After two seasons of installation, 7 stations built and many lessons learned, the Radio Neutrino Observatory Greenland (RNO-G) is now operational. In the coming years, the construction of another 28+ stations will bring the array to full capacity as an instrument with an eye towards the ultra-high energy neutrino (>10 PeV) regime, creating another link in the fast paced and rapidly changing landscape of multi-messenger astronomy. Until now, the data volume of our two initial seasons has remained manageable. However, as the array continues to grow, we need to develop faster and more efficient processes regarding how to filter our data; we must throw away the noise and identify the most promising events. Data reduction tools become crucial for anthropogenic, environmental and local noise identification/removal in order to test and monitor our instrument as we scale up. We present a convolutional encoder-decoder network that assigns an anomaly ranking to events, helping to classify different categories of background and signal.
The Radio Neutrino Observatory in Greenland (RNO-G) is an array of radio detector stations which has been designed to study ultra-high energy (𝐸 ≳ $10^{18}$ eV) neutrinos. The experiment, when completed, will have the best sensitivity in this energy range and will yield a major advancement in our understanding of the sources and propagation of the highest energy cosmic rays. While RNO-G will be sensitive to primarily 𝐸 ≳ 100 PeV neutrinos, the optical-based detectors only have a large enough exposure to study up to ∼ 1–10 PeV, leaving a gap in the energy range between the two detection methods. For RNO-G, the energy threshold is set by our ability to distinguish the Askaryan pulses, created from neutrino interactions, from the irreducible background of thermal noise. Using modern machine learning techniques, an online trigger can be implemented to identify small-amplitude pulses from in-ice cascades and thereby decrease the energy threshold of RNO-G. Such an advancement will increase the expected amount of observed neutrinos, as well as close the gap between radio- and optical-based observatories. We present a convolutional neural network for classification of neutrino events that can be run as a second-stage trigger.
Abstract Neutron monitors continuously record the hadronic part of secondary atmospheric radiation on the ground, which originates from primary cosmic rays. In Thailand, we developed a mobile neutron monitor housed inside a standard-size shipping container named “Changvan.” It contains three neutron-sensitive proportional counters set up in the typical NM64 layout. However, the central counter doesn’t have the lead producer, leading us to refer to it as a “semi-leaded” neutron monitor. We examined cosmic ray spectral variations on two latitude surveys during 2018-2019 and 2019-2020. This work examines the ratio of count rates between leaded and unleaded setups, which shows notable variation based on geomagnetic cutoff rigidity, suggesting a sensitivity to the cosmic ray spectrum. This measurement could be implemented at stationary stations. The unleaded counter, shielded by the reflector with a higher count from nearby lead, may have advantages over a bare one. Furthermore, we explore alternative techniques to identify spectral changes in Galactic cosmic rays using Changvan data. We analyze using time delay histograms to determine the leader fraction (L) of neutrons that are not preceded by another neutron from the same primary cosmic ray. We also examine other parameters, including the alpha (α) parameter and pulse rate (PR), which can be compared with count rates (CR). Our findings indicate that the ratios of L and α are not significantly affected by geomagnetic cutoff rigidity. In contrast, CR and PR exhibit significant dependency and show opposite trends.
Searches for neutrinos from gravitational wave events have been performed utilizing the wide energy range of the IceCube Neutrino Observatory. We discuss results from these searches during the third observing run (O3) of the advanced LIGO and Virgo detectors, including a low-latency follow-up of public candidate alert events in O3, an archival search on high-energy track data, and a low-energy search employing IceCube-DeepCore. The dataset of high-energy tracks is mainly sensitive to muon neutrinos, while the low energy dataset is sensitive to neutrinos of all flavors. In all of these searches, we present upper limits on the neutrino flux and isotropic equivalent energy emitted in neutrinos. We also discuss future plans for additional searches, including extending the low-latency follow-up to the next observing run of the LIGO-Virgo-KAGRA detectors (O4) and analysis of gravitational wave (GW) events using a high-energy cascade dataset, which are produced by electron neutrino charged-current interactions and neutral-current interactions from neutrinos of all flavors.
