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Detectability of Neutrinos from Failed Supernovae and Black Hole- Neutron Star Mergers Halston Lim and Jason Liang North Carolina School of Science and Mathematics 1

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Detectability of Neutrinos from Failed Supernovae and Black Hole-Neutron Star Mergers

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Detectability of Neutrinos from Failed Supernovae and Black Hole-Neutron Star Mergers

Halston Lim and Jason LiangNorth Carolina School of Science and Mathematics

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Neutrinos and their Detection• Fundamental particles

– Three flavors (νe, νμ, ντ)

– Mainly interact through weak force

• Can propagate through matter

– Useful when astrophysical phenomena are opaque to light

– Detectors use secondary particles to determine if event has occurred

• Detectors

– Water Cherenkov (Super-Kamiokande)

– Liquid argon (LBNE)

The fundamental particles of the Standard Model

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20132

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Neutrino Emission• Neutrinos emitted by

astrophysical phenomena

• Core collapse supernova (SN) –stellar collapse and explosion

• Emits neutrinos (99% of binding energy)

• Analyzed important events different from typical SN

- Failed supernovae (fSN)

- Black hole-neutron star mergers (BHNSM)

Supernova 1987A

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20133

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fSN and BHNSM• fSN

- Very high-mass star

- Site of nucleosynthesis

- Would allow for first observation of BH formation

• BHNSM

- Thought to be linked with short-period gamma-ray bursts

- Very luminous events

- Can be used to study the evolution of early universe

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Artist’s conception of a BHNSM

Artist’s conception of a fSN

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20134

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Research Goals1. Determine observability of neutrinos from fSNand BHNSM in current and proposed detectors

2. Compare detector signals from our events with signals from typical SN

3. Investigate how well the parameters of the original flux distribution can be determined from interaction rates Schematic of Super-Kamiokande

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20135

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General Methods

Use theoretical models of fSN and BHNSM to calculate the

neutrino emission

After finding the neutrinos emitted, use SNOwGLoBES to calculate the

what detectors on Earth observe

Determine the observability of

neutrinos

Consider existing astrophysical models of

fSN and BHNSM and how observations will confirm/reject these

models

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Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Neutrino event generated with Superscan event display program

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

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SNOwGLoBES(SuperNova Observatories with General Long Baseline Experiment Simulator)1

• Interaction rates calculator that we used to simulate neutrino events on Earth

• We calculated the neutrino flux from fSN and BHNSM and integrated real detectors parameters (cross sections, smearing, efficiencies)

[1] K. Scholberg, in APS April Meeting 2011 (2011), p. 1.

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Fluence Calculation

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

[4] O. L. Caballero and G. C. McLaughlin, Physical Review D 80, 123004 (2009).

[3] K. Sumiyoshi, S. Yamada, and H. Suzuki, The Astrophysical Journal 667, 32 (2007).

[2] H. Minakata et al., Journal of Cosmology and Astroparticle Physics 2008, 006 (2008).

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20138

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Examples of Flux ParameterizationsFermi-Dirac Parameterization3Garching Parameterization3

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 20139

Best to model fSN (non-thermal emission)

Best to model BHNSM(thermal emission)

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Flux Comparisons with Typical SN

• fSN and BHNSM have higher neutrino energies

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Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

SH fSN BHNSM

[5] G. Shen, arXiv Preprint arXiv:1202.5791 1–20 (2012).

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Event Rate Calculation4

Flux Interaction Cross Section x

Secondary Particle Distribution

Threshold Response xEnergy Resolution

• Calculated the flux– Applied intrinsic interaction and detection inputs for various

detectors in SNOwGLoBES6

• Main focus on liquid argon (LBNE) and water Cherenkov (Super-K) detectors

[6] K. Scholberg, arXiv Preprint arXiv:1205.6003 1–19 (2012).

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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b.a.

LEFT: The total number of events as a function of threshold energy is plotted for typical SN, fSN, and BHNSM models.

RIGHT: The total number of events is shown as a function of time for typical SN (Livermore, Basel) and fSN models (SH and LS nuclear equation of states. All events are calculated in Super-Kamiokande at 10 kpc.

Neutrino Detector Signal

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[6] J. M. Lattimer and F. D. Swesty, Nuclear Physics A 535, 331 (1991).

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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a. b.

The total number of events in various current (Super-Kamiokande, HALO, LVD, Borexino) and proposed (Hyper-Kamiokande, LENA, GLACIER, LBNE) detectors as a function of distance. Water Cherenkov (blue), liquid scintillator (red), and liquid argon (green) detectors are shown.

Viewing neutrinos from our neighbor Andromeda (700 kpc) is feasible with new detectors.

Observability

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

SH fSN BHNSM

Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 201313

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Observed Neutrino Events

Center of Milky Way (10 kpc) Andromeda (700 kpc)

fSN (32 kt Super-K) 37400 events 8 events

fSN (560 kt Hyper-K) 654000 events 134 events

BHNSM (32 kt Super-K) 9300 events 2 events

BHNSM (560 kt Hyper-K) 162000 events 33 events

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

• Supernova 1987A, which exploded in the Large MagellanicCloud 50 kpc away, only produced 20 neutrinos that were detected

• Only confirmed observation of astrophysical neutrinos to date • The next supernova event would give many more neutrinos!

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Neutronization Burst

fSN neutrino luminosities3

Neutronization burst

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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Neutronization Burst Visibility

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

Water Cherenkov (Super-K) Liquid Argon (LBNE)

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Nucleosynthesis

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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Nucleosynthesis Potential

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

FD parameterization for BHNSMGR parameterization for fSN

Temperature

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Results• Showed differences between typical SN

detector signal and fSN/BHNSM detector signals

• Calculated the number of observed events from fSN and BHNSM in current and proposed detectors

• Determined the potential for nucleosynthesisto occur in fSN and BHNSM

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Future Work

• Incorporate systematic uncertainties in parameter determinations

• Apply flavor dependent flux parameterizations to improve fits

• Use time-dependent models of BHNSM

• Incorporate neutrino oscillation

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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Credits

Dr. Kate Scholberg, Duke University

Dr. Josh Albert, Duke University

Dr. Alex Himmel, Duke University

Dr. Jonathan Bennett, NCSSM

Poison Bear

Introduction SNOwGLoBES Time EvolutionObserved Signal Parameter Determination Conclusion

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