TES-Simulations - · PDF fileTES-Simulations Jörn Wilms (FAU), P. Peille (IRAP), M....
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Transcript of TES-Simulations - · PDF fileTES-Simulations Jörn Wilms (FAU), P. Peille (IRAP), M....
TES-Simulations
Jörn Wilms (FAU), P. Peille (IRAP), M. Ceballos (IFCA), T. Brand (ECAP),T. Dauser (ECAP), S.J. Smith (GSFC), B. Cobo (IFCA), S. Bandler (GSFC),
R. den Hartog (SRON), J. de Plaa (SRON), E. Pointecouteau (IRAP),D. Barret (IRAP)
Simulation ansatz 1
X-IFU photon detection process:• sensitivity described by effective area curves
(taking into account mirror reflectivity, pixel sensitivity, gaps)
• Two (input/output-compatible) simulation approaches– xifupipeline:∗ full imaging implemented∗ fast detection simulation using response matrices=⇒Well suited for faint sources
– tessim/sirena∗ Simulation of TES physics and pulse reconstruction∗ Slower than xifupipeline, but much better physics=⇒Well suited for bright sources=⇒Well suited for engineering studies
Will soon be able to easily switch simulation between both
Simulation ansatz 2
Device Simulations: Principle
Smith (2006 PhD Leicester)
Calorimeter: measure temperature change in device with temperature T0 connected to heat bathwith temperature TS.Joule heating by current through device =⇒ T0 > TS
Absorption: temperature rises: ∆T = Eγ/CC: heat capacity
Relaxes back to T0. Typical timescale: τ = C/G.Resolution given by thermal fluctuations: ∆E = 2.35
√kT 2C
=⇒ Small (few eV) for T small (mK)
Simulation ansatz 3
Device Simulations: Principle
Kinnunen (2011, PhD Jyväskylä)negative electrotermal feedback: Oper-ate circuit at Transition Edge betweensuperconduction and normal conduction,voltage bias circuit:absorption =⇒ R ↗=⇒ Joule power PJ = I2R ↘=⇒ faster cooling than for R = constTypical time constants 75µs. . . 400µs
X-IFU Simulation 1
Tessim
1.00.80.60.40.20.0-0.2
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Time since photon impact [ms]
Pulsesh
apenorm
alized
toequalarea
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
1.00.80.60.40.20.0-0.2
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Time since photon impact [ms]
Pulsesh
apenorm
alized
topeak
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
SLA pulse shapes for 0.1, 1, 2, 3, 5,10 keV, normalized to top: equal area,bottom: peak current.
• based on GSFC code by S.J. Smith• numerical solution of differential equa-
tions for T (t), I(t) (e.g., Irwin & Hilton,2005),
CdTdt
= −Pb + PJ + P + Noise
LdIdt
= V − IRL − IR(T , I) + Noise
• linear resistance model, R(T , I;α, β)• noise treatment: Johnson of circuit, bath,
excess noise• input parameters: C, Gb, n, α, β, m, R0,
T0, Tb, Lcrit
including flexible, FITS-based library of pixel types
X-IFU Simulation 2
Tessim
1.00.80.60.40.20.0-0.2
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Time since photon impact [ms]
Pulsesh
apenorm
alized
toequalarea
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
1.00.80.60.40.20.0-0.2
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Time since photon impact [ms]
Pulsesh
apenorm
alized
topeak
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
SLA pulse shapes for 0.1, 1, 2, 3, 5,10 keV, normalized to top: equal area,bottom: peak current.
• based on GSFC code by S.J. Smith• numerical solution of differential equa-
tions for T (t), I(t) (e.g., Irwin & Hilton,2005),
CdTdt
= −Pb + PJ + P + Noise
LdIdt
= V − IRL − IR(T , I) + Noise
• linear resistance model, R(T , I;α, β)• noise treatment: Johnson of circuit, bath,
excess noise• input parameters: C, Gb, n, α, β, m, R0,
T0, Tb, Lcrit
including flexible, FITS-based library of pixel types
Linear Resistance Model:
R(T , I) = R0 +∂R∂T
∣∣∣∣I0
(T − T0) +∂R∂I
∣∣∣∣T0
(I − I0) (1)
where
α =∂ log R∂ log T
∣∣∣∣I0
=T0
R0
∂R∂T
∣∣∣∣I0
(2)
β =∂ log R∂ log I
∣∣∣∣T0
=I0R0
∂R∂I
∣∣∣∣T0
(3)
more complicated resistance models are to be implemented(BSc work Christian Kirsch)
X-IFU Simulation 3
Tessim
1.00.80.60.40.20.0-0.2
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Time since photon impact [ms]
Pulsesh
apenorm
alized
toequalarea
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
1.00.80.60.40.20.0-0.2
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Time since photon impact [ms]
Pulsesh
apenorm
alized
topeak
10.0 keV5.0 keV3.0 keV2.0 keV1.0 keV0.1 keV
SLA pulse shapes for 0.1, 1, 2, 3, 5,10 keV, normalized to top: equal area,bottom: peak current.
• based on GSFC code by S.J. Smith• numerical solution of differential equa-
tions for T (t), I(t) (e.g., Irwin & Hilton,2005),
CdTdt
= −Pb + PJ + P + Noise
LdIdt
= V − IRL − IR(T , I) + Noise
• linear resistance model, R(T , I;α, β)• noise treatment: Johnson of circuit, bath,
excess noise• input parameters: C, Gb, n, α, β, m, R0,
T0, Tb, Lcrit
including flexible, FITS-based library of pixel types
Noise terms:
• Johnson noise in the TES
• Johnson noise in the load resistor
• Thermal fluctuation noise
• Excess noise
see Irwin & Hilton (2005) for detailsrequires special care in numerical integrator
tessim 1
tessim
Translation to program: (too) many parameters ;-)
tessim 2
Configuration controlSolution to large number of parameter problem:self documentation =⇒ store all parameters in FITS files output by tessim: canreload exact pixel configuration from any stream produced by tessim:
First run tessim with propertiesonly=yes:
tessim PixType=LPA1 Ce=0.26 Gb=280 ... propertiesonly=yes... Streamfile=testpixel.fits
Later run with
tessim PixType=file:testpixel.fits[LPA1]Streamfile=simulation PixImpList=impact.fits
where ...[LPA1]: FITS selection syntax on FITS HDUNAME keyword.impact.fits
: FITS impact file with TIME and ENERGY columns.
tessim 3
Trigger
EventDetection
EventGrading
UncalibratedE
Energy
Iterative Event Detection (Triggering):
• Take derivative of TES streamtrigger=
– stream– movavg:npts:threshold:suppress– diff:npts:threshold:suppress
• remove pulses on the go to findothers
0.1040.1020.1000.0980.096
1.7
1.6
1.5
1.4
1.3
1.2
1.1
0.1040.1020.1000.0980.096
Time [s]
Current[µ
A]
tessim 4
Trigger
EventDetection
EventGrading
UncalibratedE
Energy
Iterative Event Detection (Triggering):
• Take derivative of TES streamtrigger=
– stream– movavg:npts:threshold:suppress– diff:npts:threshold:suppress
• remove pulses on the go to findothers
0.1040.1020.1000.0980.096
1.7
1.6
1.5
1.4
1.3
1.2
1.1
0.1040.1020.1000.0980.096
100
80
60
40
20
0
-20
Time [s]
Current[µ
A]
Deriv
ativ
edI/dt[µ
A/sample]
tessim 5
Trigger
EventDetection
EventGrading
UncalibratedE
Energy
Event Grading: Resolution depends on distance between different pulses.
tessim 6
Trigger
EventDetection
EventGrading
UncalibratedE
Energy
Energy calculation: Use optimal filter(Szymkowiak et al., 1993):
E ∝∑ D(f )S∗(f )
N(f )where• D(f ): data spectrum,• S(f ): template spectrum,• N(f ): noise spectrum
Caveats:• exact degradation at higher
energies not yet studied fortime reasons• reconstruction algorithms
still in development and un-der optimization, have donefirst studies using principlecomponent analysis andresistance space optimalfiltering