calorimetry in particle physics...

Calorimetry in particle physicsexperiments

Unit n. 7Front End and Trigger electronics

Roberta Arcidiacono

R. Arcidiacono Calorimetria a LHC 2

Lecture overview

● Signal processing● Introduction on calorimeter FE● Pre-amplifiers

● Charge sensitive● Current sensitive

● Readout● Shaper● Readout & ADCs● Digital filtering

● L1 Calorimeter Trigger ● NA48 example


Signal processing

Signal Processing is a way of converting an

obscure signal into useful information

Signal processing includes signal amplification,

signal shaping (filtering) and readout

The basic goal is to extract the desired information

(Amplitude, time of the signal) from the obscuring

factors (e.g. noise, pile-up)


Signal processingMost detectors provide a certain amount of (induced)

charge onto an output electrode (deriving from moving

ionization/excitation charge).

The electrode represents a certain capacitance For signal-processing point of view, they behave like capacitive

charge sources → charge generator with capacitance in parallel

Differences between detectors:• The typical charge at the detector output → can differ by six orders of magnitude • The output capacitances → can differ by the same factor • Signal dynamics• Time available for the measurement


Typical signal processing chain

• Very small charge (fC) from detector → need amplification and shaping to match the converter input characteristics

• Meas. of Amplitude (ADC) and/or Time (TDC)

DigitalSignal

Processors


Ex of Signal induced by a moving charge• https://indico.cern.ch/event/209452/contribution/0/material/slides/1.pdf

� �d

x

E

Vb

i

QA,el = -qV’A(P)

V’A

= -qx

d

QA,ion = qV’A(P)

V’A

= qx

d

Anode (A)

Cathode (C)

A constant induced current flows in the external circuit

Parallel Plate Ion Chamber

Applying

Green’s

Theorem

I=dQA ,el

dt=−qddxdt


Noise Spectra

white noise pink, 1/f noise

in the frequency domain


Noise sources

Source of serial noise related to amplification technique

Source of parallel noise due to imperfections in the amplifier or in the detector (current losses) and to parassitic resistances (Rp) of the input stage

Noise important only if it contributes to the output of the filter. It is fundamental to know the transfer function of the filter.


Signal processing

The detector signal is modeled as a current source

delivering a current pulse with time profile s(t) and

charge Q, across the parallel of the detector

capacitance Cd and the preamplifier input capacitance Ci.

Cd CiQ · s(t)

A

noiseless

preamplifier

signal

processor

i2W=b

e2W=a

parallel

white noise

series

white noise

e2f=c/|f|

i2f=d·f

series

1/f noise

parallel

f noise


Dynamic range

It is defined as:It is defined as:• Maximum signal/minimum signal (or noise)Maximum signal/minimum signal (or noise)• Typical values: 103–105

• Often specified in dB (20 log Vmax/Vmin) = 60–100 dB• Also in bits : 2n= Vmax/Vmin= 10–18 bits

• The large dynamic range is a key parameter for calorimeter FE electronics


Introduction on calorimeter FE electronics

Calorimeter readouts requirements:– Response linear over a large dynamic range (18 bit)– Noise ( ENC “equivalent noise charge”) should not

dominate the energy resolution; low coherent noise– Read-out rate capability** adapted to observed

interaction rate– Sensitivity to magnetic field, radiation,

temperature to be considered!

** occupation time, integration time, time resolution


Introduction on calorimeter FE electronics

Calorimeter readouts nowadays are characterized by:

– A large dynamic range ~ 16 bits– Low noise– Large number of channels (hundred-thousands)– High speed: the shaping times are now in the 20-

200 ns region


Pre-amplifiersMain function:

Receive weak signal from a detector, amplify it and pass it on via cables to the heart of the electronic processing system.

Mounted as close as possible to the signal source, to reduce extra noise (which will be amplified as well), and to reduce as well signal attenuation (along cables)

Impedance matching (between input stage and preamplifier) must also be achieved, to avoid pulse distortion.

Trend: integrate the whole processing chain in FE electronics

Peaking time: time required for a shaped pulse to go from the baseline to the peak


Pre-amplifiersCharge pre-amplifier:Traditionally used as FE elements of calorimeter readouts.

They are usually realized by placing a capacitor Cf as feedback element of a voltage amplifier. Voltage output for current input.

Low impedance. Configuration exhibit minimal noise: parallel noise negligible.

In fast calorimetry, the preamplifier risetime modifies the shaping time: must specify the overall peaking time tp

The main limitation is their counting rate which is limited by pileup as the signal is integrated during a long time


Pre-amplifiers

Current pre-amplifier:Overcome pileup problems with charge preamps at high

counting rate. They are usually based on the same architecture replacing the feedback capacitor by a resistor.

The noise is similar to the charge preamp except for a significant parallel noise contribution, which is usually acceptable at fast shaping time.


Pre-Amplifiers Overview

The power per channel is relatively high mW : price to pay to obtain low noise figures.


Dictionary

• ASIC = application-specific integrated circuit is an integrated circuit (IC) customized for a particular use

• Hybrid = a componenti discreti miniaturizzati• BiCMOS = Bipolar Complementary Metal Oxide Semiconductor, tecnologia

mista che integra CMOS e BJT sullo stesso chip semiconduttore.• CMOS (Complementary MOS)= tecnologia utilizzata in elettronica per la

progettazione di componenti digitali utilizzando transistor.• JFET= junction field effect transistor, tipo di transistor ad effetto di

campo, via di mezzo tra i transistor a giunzione bipolare (BJT) e i MESFET, a basso rumore.

• GaAs = compound di gallium and arsenic. Semiconduttore usato per microwave frequency integrated circuits infrared light-emitting diodes, laser diodes and solar cells.


Shapers

Shapers:The goal is to optimize the signal to noise ratio, adapting preAmps output to ADC input window. In the past complex architectures to optimize series and parallel noise contribution.In fast calorimetry parallel noise is no longer a concern. More the physics noise ( pileup of minimum bias events) is what usually determines the optimum shaping time. The earlier digitization and the progress in DSPs has boosted the use of digital filtering


Shapers: filtering noise

F(f) U(f)

F(f)

fU(f)

f

h(f)

f

Noise floorf0

f0

f0

Improved Signal/Noise Ratio

Example of noise filtering

Detector signals have very often a very large frequency spectrum

The filter (shaper) provides a limitation in bandwidth, and the output signal shape is different with respect to the input signal shape.


Shapers: filtering noise

F(f) U(f)

F(f)

fU(f)

f

h(f)

f

Noise floor

f0

f0

Improved Signal/Noise Ratio

The output signal shape is determined by:• Input signal shape (characteristic of detector)• Filter (amplifier-shaper) characteristic

The output signal shape is chosen such to satisfy the requirements: Time measurement, Amplitude measurement, Pile-up reduction, Optimized Signal-to-noise ratio


Electronic signal processing

ff0

ff0

signal BW is preserved

filter cuts inside signal

bandwidth → shape is

modified


Real-time Filters A filtering device is a signal processor, capable of signal discrimination on the basis of either frequency-domain or

time-domain characteristics.

Continuous-time (analogue) filters predominant in the past Passive: R – L - C Active: Operational Amplifiers + R-L-C or R-C

feedback

Nowadays discrete-time (sampled) filters, analogue (switched-capacitors) and digital, have replaced the analogue filters in most applications → sampled signal becomes a sequence of numbers

Advantages of Digital Filters: Finite-duration impulse responses, high-programmability, greater accuracy


Real-time Filters Disadvantages of Digital Filters: Power consumption, Limited

by A/D speed and resolution

Why Analogue circuitry?

“Real-World” signals are analogue

Analogue Signal Processing (ASP) has certain specific advantages, e.g. low-power, no A/D conversion,speed

Mixed-mode Signal Processing (i.e. combining ASP with DSP on a VLSI chip has become technologically possible, and is gaining in popularity)

Switched-Capacitor (SC) circuits (sampled-data, or discrete-time analogue circuits) have largely proved themselves in VLSI technology


Real-time Filters

What about HEP?What about HEP?

– Filtering is almost exclusively based on

continuous-time signals– Analogue filters based on “classical” architectures – Marginal use of discrete-time filters, both

analogue and digital


Analogue Shapers• Analog filters work characterized by the overall number and

arrangement of parts (the electronic filter topology) ( "order" of the filter), and by the transfer function of that order.

→ mathematical representation, in terms of spatial or temporal frequency, of the relation between the input and output of a linear time-invariant system.

• Main architectures: CR RC2 and Bessel (best approx of optimal shapers)


Shapers: optimum shaping time

• Shaper has to minimize the quadratic sum of electronics noise (which increases at fast shaping) and pileup noise (which increases at slow shaping)

• As the pileup noise is proportional to the collider luminosity the optimum shaping time should be varied as the luminosity evolves → performed by further digital filtering


On Digital Filters Digital filtering (after ADC converter stage) is a more and more widespread method of further improving the signal to noise ratio. Given the sampled signal: s(t)=Ag(t)+b → s

i=Ag

i+b

i


On Digital Filters

It consists of a linear weighted sum of N samples of the digitized signalFilter → S = Σn

i=1 aisi

with coefficients ai= ΣR-1

ij g

i

obtained by inverting the noise autocorrelation matrix R, g

i signal

shape(0, 0.63, 1, 0.8, 0.47)

The coefficients can be recalculated when the luminosity changes and optimized for various compartments of the detector which exhibit different electronics noise

cross-correlation of a signal with itself.


Read-out technique

• Experiments rely on multilinear handling of the large dynamic range of calorimeters (multi gain ADC converter stage)

• Most experiments now digitize very early often just after preamplifier/shaper

• The data storage until LV1 arrives is more often digital although analog pipelines reach excellent performance with up to 13 bits dynamic range and simultaneous readwrite operation.


Readout overview


On Trigger Systems...

• Trigger system has to identify interesting events and reject all unwanted interactions

• Nowadays, rejection factor is orders of magnitude• Cannot do it at beam crossing rate

– Algorithms are too sophisticated.

– Accelerator related backgrounds can contribute to the problem (e+e- vs pp)

• Multi-Level trigger– Algorithms can be implemented in Hardware, typically

custom boards, often matched to geometry of detector– Algorithms can be implemented in Software Farm– Or be a mix of the two


L1 Calorimeter Trigger

• Calorimeters can provide fast informations and be used successfully @ L1 stage

• @High Luminosity, Calo Pattern recognition much easier than tracker

• Typical budget time ~ 2-3 s (signal transfer time included)

→ Pipelined synchronous trigger, no dead-time, fixed latency wrt event time


L1 Calorimeter Trigger

• L1 Calo triggers at colliders (detector has projective geometry) computes:

– Electron/Photon objects– Jet/Tau objects– Missing ET/Total ET


NA48 ECAL Trigger

• Built for the selection of KBuilt for the selection of K00→→ 2 2ππ00 →→44γγ• Large reduction and high trigger efficiencyLarge reduction and high trigger efficiency• 40 MHz dead-time free pipeline40 MHz dead-time free pipeline• Computes every 25 ns:Computes every 25 ns:

– Total EnergyTotal Energy– Energy Centre of gravityEnergy Centre of gravity– Kaon lifetimeKaon lifetime– Number of peaks in calorimeter projectionsNumber of peaks in calorimeter projections


NA48 Trigger Requirements

Neutral Trigger = NUT• Particle rate in detector = 1 MHz • Data digitized and stored in 200 μs ring buffers• Neutral trigger decision every 25 ns with latency

of 3.2μs:– Select 2π0 – Suppress background 3-body decay– Small loss through accidental activity


NUT pipeline schema


NUT chain in detail

Schema of the neutral trigger signal flow in CPD

Bandbass Filter for noise reduction

Fine-time reconstruction of a peak Look-up-Tables System


NUT performance

Very high efficiency > 99.9%

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