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Simulation Issues for Radio Detection in Ice and Salt Amy Connolly UCLA May 18 th, 2005.

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Presentation on theme: "Simulation Issues for Radio Detection in Ice and Salt Amy Connolly UCLA May 18 th, 2005."— Presentation transcript:

1 Simulation Issues for Radio Detection in Ice and Salt Amy Connolly UCLA May 18 th, 2005

2 Overview During the past few years, simulations of Askaryan pulses and detection systems have become mature Large overlap in people working on simulations from GLUE, ANITA, SalSA This talk focuses on the status of simulations developed for ANITA, SalSA Lessons learned

3 Simulations - Overview Flavors treated separately EM, Had components of shower treated separately for simulating Askaryan signal, proper dN/dy used Secondary interactions included Weighting accounts for neutrino attenuation through Earth, etc. Complementary simulation programs are being developed – this is essential Interactions occur uniformly and isotropically in medium – followed by weighting [P. Gorham]

4 Anita Simulation Ray tracing through ice, firn Fresnel coefficients Attenuation lengths are depth and frequency dependent Include surface slope and roughness 40 quad ridged horn antennas arranged in 3 layers: 8,8,16 Bandwidth: 200 MHz-1200 MHz For now, signal in frequency domain Measured antenna response ice [S. Barwick]

5 Anita – Ice Properties Use measured ice and crust layer thicknesses (model is Crust 2.0 based on seismic data) Use measured attenuation lengths (frequency dependent) Rays traced in ice with depth- dependent index of refraction. n(z): 1.8 in deep ice 1.3 at surface Temperature-dependence also included: ~few hundred m in warmest ice (in firn and near bedrock) ~1300 m at mid-depth [D. Besson] [P. Gorham]

6 ANITA - Skymap For a fixed balloon position, sensitivity on sky takes a sinusoidal shape. A source between -5 and +15 declination would be observable for 5 hrs/day Over the entire balloon flight, sensitive to entire band between -10 and +15 declination.

7 Reflected Rays Work by: S. Barwick, F. Wu from University of California at Irvine TIR Micro-black holes at ANITA Energies Could measure cross section from relative rates of direct (far) to reflected (near). [S. Barwick] [S. Barwick & F. Wu] ANITA could (possibly) detect events where a signal is reflected from ice-bedrock interface Signals suffer from extra attenuation through ice and losses at reflection At SM  ’s, reflected rays not significant At large cross-sections, short pathlengths → down-going neutrinos dominate ! reflected rays important  SM 10 100 1000 1

8 View more sky! HOWEVER, more uncertainty at ice-bedrock interface For now, assuming 10% attenuation in power at interface Collaborating with UT group to understand under-ice topologies, radar reflectivities Use Brealt code to study interfaces quantitatively With reflected rays, we could observe a large portion of the sky that we could not otherwise. Reflected Rays Work by: S. Barwick, F. Wu from University of California at Irvine

9 SalSA: Benchmark Detector Parameters Overburden: 500 m Detector –Array starts at 750 m below surface –10 x 10 string square array, 250 m horiz. separation –2000 m deep, 12 “nodes”/string, 182 m vert. separation –12 antennas/node Salt extends many atten. lengths from detector walls Attenuation length: 250 m Alternating vert., horiz. polarization antennas Bandwidth: 100-300 MHz Trigger requires 5/12 antennas on node, 5 nodes to fire: V signal >2.8  £ V RMS Index of refraction=2.45 Syst. Temp=450K=300K (salt) + 150K (receiver)

10 Angular Resolution Work by: P. Gorham, University of Hawaii [P. Gorham] Performed chi-squared analysis from two hadronic shower event types –Fully contained –Parallel to a face 250 m outside array Fit to –Amplitude of Cerenkov signal –Polarization At 8£10 16 eV: –Contained: fraction of deg. –Non-contained: ~1 deg. Improves with energy

11 SalSA Cross Section Measurement Use Poisson likelihood cross section can be measured from distribution At SM , only 10% of events in sensitive region Generate distribution from simulation and throw dice for many pseudo- experiments For experiments w/ 100 events With 300 events @10 18 eV, Binning: ~2 ±  N cos  =400 § 130 km

12 Comparing Sensitivities ANITA: 45 days GLUE: 120 hrs RICE: 333 hrs SalSA (1 year) SalSA & ANITA –SalSA lower threshold –ANITA higher V , shorter livetime Two independent simulations for each experiment give similar results for ES&S “baseline” –ANITA: handful of events –SalSA: 20 events/year Having two MC’s has been essential to: –Learn about the physics of these systems –Spot bugs –Gain confidence in our results ES&S “baseline”

13 How Do We Know Our Simulations Are Correct? Resolving disagreement in peak at 1£10 20 eV Even if two independent simulations give the same answer, we should assume it is a coincidence until we compare many, many intermediate plots. ANITA Depth of Interaction (m)Balloon-to-Interaction Distance (km)

14 Validating our Simulations (cont) [P. Gorham] [S. Hoover] ANITA Projection of Askaryan signal onto the sky:

15 Summary Simulations of radio detection systems are becoming sophisticated ANITA –Reflected rays show promise for detecting high cross sections, opening up large part of the sky if ice- bedrock interface can be understood SALSA –Angular resolution ~fraction of degree for contained events, 1-2 degrees for external events –Cross section measured at 30% level with 100 events Independent simulations are essential Many intermediate plots necessary to verify simulation performance

16 Backup Slides

17 ANITA SalSA GLUE RICE

18 Impact of Salt Properties Track length L –X 0 ice =43 cm, X 0 salt =10.3 cm ! Expect L salt /L ice =0.26. Simulations show 0.34. Cerenkov index of refraction factor Cerenkov threshold Critical energy Coherence Angular scaling

19 Secondary Interactions Generate from MMC for each flavor, interaction type From MMC, also retrieve multiplicity of each type of sec. interaction Force neutrinos in our simulation to obey these distributions For now, consider interaction (primary or secondary) which contributes the strongest signal Critical for flavor ID Example Probability Distribution from MMC for muon brem. showers P. Miocinovic


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