First Look at EMEC Weights using the FFT Algorithm Combined Test Beam Meeting 19 November 2002 Naoko Kanaya & Rob McPherson University of Victoria.

Slides:



Advertisements
Similar presentations
OFDM Transmission over Wideband Channel
Advertisements

Lecture 7: Basis Functions & Fourier Series
Lecture 15 Orthogonal Functions Fourier Series. LGA mean daily temperature time series is there a global warming signal?
Sampling theory Fourier theory made easy
Sampling, Reconstruction, and Elementary Digital Filters R.C. Maher ECEN4002/5002 DSP Laboratory Spring 2002.
Event Phase Reconstruction Raw TDC Event time used for monitoring Event phase  TBPhaseRec  TBPhase Michel Lefebvre Rob McPherson University of Victoria.
Transforms: Basis to Basis Normal Basis Hadamard Basis Basis functions Method to find coefficients (“Transform”) Inverse Transform.
Normalised Least Mean-Square Adaptive Filtering
Inputs to Signal Generation.vi: -Initial Distance (m) -Velocity (m/s) -Chirp Duration (s) -Sampling Info (Sampling Frequency, Window Size) -Original Signal.
Characterization of the electrical response of Micromegas detectors to spark processes. J.Galan CEA-Saclay/Irfu For the RD51 meeting.
Discrete-Time and System (A Review)
DOLPHIN INTEGRATION TAMES-2 workshop 23/05/2004 Corsica1 Behavioural Error Injection, Spectral Analysis and Error Detection for a 4 th order Single-loop.
1 CS 551/651: Structure of Spoken Language Lecture 8: Mathematical Descriptions of the Speech Signal John-Paul Hosom Fall 2008.
W  eν The W->eν analysis is a phi uniformity calibration, and only yields relative calibration constants. This means that all of the α’s in a given eta.
Analytical vs. Numerical Minimization Each experimental data point, l, has an error, ε l, associated with it ‣ Difference between the experimentally measured.
CISE315 SaS, L171/16 Lecture 8: Basis Functions & Fourier Series 3. Basis functions: Concept of basis function. Fourier series representation of time functions.
Saturation in V-calib LED scans CALICE AHCAL – Main Meeting 2010/07/05 Jaroslav Zalesak analysis still in progress but gives global results FNAL & CERN.
C. Seez Imperial College November 28th, 2002 ECAL testbeam Workshop 1 Pulse Reconstruction Worth considering the experience of other experiments using.
Signals CY2G2/SE2A2 Information Theory and Signals Aims: To discuss further concepts in information theory and to introduce signal theory. Outcomes:
 a mathematical procedure developed by a French mathematician by the name of Fourier  converts complex waveforms into a combination of sine waves, which.
Reconstruction techniques, Aart Heijboer, OWG meeting, Marseille nov Reconstruction techniques Estimators ML /   Estimator M-Estimator Background.
Speech Signal Representations I Seminar Speech Recognition 2002 F.R. Verhage.
Chapter 4 Fourier transform Prepared by Dr. Taha MAhdy.
1 xCAL monitoring Yu. Guz, IHEP, Protvino I.Machikhiliyan, ITEP, Moscow.
1 Complex Images k’k’ k”k” k0k0 -k0-k0 branch cut   k 0 pole C1C1 C0C0 from the Sommerfeld identity, the complex exponentials must be a function.
NA62 Trigger Algorithm Trigger and DAQ meeting, 8th September 2011 Cristiano Santoni Mauro Piccini (INFN – Sezione di Perugia) NA62 collaboration meeting,
Combined HEC/EMEC testbeam data can be read and analyzed within the ATLAS Athena framework A “cookbook” gives an introduction for how to access the data.
Robotics Research Laboratory 1 Chapter 7 Multivariable and Optimal Control.
Attenuation measurement with all 4 frozen-in SPATS strings Justin Vandenbroucke Freija Descamps IceCube Collaboration Meeting, Utrecht, Netherlands September.
ES97H Biomedical Signal Processing
Offline and Monitoring Software for the EMEC+HEC Combined Run Combined Test Beam and LArg Software and Performance 11 September 2002 Rob McPherson University.
Results from particle beam tests of the ATLAS liquid argon endcap calorimeters Beam test setup Signal reconstruction Response to electrons  Electromagnetic.
2D Sampling Goal: Represent a 2D function by a finite set of points.
Sampling of Continuous-Time Signals Quote of the Day Optimist: "The glass is half full." Pessimist: "The glass is half empty." Engineer: "That glass is.
Professor A G Constantinides 1 Discrete Fourier Transforms Consider finite duration signal Its z-tranform is Evaluate at points on z-plane as We can evaluate.
Peterson xBSM Optics, Beam Size Calibration1 xBSM Beam Size Calibration Dan Peterson CesrTA general meeting introduction to the optics.
Primarily, Chapter 3 Claridge Also, Chapter 2 Claridge,
Single Correlator Based UWB Receiver Implementation through Channel Shortening Equalizer By Syed Imtiaz Husain and Jinho Choi School of Electrical Engineering.
SYSTEMS Identification Ali Karimpour Assistant Professor Ferdowsi University of Mashhad Reference: “System Identification Theory.
M. LefebvreATLAS LAr week, November 19th HEC-EMEC beam test data analysis Filtering weight synchronization and timing issues TDC timing Cubic timing.
Comparison of filters for burst detection M.-A. Bizouard on behalf of the LAL-Orsay group GWDAW 7 th IIAS-Kyoto 2002/12/19.
Development of a pad interpolation algorithm using charge-sharing.
Measuring Oscillation Parameters Four different Hadron Production models  Four predicted Far  CC spectrum.
BE/ABP/LIS section meeting - N. Mounet, B. Salvant and E. Métral - CERN/BE-ABP-LIS - 07/09/20091 Fourier transforms of wall impedances N. Mounet, B. Salvant.
Testbeam analysis Lesya Shchutska. 2 beam telescope ECAL trigger  Prototype: short bars (3×7.35×114 mm 3 ), W absorber, 21 layer, 18 X 0  Readout: Signal.
PMT time offset calibration (not completed) R.Sawada 27/Dec/2007.
Leo Lam © Signals and Systems EE235 Lecture 26.
ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ (22Δ802) Β΄ ΕΞΑΜΗΝΟ Καθηγητής Πέτρος Π. Γρουμπός  Ώρες Γραφείου: Τετάρτη Πέμπτη Παρασκευή 11:00- 12:00 Γραφείο: 1.
Searches and limit analyses with the Fourier transform Alex, Amanda, Donatella, Franco, Giuseppe, Hung-Chung, Johannes, Juerg, Marjorie, Sandro, Stefano.
Status of the measurement of K L lifetime - Data sample (old): ~ 440 pb -1 ( ) - MC sample: ~125 pb -1 ( mk0 stream ) Selection: standard tag (|
28/11/02Guy Dewhirst1 Ultra Light Weights Method Guy Dewhirst Imperial College London.
3/06/06 CALOR 06Alexandre Zabi - Imperial College1 CMS ECAL Performance: Test Beam Results Alexandre Zabi on behalf of the CMS ECAL Group CMS ECAL.
11 june 2002 CMS ECAL : Patrick Jarry ( Saclay) 1 Pulse shape reconstruction : CMS ECAL Introduction –the problem –the tools –experimental pulse shape.
1 First look on the experimental threshold effects Straw WG meeting Dmitry Madigozhin, JINR.
CS 591 S1 – Computational Audio
on behalf of ATLAS LAr Endcap Group
The general linear model and Statistical Parametric Mapping
Design of Digital Filter Bank and General Purpose Digital Shaper
لجنة الهندسة الكهربائية
Interpolation and Pulse Compression
NanoBPM Status and Multibunch Mark Slater, Cambridge University
EM Linearity using calibration constants from Geant4
Gary Margrave and Michael Lamoureux
Microphone Array Project
8.6 Autocorrelation instrument, mathematical definition, and properties autocorrelation and Fourier transforms cosine and sine waves sum of cosines Johnson.
Slope measurements from test-beam irradiations
Dilepton Mass. Progress report.
Programs for the calibration of TOF measurement with the new pulser
Signals and Systems Revision Lecture 1
Speech Processing Final Project
Presentation transcript:

First Look at EMEC Weights using the FFT Algorithm Combined Test Beam Meeting 19 November 2002 Naoko Kanaya & Rob McPherson University of Victoria

Rob McPherson2 Overview  Want to predict physics pulse shape from calibration pulse shape Use this for determining “optimal” filtering weights  At least two methods “on the market” Time domain convolution “NR” method Kurchaninov/Strizenec used in previous HEC analyses Fourier transform “FFT” method Neukermans, Perrodo, Zitoun, used in EM community  Here we look at the FFT method for the EMEC in the 2002 combined run

Rob McPherson3 FFT Method I  See: ATL-LARG Idea is to simplify the complicated picture:

Rob McPherson4 FFT Method II  Very simplified picture:  Calibration current:   Exponential  Ical(t)  Y (t) (  + (1-  )exp(-t/  c ))  Physics current:  Triangle  Iphy(t)  Y (t) Y(-t)(1-t/  d )) MeasureMeasure Uphy(t)Uphy(t) Ucal(t)Ucal(t)

Rob McPherson5 FFT Method III  Use FT “transfer function” property: Time Domain Convolution  Frequency Domain Multiplication  Ingredients Measured physics pulse shape Uphy(t) (take 128 samples in 1nsec bins) Calculate (discrete) FFT[Uphy(t)] = DFT[Uphy](  ) Assume: FT[Uphy](  ) = FT[Iphy](  ) x H det  ) x H ro  ) Measured calibration pulse shape from delay runs Ucal(t) (also 128 x 1 nsec) Calculate (discrete) FFT[Uphy(t)] = DFT[Ucal](  ) Assume: FT[Ucal](  ) = FT[Ical](  ) x H cal  ) x H ro  )

Rob McPherson6 FFT Method IV  Finally: FT[Uphy](  ) = FT[Ucal](  ) x G (    In simple model: G ana (   FT[Iphy](  ) /FT[Ical](  ) x  0 2 / (   2 ) With  0 2 = 1 / LC  Algorithm: 1) Measure Ucal(t) with delay run 2) Calculate DFT[Ucal](  3) Predict FT[Uphy](   2 free parameters: LC and t 0 (Iphy – Ical) 4) Calculate predicted Uphy(t  5) Minimize predicted-measured Uphy(t   Using uncorrelated  2 with unit error for now

Rob McPherson7 Data and Tools  Physics and calibration data athena  root file  In root: use “fftw” ( )  Physics run: in middle gain 120 GeV e, events channel : 897 (  = 8  =17 layer=2(middle)) E cubic >0.4*Etot TDC: wac=720, guard_region=20)  Calibration run: 12836(middle gain) DAC DAC10

Rob McPherson8 First attempt at fit  See oscillations in best predicted pulse:  Best parameters, fixed = 450 nsec, =370 nsec: (note: not yet fitted, just scanned and minimum found)  Best parameters, fixed  d = 450 nsec,  c =370 nsec: (note: not yet fitted, just scanned and minimum found) LC = 19.5 nHxnF t 0 = 6.1 nsec  Maximum residual: 7.1%

Rob McPherson9 Look in frequency domain  PHYSICS  1 / f ??? CALIB

Rob McPherson10 Transfer function G(f)  Data  1 / f 2 ??? Analytic

Rob McPherson11 Flaw in the model or method?  Many theories: Discrete FT windowing  leakage Reduce pole height Affect high/low frequency But not an offset  Side note: the authors of this talk disagree on this point … Or … maybe simple model not powerful enough? ????

Rob McPherson12 Modified simple model  Allow a high frequency physics tail with extra resistor: r

Rob McPherson13 New G(  ) I With  0 2 = 1 / LC 1/rC New time constant 1/rC 02020202 (   2 )   j  /(rC) (   2 ) + j  /(rC) LC = 29.5 nHnF rC = 0.02 nsec t 0 = 0.5 nsec Maximum residual: 1.8% (t < 120 nsec) Best parameters (fixed = 450 nsec, =370 nsec): Best parameters (fixed  d = 450 nsec,  c =370 nsec):

Rob McPherson14 New G(  ) II  New fit: No visible oscillation Fitted LC corresponds well with peak in FFT[Uphy] / FFT[Ucal LC = 29.6 nHnF  f 0 = Hz = 3.74/128 Hz

Rob McPherson15 Measure LC ? I  In principle: instead of fit, can measure LC from: FFT[Uphy] / FFT[Ucal]  But the peak isn’t obvious: Can we do better?

Rob McPherson16 Measure LC ? II  Know from Fourier Transform theory Time shift  Frequency oscillation And we don’t know t 0 very well  Average over range of min time bins used in FFTs to smooth out oscillation (frequency pole is independent) Recall: “best” LC was 3.7/128 … seems promising

Rob McPherson17 Conclusions  Implemented FFT calibration for EMEC in combined run  “out of the book” FFT method seems to have (known) problems  EM community has some fixes that (I think) use more model parameters / complication  Have instead made small change to the simple model (extra resistor in parallel with L)  Seems to be a promising way of directly measuring LC with no fit at all!  Next: will pursue as far as filtering weight calculation