Abstract Active galactic nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos, since they provide environments rich in matter and photon targets where cosmic-ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the Swift-BAT Spectroscopic Survey catalog of hard X-ray sources and 12 yr of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, the Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux = TeV−1cm−2s−1normalized at 1 TeV, with a power-law spectral indexγ= 3.10 , consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a posttrial significance of 2.9σ. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux = TeV−1cm−2s−1normalized at 1 TeV andγ= 2.83 .
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Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors
Abstract We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For keV electrons in a 70% He 30% CO2gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26∘) compared to 0.556 (64∘) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.
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- Award ID(s):
- 2238375
- PAR ID:
- 10522797
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 035009
- Size(s):
- Article No. 035009
- Sponsoring Org:
- National Science Foundation
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