A neural network based UHE neutrino reconstruction method for the Askaryan Radio Array (ARA)

Pan, Y.; Allison, P.; Archambault, S.; Beatty, J. J.; Beheler-Amass, M.; Besson, D. Z.; Chen, C.H.; Chen, P.; Chen, Y.C.; Clark, Brian; Clay, W.; Connolly, A.; Cremonesi, L.; Dasgupta, P.; Davies, J.; Kockere, Simon De; Vries, Krijn de; Deaconu, C.; DuVernois, M. A.; Flaherty, J.; Friedman, E.; Gaïor, R.; Hanson, Jordan C.; Hanson, K.; Harty, N.; Hendricks, B. Clifford; Hoffman, K. D.; Hokanson-Fasig, B.; Hong, Eugene; Hsu, Shih-Ying; Huang, J. J.; Huang, M.; Hughes, K.; Ishihara, A.; Karle, A.; Kelley, J. L.; Khandelwal, Rishabh; Kim, K.-C.; Kim, M.-C.; Krebs, Ryan; Kravchenko, I.; Ku, Yun Hyi; Kuo, Chih‐Ming; Kurusu, K.; Latif, Uzair Abdul; Laundrie, A.; Landsman, H.; Lu, M.-Y.; Liu, T.-C.; Madison, B.; Mase, K.; Meures, T.; Nam, J. W.; Novikov, Alexander; Nichol, R. J.; Nir, G.; Nozdrina, A.; Oberla, E.; O’Murchadha, A.; Osborn, J. D.; Pan, Y.; Pfendner, C.; Punsuebsay, N.; Roth, J. D.; Sandstrom, P.; Seckel, D.; Shiao, Y.-S.; Shultz, A.; Smith, D. J.B.; Torres, Jorge; Toscano, S.; Touart, J.; Eijndhoven, N. van; Varner, G.; Vieregg, A. G.; Wang, M.-Z.; Wang, S.-H.; Wang, Y.H.; Wissel, Stephanie; Xie, Changqing; Young, Robert D.; Yoshida, Shigeru

doi:10.22323/1.395.1157

Cited by 7 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We characterize the signal in each antenna by its maximum amplitude and time which we extract from the maximum of the Hilbert envelope of each antenna's simulated signal trace. This is similar to a deep learning-based analysis performed for the ARA experiment [36].…”

Section: Simplified Datasetssupporting

confidence: 58%

Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors

Glaser¹,

McAleer²,

Stjärnholm³

et al. 2022

Preprint

View full text Add to dashboard Cite

Ultra-high-energy (UHE) neutrinos (> 10 16 eV) can be measured cost-effectively using in-ice radio detection, which has been explored successfully in pilot arrays. A large radio detector is currently being constructed in Greenland with the potential to measure the first UHE neutrino, and an order-of-magnitude more sensitive detector is being planned with IceCube-Gen2. For such shallow radio detector stations, we present an end-to-end reconstruction of the neutrino energy and direction using deep neural networks (DNNs). The DNN determines the energy with a standard deviation of a factor of two around the true energy, which meets the science requirements of UHE neutrino detectors. For the first time, we are able to predict the neutrino direction well for all event topologies including the complicated electron neutrino charged-current (ν e -CC) interactions, a significant improvement compared to previous approaches. The obtained angular resolution follows a Gaussian distribution with σ ≈ 0.6°(0.8°) with extended tails that push the 68% quantile to 4°(5°) for non-ν e -CC and ν e -CC interactions, respectively. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling measurement of neutrino energy and direction.

show abstract

Section: Simplified Datasetssupporting

confidence: 58%

Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors

Glaser¹,

McAleer²,

Stjärnholm³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The ability to measure the viewing angle and to combine all individual measurements to estimate the arrival direction was quantified in simulation studies using the forward folding technique [81,197,199,210] and deep neural networks [182,212]. The best resolution of the direction was obtained with a shallow detector station with a 68% quantile of 3 • , which translates into an uncertainty of the zenith angle of σ θz = 2 • .…”

Section: Reconstruction Of Energy and Directionmentioning

confidence: 99%

The ultra-high-energy neutrino-nucleon cross section: measurement forecasts for an era of cosmic EeV-neutrino discovery

Valera,

Bustamante,

Glaser

2022

Preprint

View full text Add to dashboard Cite

Neutrino interactions with protons and neutrons probe their deep structure and may reveal new physics. The higher the neutrino energy, the sharper the probe. So far, the neutrino-nucleon (νN ) cross section is known across neutrino energies from a few hundred MeV to a few PeV. Soon, ultrahigh-energy (UHE) cosmic neutrinos, with energies above 100 PeV, could take us farther. So far, they have evaded discovery, but upcoming UHE neutrino telescopes endeavor to find them. We present the first detailed measurement forecasts of the UHE νN cross section, geared to IceCube-Gen2, one of the leading detectors under planning. We use state-of-the-art ingredients in every stage of our forecasts: in the UHE neutrino flux predictions, the neutrino propagation inside Earth, the emission of neutrino-induced radio signals in the detector, their propagation and detection, and the treatment of backgrounds. After 10 years, if at least a few tens of UHE neutrino-induced events are detected, IceCube-Gen2 could measure the νN cross section at center-of-mass energies of √ s ≈ 10-100 TeV for the first time, with a precision comparable to that of its theory prediction.

show abstract

“…To produce our main results, we set σ θz = 2 • and σ E sh = 10 σ E sh , where σ = 0.1 is the spread in the ratio ≡ log 10 (E rec sh /E sh ). These values are chosen based on simulations [89,[106][107][108][109][110][111][112][113]. To produce our results, we use the total event rate, summing Eq.…”

Section: Appendix B: Radio-detecting Ultra-high-energy Neutrinos In I...mentioning

confidence: 99%

“…Due to limited detector resolution, the shower energy and direction are reconstructed imperfectly. Our choices of detector resolution are informed by simulations [89,[106][107][108][109][110][111][112][113]; they are the same as in Ref. [89].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Near-future discovery of point sources of ultra-high-energy neutrinos

Fiorillo¹,

Bustamante²,

Valera³

2022

Preprint

View full text Add to dashboard Cite

Upcoming neutrino telescopes may discover ultra-high-energy (UHE) cosmic neutrinos, with energies beyond 100 PeV, in the next 10-20 years. Finding their sources would expose the long-sought origin of UHE cosmic rays. We search for sources by looking for multiplets of UHE neutrinos arriving from similar directions. Our forecasts are state-of-the-art, geared at neutrino radio-detection in IceCube-Gen2. They account for detector energy and angular response, and for critical, but uncertain backgrounds. We report powerful insight. Sources at declination of −45 • to 0 • will be easiest to discover. Discovering even one steady-state source in 10 years would disfavor most known steady-state source classes as dominant. Discovering no transient source would disfavor most known transient source classes as dominant. Our results aim to inform the design of upcoming detectors.

show abstract

A neural network based UHE neutrino reconstruction method for the Askaryan Radio Array (ARA)

Abstract: A neural network based UHE neutrino reconstruction method for the Askaryan Radio Array (ARA) Yue Pan * on behalf of the ARA Collaboration

Cited by 7 publications

References 2 publications

Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors

Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors

The ultra-high-energy neutrino-nucleon cross section: measurement forecasts for an era of cosmic EeV-neutrino discovery

Near-future discovery of point sources of ultra-high-energy neutrinos

Contact Info

Product

Resources

About