Detector Design Studies for a Cubic Kilometre Deep Sea Neutrino Telescope – KM3NeT
Status and Recent Results of the Acoustic Neutrino ... · AMADEUS too small, “2D-geometry”...
Transcript of Status and Recent Results of the Acoustic Neutrino ... · AMADEUS too small, “2D-geometry”...
Robert Lahmann ARENA 2014�Annapolis, 09-June-2014
Status and Recent Results of the Acoustic Neutrino Detection Test System AMADEUS of ANTARES
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Outline
● Introduction: Acoustic Neutrino Detection and AMADEUS ● Ambient Noise and Transient Background Investigations ● Lessons Learned ● Conclusions and Outlook
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Acoustic Detection of Neutrinos
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-0.08-0.06-0.04-0.02
0 0.02 0.04 0.06 0.08
0.1 0.12
-0.04 -0.02 0 0.02 0.04
Pres
sure
[Pa]
Time [ms]
1011GeV @ 1000m
Hadronic cascade: ~10m length, few cm radius Pressure field: Characteristic “pancake” pattern Long attenuation length (~5 km @ 10 kHz)
Thermo-acoustic effect: (Askariyan 1979) energy deposition ð local heating (~µK) ð expansion ð pressure signal
ν ~1o
Time [ms]
Pres
sure
[Pa]
Bipolar Pressure Signal (BIP)
The AMADEUS System of the ANTARES Detector
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AMADEUS : • Total of 6 “acoustic storeys” • Total of 36 hydrophones • Continuous sampling • Online filter selects ~1% of data volume for storage
ANTARES site: • 2500m depth, 30km offshore
ANTARES site
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Operation of AMADEUS
● Main objective: feasibility study for a potential future large-scale acoustic neutrino detector ● Investigate background conditions ● Determine energy threshold for neutrino detection ● Devise high efficiency, high purity neutrino detection algorithms
● Data from first line with acoustic sensors: Dec 2007 Data from two lines: Nov. 2009 – Oct. 2010 Since April 2013 (new position of IL)
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ANTARES: New Geometry since April 2013
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“Instrumentation Line” was redeployed at new position:
Distance between lines with acoustic storeys
220m 150m
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Background for Acoustic Detection in the Sea
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Transient background
Bipolar Pressure Signals (BIPs)
ðDetermines fake neutrino rate Suppress by � clustering
� signal classification � fiducial volume cuts
ðDetermines intrinsic energy threshold Use “effective volume” for estimate Depends on “sea state” (surface agitation) (see talk by Dominik Kiessling)
Ambient noise
Frequency [ kHz ]5 10 15 20 25 30 35 40 45 50
/Hz
]2
Paµ
Pow
er le
vel [
dB
re 1
0
10
20
30
40
50
60
70Sea state 0
Sea state 2
Sea state 4
Model ss0
Model ss2
Model ss4
Measured noise spectra and model
Signal (a.u.), schematic
Transient Background: Properties
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• Very diverse Shipping traffic, marine mammals, … ð perform signal classification
• Mostly originating from near surface ð “straight forward” approach: Impose cut based on source location
~500
m
“qu
iet z
one”
em
issi
ons,
re
flect
ions
Transient Background: Position Reconstruction
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• For events selected by online filter, reconstruct direction for individual storeys
• When directions reconstructed by more than one storey get source location
Data: 156 days of measuring time from Nov. 2009 to Oct. 2010
Source Localization
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Problem: Small size of AMADEUS device ð large errors in z, despite good angular
resolution for direction reconstruction: in zenith
in azimuth Solution: Project positions to sea surface and remove event clusters from moving sound emitters
Δθ = 0.6± 0.2
Δϕ =1.6± 0.2
Cluster Analysis of Moving Sound Emitting Objects
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x [m]
y [m
]
Nov. 2009
Oct. 2010
Signal Classification with Machine Learning Algorithms
● Classification: neutrino candidate (BIP) background
● Different algorithms have been investigated: ● Random Forest ● Boosted Trees ● Naïve Bayes ● Decision Tree ● Support Vector Machine
● Recognition Error: ● For individual sensors < 10% ● For clusters of sensors < 2%
best performance
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Spatial Distribution of Transient Background
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Dep
th z
[m]
Distance r [m]
-2500-2000-1500-1000
-500 0
500 1000
0 2000 4000 6000 8000 10000 1e-08
1e-07
1e-06
1e-05
0.0001
0.001
Even
t den
sity
[m-3
]
All clustered events
Dep
th z
[m]
Distance r [m]
-2500-2000-1500-1000
-500 0
500 1000
0 2000 4000 6000 8000 10000 1e-08
1e-07
1e-06
1e-05
0.0001
0.001
Even
t den
sity
[m-3
]
All reconstructed events 15·103 km−3 year−1
After signal classification and cluster analysis 100 km−3 year−1
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Search for a Fiducial Volume - Motivation
● Using signal classification and cluster analysis for the identification of neutrino-like bipolar signals
● Remaining events density: ~100 events/km3/yr ● Need for further reduction ð cut on the volume
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Dep
th z
[m]
Distance r [m]
-2500-2000-1500-1000
-500 0
500 1000
0 2000 4000 6000 8000 10000 1e-08
1e-07
1e-06
1e-05
0.0001
0.001
Even
t den
sity
[m-3
]
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z [m
]
Distance r [m]
-2500-2000-1500-1000
-500 0
500 1000
0 2000 4000 6000 8000 10000 12000 1e-50 1e-45 1e-40 1e-35 1e-30 1e-25 1e-20 1e-15 1e-10 1e-05 1
Even
t den
sity
[m-3
]
Search for a Fiducial Volume - PSF
Optimize fiducial volume for minimal background content: ● Point spread function calculated from MC Simulations ● Deconvolution of the PSF using Richardson-Lucy-Algorithm
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Preliminary
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Search for a Fiducial Volume - Cut Strategy
● Optimization problem: ● Minimal number of events and ● Maximal remaining volume after applying the cut
● Using a Genetic Algorithm to solve the optimization problem ● Remaining event density after cut: ~0.05 events/km3/yr
(but volume closest to sensors is removed)
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z [m
]
Distance r [m]
-2500-2000-1500-1000
-500 0
500 1000
0 2000 4000 6000 8000 10000 12000 1e-50 1e-45 1e-40 1e-35 1e-30 1e-25 1e-20 1e-15 1e-10 1e-05
Even
t den
sity
[m-3
]
Preliminary
Further Reduction of Transient Background
Search for characteristic geometry of pressure field from neutrino interaction (“pancake”) ● AMADEUS too small, “2D-geometry” ● investigations with Monte Carlo simulations (input from AMADEUS) ● KM3NeT: Combined system for acoustic positioning and neutrino
detection planned ð test bed for algorithm development
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See talk by Dominik Kiessling
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Effective Volume
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Ve↵ =
Pp(E,x, ep)�sel
NgenVgen
Effective Volume
Number of Neutrinos: 107
Volume in which the Neutrinos are generated: 1200km3
Probability of the neutrino reaching the vertex
only counted if signal is detected;
Earth
ν
Water
10°
Earth density model (PREM)
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AMADEUS Effective Volume
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Level 1:
• low ambient noise • minimal filter
Level 2: • noise model (annual distr.) • std. filter
10-5
10-4
10-3
10-2
10-1
100
101
9 9.5 10 10.5 11 11.5 12
V eff(
E) [k
m3 ]
E [log(E/Gev)]
Preliminary
Veff(Ei) Level 1Random Coincidences Level 1Veff(Ei) Level 2Random Coincidences Level 2
Preliminary
AMADEUS: Lessons Learned
● Ambient background: GZK neutrinos (for pure proton flux) detectable, reduction of threshold crucial ð bigger detector, use signals from more sensors
● Transient noise: High level of background (mainly dolphins); High level of reduction already achieved with AMADEUS, for competitive flux limits recognition of “acoustic pancake” crucial
● Road ahead: Apply knowledge about ambient noise and transient background data to simulations: ● KM3NeT acoustic system for positioning/neutrino detection ● large scale fiber-based acoustic neutrino telescope?
(see talk on behalf of E.J. Buis)
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Conclusions and Outlook
• Ambient noise: Smaller effect on neutrino detection than assumed
• Transient background: Strong suppression achieved, further reduction requires larger detectors
• Monte Carlo simulations developed and energy threshold derived from effective volume of AMADEUS
• Next step KM3NeT: Combined system for acoustic positioning and neutrino detection planned
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Thank you for your attention
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