1933, the FRC authorized KSTP to increase its daytime power to 25 KW  In 1938 and 1939 KSTP also operated a high-fidelity AM "experimental audio broadcasting station" Apex station, W9XUP, originally on 25,950 kHz and later on 26,150 kHz  In 1941, as part of the implementation of the North American Regional Broadcasting Agreement, KSTP was assigned to its current "clear channel" frequency of 1500 kHz, with the provision that it and WJSV, as "Class I-B" stations, had to maintain directional antennas at night in order to mutually protect each other from interference  An FM station, KSTP-FM, was founded in 1946 but shut down in 1952 

Hubbard reportedly acquired an RCA TV camera in 1939, and started experimenting with television broadcasts   But World War II put a hold on the development of television   In 1948, with the war over, KSTP-TV became the first television station in Minnesota   With KSTP 1500 already associated with NBC Radio, KSTP-TV became an NBC Television Network affiliate   From 1946 to 1952, KSTP also had an FM counterpart   KSTP-FM 102 1 was only on the air four years   There were few radios equipped to receive FM signals in that era, and management decided to discontinue FM broadcasts 

MOR and Top 40
As network programming moved from radio to television, KSTP programmed a full service Middle of the Road (MOR) radio format, in the shadow of its chief competitor, CBS Radio affiliate 830 WCCO  In 1965, a new FM station, reviving the KSTP-FM call sign, was put on the air, largely simulcasting the AM station   But by the late 1960s, KSTP-FM began a separate format of beautiful music   KSTP was the radio home of the Minnesota Vikings football team from 1970 to 1975  

In 1973, KSTP broke away from its longtime adult MOR sound and became one of four area stations at the time to program a Top 40 format  "15 KSTP, The Music Station" competed with Top 40 AM rivals WDGY, KDWB and later, WYOO  The competition would eventually shake itself out, with outrageous rocker WYOO dropping out after being sold in 1976, and then the staid WDGY switching to country music the following year   As for uptempo hits station 15 KSTP, it went from a tight Top 40 format to leaning adult rock in 1978, to leaning adult contemporary in 1979, to evolving into adult contemporary/talk by 1980   In 1982, it officially shifted to talk   Most Top 40 rock music, by this time, had moved to the FM band 

Past Personalities

Notable hosts who have been on KSTP include John Hines, Jesse Ventura, Larry Carolla, Tom Barnard, Big Al Davis, Don Vogel, John MacDougall, Griff, Mike Edwards, Geoff Charles, Joe Soucheray, James Lileks, Leigh Kamman, Barbara Carlson, Peter Thiele, Tom Mischke, Jason Lewis, Chuck Knapp, Machine Gun Kelly, Charle Bush, Mark O'Connell and Paul Brand  These broadcasters were supported by producers such as Bruce Huff, Rob Pendleton, Alison Brown, Jean Bjorgen, David Elvin (who Vogel dubbed the "Steven Spielberg of Talk Radio"), Mitch Berg and others 

The station has, for the most part, emphasized local hosts over the years   But in 1988, KSTP was one of Rush Limbaugh's first affiliates when his conservative talk show was rolled out for national syndication   (Clear Channel-owned KTLK-FM took over rights to Limbaugh's show in January 2006)  Other syndicated hosts previously heard on KSTP include Sean Hannity, Bruce Williams, Larry King, and Owen Spann 

Sports Radio
KSTP switched to Sports Radio on February 15, 2010   As the station had to wait for ESPN's contract with rival KFAN and its sister station KFXN to expire, it did not become an ESPN Radio affiliate until April 12, the same day that the Minnesota Twins were scheduled to play the first game in their new ball park, Target Field, against the Boston Red Sox   As a result Coast to Coast AM and Live on Sunday Night, it's Bill Cunningham were retained during this period   One ESPN Radio network program, The Herd with Colin Cowherd, was picked up by KSTP immediately following the format change 

In 2018, the station was approved for an FM translator on 94 1 FM, broadcasting from a transmitter atop the IDS Center in downtown Minneapolis  The two-watt signal threw most of its power to the west, preventing interference to low powered FM stations on the same channel including WFNU-LP in St  Paul  With only two watts of power, however, the signal was limited to the immediate downtown area surrounding the IDS Center   It later acquired a 250 watt translator, K235BP at 94 9 MHz   The original translator was discontinued 

On January 15, 2019, KSTP rebranded as "SKOR North" (a reference to the Vikings team song/chant, "Skol, Vikings"), with local programming between 12 noon and 7 pm  About a year later, in May of 2020, KSTP suspended most of its local programming and laid off nearly all of its local staff  Station management cited the economic toll of the coronavirus for the changes  Sports broadcasting continues, primarily composed of ESPN radio network broadcasts 

Sports Teams

KSTP-AM served as the radio flagship for the Minnesota Vikings football team from 1970 to 1975 

On August 1, 2006, the station announced that it would be the new flagship station for the Minnesota Twins baseball team, effective with the start of the 2007 season  The Twins had been on rival WCCO since arriving in Minnesota in 1961   KSTP served as the flagship for the Twins until the end of the 2012 season, when games moved to 96 3 KTWN-FM (now KMWA)   The Twins have since returned to WCCO 830 

The switch to a fairly weak FM station caused dissent among some listeners, particularly in communities that had trouble picking up KSTP 1500   Although KSTP is the state's second most powerful AM station, it must operate directionally at night, delivering a reduced signal to parts of the market  WCCO, by comparison, offers a signal with a wider coverage area during the day than KSTP does, with WCCO's non-directional 50,000 watt signal  In response, the Twins have expanded the number of affiliates 

On March 9, 2011, KSTP announced it would be the new flagship for the University of Minnesota Golden Gophers men's and women's basketball and men's ice hockey, ending a 68-year run on WCCO  The rights have since moved to KFXN-FM, which already aired Gopher football 

On March 2, 2017, KSTP announced it would be the first radio broadcaster for Minnesota United FC   The move brings live soccer action to 1500 AM 

Previous logos

References

External links
KSTP website

FCC History Cards for KSTP (covering 1928-1980)
Radiotapes com Historic Minneapolis/St  Paul airchecks dating back to 1924 including KSTP and other Twin Cities radio stations 
Rick Burnett's TwinCitiesRadioAirchecks com has additional airchecks of KSTP and other Twin Cities radio stations from the '60s and '70s, including Chuck Knapp's 2nd show on KSTP 

Hubbard Broadcasting
ESPN Radio stations
Peabody Award winners
Radio stations in Minneapolis–Saint Paul
Radio stations established in 1925
1925 establishments in Minnesota
Minnesota Kicks
Sports radio stations in the United States
Clear-channel radio stations 



ground_truth: Verifying other meta-information such as patient's gender, age, race, etc.

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: How many years has KSTP-FM 102.1 been on the air?

Read the following text and answer briefly.

Context 3: \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}





ground_truth: Four years.

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 4:  The systematic error includes the unknown phase between the
two resonances  Work is in progress on extracting the two Breit-Wigner
amplitudes 


\begin{figure}[htb]
\vspace*{-2mm}
\hspace*{-3mm}
\includegraphics[height=0 28\textheight,width=8 3cm]  {B08F02 eps}

\vspace*{-1cm}
\caption{Mass difference $\Delta M = M(B\pi)-M(B)$ for exclusive $B$ decays 
The background-subtracted signal is a sum of 
$B^*_1 \rightarrow B^* \pi$, $B^* \rightarrow B \gamma $ (open area)
and $B^*_2 \rightarrow B^*\pi$ $B^*\rightarrow B \gamma$ (lower peak in the shaded area)
and $B^*_2 \rightarrow B \pi$ (upper peak in the shaded area)  
(D\O) }
\label{fig:d0_bexc}
\end{figure}


\begin{figure}[htb]
\includegraphics[height=0 25\textheight,width=7 5cm]  {B05F03 eps}

\vspace*{-1cm}
\caption{The invariant mass distribution of
$(D^*,\pi)$ pairs, opposite sign (points) and same-sign (solid histogram) }
\label{fig:d0_dstst}
\end{figure}






\subsection{Lifetimes}


CDF and D\O\ have measured  lifetimes of $b$ hadrons through the exclusively
reconstructed decays $B^+ \rightarrow J/\psi K^+$, $B^0 \rightarrow J/\psi K^{*0}$,
$B_s \rightarrow J/\psi \phi$, 
and $\Lambda_b \rightarrow J/\psi \Lambda$
(Fig  \ref{fig:d0_lbctau}) 
The latest results are:  \\



 $\tau(B^+)$=1 65 $\pm$ 0 08 $^{+0 096}_{-0 123}$  ps ~(D\O\ 2003),

 $\tau(B^+)$=1 662 $\pm$ 0 033  $\pm$ 0 008  ps ~(CDF),

 $\tau(B^0_d)$=1 473  $^{+0 052}_{-0 050}$ $\pm$ 0 023    ps ~(D\O) 

 $\tau(B^0_d)$=1 539 $\pm$ 0 051  $\pm$ 0 008  ps ~(CDF),

 $\tau(B^0_s)$=1 444   $^{+0 098}_{-0 090}$ $\pm$ 0 020   ps ~(D\O),

 $\tau(B^0_s)$=1 369 $\pm$ 0 100 $\pm$ $^{+0 008}_{0 010}$  ps ~(CDF),


 $\tau(\Lambda_b)$=1 221 $^{+0 217}_{-0 179}$ $\pm$ 0 043  ps ~(D\O),


 $\tau(\Lambda_b)$=1 25 $\pm$ 0 26 $\pm$ 0 10  ps ~(CDF 2003) \\



The measured lifetimes correspond to the following lifetime ratios:\\

$\tau(B^+)/\tau(B^0_d)$   =  1 080$\pm$0 042     ~(CDF),
 
$\tau(B^0_s)/\tau(B^0_d)$ =  0 890$\pm$0 072    ~(CDF),

$\tau(B^0_s)/\tau(B^0_d)$ = 0 980$ ^{+0 075}_{-0 070}   \pm$0 003    ~(D\O),

$\tau(\Lambda_b)/\tau(B^0_d)$ =  0 874$ ^{+0 169}_{-0 142}   \pm$0 028    ~(D\O) \\



\begin{figure}[htb]
\includegraphics[height=0 3\textheight,width=8 2cm]  {d0_lbctau_B11F02 eps}
\vspace*{-1cm}

\caption{ Fit projection on  $c\tau$ for the $\Lambda_b$ candidates   (D\O)}
\label{fig:d0_lbctau}
\end{figure}


The $B_s$ lifetime measurements listed above are results of
a single-lifetime fit to data, integrated over the decay angles 
Because  of the presence of  final
states  common to ${B_s^0}$\ and its charge conjugate ${\overline{B}_s^0}$,
the two meson states   are expected
to mix in such a way that the two CP  eigenstates may have a relatively
large lifetime difference 
It is possible to
separate the two CP components of ${B_s^0 \rightarrow J/\psi \phi}$\ and thus to measure the
lifetime difference by studying the time evolution of the
polarization states of the vector mesons in the final state 
CDF has carried out a combined analysis of $B_s$ lifetimes 
and polarization amplitudes  The results for the lifetimes of the
low mass (CP even) and high mass (CP odd) eigenstates, and the relative 
width difference are:\\

 $\tau_L = 1 05 ^{+0 16}_{-0 13} \pm 0 02$ ~ps,
 
 $\tau_H = 2 07 ^{+0 58}_{-0 46} \pm 0 03$ ~ps,

 $\Delta \Gamma /\overline \Gamma   = 0 65 ^{+0 25}_{-0 33} \pm 0 01$ \\

Figure \ref{fig:cdf_dg} shows  the scan of the likelihood function 
for $\Delta \Gamma /\overline \Gamma$ 
Pseudoexperiments tossed with $\Delta \Gamma /\overline \Gamma =0$
yield the betting odds for observing the above results at
1/315  For $\Delta \Gamma /\overline \Gamma = 0 12$ (SM prediction,
which has recently been updated to 0 14$\pm$0 05~\cite{dg_un}) the betting odds are
1/84 

\begin{figure}[htb]
\vspace*{-1mm}
\includegraphics[height=0 3\textheight,width=8 2cm]  {cdf_scan-dg-un eps}

\vspace*{-1cm}
\caption{Scan of the likelihood function 
for $\Delta \Gamma /\overline \Gamma$ (CDF) 
}
\label{fig:cdf_dg}
\end{figure}




D\O\ has used a novel technique to  measure the lifetime ratio
of the charged and neutral $B$ mesons, exploiting the large
semileptonic sample  $B$ hadrons were reconstructed in the channels
$B\rightarrow \mu^+ \nu D^*(2010)^-X$, which are dominated by $B^0$ decays, 
and  $B\rightarrow \mu^+ \nu D^0X$, which are dominated by $B^+$ decays 
The lifetime ratio was
obtained from the variation of the ratio of the number of events in these two
processes at different decay lengths 
The result is \\


$\tau(B^+)/\tau(B^0_d)$   =  1 093$\pm$0 021$\pm$0 022       ~(D\O)




\subsection{Towards $B_s$ mixing}

Measurement of the $B_s$ oscillation frequency via ${B_s^0}$ -${\overline{B}_s^0}$ ~mixing
will provide an important constraint on the CKM matrix  The oscillation
frequency is proportional to the mass difference between the mass eigenstates,
$\Delta m_s$, and is related to the CKM matrix through 
$\Delta m_s \propto |V_{tb}V_{ts}|$  When combined with the
$B_d$ mass difference, $\Delta m_d$ it helps in extraction of $|V_{td}|$,
and thereby the CP violating phase  

As a benchmark for future $B_s$ oscillation measurement, both groups
study  $B_d$ mixing, gaining an understanding of the different components
of a $B$ mixing analysis (sample composition, flavor tagging, vertexing,
asymmetry fitting)  For a sample of partially reconstructed decays
$B\rightarrow D^*(2010)^+\mu^-X$, D\O\ obtains 
$\Delta m_d = 0 506 \pm 0 055 (stat) \pm  0 049 (syst))$ ps$^{-1}$ and
$\Delta m_d = 0 488 \pm 0 066 (stat) \pm  0 044 (syst))$ ps$^{-1}$
when employing  opposite side muon tagging and the same side tagging,
respectively 

The CDF result for semileptonic channels is
$\Delta m_d = 0 536 \pm 0 037 (stat) \pm  0 009 (s c ) \pm 0 015 (syst)$ ps$^{-1}$ 
CDF also reports a result on $B$ oscillations using fully reconstructed
decays:
$\Delta m_d = 0 526 \pm 0 056 (stat) \pm  0 005 (syst))$ ps$^{-1}$ 

Reconstructing $B_s$ decays into different final states is another
important
 step in the ${B_s^0}$ -${\overline{B}_s^0}$ ~mixing analysis 
Thanks to the  large muon and tracking coverage,   D\O\ is accumulating
a  high statistics sample of semileptonic $B_s$ decays 
D\O\ reconstructs the $B_s \rightarrow D^+_s \mu^- X$ decays, with
$D^+_s \rightarrow \phi \pi^+ $ and
$D^+_s \rightarrow K^* K^+ $,
at a rate of $\approx$ 40(25) events per pb$^{-1}$,  respectively 
Figure \ref{fig:d0_bsdsphipi} shows the mass distribution of the
$D^+_s \rightarrow \phi \pi$ candidates 


\begin{figure}[htb]
\vspace*{-5mm}
\includegraphics[height=0 3\textheight,width=8 0cm]  {blds-250 eps}
\vspace*{-1 2cm}
\caption{  $D^+_s \rightarrow \phi \pi^+$  signal  (D\O)}
\label{fig:d0_bsdsphipi}
\end{figure}


\begin{figure}[htb]
\vspace*{-10mm}
\hspace*{-4mm}
\includegraphics[height=0 35\textheight,width=7 9cm]  {cdf_Bs-DsPi-PhiPi eps}

\vspace*{-1 0cm}
\caption{ $B_s \rightarrow D_s \pi$, $D_s \rightarrow \phi \pi$  signal   (CDF)}
\label{fig:cdf_bsdsphipi}
\end{figure}


CDF has clean signals for fully hadronic, flavor-specific  $B_s$ decays,
providing the best sensitivity to $B_s$ oscillations at high
$\Delta m_s$  Figure \ref{fig:cdf_bsdsphipi} shows the signal for
the best channel, $B_s \rightarrow D_s \pi$, $D_s \rightarrow \phi \pi$ 

\clearpage


\subsection{Rare decays}

The purely leptonic decays $B_{d,s}^0 \rightarrow \mu^+
\mu^-$ are flavor-changing neutral current (FCNC) processes 
In the standard model, these decays are forbidden at the tree level and
proceed at a very low rate through higher-order diagrams 
The latest SM prediction~\cite{sm_ref3}
is ${\cal B}(B^0_s \rightarrow \mu^+ \mu^-)=(3 42\pm 0 54)\times
10^{-9}$, where the error is dominated by non-perturbative uncertainties  The
leptonic branching fraction of the $B_d^0$ decay is suppressed by CKM matrix elements $|V_{td}/V_{ts}|^2$
leading to a predicted SM branching fraction of $(1 00\pm0 14)\times 10^{-10}$ 
The best published experimental bound (Fig ~\ref{fig:cdf_bsmumu})
 for the branching fraction
of $B^0_s$ $(B^0_d)$ is presently
${\cal B}(B^0_s \, (B^0_d) \rightarrow \mu^+\mu^-)<7 5\times 10^{-7}\, 
(1 9\times 10^{-7})$ at the 95\% C L ~\cite{cdfII} 
The decay amplitude of $B^0_{d,s} \rightarrow \mu^+ \mu^-$ can be
significantly enhanced in some extensions of the SM  

\begin{figure}[htb]
\includegraphics[height=8 3cm,width=7 9cm]  {cdfbsmumu_results_prl eps}

\vspace*{-1cm}
\caption{Invariant mass for the events passing all requirements  (CDF)}
\label{fig:cdf_bsmumu}
\end{figure}


Assuming no contributions 
from the decay $B^0_d\rightarrow \mu^+\mu^-$ in the signal region,
D\O\  finds the conservative upper limit on the branching fraction 
to be ${\cal B}(B^0_s \rightarrow \mu^+ \mu^-) \leq 4 6\times 10^{-7}$ 
at the 95\% C L  (Fig ~\ref{fig:d0_bsmumu}) 






\begin{figure}[htb]
\includegraphics[height=5 0cm,width=8 0cm]  {B06F03 eps}
\vspace*{-1cm}
\caption{Invariant mass for the events  passing all requirements  (D\O)}
\label{fig:d0_bsmumu}
\end{figure}





ground_truth: 4.5x10^8 cm.