Introduction to Trigger Data Acquisition Data Analysis EIROforum

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 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 theory Trigger / DAQ / Analysis ESI 2009, Niko Neufeld 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 – bw. Tycho = ~ 100 Bytes / h (compare with LHCb 40. 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 3 D 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 Clear Q 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 Discriminator Delay ADC Start Full and Busy Logic FIFO Processing Data. Ready 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 Trigger Delay ADC Start Busy Logic and Processing Interrupt Ready Set Clear not Q storage Discriminator Delay Discriminator ADC FIFO Processing Start and Busy Logic Full Data. Ready 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 Pt 100 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->next) for (dev = bus->devices; dev = dev>next) if (dev->descriptor. id. Vendor == HOBBICO) vmh = usb_open(dev); usb_bulk_read(vmh , 3, &u, sizeof(float), 500); USB/RS 232 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” 7 U VME Crate (a. k. a. “Subrack”) 7 U • Buy or build a compact multiport volt-meter module, e. g. 16 inputs • Put many of these multi-port modules together in a common chassis or crate • The modules need Backplane Connectors (for power and data) VME Board Plugs into Backplane – 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 19 in 49 U 1 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 22 Slave Master Device 3 Device 44 Data. Lines Trigger / DAQ / Analysis ESI 2009, Niko Neufeld Select. Line 31

Buses • Famous examples: PCI, USB, VME, SCSI – older standards: CAMAC, ISA – upcoming: ATCA – many more: Fire. Wire, I 2 C, Profibus, etc… • Buses can be – – local: PCI external peripherals: USB in crates: VME, compact. PCI, ATCA long distance: CAN, Profibus Trigger / DAQ / Analysis ESI 2009, Niko Neufeld 32

The VME Bus 0 x 000 -0 x 1 ff 0 x 200 -0 x 2 ff 0 x 300 -0 x 3 ff 0 x 400 -0 x 4 ff 0 x 500 -0 x 5 ff 0 x 600 -0 x 6 ff • 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 (VME 64) 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-to-point 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 • Unreliable – but in practice transmission errors are very low • Available in many different speeds and physical media – 10, 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 Remember: YMMV 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 * 40 * 1, 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 L 1 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, L 1) 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 Te. V at 1034 cm-2 s-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 And this (not the H though…) repeats every 25 ns… @2 x 1033 cm-2 -1 isn’t much nicer and in Alice (Pb. Pb) 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 time-slots: Barrel Shifting Collision rate Level-1 Maximum trigger rate Average event size ≈ Event Flow Control ≈ Mssg/s 40 MHz 100 k. Hz 1 Mbyte 106 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 SI 95 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 there are 2000 co-authors ) 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: 0 x 80001000 0 x. FEEDBABE 0 x. CAFE 0 x. DEADBEEF “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 61 Trigger / DAQ / Analysis ESI 2009, Niko Neufeld

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, cluster shape, Trigger etc / DAQ / Analysis ESI 2009, Niko Neufeld 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 0 +1 50%, 50% Cluster at 0, evenly split 85%, 15% Cluster at -0. 5, unevenly split 100%, 0% Cluster at -1 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 • • Trigger / DAQ / Analysis ESI 2009, Niko Neufeld 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(N 4) lots of CPU needed! 67

A very nice example by Bob Jacobson

A simple analysis: What’s BR(Z->m+m-)? • Measure: • 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

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Not Z->m+m- Trigger / DAQ / Analysis ESI 2009, Niko Neufeld 71

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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: – 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? • Due to detector imperfections, poor understanding, etc? Trigger / DAQ / Analysis ESI 2009, Niko Neufeld 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 key-concepts 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 • IEEE Realtime ICALEPCS CHEP IEEE NSS-MIC Journals – IEEE Transactions on Nuclear Science, in particular the proceedings of the IEEE Realtime conferences – IEEE Transactions on Communications Network and Protocols – Ethernet “Ethernet: The Definitive Guide”, O’Reilly, C. Spurgeon – TCP/IP “TCP/IP Illustrated”, W. R. Stevens – Protocols: RFCs www. ietf. org in particular RFC 1925 http: //www. ietf. org/rfc 1925. txt “The 12 networking truths” is required reading • – – Game: http: //www. happypenguin. org/show? Trigger – VME: http: //www. vita. com/ – PCI http: //www. pcisig. com/ Conferences • 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