Status of TPC Signal Simulation & Processing Jyoti Joshi - - PowerPoint PPT Presentation

Status of TPC Signal Simulation & Processing

Jyoti Joshi

Brookhaven National Laboratory LBL/Sim/Reco Meeting, 11/07/2016

* Signal Processing Method * LArTPC Noise Experience

* Signal Processing Challenges

Outline

2

* TPC signal consists of >me and charge informa>on from induc>on and collec>on planes

• Same amount of charge seen by all wire planes

* The goal of signal processing is to extract both >me and charge informa>on reliably

Introduc>on: Signal Processing

Cathode Plane

Edrift

U V Y

Liquid Argon TPC Y wire plane waveforms V wire plane waveforms Sense Wires

t

Incoming Neutrino Charged Particles

3

TPC Signal Size

• E"field:(

– Electron(dri/(velocity( – Recombina6on(factor(

• Electron(life6me(

– Dri/(distance(!(dri/(6me(!(signal(size(

• Track(angle(

– Time(structure(of(signal(

• Minimum(ionizing(vs.(heavy(ionizing(

– Recombina6on(etc.(

• Diffusion

4

Field and Electronic Response Field and Electronic Response

Field Response Func>on Electronics Shaping Func>on

* Using 2D garfield simula2on with 3mm

wire pitch

* Charge vs. Time averaged for a single

electron

* Further stretched signal for U and V

considering 3D wires

* Cold electronics:

• Four shaping 2me - 0.5, 1, 2, 3 us
• Four gain seHngs - 4.7, 7.8, 14, 25 mV/fC

5

Deconvolu>on

6

DPF, 2015 Jyo> Joshi

Deconvolu)on*

1*

M( ) ( ) ( )

t

t R t t S t dt = − ⋅ ⋅

∫

( ) R( ) S( ) M ω ω ω = ⋅

Fourier*transforma)on*

Time*domain* Frequency*domain*

S(t)

Back*to*)me*domain*

An);Fourier** transforma)on**

M( ) S( ) R( ) ( ) F ω ω ω ω = ⋅

The goal of the deconvolu>on process is to extract charge and >me informa>on from the TPC signals

Deconvolu>on

6

Deconvolu>on Filter

7

DPF, 2015 Jyo> Joshi

Perfect Signal Adding Integer ADC Add Random Noise Add Filter

7

Noise Sources in Detector

Electronics Noise (ENC)

* Dominant noise sources are from the circuits and components directly connected to input node

• i2n arises in the sensor, e.g, from the leakage current; i2nF may arise in the feedback circuit
• i2diel a thermal fluctua2ons in dielectrics
• e2n associated with the gain mechanism in the input transistor (known as “series noise”)

Digi>za>on Noise

* Digi2za2on noise is due to the signal digi2zed using 12-bit ADC. * Digi2za2on noise is usually made smaller than the electronics noise.

8

9

* Noise associated with first transistor of the cold ASIC

• Unavoidable
• Expected ENC ~500 electrons at LAr temp (for 150 pF)
• Depends on shaping 2me, wire length and TPC geometry

* Noise from warm shaping amplifier & ADC

• Negligible as compared to first transistor

* Noise from other circuits in readout chain

• Low frequency coherent noise from voltage regulator

* Noise from wire bias power supplies

• Negligible

* Noise from cathode HV

• Anode sensi2vity due to ripple from HV

* ASIC satura2on due to wire mo2on

• Charge generated due to wire mo2on in E.field

Excess Noise Sources

*

ENC acer noise filtering is around 400 electrons for 85% of channels

*

~ 10% Non-func2onal channels

*

Measured efficiency requiring two wire planes with real loca2on of dead channels is about 97.3%

Noise Performance in MicroBooNE

PSNR: Peak Signal to Noise RMS

MicroBooNE- NOTE-1016-PUB.pdf

10

* Socware noise filter is applied which improves peak-signal-to-noise

ra2o by a factor of 2

11

MicroBooNE- NOTE-1016-PUB.pdf

12

* Distor2on of Signal due to regulator noise removal specially when par2cle traveling

parallel to the wire plane i.e, when signal is also coherent across many wires

* Effect Signal Protec2on decreases with signal size closer to coherent noise

U-Plane V-Plane Y-Plane

Worst Case Scenario

Impact of Coherent Noise Filtering on Signal

MicroBooNE- NOTE-1016-PUB.pdf

Impact of Non-Func>onal Channels

Volume efficiency of a detector with can be es2mated as:

where, p is efficiency for a single plane & n is number of planes

Efficiency, if there are less number of planes: But requiring less number of planes implies increase in ambigui2es (i.e, fake hits) And number of fake hits can be es2mated as:

• X. Qian

13

Poten2al hits vs. real hits for 1% and 5% dead region See difference b/w solid & dashed lines

• X. Qian

Hence the reconstruc2on becomes very challenging!

14

Signal Processing Challenge: Dynamic Induced Charge

15

DPF, 2015 Jyo> Joshi

* The field response model shown before neglected

the induced charge contribu2ons from the adjacent wires

* The induced current on each wire can be derived

by Shockley-Ramo therorem: The electron dric lines (orange color) are superimposed on the weigh2ng field contours

1.7 deg track from ver2cal

* Induc2on signal strongly depends on local charge distribu2on * Due to this induced signal on adjacent wire, the digi2zed signal on wires is more complicated

and strongly depends on track angle. Garfield Simula2ons

15

MicroBooNE- NOTE-1017-PUB.pdf

16

Deconvolu>on Scheme with DIC

17

DPF, 2015 Jyo> Joshi

* Including effects of induced charge from adjacent wires :

Mi(t0) = R0(t −t0)⋅ Si(t)+ R1(t− t0)⋅ Si+1(t)+...

( )⋅

t

∫

dt Mi(ω) = R0(ω)⋅ Si(ω)+ R1(ω)⋅Si+1(ω)+...

* With induced signals, the signal is linear sum of direct signal and induced signal,

can be represented in a matrix form:

1 1 1 1 2 1 2 1 2 1 1 2 1 1 1 1

( ) ( ) ( ) ... ( ) ( ) ( ) ( ) ( ) ( ) ... ( ) ( ) ( ) ... ... ... ... ... ... ... ( ) ( ) ( ) ... ( ) ( ) ( ) ( ) ( ) ( ) ... ( ) ( )

n n n n n n n n n n n n

M R R R R S M R R R R S M R R R R S M R R R R S ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω ω

− − − − − − − −

# $ # $ % & % & % & % & % & % & = ⋅ % & % & % & % & % & % & ( ) ( ) ( ) ω # $ % & % & % & % & % & % & ( )

* Inversion of matrix `R’ can be done with deconvolu2on through 2D Fast Fourier

Transforms (FFT)

17

Challenges in Signal Processing: 2D Deconvolu>on

Raw signal is very small: Track traveling perpendicular to wire plane

MicroBooNE- NOTE-1017-PUB.pdf

18

Exercised Two-dimensional deconvolu>on technique to extract number of ionized electronics from wire planes

19

Challenge with Induc>on Plane

Low Frequency filter in deconvolu2on step, can remove the long signal. Understanding

• f response func2on and Region of Interest (ROI) selec2on is very important

20

Current Implementa>on in dunetpc code

* Noise model used is simple white gaussian noise * There is also another noise model (acer noise filtering) based

• n coherent noise removal which has exponen2al feature in

low frequency from 35ton data

* Field response func2on used is the average one, currently no

contribu2on from induced charge from adjacent wires and hence 1D-deconvolu2on * More code structure improvements (David Adams)

21

Summary

* Many Challenges in TPC Signal Simula2on and Processing * Noise model from data (acer noise filtering) already exists * Work is ongoing on 3D field response calcula2ons (Bres & Leon) * New improvements in simula2on code structure (David Adams) * More realis2c Signal Simula2on code with data-drive noise

model and detailed response func2on development in Wire-Cell framework (Xiaoyue)

BACK-UP

22

Fast Fourier Transformation (FFT) & AutoCorrelation

23

* FFT is an efficient algorithm to compute the discrete Fourier transform (DFT)

and its inverse. Fourier analysis converts a function from time domain to the frequency domain by factorizing the matrix into a product of mostly zero factors. FFT computes the same result in O(N logN) operations which DFT computes in O(N2) * Autocorrelation is the cross-correlation of a signal with itself. Basically, it is the representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals.

According to Weiner-Khintchine theorem, the autocorrelation function of a random process is the Fourier transform of its power spectrum.

Relation b/w FFT & AutoCorrelation

Gain and Shaping >me

• Choice of gain doesn’t impact on the S/N ra2o, higher gain can be

sensi2ve to ADC overflow

• Longer Shaping 2me has smaller noise (higher S/N ra2o), but slightly

worse two peak separa2on

Bo Yu

Simulated Double Track Waveform :

24

Raw and Convoluted Signal

Raw Signal - MIP Convoluted Signal

25