Natural iron enrichment around the Antarctic Peninsula in the Southern Ocean

As part of the US-AMLR program in January-February of 2006, 99 stations in the South Shetland Islands-Antarctic Peninsula region were sampled to understand the variability in hydrographic and biological properties related to the abundance and distribution of krill in this area. Concentrations of dissolved iron (DFe) and total acid-leachable iron (TaLFe) were measured in the upper 150 m at 16 of these stations (both coastal and pelagic waters) to better resolve the factors limiting primary production in this area and in downstream waters of the Scotia Sea. The concentrations of DFe and TaLFe in the upper mixed layer (UML) were relatively high in Weddell Sea Shelf Waters (~0.6 nM and 15 nM, respectively) and low in Drake Passage waters (~0.2 nM and 0.9 nM, respectively). In the Bransfield Strait, representing a mixture of waters from the Weddell Sea and the Antarctic Circumpolar Current (ACC), concentrations of DFe were ~0.4 nM and of TaLFe ~1.7 nM. The highest concentrations of DFe and TaLFe in the UML were found at shallow coastal stations close to Livingston Island (~1.6 nM and 100 nM, respectively). The ratio of TaLFe:DFe varied with the distance to land: ~45 at the shallow coastal stations, ~15 in the high-salinity waters of Bransfield Strait, and ~4 in ACC waters. Concentrations of DFe increased slightly with depth in the water column, while that of TaLFe did not show any consistent trend with depth. Our Fe data are discussed in regard to the hydrography and water circulation patterns in the study area, and with the hypothesis that the relatively high rates of primary production in the central regions of the Scotia Sea are partially sustained by natural iron enrichment resulting from a northeasterly flow of iron-rich coastal waters originating in the South Shetland Islands-Antarctic Peninsula region

Experiment Simulation Configurations Used in DUNE CDR

The LBNF/DUNE CDR describes the proposed physics program and experimental design at the conceptual design phase. Volume 2, entitled The Physics Program for DUNE at LBNF, outlines the scientific objectives and describes the physics studies that the DUNE collaboration will perform to address these objectives. The long-baseline physics sensitivity calculations presented in the DUNE CDR rely upon simulation of the neutrino beam line, simulation of neutrino interactions in the far detector, and a parameterized analysis of detector performance and systematic uncertainty. The purpose of this posting is to provide the results of these simulations to the community to facilitate phenomenological studies of long-baseline oscillation at LBNF/DUNE. Additionally, this posting includes GDML of the DUNE single-phase far detector for use in simulations. DUNE welcomes those interested in performing this work as members of the collaboration, but also recognizes the benefit of making these configurations readily available to the wider community.Comment: 9 pages, 4 figures, configurations in ancillary file

Arterial oxygen content is precisely maintained by graded erythrocytotic responses in settings of high/normal serum iron levels, and predicts exercise capacity: an observational study of hypoxaemic patients with pulmonary arteriovenous malformations.

Oxygen, haemoglobin and cardiac output are integrated components of oxygen transport: each gram of haemoglobin transports 1.34 mls of oxygen in the blood. Low arterial partial pressure of oxygen (PaO2), and haemoglobin saturation (SaO2), are the indices used in clinical assessments, and usually result from low inspired oxygen concentrations, or alveolar/airways disease. Our objective was to examine low blood oxygen/haemoglobin relationships in chronically compensated states without concurrent hypoxic pulmonary vasoreactivity.165 consecutive unselected patients with pulmonary arteriovenous malformations were studied, in 98 cases, pre/post embolisation treatment. 159 (96%) had hereditary haemorrhagic telangiectasia. Arterial oxygen content was calculated by SaO2 x haemoglobin x 1.34/100.There was wide variation in SaO2 on air (78.5-99, median 95)% but due to secondary erythrocytosis and resultant polycythaemia, SaO2 explained only 0.1% of the variance in arterial oxygen content per unit blood volume. Secondary erythrocytosis was achievable with low iron stores, but only if serum iron was high-normal: Low serum iron levels were associated with reduced haemoglobin per erythrocyte, and overall arterial oxygen content was lower in iron deficient patients (median 16.0 [IQR 14.9, 17.4]mls/dL compared to 18.8 [IQR 17.4, 20.1]mls/dL, p<0.0001). Exercise tolerance appeared unrelated to SaO2 but was significantly worse in patients with lower oxygen content (p<0.0001). A pre-defined athletic group had higher Hb:SaO2 and serum iron:ferritin ratios than non-athletes with normal exercise capacity. PAVM embolisation increased SaO2, but arterial oxygen content was precisely restored by a subsequent fall in haemoglobin: 86 (87.8%) patients reported no change in exercise tolerance at post-embolisation follow-up.Haemoglobin and oxygen measurements in isolation do not indicate the more physiologically relevant oxygen content per unit blood volume. This can be maintained for SaO2 ≥78.5%, and resets to the same arterial oxygen content after correction of hypoxaemia. Serum iron concentrations, not ferritin, seem to predict more successful polycythaemic responses

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA.

The NOvA experiment has seen a 4.4σ signal of ν[over ¯]_{e} appearance in a 2&nbsp;GeV ν[over ¯]_{μ} beam at a distance of 810&nbsp;km. Using 12.33×10^{20} protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ν[over ¯]_{μ}→ν[over ¯]_{e} candidates with a background of 10.3 and 102 ν[over ¯]_{μ}→ν[over ¯]_{μ} candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm_{32}^{2}|=2.48_{-0.06}^{+0.11}×10^{-3}  eV^{2}/c^{4} and sin^{2}θ_{23} in the ranges from (0.53-0.60) and (0.45-0.48) in the normal neutrino mass hierarchy. The data exclude most values near δ_{CP}=π/2 for the inverted mass hierarchy by more than 3σ and favor the normal neutrino mass hierarchy by 1.9σ and θ_{23} values in the upper octant by 1.6σ

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

IntroductionReconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications.MethodsWe observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets.ResultsOur experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset.DiscussionBy assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes

Observation of seasonal variation of atmospheric multiple-muon events in the NOvA Near Detector

Using two years of data from the NOvA Near Detector at Fermilab, we report a seasonal variation of cosmic ray induced multiple-muon (Nμ≥2) event rates which has an opposite phase to the seasonal variation in the atmospheric temperature. The strength of the seasonal multiple-muon variation is shown to increase as a function of the muon multiplicity. However, no significant dependence of the strength of the seasonal variation of the multiple-muon variation is seen as a function of the muon zenith angle, or the spatial or angular separation between the correlated muons

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model's strengths and weaknesses are discussed, and plans for developing this technique further are summarised.Comment: 7 pages, 3 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physic

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at arXiv:1802.0870

Shelf-derived iron inputs drive biological productivity in the southern Drake Passage

Author Posting. © American Geophysical Union, 2009. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 23 (2009): GB4014, doi:10.1029/2008GB003406.In the Southern Ocean near the Antarctic Peninsula, Antarctic Circumpolar Current (ACC) fronts interact with shelf waters facilitating lateral transport of shelf-derived components such as iron into high-nutrient offshore regions. To trace these shelf-derived components and estimate lateral mixing rates of shelf water, we used naturally occurring radium isotopes. Short-lived radium isotopes were used to quantify the rates of shelf water entrainment while Fe/228Ra ratios were used to calculate the Fe flux. In the summer of 2006 we found rapid mixing and significant lateral iron export, namely, a dissolved iron flux of 1.1 × 105 mol d−1 and total acid leachable iron flux of 1.1 × 106 mol d−1 all of which is transported in the mixed layer from the shelf region offshore. This dissolved iron flux is significant, especially considering that the bloom observed in the offshore region (0.5–2 mg chl a m−3) had an iron demand of 1.1 to 4 × 105 mol Fe. Net vertical export fluxes of particulate Fe derived from 234Th/238U disequilibrium and Fe/234Th ratios accounted for only about 25% of the dissolved iron flux. On the other hand, vertical upward mixing of iron rich deeper waters provided only 7% of the lateral dissolved iron flux. We found that similarly to other studies in iron-fertilized regions of the Southern Ocean, lateral fluxes overwhelm vertical inputs and vertical export from the water column and support significant phytoplankton blooms in the offshore regions of the Drake Passage.This work was funded by the National Science Foundation (ANT-0443869 to M.A.C.)