Transcript trigger-daq-analysis-esi09-neufeld1 - Indico

Introduction to Trigger /
Data Acquisition / Data
Analysis
EIROforum School on Instrumentation
ESI 2009
Niko Neufeld, CERN-PH
Introduction
• Trigger & Data Acquisition are indispensible
parts of most electronically read out
experiments. They can be anything from
trivial side-aspects to vast (wo-)man-century
collective efforts
• Technically they consist mainly of electronics,
computer science, networking and (we
hope) a bit of (insight into) physics
• Analysis of the data is what links our
theoretical ideas to our observations and
measurements
• Some material and lots of inspiration for this
lecture was taken from lectures by my
predecessors and colleagues in the CERN
summer-school programme
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
2
Outline
• Introduction
– Trigger
– Data acquisition
– The first data acquisition
campaign
• A simple DAQ system
– One sensor
– More and more sensors
• Read-out with buses
• Some hints for your own
DAQ and a look at a
large system
• Data Analysis
– General remarks
– Clusterfinding in a
Calorimete
– A nice example by B.
Jacobson
– Crates & Mechanics
– The VME Bus
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
3
Disclaimer
• Trigger, DAQ and (even more so) Analysis are vast
subjects covering a lot of physics, electronics,
computing and mathematics
• Based entirely on personal bias I have selected a few
topics
• While most of it will be only an overview at a few
places we will go into some technical detail
• Some things will be only touched upon or left out
altogether – information on those you will find in the
references at the end
– Electronics (lectures by J. Christiansen)
– High Level Trigger
– Experiment Control (= Run Control + Detector Control / DCS)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
4
Historical introduction
Tycho Brahe and the Orbit of
Mars
I've studied all available charts of the planets and stars and
none of them match the others. There are just as many
measurements and methods as there are astronomers and all
of them disagree. What's needed is a long term project with
the aim of mapping the heavens conducted from a single
location over a period of several years.
Tycho Brahe, 1563 (age 17).
• First measurement campaign
• Systematic data acquisition
– Controlled conditions (same time of the day and
month)
– Careful observation of boundary conditions
(weather, light conditions etc…) - important for
data quality / systematic uncertainties
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
6
The First Systematic Data
Acquisition
• Data acquired over 18 years, normally e every month
• Each measurement lasted at least 1 hr with the naked eye
• Red line (only in the animated version) shows comparison with modern
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
theory
7
Tycho’s Trigger & DAQ in
Today’s Terminology
• Bandwith (bw) = Amount of data
transferred / per unit of time
– “Transferred” = written to his logbook
– “unit of time” = duration of measurement
– bwTycho = ~ 100 Bytes / h (compare with LHCb
40.000.000.000 Bytes / s)
• Trigger = in general something which tells
you when is the “right” moment to take
your data
– In Tycho’s case the position of the sun,
respectively the moon was the trigger
– the trigger rate ~ 3.85 x 10-6 Hz (compare with
LHCb 1.0 x 106 Hz)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
8
Some More Thoughts on Tycho
• Tycho did not do the correct analysis
of the Mars data, this was done by
Johannes Kepler (1571-1630),
eventually paving the way for
Newton’s laws
• Morale: the size & speed of a DAQ
system are not correlated with the
importance of the discovery!
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
9
Raw data  physics
• These are Tycho’s raw data
• We need to convert them to orbital
coordinates around the earth (or sun) to
confront them with theory
• And of course correct for quality (“minus
bona”), time, etc…
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
10
Tycho’s theory
Kepler’s Laws
1) Planets move in ellipses
with the Sun at one focus
2) Planets in their orbits sweep
out equal areas in equal
times
3) A planets rotational period
squared equals the third
power of its semi major-axis
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
11
Trigger
What is a trigger?
An open-source
3D rally game?
An important part
of a Beretta
The most famous
horse in
movie history?
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
13
What is a trigger?
Wikipedia: “A trigger is a system that
uses simple criteria to rapidly decide
which events in a particle detector to
keep when only a small fraction of the
total can be recorded. “
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
14
Trigger
• Simple
• Rapid
• Selective
• When only a small fraction can be
recorded
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
15
Trivial DAQ
External View
sensor
Physical View
sensor
ADC Card
CPU
disk
Logical View
ADC
Processing
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
storage
16
Trivial DAQ with a real trigger
Sensor
Trigger
Delay
ADC
Processing
Discriminator
Start
Interrupt
storage
What if a trigger is produced when the ADC or
processing is busy?
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
17
Trivial DAQ with a real trigger 2
Sensor
Trigger
Delay
ADC
Processing
Start
Interrupt
Ready
Discriminator
Busy Logic
and
not
Set
ClearQ
storage
Deadtime (%) is the ratio between the time the DAQ
is busy and the total time.
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
18
Trivial DAQ with a real trigger 3
Sensor
Trigger
Delay
ADC
FIFO
Processing
Discriminator
Start
Full
and
Busy Logic
DataReady
storage
Buffers are introduced to de-randomize data,
to decouple the data production from the data
consumption. Better performance.
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
19
Effect of derandomizing
Sensor
Sensor
Trigger
Trigger
Delay
ADC
Processing
Start
Busy Logic
and
Interrupt
Ready
not
Set
Q
Clear
storage
Discriminator
Delay
Discriminator
ADC
FIFO
Processing
Start
and
Busy Logic
Full
DataReady
storage
The system is busy during the ADC
conversion time + processing time
until the data is written to the
storage
The system is busy during the ADC
conversion time if the FIFO is not full
(assuming the storage can always
follow!)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
20
Choosing a trigger
• Keep it simple! (Remember Einstein:
“As simple as possible, but not
simpler”)
• Even though “premature optimization
is the root of all evil”, think about
efficiency (buffering)
• Try to have few adjustable parameters:
scanning for a good working point will
otherwise be a night-mare
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
21
A Very Simple Data
Acquisition System
Measuring Temperature
• Suppose you are given a Pt100
thermo-resistor
• We read the temperature as a
voltage with a digital
voltmeter
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
23
Reading Out Automatically
Note how small
the sensor has
become.
In DAQ we
normally need
not worry about
the details of
the things we
readout
#include <libusb.h>
struct usb_bus *bus;
struct usb_device *dev;
usb_dev_handle *vmh = 0;
usb_find_busses(); usb_find_devices();
for (bus = usb_busses; bus; bus = bus->next)
for (dev = bus->devices; dev; dev = dev>next)
if (dev->descriptor.idVendor ==
HOBBICO) vmh = usb_open(dev);
usb_bulk_read(vmh ,3,&u,sizeof(float),500);
USB/RS232
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
24
Read-out 16 Sensors
• Buy 4 x 4-port USB
hub (very cheap) (+
15 more voltmeters)
• Adapt our little DAQ
program
• No fundamental
(architectural)
change to our DAQ
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
25
Read-out 160 Sensors
• For a moment we (might) consider to
buy 52 USB hubs, 160 Voltmeters
• …but hopefully we abandon the idea
very quickly, before we start cabling
this!
• Expensive, cumbersome, fragile 
our data acquisition system is not
scalable
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
26
Read-out with Buses
A Better DAQ for Many
(temperature) Sensors
19”
7U
7U VME Crate
(a.k.a. “Subrack”)
Backplane Connectors
(for power and data)
VME Board
Plugs into Backplane
• Buy or build a compact
multi-port volt-meter module,
e.g. 16 inputs
• Put many of these multi-port
modules together in a
common chassis or crate
• The modules need
– Mechanical support
– Power
– A standardized way to
access their data (our
measurement values)
• All this is provided by
standards for (readout)
electronics such as VME (IEEE
1014)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
28
DAQ for 160 Sensors Using VME
• Readout boards
in a VME-crate
– mechanical
standard for
– electrical
standard for
power on the
backplane
– signal and
protocol standard
for
communication
on a bus
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
29
A Word on Mechanics and
Pizzas
• The width and height of racks and
crates are measured in US units: inches
(in, '') and U
– 1 in = 25.4 mm
– 1 U = 1.75 in = 44.45 mm
• The width of a "standard" rack is 19 in.
• The height of a crate (also sub-rack) is
measured in Us
• Rack-mountable things, in particular
computers, which are 1 U high are
often called pizza-boxes
• At least in Europe, the depth is
measured in mm
• Gory details can be found in IEEE
1101.x (VME mechanics standard)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
49 U
30
Communication in a Crate: Buses
• A bus connects two or more devices and allows the
to communicate
• The bus is shared between all devices on the bus 
arbitration is required
• Devices can be masters or slaves (some can be
both)
• Devices can be uniquely identified ("addressed") on
the bus
Master
Device 1
Slave
Device
Device22
Slave
Master
Device 3
Device
Device44
Data
DataLines
Lines
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
Select
SelectLine
Line
31
Buses
• Famous examples: PCI, USB, VME, SCSI
– older standards: CAMAC, ISA
– upcoming: ATCA
– many more: FireWire, I2C, Profibus, etc…
• Buses can be
–
–
–
–
local: PCI
external peripherals: USB
in crates: VME, compactPCI, ATCA
long distance: CAN, Profibus
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
32
The VME Bus
0x000-0x1ff
0x200-0x2ff
0x300-0x3ff
0x400-0x4ff
0x500-0x5ff
0x600-0x6ff
• In a VME crate we can find
three main types of modules
– The controller which monitors
and arbitrates the bus
– Masters read data from and
write data to slaves
– Slaves send data to and receive
data from masters
• Addressing of modules
– In VME each module occupies a
part of a (flat) range of
addresses (24 bit to 32 bit)
– Address range of modules is
hardwired (conflicts!)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
33
VME protocol 1) Arbitration
• Arbitration: Master asserts*) BR#, Controller answers by
asserting BG#
• If there are several masters requesting at the same
time the one physically closest to the controller wins
• The winning master drives BBSY* high to indicate that
the bus is now in use
Pictures from http://www.interfacebus.com
*) assert means driving the line to logical 0 (VME control lines are inverted or active-low)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
34
VME protocol 2) Write transfer
• The Master writes data
and address to the
data / respectively
address bus
• It asserts DS* and AS*
to signal that the data
and address are valid
• The slave reads and
acknowledges by
asserting DTACK
• The master releases
DS*, AS* and BSBSY*,
the cycle is complete
• Note: there is no clock!
The slave can respond
whenever it wants.
VME is an
asynchronous bus
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
35
Speed Considerations
• Theoretically ~ 16 MB/s can be
achieved
– assuming the databus to be full 32-bit
wide
– the master never has to relinquish bus
master ship
• Better performance by using blocktransfers
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
36
VME protocol 3) Block transfer
• Block transfers are essential
for Direct Memory Access
(DMA)
• More performance can be
gained by using the address
bus also for data (VME64)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
• After an address
cycle several (up to
256) data cycles are
performed
• The slave is supposed
to increment the
address counter
• The additional delays
for asserting and
acknowledging the
address are removed
• Performance goes
up to 40 MB/s
• In PCI this is referred
to as "burst-transfer"
37
Dis-/Advantages of buses
• Relatively simple to implement
– Constant number of lines
– Each device implements the same interface
• Easy to add new devices
– topological information of the bus can be used
for automagically choosing addresses for bus
devices: this is what plug and play is all about
• One misbehaving device can bring the entire
crate down
• At best the scaling for readout from a single
unit is 1 / n
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
38
Switched Networks
• In a switched network each node is
connected either to another node or
to a switch
• Switches can be connected to other
switches
• A path from one node to another
leads through 1 or more switches (this
number is sometimes referred to as the
number of "hops" )
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
39
Connecting Devices in a
Network
• In a network a device is identified by a
network address
– eg: our phone-number, the MAC address of your
computer
• Devices communicate by sending
messages (frames, packets) to each other
• Some establish a connection lilke the
telephone network, some simply send
messages
• Modern networks are switched with point-topoint links
– circuit switching, packet switching
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
40
A Switched Network
3
4
2
1
5
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
• While 2 can
send data to 1
and 4, 3 can
send at full
speed to 5
• 2 can distribute
the share the
bandwidth
between 1 and
4 as needed
41
Network Technologies
• Examples:
–
–
–
–
–
–
The telephone network
Ethernet (IEEE 802.3)
ATM (the backbone for GSM cell-phones)
Infiniband
Myrinet
many, many more
• Note: some of these have "bus"-features as well
(Ethernet, Infiniband)
• Network technologies are sometimes functionally
grouped
– Cluster interconnect (Myrinet, Infiniband) 15 m
– Local area network (Ethernet), 100 m to 10 km
– Wide area network (ATM, SONET) > 50 km
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
42
Ethernet
• Cheap, cheap, cheap
• Unreliable – but in practice transmission errors are
very low
• Available in many different speeds and physical
media
– 10, 100, 1000 MBit, 10 Gbit and soon up to 100 Gbit/s
on a single link
– optical fibres
– copper unshielded twisted pairs
– power-lines
– wireless
• We use IP or TCP/IP over Ethernet
• By far the most widely used local area network
technology (even starting on the WAN)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
43
Raw data format
There are 10 kinds of people in the world
Those who can read binary and those who cannot
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
44
Binary vs Text
• 11010110 Pros:
– compact
– quick to write & read (no
conversion)
• Cons:
– opaque (humans need
tool to read it)
– depends on the
machine architecture
(endinaness, floating
point format)
– life-time bound to
availability of software
which can read it
• <TEXT></TEXT> Pros:
– universally readable
– can be parsed and
edited equally easily by
humans and machines
– long-lived (ASCII has not
changed over decades)
– machine independent
• Cons:
– slow to read/write
– low information density
(can be improved by
compression)
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
45
A little checklist for your DAQ
Data can be acquired
with PC hardware
Yes
Data
rate
(MB/s)
A single PC
suffices?
Can be done
with several
PCs?
No
Yes
No
Yes
Use crate-based
Electronics
(CompactPCI/
VME)
Do it yorself
in Linux
No
Standard
software
available?
Yes
Raw data > 1 MB /
day
Connect
them via
Ethernet
Use it (e.g.
Labview)
Yes
Remember:
YMMV
No
Use
binary
Use text
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
46
Data Acquisition for a Large
Experiment
Moving on to Bigger Things…
The CMS Detector
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
48
Moving on to Bigger Things…
•
•
•
15 million detector channels
@ 40 MHz
= ~15 * 1,000,000 * 40 * 1,000,000 bytes
• = ~ 600 TB/sec
?
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
49
Designing a DAQ System for
a Large HEP Experiment
• What defines "large"?
– The number of channels: for LHC experiments
O(107) channels
• a (digitized) channel can be between 1 and ~ 14 bits
– The rate: for LHC experiments everything
happens at 40.08 MHz, the LHC bunch crossing
frequency (This corresponds to 24.9500998 ns or 25 ns among
friends)
• HEP experiments usually consist of many
different sub-detectors: tracking,
calorimetry, particle-ID, muon-detectors
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
50
Challenges for the L1 at LHC
• N (channels) ~ O(107); ≈20 interactions every 25 ns
– need huge number of connections
• Need to synchronize detector elements to (better
than) 25 ns
• In some cases: detector signal/time of flight > 25 ns
– integrate more than one bunch crossing's worth of
information
– need to identify bunch crossing...
• It's On-Line (cannot go back and recover events)
– need to monitor selection - need very good control over all
conditions
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
51
Trigger
• No (affordable) DAQ system
could read out O(107)
channels at 40 MHz  400
TBit/s to read out – even
assuming binary channels!
• What’s worse: most of these
millions of events per second
are totally uninteresting: one
Higgs event every 0.02
seconds
• A first level trigger (Level-1,
L1) must somehow select the
more interesting events and
tell us which ones to deal
with any further
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
Black magic
happening here
52
Know Your Enemy: pp Collisions at 14
TeV at 1034 cm-2s-1
• (pp) = 70
mb --> >7 x
108 /s (!)
• In ATLAS
and CMS*
20 min bias
events will
overlap
• HZZ
Z mm
H 4 muons:
the cleanest
(“golden”)
signature
*)LHCb
Reconstructed tracks
with pt > 25 GeV
And this
(not the H though…)
repeats every 25 ns…
@2x1033 cm-2-1 isn’t much nicer and in Alice (PbPb) it will be even worse
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
53
How to defeat minimum bias:
transverse momentum pt
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
54
CMS DAQ
Congestion is handled by
synchronizing the sources
to send in discrete timeslots: Barrel Shifting
Collision rate
40 MHz
Level-1 Maximum trigger rate
100 kHz
Average event size
≈ 1 Mbyte
Event Flow Control
≈ 106
Mssg/s
No. of In-Out units
Readout network bandwidth
Event filter computing power
Data production
No. of PC motherboards
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
≈
≈
≈
≈
512
1 Terabit/s
106 SI95
Tbyte/day
Thousands
55
WE GOT THE DATA.
WHAT NOW?
Reconstruction and Analysis
Apologies for this
being HEP biased
Difference between Reconstruction
and
Analysis is sociologico-technical:
Usually reconstruction is done in
common for all analysis done on a
specific set of data.
Analysis is something very personal:
(even then there are 2000 coauthors )
And that in simple terms is how we analyse
the data
Reality
We use experiments to inquire
about what “reality” does.
• We intend to fill this
gap
Theory &
Parameters
The goal is to understand in
the most general; that’s
usually also the simplest.
- A. Eddington
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
58
Theory
Particle Data Group,
Barnett et al
• “Clear statement of
how the world
works”
Additional term goes
here
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
59
What does the data mean?
Digitized
data:
0x80001000 0xFEEDBABE 0xCAFECAFE 0xDEADBEEF
“Address”: what
detector element
took the reading
“Value”: What the
electronics wrote
down
Look up type, calibration info
Look up/calculate spatial position
Check valid,
convert to
useful units/form
Draw
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
60
It’s a long way to pμ
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
61
Reconstruction: Calorimeter Energy
• Goal is to measure particle properties in the event
– “Finding” stage attempts to find patterns that indicate what happened
– “Fitting” stage attempts to extract the best possible measurement from
those patterns.
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
62
Finding
• Clusters of energy in a calorimeter are due to the original
particles
– Clustering algorithm groups individual channel energies
– Don’t want to miss any; don’t want to pick up fakes
• Many algorithms exist
– Scan for one or more channels above a high threshold as
“seeds”
– Include channels on each side above a lower threshold:
Not perfect! Doesn’t use prior knowledge about event,
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
cluster shape,
etc
63
One lump or two?
• Hard to tune thresholds to get this right.
• Perhaps a smarter algorithm would do
better?
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
64
Fitting
• From the clusters, fit for energy and position
– Complicated by noise & limited information
• Simple algorithm: Sum of channels for energy,
average for position
-1
50%, 50%
0
+1
Cluster at 0, evenly split
x  50%(1)  50%(1)  0.0
85%, 15%
Cluster at -0.5, unevenly split

100%, 0%
Cluster at -1

x  85%(1)  15%(1)  0.70
x  100%(1)  0(1)  1.0
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
65
RICH
• Probably the most
complex detector
• Measures the true
velocity of a
particle
cosθ=1/n β
• Typically 5 stable
charged particles
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
66
RICH pattern recognition
• Staggering
complexity
• For each
track (20 to
50) consider
each photon
(100 to 200)
under each
hypothesis (5)
and
minimize!
• Involves ray
tracing the
photon
through the
detector
• O(N4) lots of
CPU needed!
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
67
A very nice example by
Bob Jacobson
A simple analysis:
What’s BR(Z->m+m-)?
• Measure:
 
Number
of
m
m events
0
 
BRZ  m m  
Total number of events
• Take a sample of events, and count those with a mm final
state.
– Two tracks, approximately back-to-back with the expected
|p|
• Empirically, other kinds of events have more tracks
– Right number of muon hits in outer layers
• Muons are very penetrating, travel through entire detector
– Expected energy in calorimeter
• Electrons will deposit most of their energy early in the
calorimeter; muons leave little
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
69
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
70
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
71
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
72
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
73
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
74
Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
75
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
76
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
77
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
78
Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
79
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
80
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
81
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
82
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
83
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
84
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
85
Not Z->m+m-
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
86
Summary
• We have a result: BR(Z->m+m-) = 2/45
• But there’s a lot more to do!
• Statistical error
– We saw 2 events, but it could easily have been 1 or 3
– Those fluctuations go like the square-root of the number of
events:
Nmm
N mm
BRZ  m m  

Ntotal Ntotal

0

– To reduce that uncertainty, you need lots of events
• Need to record lots of events in the detector, and then process
them
• Systematic error
– What if you only see 50% of the m+m- events?
Nmm seen  N mm
• Due to detector imperfections, poor understanding, etc?
Nseen 
BRZ  m m  
Ntotal
0


Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
  0.50  0.05
87
This is only the beginning…
• Statistics
• Systematic errors need a case-by-case
treatment
• Complex detector response can only
be estimated by simulation
• etc…
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
88
Summary
• We have touched on a few keyconcepts of trigger, DAQ and analysis of
data
• While there is literature and of course a
strong underpinning in science and
engineering, these three fields are
essentially crafts
• The best thing is to learn them from others
and by doing them
• Have fun!
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
89
Further Reading
•
Trigger
•
Buses
•
–
–
–
VME: http://www.vita.com/
PCI
http://www.pcisig.com/
Conferences
•
Journals
–
–
–
–
IEEE Realtime
ICALEPCS
CHEP
IEEE NSS-MIC
–
IEEE Transactions on Nuclear
Science, in particular the
proceedings of the IEEE Realtime
conferences
IEEE Transactions on
Communications
Network and Protocols
–
–
–
•
Game:
http://www.happypenguin.org/show?Trig
ger
•
Ethernet
“Ethernet: The Definitive Guide”,
O’Reilly, C. Spurgeon
TCP/IP
“TCP/IP Illustrated”, W. R. Stevens
Protocols: RFCs
www.ietf.org
in particular RFC1925
http://www.ietf.org/rfc/rfc1925.txt
“The 12 networking truths” is
required reading
–
• Analysis & Statistics
–
–
Fruehwirth & Regler “Data Analysis
Techniques for High Energy Physics”
G. Cowan “Statistical Data
Analysis”
Wikipedia (!!!) and references
therein – for all computing related
stuff this is usually excellent
Trigger / DAQ / Analysis ESI 2009, Niko Neufeld
90