Detecting Supernovae with IceCube Kael Hanson University of Wisconsin HEP Seminar March 26, 2007

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Detecting Supernovae with IceCube

Kael HansonUniversity of Wisconsin

HEP Seminar March 26, 2007

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Overview

• The IceCube neutrino observatory is currently under construction at the South Pole. To date approximately 30% of the detector is installed and over 50% of the instrumentation has been produced.

• Science operation begins 4/1/2007• As a UHE telescope, IceCube is gigaton detector with effective

area in excess of 1 km2 to > TeV muons.• First proposed by Jacobsen, Halzen, Zas PRD 49 (1994),

possibility to use background counting of optical detectors for detection of MeV-scale neutrinos from galactic supernovae.

• This technique utilized by AMANDA detector since 1998.• With est. 3 Mton effective volume for low-energy ν, IceCube

potentially provides detailed information for modeling supernovae:– Bigger, better, lower-noise optical detectors– Data acquisition at fine timescales - 1.6 ms / bin

• Supernova detection would provide high resolution, high statistics data for supernova burst models.

• Sensitivity may be sufficient for particle physics investigations

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The IceCube UHE Neutrino Observatory

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Both representations not to scale - nevertheless nicely illustrate salient detector characteristics.

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IceCube UHE Neutrino Observatory (2)

• 1st string deployed Jan-2005• Last string deployed Jan-2011• 4500 deep-ice optical modules along 75 strings• Depth ranges 1450 m - 2450 m with 17 m spacing.• 320 surface modules in 80 stations - IceTop• Detector is hybridizing already - this year radio and

acoustic test modules deployed and are working well.

• IceCube optimized for detection of TeV and PeV-scale neutrinos of cosmic origin: point sources, diffuse HE neutrinos, GRBs, &c.

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IceCube Integrated Volume (Projected)

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km3·yr Strings

Graph shows cumulative km3·yr of exposure × volume

# of strings per year is based on latest “best guess” deployment rate of 12 strings 2007 (13) and 14 strings per season thereafter.

1 km3·yr reached 2 years before detector is completed

Close to 4 km3·yr at the beginning of 2nd year of full array operation.

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AMANDA

• 677 analog OMs deployed along 19 strings– 10 strings 1997 (AMANDA B10)– 3 strings 1998 (AMANDA B13)– 6 strings 2000 (AMANDA II)

AMANDA supernova analyses typically employ between 400-500 of these channels due to instabilities in some.

• Analog PMT signals using electrical and optical transmission lines.

• 200 m diameter, 500 meters height; AMANDA II encompasses 20 Mton instrumented ice volume: 6 times more dense than IceCube.

• AMANDA will remain operational and form IceCube Inner Core Detector for low E physics (~ 100 GeV - WIMPs, &c)

• IceCube surrounding strings provide effective veto – lower background and can push AMANDA energy threshold down.

• Conventional TDC / ADC technology for AMANDA has been entirely replaced by TWR system.

• Beginning 2007 season, AMANDA / IceCube data streams are conjoined; detector subsystems will share trigger information.

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The AMANDA/IceCube Authors

A. Achterberg31, M. Ackermann33, J. Adams11, J. Ahrens21, K. Andeen20, D. W. Atlee29, J. N. Bahcall25,a, X. Bai23, B. Baret9, M. Bartelt13,

S. W. Barwick16, R. Bay5, K. Beattie7, T. Becka21, J. K. Becker13, K.-H. Becker32, P. Berghaus8, D. Berley12, E. Bernardini33, D. Bertrand8, D. Z. Besson17, E. Blaufuss12, D. J. Boersma20, C. Bohm27, J. Bolmont33, S. Böser33, O. Botner30, A. Bouchta30, J. Braun20, C. Burgess27,

T. Burgess27, T. Castermans22, D. Chirkin7, B. Christy12, J. Clem23, D. F. Cowen29,28, M. V. D'Agostino5, A. Davour30, C. T. Day7, C. De Clercq9, L. Demirörs23, F. Descamps14, P. Desiati20, T. DeYoung29, J. C. Diaz-Velez20, J. Dreyer13, J. P. Dumm20, M. R. Duvoort31, W. R. Edwards7,

R. Ehrlich12, J. Eisch26, R. W. Ellsworth12, P. A. Evenson23, O. Fadiran3, A. R. Fazely4, T. Feser21, K. Filimonov5, B. D. Fox29, T. K. Gaisser23, J. Gallagher19, R. Ganugapati20, H. Geenen32, L. Gerhardt16, A. Goldschmidt7, J. A. Goodman12, R. Gozzini21, S. Grullon20, A. Groß15,

R. M. Gunasingha4, M. Gurtner32, A. Hallgren30, F. Halzen20, K. Han11, K. Hanson20, D. Hardtke5, R. Hardtke26, T. Harenberg32, J. E. Hart29, T. Hauschildt23, D. Hays7, J. Heise31, K. Helbing32, M. Hellwig21, P. Herquet22, G. C. Hill 20, J. Hodges20, K. D. Hoffman12, B. Hommez14,

K. Hoshina20, D. Hubert9, B. Hughey20, P. O. Hulth27, K. Hultqvist27, S. Hundertmark27, J.-P. Hülß32, A. Ishihara20, J. Jacobsen7, G. S. Japaridze3, H. Johansson27, A. Jones7, J. M. Joseph7, K.-H. Kampert32, A. Karle20, H. Kawai10, J. L. Kelley20, M. Kestel29, N. Kitamura20,

S. R. Klein7, S. Klepser33, G. Kohnen22, H. Kolanoski6, L. Köpke21, M. Krasberg20, K. Kuehn16, H. Landsman20, H. Leich33, I. Liubarsky18, J. Lundberg30, J. Madsen26, K. Mase10, H. S. Matis7, T. McCauley7, C. P. McParland7, A. Meli13, T. Messarius13, P. Mészáros29,28,

H. Miyamoto10, A. Mokhtarani7, T. Montaruli20,b, A. Morey5, R. Morse20, S. M. Movit28, K. Münich13, R. Nahnhauer33, J. W. Nam16, P. Nießen23, D. R. Nygren7, H. Ögelman20, A. Olivas12, S. Patton7, C. Peña-Garay25, C. Pérez de los Heros30, A. Piegsa21, D. Pieloth33,

A. C. Pohl30,c, R. Porrata5, J. Pretz12, P. B. Price5, G. T. Przybylski7, K. Rawlins2, S. Razzaque29,28, F. Refflinghaus13, E. Resconi15, W. Rhode13, M. Ribordy22, A. Rizzo9, S. Robbins32, P. Roth12, C. Rott29, D. Rutledge29, D. Ryckbosch14, H.-G. Sander21, S. Sarkar24, S. Schlenstedt33, T. Schmidt12, D. Schneider20, D. Seckel23, S. H. Seo29, S. Seunarine11, A. Silvestri16, A. J. Smith12, M. Solarz5, C. Song20, J. E. Sopher7,

G. M. Spiczak26, C. Spiering33, M. Stamatikos20, T. Stanev23, P. Steffen33, T. Stezelberger7, R. G. Stokstad7, M. C. Stoufer7, S. Stoyanov23, E. A. Strahler20, T. Straszheim12, K.-H. Sulanke33, G. W. Sullivan12, T. J. Sumner18, I. Taboada5, O. Tarasova33, A. Tepe32, L. Thollander27,

S. Tilav23, M. Tluczykont33, P. A. Toale29, D. Tur_an12, N. van Eijndhoven31, J. Vandenbroucke5, A. Van Overloop14, B. Voigt33, W. Wagner13, C. Walck27, H. Waldmann33, M. Walter33, Y. -R. Wang20, C. Wendt20, C. H. Wiebusch1, G. Wikström27, D. R. Williams29, R. Wischnewski33,

H. Wissing1, K. Woschnagg5, X. W. Xu4, G. Yodh 16, S. Yoshida10, J. D. Zornoza20,d

1. RWTH Aachen University

2. University of Alaska Anchorage

3. CTSPS, Clark-Atlanta University, Atlanta

4. Southern University, Baton Rouge

5. Univer sity of California, Berkeley

6. Humboldt Universität zu Berlin

7. Lawrence Berkeley National Laboratory

8. Université Libre de B ruxelles

9. Vrije Universiteit Brussel

10. Chiba U niversity

11. University of Canterbury

12. University of Maryland

13. Universität Dortmund

14. University of Gent

15. Max-Planck -Institut für Kernphysik

16. University of Ca lifornia, Irvine

17. University of Kansas, Lawrence

18. Imper ial C ollege London

19. Dept. of Astron ., University of W isconsin, Madison

20. Dept. of Physics, University of Wisconsin , Madison

21. University of Mainz

22. University of Mons -Hainaut,

23. Bartol Research Institute, University of Delaware

24. University of Oxford

25. Institute for Adva nced Study, Princeton

26. University of Wisconsin, River Falls

27. Stockholm University

28. Dept. of Astronomy and Astrophysics, PSU

29. Dept. of Physics, P SU

30. Uppsala University

31. Utrecht University/SRON

32. University of Wuppertal

33. DESY Zeuthen

a. deceased

b. on leave of absence from Università di Bari,

Dipartimento di Fisica, I -70126, Bari, Italy

c. affiliated with Dept. of Chemical and Biomedical

Sciences, Kalmar University, S -39182 Kalmar, Sweden

d. affiliated with IFIC (CSIC -Universitat de València), A.

C. 22085, 46071 Valencia, Spain

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Optical Properties of Glacial Ice

Why deploy in ice? Deep glacial ice is optically transparent. Two mechanisms: scattering length ~ 20 m, absorption ~ O(100) m. Ice has several layers of dust from prehistoric events. Monte Carlo detector simulation must account for this. Reconstruction methods involving maximum likelihood tests against hypotheses have been developed to overcome difficulties posed by photon scattering.

Plots above from in situ measurements using artificial light sources in AMANDA. “Hole ice” around deployed modules must also be taken into account.

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The Enhanced Hot Water Drill (EHWD)

Supply: 200 GPM @ 1000 psi, 190 °FReturn: 192 GPM @ 33 °F Make-Up:

8 GPM @ 33 °F

Thermal Power:Thermal Power: 4.5 4.5 MegawattMegawatt

EHWD designed to drill a 2450 m × 60 cm hole in ~30 hr. Fuel budget is 7200 gal per hole. Shown above is drill camp and tower site (inset), both mobile field arrays. Everything must fit into LC-130 for transport to Pole.

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Drilling

Ted Schultz

Top layer of packed snow is called firn. Hot water drill designed for ice drilling – it gets starter hole from firn drill (lower right). (Top left and top right) EHWD drill head entering hole.

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Deployment

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2005, 2006, 2007 Deployments

AMANDA

2006 - 8 strings

IceTop only 2007

1424 DOMs deployed to date1320 DOMs on 22 deep ice strings>99% DOM survival rate

Following deployment years will target ≥ 14 strings / year until a ~75 strings are installed.

Final detector will include ~4500 in-ice optical modules and 500 surface modules.

2005 - 1 string

2007 - 13 strings!

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The IceCube Digital Optical Module (DOM)

Large Area Photocathode10” (500 cm2) Hamamatsu R7081-02 bialkali PMT (peak QE 24% @ 420 nm)

Low noise< 300 Hz background counting rate in-ice (with deadtime - see later)

Glass / Gel ImprovementsBetter transmission in 330 - 400 nm relative to AMANDA OM

Optical calibrationEach DOM is calibrated ε(λ) in the lab to about 7%; in-situ flasher board additionally permits in-ice measurements

DOM Highlights - Optical

“Smart” sensor digital technologyVersatile FPGA design with option to expand / change programming at any point in lifecycle. Core of supernova DAQ resides inside DOM itself.

Array TimingHandled in DOM logic - DOM-to-DOM timing good to 2-3 ns using RAPCal method.

Low power - 3.75 W / DOM

DOM Highlights - Electronics

The DOM has been in production at UW, DESY-Zeuthen, and Stockholm since mid-2004 with very little change. It has met or exceeded design requirements, and, despite harsh re-freeze conditions 2500 m deep, we’ve lost < 0.5 % of the units during this critical phase.

To date, 3000 of approx. 5000 units have been produced.

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Reducing Background Counting - Deadtime

Measurements counting all PMT pulses in deployed DOMs yield average rate of approx. 700 Hz / module. Study of the time structure of this noise clearly indicates correlated noise patterns:

PMT AfterpulsesWell-known that ionized residual gases in PMTs cause afterpulses on timescale (for large PMTs) of 6-10 µs.

Glass ScintillationSmall contamination of rare-earth oxides produces further tail out to 100’s of µs due to scintillation processes.

To reduce noise rate and restore Poissonian behavior to fluctuations ,it is necessary to apply afterpulse inhibit (deadtime) when pulse counting. Analysis of real data demonstrates that 200 µs afterpulse suppression window reduces background by factor of 4 while sacrificing only few percent of signal.

Correlated noise in PMTs

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Dom Temperature vs Depth

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49-55 Fusilli

One DOM didn’t freeze-in until

May!

DOM Temperatures in the Ice

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Dom Rate vs Dom Depth 5.14.06(calibrated)

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Importance of noise rates: 1.) noise rate w/o dead time: 700 Hz, important for DAQ bandwidth

2.) noise rate w/suppression of 50µs: 300Hz, important for event reconstruction and in particular for supernova sensitivity.

Two Icecube strings equivalent or more sensitive than all of AMANDA to SN.

Singles Counting Rates vs. Depth

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The Supernova DAQ

FPGAIntegrate into 1.6384 ms (216 / 40 MHz) bins SPE/MPE discriminator crossings with application of 200 µs deadtime. This counting operation executes in parallel with digitization and readout activity of main DAQ core and does not depend on its state.

CPUInterrupted every 6.5 ms (4 bins). Bins are timestamped with ~ 10 ns precision and copied into SDRAM where they await ~1 Hz commands from surface to readout accumulated bins.

In the ice - counting

Surface DAQ for supernova is 2-stage assembly of disparate data packets from individual DOM channels:

StringHubIssue periodic readouts to string of 60 DOMs. Use RAPCal information to translate DOM timestamps to UTC then perform merge and sort into single stream of data which is sent downstream.

SupernovaBuilderFurther merge-and-sort of StringHub streams into final stream written to tape.

At the surface - assembly

Began supernova-mode data-taking last September with 9-string 540-module detector. This year’s run is set to begin 5/1. Taking full supernova data (1 MB/sec) since 3/18.

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The Online Trigger and the SNEWS Connection

Don’t stop there: real-time detection and reporting of burst candidates gives “heads-up” to optical observers.

In addition, combining many, distributed observatories makes more sensitive, robust alert.

SuperNova Early Warning System [NJP 6 (2004)114] … Strict requirement that individual detector produce no more than 1 false alarm / week.

Real-time detection of SNeAMANDA official participant since 6/2005.

Near real-time delivery of alerts possible through Iridium link to pole (24/7; low-bandwidth)

Noise rates in AMANDA can be highly variable. Real-time detection of noisy channels necessary; additionally, detector chi-square reports uniformity of signal in detector (good SN totally uniform).

Statistics based on Gaussian approx.; OK for large bins.

SNEWS and AMANDA



Mainz supernova group adapting AMANDA online trigger to handle data feed from IceCube DAQ.

Necessary to thoroughly evaluate system to demonstrate that we meet the strict requirement of < 1 false alarm / week.

22-string IceCube will join end of year 2007.

SNEWS and IceCube

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Supernovae

• Categorization of SNe historically based on presence of hydrogen in spectroscopic lines:– Type I SNe don’t have it

• Type Ia probably from dwarf stars accreting mass to Chandrasekhar limit, then exploding.

• Type Ib/c core collapse SNe but have lost hydrogen envelope because of stellar winds (Wolf-Rayet stars) or perhaps mass transfer to companion star. Incidentally, recent investigations associate these types with GRBs (e.g. Woosley and Bloom astro-ph/0609142); variations with slower jets may further be γ dark but still produce TeV ν detectable in IceCube and may be much more common.

– Type II SNe have the hydrogen lines - these are likely massive stars with hydrogen envelopes intact that undergo core collapse

• Intense neutrino luminosities only with core collapse SNe Type Ib/c and Type II.

• Galactic rate of core collapse SNe given by INTEGRAL measurements - 1-3 per century (Nature 439 (2006) 45). Optimistically, given IceCube lifetime of 15 years - 40% chance of observing galactic event.

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Core Collapse - Basic Features

• Details of physics of core collapse not completely understood due to lack of observational data. 19 neutrino events detected in Kamiokande-II and IMB from SN1987A bolster support for general model but were too few to provide specific insights (see Yuksel and Beacom astro-ph/0702613v2 for recent discussion of SN1987A data). – Massive star (> 8 M⊙) has developed 1.5 M⊙ Fe core which is beginning

to neutronize.– “Homologous” core becomes unstable as Fe nuclei leech out electrons

and photo-disintegration processes occur and collapses to ~30 km where nuclear repulsion causes ‘core bounce.’ Lots of neutrinos of all flavors inside simmering proto-neutron star.

– Shock wave from rebound and collapsing outer portion of core. When shock penetrates “neutrinosphere,” initial neutronization burst escapes.

– Core accretes infalling material and begins to radiate ~1053 erg in neutrinos over the next ~ 0.5 s until explosion.

– The remainder of the energy is emitted over timescales of 10’s of seconds as the newly-formed neutron star cools

• Optical emission some ~10 hr later.• Models still fail, unmanipulated, to provoke explosion - this is

continuing mystery which might be unraveled given adequate input data provided by IceCube or future SN neutrino detector.

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The Supernova - GRB Connection

GRBs

SNe ?

Mounting evidence that Type Ib/c SNe produce GRB. Rate of GRB is 1%. May be many more with mildly relativistic jets (Γ ~ few) which don’t produce significant EM component.

TeV neutrinos detectable from nearby objects (D < 20 Mpc) at the rate of ≈ 1 SN / year.

[ Razzaque, Mezaros, Waxman PRL (2004) ]

TeV Neutrinos from Nearby SNe

Ando & Beacom PRL2005

Sensitivity can be doubled

by optical follow-up! MK, A. Mohr, astro-ph/0701618

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MeV Supernova Neutrinos in AMANDA

SN neutrino signal simulation

center of galaxy, normalized to SN1987A

2000-2003preliminary

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MeV Neutrinos in IceCube

• Jacobsen, Halzen, Zas PRD 49 (1994) 1758 first proposed. Follow-up calculation for IceCube presented in JCAP 6 (2003) - Dighe, Keil, Raffelt. Unfortunately both groups have some incorrect assumptions.

• IceCube supernova analysis group has done preliminary update to IceCube of more detailed work from AMANDA Ph.D. thesis by T. Feser.

• Some points from all works:– Neutrino effective volume ∝ E3; 2 powers from σ 1 power from

electron/positron tracklength. Thus, detection is sensitive to neutrino energy spectra - or stated another way, effective volumes are all dependent on SN models / oscillations, &c.

– Effective volume ∝ Λabs the optical pathlength in the ice– For SN models this yields approx. 700 m3 Veff

- or a sphere around each module of 5 m radius. As such, each module may be treated independently.

– Principal detection channel is inverse beta decay (ref cross-sections slide) - this makes detection of neutronization peak difficult.

– Currently, with ~ 1300 deep ice modules detector mass is 800 kton.

– Full IceCube ~ 4500 modules detector mass 3 Mton.

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MeV Neutrinos in IceCube (continued)

• The detection comes from increase in background counts across the entire array.

• Disadvantages - you have no pointing or energy reconstruction as in Super-K

• Advantage is that enormous volume provides high-statistics measurement. Time binning can be made fine.

• Signal from 1987A SN at galactic center would produce 475k excess counts in ~10 sec window on a background of 12 ×106 counts from noise - S/N ~ 150:1 in full IceCube;

• In current 22-string detector signal is still very significant: 140k excess counts giving S/N ~ 75:1.

• Gain comes when decimating signal in time to study time evolution

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MeV Neutrino Cross Sections on Water



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Signal Predictions



From Dighe, et. al. JCAP 06 (2003)005. Note that predicted rate should be scaled dow by 3x - cf. previous slide. Also statistical errors for 50 ms bins are +/- 250 counts.



From Dighe, et. al. JCAP 06 (2003)005. Their prediction of earth modulation effect for particular choice of neutrino mixing angle θ13 could render IceCube sensitive to neutrino mass hierarchy.

IceCube detector sensitive to modulation effect; however, model-dependence of the neutrino luminosity would almost certainly require contemporaneous detection of SN at another detector such as Super-K or Hyper-K.

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Signal Predictions - IceCube SN group calculation

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Detection of neutronization peak

Neutronization fluence is largely independent of SN model and progenitor mass - useful as a neutrino standard candle. IceCube detection is marginal.

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Neutrino Oscillation in Star





From Kachelreiß & Tomàs PRD 71 (2005)

-30-

Dete

cing S

upern

ovae w

ith Ice

Cube

K.

Hanso

n -

UW

HEP S

em

inar

20

07

-03

-26

Conclusions

• IceCube gigaton high energy neutrino observatory doubles nicely as megaton low energy detector

• High-statistics measurements are possible which can at the very least provide detailed measurements of neutrino luminosity vs time.

• IceCube longevity gives good chance of observing significant galactic core collapse event.

• IceCube is operating and is sensitive to GC events NOW! With 1300 modules deployed in ice this year sensitive to 100% of Milky Way.

• SN analysis group working to improve outdated simulations and bring up online supernova trigger

• Participation in SNEWS later this year.

Documents

Detecting Supernovae with IceCube Kael Hanson University of Wisconsin HEP Seminar March 26, 2007