## A. *FNAL D0:* Experiment and Upgrade

The D0 collaboration is in completing the upgrade of the detectoror and
software for Run II of the Fermilab collider. We expect to begin running
on March 1, 2001. In the past year, two Rice graduate students have
obtained their M.S. degrees for work on the D0 experiment (and will
continue on to obtain Ph.D.'s). We have also started to prepare for the
analysis of the data from Run II.

### Physics Goals 

D0 is a large general-purpose detector for studying
![](media/image1.wmf){width="0.2222222222222222in"
height="0.18055555555555555in"} collisions at the 1.8 TeV Fermilab
collider. The detector design emphasizes the following capabilities:

> 1\. good electron and muon identification over the full solid angle
> (leptons are excellent signatures for new physics),
>
> 2\. precision calorimetry with fine segmentation and uniform, stable
> response (for good resolution in jet energy and position, high
> multi-jet efficiency, and single photon detection),
>
> 3\. high resolution tracking, and
>
> 4\. excellent missing transverse energy resolution (for detection of
> neutrinos and other non-interacting particles).

The central detector in D0 consists of a silicon microstrip system
inside a scintillating fiber tracker embedded in a 2.0 Tesla solenoid.
The tracker is surrounded by one central and two endcap uranium--liquid
argon calorimeters for electromagnetic and hadronic particle
identification and energy measurement. The calorimeters are segmented to
form towers of pseudorapidity and azimuth of ![](media/image2.wmf). The
full calorimeter coverage extends out to
![](media/image3.wmf){width="0.44375in" height="0.2361111111111111in"}.
Scintillating fiber preshower detectors sit on the inside face of the
calorimeters. Outside the calorimeters there is the muon system
consisting of three large and two small magnetized toroids together with
three layers of proportional tube chambers.

The physics goals of the D0 experiment include the following topics.

1*. Top quark.* The discovery of the long sought-after top quark was one
of the highlights of run I. During Run II, we will confirm this
discovery and improve the precision of the mass measurement.

2\. *Precision studies of electroweak physics.* Topics include
production properties of *W* and *Z* (mass, width, cross-section, *pt*
dependence) using both electrons and muons. For example, D0 hopes to
measure the *W* mass to within 0.25%. This mass combined with that of
the top quark would constrain the Higgs mass. Other topics to be pursued
are charge asymmetries in
![](media/image4.wmf){width="0.6805555555555556in" height="0.19375in"}
and ![](media/image5.wmf){width="0.6527777777777778in"
height="0.19375in"}.

3\. *QCD studies / jet production.* D0 is able to measure inclusive jet
cross-sections over a wide angular range, out to \| η \| = 3.7. This
allows precision tests of QCD well outside the central region.
Multi--jet production and direct photon production are studied over the
same range.

4\. Search for new physics. D0 looks for evidence for supersymmetry
(squarks, gluinos,\...), leptoquarks and for new heavy W's and Z's. In
the absence of signals new search limits are established for these
particles. The upgraded detector will allow searches that were not
possible in the past, such as looking for decays of supersymmetric
particles to tau leptons.

Complete information about the D0 experiment may be found at
[http://www-d0.fnal.gov](http://www-d0.fnal.gov/) .

## Scintillating fiber tracker

Rice originally joined the D0 collaboration as part of a group proposing
to build a scintillating fiber tracking system for Run II. The difficult
R&D issues have been overcome and the design and construction are
complete. We expect the detector to be robust and to provide high
efficiency and high resolution

### Rice contributions

In previous years the Rice group has been responsible for the following
tasks.

> 1\. managing the off-line software for the scintillating fiber
> tracker,
>
> 2\. managing the off-line software for global track finding, and
>
> 3\. providing a test stand for the scintillating fiber photodetectors
> (VLPC's).

In the past year, Rice has continued its work on the CFT. Our
contributions included

1.  Analysis of x-ray test stand data.

2.  Analysis of cosmic ray test stand data.

3.  Testing software for the Digital Front End (DFE) boards.

4.  Contibuting to the assembly of VLPC cassettes.

5.  Testing of VLPC cassettes.

6.  Commissioning of the Detector.

### Analysis of Scintillating Fiber X-ray Test Stand Data.

During the year 2000, the scintillating fiber ribbons for the Central
Fiber Tracker (CFT) were mass produced. It became apparent that the the
light yield of the fibers was 20% to 35% less than what had been
obtained with the prototypes six years ago. An intensive effort was made
to understand the discrepancy in light yield. Rice Postdoc, Bryan Smith,
analyzed all of the X-ray test stand quality control data in an attempt
to find trends in the light yield, attenuation length, and reflectivity
versus time of the fibers which might help explain the lower light
yield.

![](media/image6.png){width="5.35625in" height="3.626388888888889in"}

The current understanding of the CFT group is that the waveguide fibers,
and not the scintillating fibers, are the origin of the lower light
yield. The outer cladding of the production waveguide fibers does not
seem to transmit light as well as the prototype waveguide fibers; the
effect is large enough to explain the light yield discrepancy. It is
fortunate the waveguide fibers are the problem and not the scintillating
fibers, for the later would be next to impossible to replace. The
waveguide fibers can be replaced in a future upgrade.

Nevertheless, to the extent one is limited by the quality of the X-ray
test stand data, there is evidence that the scintillating fiber light
yield has slightly changed for different cylinders during the year long
production of ribbons. That is important information because it means
the calibration and simulation of the CFT must be sophisticated in order
to account for the non-uniformity. Further, one now has a database of
dead and damaged fibers, which can be compared with the online
calibration information once the detector is instrumented

**Cosmic Ray Test of Cylinder 3b**.

A realistic cosmic ray test of 3000 channels of Cylinder 3b has been
performed. It was a very important test because it allowed the
resolution, efficiency, light yield uniformity, and behavior in dry and
humid conditions to be studied. The data analysis of the cosmic ray
running was performed by Bryan Smith.

![](media/image7.jpeg){width="3.9451388888888888in"
height="4.7444444444444445in"}

The D0 silicon vertex detector dissipates about 3600W of power which is
removed with -10C water. Since this temperature is low enough to cause
dew point condensation or even ice formation, it was decided to run the
tracker in dry air (100% nitrogen). Ice formation and dew point
condensation are serious concerns; however, the CFT was built in humid
air and drying in out will introduce internal stresses on the cylinders.

In order to test for change in the CFT performance versus humidity,
Cylinder 3b was dried out, and data was taken as it dried to check for
deformation. Great care was taken not to move the cylinder and to keep
the temperature constant during the tests. Ten micron global shifts in
the mean position of the fibers were measured as the cylinders dried.
When one looks at the profile distributions (which are sensitive to the
tails of the distribution) along the length of the ribbons, there are
local shifts of up to 100 microns; conversely, when one fits Gaussians
to the peaks, the local shifts are much smaller. Clearly, there was
fiber motion as the cylinder dried. After lengthy deliberation, D0
management decided the risk of dew point condensation out-weighed the
risk of fiber motion and the whole D0 tracker will now run in 100%
nitrogen.

The measured fiber \`\`doublet\'\' layer resolution of 110 microns is
slightly worse than the 90 micron design value, yet it is completely
explained by wiggle along the length of the ribbons (the cosmic ray test
stand was not ideal for track fitting because there was not enough
redundancy, hence the track fitting was hard to self-calibrate). The
doublet layer efficiencies measured indicate it is absolutely critical
that the AFE boards operate at low photo-electron thresholds or the
trigger efficiency will suffer

### 

### Testing Software

During the summer of 2000 a Rice Undergraduate was sent to FNAL whose
primary focus was construction of a testing platform for the Digital
Front End (DFE) boards in the trigger system of the D0 experiment. The
Digital Front End boards read in data from their analog counterparts and
trigger on events of interest. A prototype DFE board, constructed by
Jamieson Olsen, and Kip Yip of FNAL, has firmware that confirms its
operation. There will be forty boards used in the D0 experiment, and our
job was to create a software platform that comprehensively tests the
functionality of the manufactured DFE boards that will be used in the
experiment.

We devised two sets of tests to confirm the proper operation of the DFE
boards. The first set, known as the link tests, does not test the DFE
board itself, but confirms that the links are accurately transmitting
and receiving data. If there are errors in bit transmission through the
DFE board, this test will recognize their location on the board. The
second set of tests sends a battery of sample tracks (in the form of
fiber data hits) through the DFE board, and determines whether the DFE
board can recognize them. This set of tests takes advantage of the
software and DFE firmware written by Kin Yip which analyzes incoming
link data to check for tracks. The software we developed applies Yip's
firmware to a variety of input tracks. This confirms that the DFE board
is effectively signaled at all desired events. If a certain track is
undetected in the DFE board, the error is noted in a log and causes can
be further analyzed.

The testing software uses a Visual C++ interface and runs in a Windows
95/NT environment. The program prompts the testing technician to input
the DFE board serial number and then performs the two sets of tests.
When all tests are complete for a particular board, the program
determines the functionality of the board (i.e. did all tests pass?),
and any errors are written to a file. The data in the error file can be
analyzed by the technician to determine hardware errors on the DFE
board.

### Cassette Assembly

Rice University's contribution to the central fiber tracker (CFT)
cassette assembly was mainly in the inspection of the 128 fiber bundles
by Rice Graduate Student Andrew Askew. These optical fibers, once
installed inside the cassette assembly, transmit the light from the CFT
waveguides (warm end) to the visible light photon counter (VLPC) chips
(cold end). The assembly of the fiber bundles into the mechanical
support of the cassette body was also part of our participation.

The fiber bundle inspection may be divided into two parts: the visual
inspection of the fiber assemblies and the optical stand tests. In the
visual inspection of the fiber bundles, the warm end optical connectors,
which connect to the CFT waveguides, are visually (and in some cases,
tactilely) inspected for proper polishing. The fiber ends at the
connector are inspected to ascertain whether any are optically
compromised. The plug assemblies (which will fit into the 'boot'
assembly attached to the VLPC hybrid) were then inspected for proper
positioning, proper orientation and proper height. Once these visual
inspections were carried out, the alignment pin was installed in the
warm end connector for the eventual interface to the waveguides. Then
the 128-fiber bundle was installed into the optical test stand. Two
tests are performed at this stage, using a computer controlled data
acquisition system, the uniformity test, wherein all of the channels are
supplied a uniform amount of light and the channels are read out by an
array of photodiodes in place of the VLPCs. Then each channel is
illuminated individually and the corresponding channel read out, to
ensure that all of the fibers were properly routed to their
corresponding places on the chips. Once the fiber bundle passed all of
the tests, it was properly stored until assembled into a cassette. Even
if a given fiber bundle did not pass some part of the visual inspection,
the information on its routing was saved for production statistics.

The cassette assembly itself involved a number of hardware inspections
to ensure that the proper bundles were installed, with their
corresponding kapton flex circuits. Any strain observed on the flex
circuit was relieved by repositioning the circuit with respect to the
cassette supports and pass throughs. The chip assemblies, which included
the VLPC hybrids and their electrical connections, were attached to the
plug ends of the 128-fiber bundles, and the connections inspected for
the proper tension of the springs holding the isotherm flush to the
fiber bundle. The cassette body was then closed and prepared for
additional checks (gas leakage, electrical etc.), after a final visual
inspection of the warm-end optical connectors for compromised channels.

### Commissioning of the CFT at D0

A great deal of control and downloading software is being written in
order to readout and correctly operate the CFT. The bias voltages on the
Visible Light Photon Counters (VLPCs), the Cryostat temperature, the
SIFT (trigger chip) thresholds, and the SVX parameters (readout and
digitization ADCs) must be set and monitored. Code for the
initialization and control of the Analog Front End Board, Sequencer
(reads out the SXV ADCs and ships to level 2), Sequencer Controller, and
Level 2 readout (VRB, VRBC, and VBD) is needed. Bryan Smith has been
contributing to the commissioning of the downloading code.

EPICS (Experimental Physics and Industrial Control System) is being used
to insulate the users from the details of the hardware and facilitate
downloading. The D0 product, COMICS (COmmunicate with epICS), is being
used to interface the database of download information to EPICS.

## Progress on Software and Analysis

During the past year we have continued our work on the development and
application of multivariate analysis techniques. These are based on the
PDE method that was developed at Rice. (We remind the reader that PDE is
a multivariate method based on discriminant analysis, and is designed to
identify rare signals in the presence of large backgrounds). In the past
year we have shown that for tau and electron identification this method
gives the same results as a neural network. This was the subject of a
M.S. thesis and will be described below.

Also Paul Padley has continued as co leader of the Tau ID group at D0.
The object group\'s responsibilities for its object include:

-   Algorithms for L1, L2, and L3 triggers.

-   Tools for estimating rates and efficiencies of the triggered object.

-   Algorithms for offline reconstruction relevant to identifying the
    object.

-   Definition of the object classes.

-   Development of analysis tools that define the quality of the object
    and making sure that the event information needed for those tools is
    available for analysis (through the object classes).

-   Development of tools that facilitate estimating the efficiency of
    post-reconstruction selection of objects as a function of object
    quality.

-   Calibration of the objects.

These groups will persist into Run II and will be responsible for
verifying analyses that use their objects. Other Object ID groups that
exist include electromagnetic particles, jets, secondary vertices and so
on. In Run II it is believed that tau's will play a more important role
than in the past at D0. In part this is due to the fact that upgraded
detector better allows this possibility. In addition it has been
realized that many supersymmetric models have dominant decay modes
involving taus. One of our students has completed an M.S. thesis on
conventional analysis of tau's which will now be described

### Trigger: L2 Tau Study 

The potential importance of events with Tau leptons in them for testing
theories beyond the Standard Model at Run II of the Tevatron has been
clearly stated, see the Fermilab Run II Physics web page for examples,
http://fnth37.fnal.gov/run2.html. Obviously, before doing physics
analysis with events containing Tau leptons, the events must make it to
storage for offline analysis, which will only happen if the trigger has
been correctly designed and implemented.

Bryan Smith, using the L2 calorimeter trigger simulation, has made a
very preliminary L2 Tau trigger study. Trigger efficiencies and rates
were simulated versus jet width, ME_t, and jet E_t cuts for a number of
processes.Clearly, a Level 2 Tau trigger should use the complementary
tracking and calorimeter information. The L2 hadronic Tau trigger
strategy will be rather straightforward. First, one finds locally
isolated Tau candidates that have decayed hadronically using one of
three conditions: tight calorimeter (small jet width), tight track (low
track multiplicity in a cone), or both calorimeter and track, but with
less tight cuts. To trigger, one takes the tau candidates just found and
requires either multiple tau candidates or requires additional ME_t,
jets, leptons, or photons in the event.

### PMCS Tau

Using a Geant based Monte Carlo detector simulation; one can simulate in
fine detail the interactions of particles with the detector. One also
tries to simulate the digitization and readout as close as possible to
the actual hardware. The care for detail means a huge amount of CPU time
to simulate each event. Given the huge Run II event samples, it may not
always be possible to generate all of the necessary simulated events
needed to do a physics analysis; therefore, one resorts to a
parameterized Monte Carlo simulation (PMCS) to generate simulated
events, which can be up to 1000 times faster. Obviously, the trick is to
parameterize the detector well enough so that analyses are not biases.
Bryan Smith has been working in context of the PMCS group to simulate
Tau leptons at D0. Progress has been made: Qualitatively, the behavior
of the reconstruction program has been simulated with the PMCS Tau
package, but much work is left to do in order to make PMCS Tau a useful
tool.

### Conventional Cut Optimization for Tau ID

# Waiting for Summary of Naresh's thesis

### 

### Multivariate Analysis

In the past we have implemented PDE as a shareable object that can be
invoked by the ROOT system. ROOT is the data visualization tool at CDF
and D0 and with the ability to easily perform PDE analysis within this
tool we expect the use of PDE to expand. This "ROOTPDE" package allows
for the easy visualization of the density functions used in the
analysis. Fig. ? shows an example plot. Also in the past we have
developed a more sophisticated version of the PDE method which uses
adaptive kernels. This should be more reliable in regions of parameter
space where there is only sparse data.

![](media/image8.png){width="5.053472222222222in"
height="3.9472222222222224in"}

To test the effectiveness of ROOTPDE we have, in the past year,
demonstrated that it provides the same discrimination ability as a
neural network for two different particle ID selections. This is
reported in the M.S. thesis of Andrew Askew, "A Comparison of
Multivariate Data Analysis Techniques as Applied to the Identification
of Electrons and Tau Leptons". This thesis compares the performance of
Neural Networks and Probability Density Estimation for the
identification of electrons and tau leptons. These techniques were used
to attempt to gain better efficiency in the discrimination between
signal and background processes by more closely fitting the structure of
the data.

The data samples that this comparison used were all simulated data based
on the best understanding of the detector's response to the signal and
background processes. The identification of electrons was performed with
two separate backgrounds, QCD jets and τ−\>π^0^πν−\>γγπν. The
identification of tau leptons was performed using a background of QCD
jets. The neural networks were created using the Physics Analysis
Workstation (PAW) package with MLPfit. The probability density
estimation (PDE) method was implemented in C++ code and put into a
shared object for the ROOT analysis environment. Two PDE analyses were
performed, fixed kernel and adaptive kernel. The PDE and neural network
analyses used the same testing and training data, and the same variable
sets. The variables used in all three comparisons were the sets under
study when the analyses were performed. The optimization of both
techniques was considered (chosen by signal efficiency multiplied by
purity of test sample, versus the parameters modified: nodes, cycles,
kernel smoothing parameter etc.), and after selection of the best
parameters the analysis was performed and the comparison of the best
performance from each technique was made.

The methods show comparable performance in the identification of both
types of particles. These multivariate data analysis techniques show an
improvement of prior studies performed using Hmatrix techniques and
linear cuts. The PDE method has the advantage over a Neural Network of
not having a large number of parameters to be tuned. (In a neural
network, things such as number of layers, number of nodes, number of
hidden nodes all have to be chosen)

  -------------------- ------------------- --------- --------- -------------
  Multivariate                                                 
  Comparison Results                                           

  τ Versus QCD jets:                                           

                       Maximum Purity x    ε~s~      ε~b~      ε~s~/ ε~b~
                       ε~s~                                    

  Fixed Kernel PDE     .731                .879      .178      4.94

  Adaptive Kernel PDE  .781                .861      .0886     9.72

  MLPft neural network .793                .883      .100      8.83

  Electrons Versus QCD                                         
  jets:                                                        

  Fixed Kernel PDE     .953                .982      .0299     32.8

  Adaptive Kernel PDE  .957                .982      .0260     37.8

  MLPft neural network .958                .993      .0365     27.2

  Electrons Versus                                             
  τ−\>π^0^πν−\>γγπν:                                           

  Fixed Kernel PDE     .844                .935      .101      9.26

  Adaptive Kernel PDE  .871                .958      .0957     10.0

  MLPft neural network .882                .952      .0750     12.7
  -------------------- ------------------- --------- --------- -------------

\[1\] B. Knuteson, H. Miettinen, and L. Holmstrom, "Mass Analysis and
Parameter Estimation with PDE", D0 Note 3396, September 1998.