3 |
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|
4 |
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%{\bf \color{red} The numbers in this Section need to be double checked.} |
5 |
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|
6 |
+ |
\subsection{Limit on number of events} |
7 |
+ |
\label{sec:limnumevents} |
8 |
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As discussed in Section~\ref{sec:results}, we see one event |
9 |
|
in the signal region, defined as SumJetPt$>$300 GeV and |
10 |
|
\met/$\sqrt{\rm SumJetPt}>8.5$ GeV$^{\frac{1}{2}}$. |
24 |
|
and with the observation of one event in the signal region. |
25 |
|
We calculate a Bayesian 95\% CL upper limit\cite{ref:bayes.f} |
26 |
|
on the number of non SM events in the signal region to be 4.1. |
27 |
< |
We have also calculated this limit using a profile likelihood method |
28 |
< |
as implemented in the cl95cms software, and we also find 4.1. |
27 |
> |
We have also calculated this limit using |
28 |
> |
% a profile likelihood method |
29 |
> |
% as implemented in |
30 |
> |
the cl95cms software\cite{ref:cl95cms}, |
31 |
> |
and we also find 4.1. (This is not surprising, since cl95cms |
32 |
> |
also gives baysean upper limits with a flat prior). |
33 |
|
These limits were calculated using a background prediction of $N_{BG} = 1.4 \pm 0.8$ |
34 |
|
events, the error-weighted average of the ABCD and $P_T(\ell\ell)$ background |
35 |
|
predictions. The upper limit is not very sensitive to the choice of |
42 |
|
are from energy scale (Section~\ref{sec:systematics}), luminosity, |
43 |
|
and lepton efficiency. |
44 |
|
|
39 |
– |
We also performed a scan of the mSUGRA parameter space. We set $\tan\beta=10$, |
40 |
– |
sign of $\mu = +$, $A_{0}=0$~GeV, and scan the $m_{0}$ and $m_{1/2}$ parameters |
41 |
– |
in steps of 10~GeV. For each scan point, we exclude the point if the expected |
42 |
– |
yield in the signal region exceeds 4.7, which is the 95\% CL upper limit |
43 |
– |
based on an expected background of $N_{BG}=1.4 \pm 0.8$ and a 20\% acceptance |
44 |
– |
uncertainty. |
45 |
– |
The results are shown in Fig.~\ref{fig:msugra}. |
46 |
– |
This figure is still preliminary: |
47 |
– |
\begin{itemize} |
48 |
– |
\item The process dependent k-factors from Prospino were not yet available |
49 |
– |
when the figure was made. We took a flat k=1.4. |
50 |
– |
\item The PDF uncertainties were still missing. |
51 |
– |
\item The limits from other experiments are missing. Wwe are hoping to |
52 |
– |
converge on a common format for this plot with other SUSY analyses, so |
53 |
– |
we have not made any attempt to make the plot look pretty (!). |
54 |
– |
\item As mentioned above, we took a constant acceptance uncertainty |
55 |
– |
instead of calculating the uncertainty point by point. |
56 |
– |
\end{itemize} |
57 |
– |
|
58 |
– |
\begin{figure}[tbh] |
59 |
– |
\begin{center} |
60 |
– |
\includegraphics[width=0.6\linewidth]{msugra.png} |
61 |
– |
\caption{\label{fig:msugra}\protect Exclusion curve in the mSUGRA parameter space, |
62 |
– |
assuming $\tan\beta=10$, sign of $\mu = +$ and $A_{0}=0$~GeVs.} |
63 |
– |
\end{center} |
64 |
– |
\end{figure} |
65 |
– |
|
45 |
|
|
46 |
+ |
\subsection{Outreach} |
47 |
+ |
\label{sec:outreach} |
48 |
|
Conveying additional useful information about the results of |
49 |
|
a generic ``signature-based'' search such as the one described |
50 |
< |
in this note is a difficult issue. The next paragraph represent |
51 |
< |
our attempt at doing so. |
50 |
> |
in this note is a difficult issue. |
51 |
> |
Here we attempt to present our result in the most general |
52 |
> |
way. |
53 |
|
|
54 |
< |
Other models of new physics in the dilepton final state |
54 |
> |
Models of new physics in the dilepton final state |
55 |
|
can be confronted in an approximate way by simple |
56 |
|
generator-level studies that |
57 |
|
compare the expected number of events in 34.0~pb$^{-1}$ |
146 |
|
% 1 29.995 0.50457E-06 |
147 |
|
% |
148 |
|
% limit: less than 4.689 signal events |
149 |
+ |
|
150 |
+ |
|
151 |
+ |
\subsection{mSUGRA scan} |
152 |
+ |
\label{sec:mSUGRA} |
153 |
+ |
We also perform a scan of the mSUGRA parameter space, as recomended |
154 |
+ |
by the SUSY group convenors\cite{ref:scan}. |
155 |
+ |
The goal of the scan is to determine an exclusion region in the |
156 |
+ |
$m_0$ vs. $m_{1/2}$ plane for |
157 |
+ |
$\tan\beta=3$, |
158 |
+ |
sign of $\mu = +$, and $A_{0}=0$~GeV. This scan is based on events |
159 |
+ |
generated with FastSim. |
160 |
+ |
|
161 |
+ |
The first order of business is to verify that results using |
162 |
+ |
Fastsim and Fullsim are compatible. To this end we compare the |
163 |
+ |
expected yield for the LM1 point in FullSim (3.56 $\pm$ 0.06) and |
164 |
+ |
FastSim (3.29 $\pm$ 0.27), where the uncertainties are statistical only. |
165 |
+ |
These two numbers are in agreement, which gives us confidence in |
166 |
+ |
using FastSim for this study. |
167 |
+ |
|
168 |
+ |
The FastSim events are generated with different values of $m_0$ |
169 |
+ |
and $m_{1/2}$ in steps of 10 GeV. For each point in the |
170 |
+ |
$m_0$ vs. $m_{1/2}$ plane, we compute the expected number of |
171 |
+ |
events at NLO. We then also calculate an upper limit $N_{UL}$ |
172 |
+ |
using cl95cms at each point using the following inputs: |
173 |
+ |
\begin{itemize} |
174 |
+ |
\item Number of BG events = 1.40 $\pm$ 0.77 |
175 |
+ |
\item Luminosity uncertainty = 11\% |
176 |
+ |
\item The acceptance uncertainty is calculated at each point |
177 |
+ |
as the quadrature sum of |
178 |
+ |
\begin{itemize} |
179 |
+ |
\item The uncertainty due to JES for that point, as calculated |
180 |
+ |
using the method described in Section~\ref{sec:systematics} |
181 |
+ |
\item A 5\% uncertainty due to lepton efficiencies |
182 |
+ |
\item An uncertaity on the NLO cross-section obtained by varying the |
183 |
+ |
factorization and renormalization scale by a factor of two\cite{ref:sanjay}. |
184 |
+ |
\item The PDF uncertainty on the product of cross-section and acceptance |
185 |
+ |
calculated using the method of Reference~\cite{ref:pdf}. |
186 |
+ |
\end{itemize} |
187 |
+ |
\item We use the ``log-normal'' model for the nuisance parameters |
188 |
+ |
in cl95cms |
189 |
+ |
\end{itemize} |
190 |
+ |
|
191 |
+ |
An mSUGRA point is excluded if the resulting $N_{UL}$ is smaller |
192 |
+ |
than the expected number of events. Because of the quantization |
193 |
+ |
of the available MC points in the $m_0$ vs $m_{1/2}$ plane, the |
194 |
+ |
boundaries of the excluded region are also quantized. We smooth |
195 |
+ |
the boundaries using the method recommended by the SUSY |
196 |
+ |
group\cite{ref:smooth}. In addition, we show a limit |
197 |
+ |
curve based on the LO cross-section, as well as the |
198 |
+ |
``expected'' limit curve. The expected limit curve was |
199 |
+ |
calculated using the CLA function also available in cl95cms. |
200 |
+ |
In general we found that the ``expected'' limit is very close |
201 |
+ |
to the observed limit, which is not surprising since the |
202 |
+ |
expected BG (1.4 $\pm$ 0.8 events) is fully consistent |
203 |
+ |
with the observation (1 event). Because of the quantization, |
204 |
+ |
we find that the expected and observed limits are either |
205 |
+ |
identical or differ by one or at most two grid points. |
206 |
+ |
We have approximated the expected limit as the observed limit |
207 |
+ |
minus 10 GeV\footnote{We show the expected limit only because |
208 |
+ |
this is what is recommended by SUSY management. We believe that |
209 |
+ |
quoting the agreement between the expected BG and the |
210 |
+ |
observation should be enough....}. |
211 |
+ |
Finally, we note that the sross-section uncertainties due to |
212 |
+ |
variations of the factorization |
213 |
+ |
and renormalization scale are not included for the LO curve. |
214 |
+ |
The results are shown in Figure~\ref{fig:msugra} |
215 |
+ |
|
216 |
+ |
|
217 |
+ |
\begin{figure}[tbh] |
218 |
+ |
\begin{center} |
219 |
+ |
\includegraphics[width=\linewidth]{exclusion_noPDF.pdf} |
220 |
+ |
\caption{\label{fig:msugra}\protect Exclusion curves in the mSUGRA parameter space, |
221 |
+ |
assuming $\tan\beta=3$, sign of $\mu = +$ and $A_{0}=0$~GeVs. THIS IS STILL MISSING |
222 |
+ |
THE PDF UNCERTAINTIES. WE ALSO WANT TO IMPROVE THE SMOOTHING PROCEDURE.} |
223 |
+ |
\end{center} |
224 |
+ |
\end{figure} |
225 |
+ |
|
226 |
+ |
|
227 |
+ |
\subsubsection{Check of the nuisance parameter models} |
228 |
+ |
We repeat the procedure outlined above but changing the |
229 |
+ |
lognormal nuisance parameter model to a gaussian or |
230 |
+ |
gamma-function model. The results are shown in |
231 |
+ |
Figure~\ref{fig:nuisance}. (In this case, |
232 |
+ |
to avoid smoothing artifacts, we |
233 |
+ |
show the raw results, without smoothing). |
234 |
+ |
|
235 |
+ |
\begin{figure}[tbh] |
236 |
+ |
\begin{center} |
237 |
+ |
\includegraphics[width=0.5\linewidth]{nuissance.png} |
238 |
+ |
\caption{\label{fig:nuisance}\protect Exclusion curves in the |
239 |
+ |
mSUGRA parameter space, |
240 |
+ |
assuming $\tan\beta=3$, sign of $\mu = +$ and $A_{0}=0$~GeVs |
241 |
+ |
using different models for the nuisance parameters. |
242 |
+ |
PDF UNCERTAINTIES ARE NOT INCLUDED.} |
243 |
+ |
\end{center} |
244 |
+ |
\end{figure} |
245 |
+ |
|
246 |
+ |
We find that different assumptions on the PDFs for the nuisance |
247 |
+ |
parameters make very small differences to the set of excluded |
248 |
+ |
points. |
249 |
+ |
Following the recommendation of Reference~\cite{ref:cousins}, |
250 |
+ |
we use the lognormal nuisance parameter model as the default. |
251 |
+ |
|
252 |
+ |
|
253 |
+ |
% \clearpage |
254 |
+ |
|
255 |
+ |
|
256 |
+ |
\subsubsection{Effect of signal contamination} |
257 |
+ |
\label{sec:contlimit} |
258 |
+ |
|
259 |
+ |
Signal contamination could affect the limit by inflating the |
260 |
+ |
background expectation. In our case we see no evidence of signal |
261 |
+ |
contamination, within statistics. |
262 |
+ |
The yields in the control regions |
263 |
+ |
$A$, $B$, and $C$ (Table~\ref{tab:datayield}) are just |
264 |
+ |
as expected in the SM, and the check |
265 |
+ |
of the $P_T(\ell \ell)$ method in the control region is |
266 |
+ |
also consistent with expectations (Table~\ref{tab:victory}). |
267 |
+ |
Since we have two data driven methods, with different |
268 |
+ |
signal contamination issues, giving consistent |
269 |
+ |
results that are in agreement with the SM, we |
270 |
+ |
argue for not making any correction to our procedure |
271 |
+ |
because of signal contamination. In some sense this would |
272 |
+ |
be equivalent to using the SM background prediction, and using |
273 |
+ |
the data driven methods as confirmations of that prediction. |
274 |
+ |
|
275 |
+ |
Nevertheless, here we explore the possible effect of |
276 |
+ |
signal contamination. The procedure suggested to us |
277 |
+ |
for the ABCD method is to modify the |
278 |
+ |
ABCD background prediction from $A_D \cdot C_D/B_D$ to |
279 |
+ |
$(A_D-A_S) \cdot (C_D-C_S) / (B_D - B_S)$, where the |
280 |
+ |
subscripts $D$ and $S$ refer to the number of observed data |
281 |
+ |
events and expected SUSY events, respectively, in a given region. |
282 |
+ |
We then recalculate $N_{UL}$ at each point using this modified |
283 |
+ |
ABCD background estimation. For simplicity we ignore |
284 |
+ |
information from the $P_T(\ell \ell)$ |
285 |
+ |
background estimation. This is conservative, since |
286 |
+ |
the $P_T(\ell\ell)$ background estimation happens to |
287 |
+ |
be numerically larger than the one from ABCD. |
288 |
+ |
|
289 |
+ |
Note, however, that in some cases this procedure is |
290 |
+ |
nonsensical. For example, take LM0 as a SUSY |
291 |
+ |
point. In region $C$ we have a SM prediction of 5.1 |
292 |
+ |
events and $C_D = 4$ in agreement with the Standard Model, |
293 |
+ |
see Table~\ref{tab:datayield}. From the LM0 Monte Carlo, |
294 |
+ |
we find $C_S = 8.6$ events. Thus, including information |
295 |
+ |
on $C_D$ and $C_S$ should {\bf strengthen} the limit, since there |
296 |
+ |
is clearly a deficit of events in the $C$ region in the |
297 |
+ |
LM0 hypothesis. Instead, we now get a negative ABCD |
298 |
+ |
BG prediction (which is nonsense, so we set it to zero), |
299 |
+ |
and therefore a weaker limit. |
300 |
+ |
|
301 |
+ |
|
302 |
+ |
|
303 |
+ |
|
304 |
+ |
\begin{figure}[tbh] |
305 |
+ |
\begin{center} |
306 |
+ |
\includegraphics[width=0.5\linewidth]{sigcont.png} |
307 |
+ |
\caption{\label{fig:sigcont}\protect Exclusion curves in the |
308 |
+ |
mSUGRA parameter space, |
309 |
+ |
assuming $\tan\beta=3$, sign of $\mu = +$ and $A_{0}=0$~GeVs |
310 |
+ |
with and without the effects of signal contamination. |
311 |
+ |
PDF UNCERTAINTIES ARE NOT INCLUDED.} |
312 |
+ |
\end{center} |
313 |
+ |
\end{figure} |
314 |
+ |
|
315 |
+ |
A comparison of the exclusion region with and without |
316 |
+ |
signal contamination is shown in Figure~\ref{fig:sigcont} |
317 |
+ |
(with no smoothing). The effect of signal contamination is |
318 |
+ |
small, of the same order as the quantization of the scan. |
319 |
+ |
|
320 |
+ |
|
321 |
+ |
\subsubsection{mSUGRA scans with different values of tan$\beta$} |
322 |
+ |
\label{sec:tanbetascan} |
323 |
+ |
|
324 |
+ |
For completeness, we also show the exclusion regions calculated |
325 |
+ |
using $\tan\beta = 10$ (Figure~\ref{fig:msugratb10}). |
326 |
+ |
|
327 |
+ |
\begin{figure}[tbh] |
328 |
+ |
\begin{center} |
329 |
+ |
\includegraphics[width=\linewidth]{exclusion_tanbeta10.pdf} |
330 |
+ |
\caption{\label{fig:msugratb10}\protect Exclusion curves in the mSUGRA parameter space, |
331 |
+ |
assuming $\tan\beta=10$, sign of $\mu = +$ and $A_{0}=0$~GeVs. THIS IS STILL MISSING |
332 |
+ |
THE PDF UNCERTAINTIES. WE ALSO WANT TO IMPROVE THE SMOOTHING PROCEDURE.} |
333 |
+ |
\end{center} |
334 |
+ |
\end{figure} |
335 |
+ |
|
336 |
+ |
|
337 |
+ |
|
338 |
+ |
|
339 |
+ |
|
340 |
+ |
|