Search NASA⌕ Search

SEARCH · Search NASA

Results for “method of averaging”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Using an Absolute Cavity Pyrgeometer to Calibrate Pyrgeometers Outdoors with Respect to the International System of Units

Accurate measurement of the atmospheric longwave irradiance is important for renewable energy and atmospheric science applications. Pyrgeometers are deployed outdoors all over the world to measure the atmospheric longwave irradiance and presently are calibrated with traceability to the interim standards for atmospheric longwave radiation measurement, the standards are based on four pyrgeometers and their average irradiance is the World InfraRed Standard Group (WISG) which is developed and maintained by The Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC). Since 2013 the InfraRed Integrating Sphere (IRIS) developed by PMOD/WRC and the Absolute Cavity Pyrgeometer (ACP) developed by the National Renewable Energy Laboratory (NREL) have been compared outdoors six times at different locations and the difference between the measured atmospheric longwave irradiance by ACP and IRIS was less than 2 w/m2 with traceability to the International System of Units (SI). During the six comparisons the irradiance measured by the interim WISG was 5 w/m2 lower than the irradiance measured by the average irradiance measured by the ACP and IRIS [1]. Based on this discrepancy, the World Meteorological Organization's Commission for Instruments and Methods of Observation (CIMO) recommended that the interim WISG should be adjusted to be traceable to SI units [2]. In anticipation of CIMO's expert team agreement on establishing the world reference using the average irradiance measured by ACP and IRIS in this article we describe a procedure to calibrate pyrgeometers with traceability to SI. One Absolute Cavity Pyrgeometer (ACP95F3) was used to calibrate four pyrgeometers traceable to SI units. Three Eppley PIRs and one Kipp&Zonen CG4 were originally calibrated with traceability to the interim WISG. Using the described procedure below, the responsivity of each pyrgeometer was then adjusted to match the irradiance measured by ACP. Outdoor data was collected during one clear sky night monitored by the output thermopile voltage of ACP95F3. The irradiance measured by the PIRs and CG4 was calculated using NREL equation. The calculated uncertainty (U95) of the PIRs varied from 2.43 w/m2 to 2.67 w/m2 , and for the CG4 equals 1.97 w/m2 with respect to SI.

absolute cavity pyrgeometer↗
DOE OSTI2024

ATR-SEIRAS Method to Measure Interfacial pH during Electrocatalytic Nitrate Reduction on Cu

This study reports the accuracy and applications of an attenuated total reflectance–surface-enhanced infrared absorption spectroscopy (ATR–SEIRAS) technique to indirectly measure the interfacial pH of the electrolyte within 10 nm of the electrocatalyst surface. This technique can be used in situ to study aqueous electrochemical reactions with a calibration range from pH 1–13, time resolution down to 4 s, and an average 95% confidence interval of 14% that varies depending on the pH region (acidic, neutral, or basic). The method is applied here to electrochemical nitrate reduction at a copper cathode to demonstrate its capabilities, but is broadly applicable to any aqueous electrochemical reaction (such as hydrogen evolution, carbon dioxide reduction, or oxygen evolution) and the electrocatalyst may be any SEIRAS-active thin film (e.g., silver, gold, or copper). The time-resolved results show a dramatic increase in the interfacial pH from pH 2–7 in the first minute of operation during both constant current and pulsed current experiments where the bulk pH is unchanged. Attempts to control the pH polarization at the surface by altering the electrochemical operating conditions—lowering the current or increasing the pulse frequency—showed no significant change, demonstrating the challenge of controlling the interfacial pH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

ZrCoBi based half Heuslers with high thermoelectric conversion efficiency

A ZrCoBi-based p-type half-Heusler material can have a formula: ZrCoBi$_{1-x-y}$Sn$_{x}$Sb$_{y}$, where x can vary between 0.01 and 0.25, and y can vary between 0 and 0.2. An average dimensionless figure-of-merit (ZT) for the material can be greater than or equal to about 0.80 as calculated by an integration method for temperatures between 300 and 973 K. A ZrCoBi-based n-type half-Heusler material can have a formula: ZrCo$_{1-x}$Ni$_{x}$Bi$_{1-y}$Sb$_{y}$, where x can vary between 0.01 and 0.25, and y can vary between 0 and 0.3. The material has an average dimensionless figure-of-merit (ZT) is greater than or equal to about 0.65 as calculated by an integration method for temperatures between 300 and 973 K.

Ren, Zhifeng↗
DOE OSTI2025

Measurements of Pion and Muon Nuclear Capture at Rest on Argon in the LArIAT Experiment

We report the measurement of the final-state products of negative pion and muon nuclear capture at rest on argon by the LArIAT experiment at the Fermilab Test Beam Facility. We measure a population of isolated MeV-scale energy depositions, or blips, in 296 LArIAT events containing tracks from stopping low-momentum pions and muons. The average numbers of visible blips are measured to be 0.74 ± 0.19 and 1.86 ± 0.17 near muon and pion track endpoints, respectively. The 3.6⁢𝜎 statistically significant difference in blip content between muons and pions provides the first demonstration of a new method of pion-muon discrimination in neutrino liquid argon time projection chamber experiments. LArIAT Monte Carlo simulations predict substantially higher average blip counts for negative muon (1.22 ± 0.08) and pion (2.34 ± 0.09) nuclear captures. We attribute this difference to geant4’s inaccurate simulation of the nuclear capture process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Techniques for improved statistical convergence in quantification of eddy diffusivity moments

While recent approaches, such as the macroscopic forcing method (MFM) or Green's function-based approaches, can be used to compute Reynolds-averaged Navier-Stokes closure operators using forced direct numerical simulations, MFM can also be used to directly compute moments of the effective nonlocal and anisotropic eddy diffusivities. The low-order spatial and temporal moments contain limited information about the eddy diffusivity but are often sufficient for quantification and modeling of nonlocal and anisotropic effects. However, when using MFM to compute eddy diffusivity moments, the statistical convergence can be slow for higher-order moments. In this work, we demonstrate that using the same direct numerical simulation (DNS) for all forced MFM simulations improves statistical convergence of the eddy diffusivity moments. We present its implementation in conjunction with a decomposition method that handles the MFM forcing semianalytically and allows for consistent boundary condition treatment, which we develop for both scalar and momentum transport. We demonstrate that for a two-dimensional Rayleigh-Taylor instability case study, using the same DNS for all forced MFM simulations results in convergence with 𝒪⁡(100) simulations rather than 𝒪⁡(1000) simulations. In conclusion, we then demonstrate the impacts of improved convergence on the quantification of the eddy diffusivity.

general physics↗
DOE OSTI2024

Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

Gaussian process↗
DOE OSTI2023

Predictive models of long COVID

Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models — logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the ‘long COVID’ label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). Findings: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. Interpretation: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The effects of different types of mulch on soil properties and tea production and quality

Abstract BACKGROUND Tea is an important economic crop in China. Mulching, a modern agricultural practice, can modify the soil microenvironment and maintain the crop yield. RESULTS To investigate the effect of different mulching modes on tea plant growth, filed experiments were conducted in a Shizipu tea plantation located in Langxi Country (Xuanchen City, Anhui Province, China). Five treatments were carried out in a randomized complete block arrangement: (i) clean tillage (control); (ii) black plastic film; (iii) weed barrier fabric; (iv) rice straw mulch; and (v) intercropping with Vulpia myuros . The effects of different mulch modes on soil temperature, water moisture, soil compactness, root activity, soil enzyme activity and nutrition status on tea yield, quality and economic benefits were compared at the harvest stage. In the present study, compared with other mulch treatments, intercropping with V. myuros significantly reduced the topsoil temperature (to an optimum temperature) and soil compactness, and increased the water holding capacity in the deep soil layer, which contributed to increased tea root activity and respiration. Furthermore, intercropping with V. myuros significantly increased soil enzymes activity, soil organic matter, and the available nitrogen and phosphorus concentrations in the main root zone. Therefore, the stronger tea root activity accelerated nutrition uptake and increased the tea yield and quality‐related components of the tea, thus resulting in a larger average net income. CONCLUSION Intercropping with V. myuros could serve as a profitable agricultural method for tea production. © 2020 Society of Chemical Industry

Xianchen, Zhang↗
DOE OSTI2024

Applying queueing theory to evaluate wait-time-savings of triage algorithms

Abstract In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Thompson, Yee Lam Elim (ORCID:0000000196537707)↗
DOE OSTI2019

Agglomeration Mechanism of Complex Ti-Al Oxides in Liquid Ferrous Alloys Considering High-Temperature Interfacial Phenomenon

This work presents the agglomeration mechanism of complex Ti-Al oxides in the liquid ferrous alloy. Cluster characteristics were investigated using Al and Ti/Al-complex deoxidation method in lab scale. The time-dependent size distribution, total number per volume, average size, and circularity of the clusters were quantitatively analyzed. Furthermore, high-temperature confocal laser scanning microscopy was utilized to directly observe the cluster formation of Ti-Al oxides. A capillary force model including wettability parameters was applied to compare the agglomeration capabilities of different types of non-metallic inclusions. When a low Ti is added into melt, the agglomeration of TiO{sub x}·FeO liquid inclusions is one of the key factors to decrease the frequency of cluster formation. When the Al is added into melt, the heterogeneous precipitation on TiO{sub x}·FeO surfaces is the main reaction process. Ti-Al oxides have lower agglomeration ability than that of Al{sub 2}O{sub 3}, which in turn, contribute to a low agglomeration frequency as well.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The practice and economics of hybrid poplar biomass production for biofuels and bioproducts in the Pacific Northwest

Hybrid poplar demonstration-scale farms were managed in Oregon, Washington, Idaho, and California to establish management practices, yields, harvesting methods, and the economics of biomass production. Yield during the 2-year establishment cycle averaged 3.5 dry Mg ha –1 year –1 increasing to 11.6 Mg ha –1 year –1 in the ensuing 3-year coppice cycle. Populus deltoides (Bartram ex Marsh.) × P. maximowiczii (Henry) varieties preformed best in Oregon during the coppice cycle with the best variety producing 18.1 Mg ha –1 year –1 , while P. ×generosa (Henry) varieties maximized yields in Washington at 22.1 Mg ha –1 year –1 . P. ×canadensis (Moench) varieties excelled in Idaho and California with upper yields of 13.6 Mg ha –1 year –1 and 12.9 Mg ha –1 year –1 , respectively. Stands were cut with a single-pass harvester 2 years after planting and a second time after 3 years of coppice growth; material capacity, limited by poor ground conditions, varied between 21.7 to 31.3 green Mg h –1 . Chemical composition averaged 1.87% inorganics, 7.74% extractives, 26.90% lignin, 38.07% glucan, 13.66% xylan, 1.61% galactan, 1.14% arabinan, and 2.76% mannan. Production costs (USD) projected over a 20-year rotation of six coppice cycles were $71.81 Mg –1 in Washington, $89.91 Mg –1 in Oregon, $98.76 Mg–1 in Idaho, and 179.07 Mg–1 in California. Here, land rental, establishment, crop care, harvest, transportation, and land restoration, respectively, accounted for 23%, 5%, 19%, 30%, 17%, and 6% of total feedstock cost. Farms were successfully restored to conditions existing before poplar conversion. In the absence of fertilization, increases in soil pH and decreases in nitrate-nitrogen, zinc, iron, and organic matter were consistently noted but could not be associated with poplar production alone.

09 BIOMASS FUELS↗
DOE OSTI2021

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗
DOE OSTI2022

The 40 Ar(d,p) 41 Ar cross section between 3–7 MeV

To determine the safety of using argon as a deuteron beam stopping material, the 40 Ar(d,p) 41 Ar cross section was measured at average deuteron energies of 3.6 MeV, 5.5 MeV, and 7.0 MeV using an activation method. A 16-MeV deuteron beam produced by Lawrence Berkeley National Laboratory’s 88-Inch Cyclotron was degraded to each energy by nickel foils and the front wall of an aluminum gas chamber. The reduced-energy deuterons were used to activate a sample of nat Ar gas. After each irradiation, the gas chamber’s 41 Ar activation was measured with a high-purity germanium detector. The cross sections measured were larger than a previous measurement by ~40%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Arbuscular mycorrhizal trees cause a higher carbon to nitrogen ratio of soil organic matter decomposition via rhizosphere priming than ectomycorrhizal trees

Tree roots and their associated microbes can significantly influence soil organic matter (SOM) decomposition, i.e., the rhizosphere priming effect. This effect is expected to be greater in trees associated with arbuscular mycorrhizal (AM) fungi, which produce higher extracellular enzymes especially surrounding hyphae, than in trees associated with ectomycorrhizal (ECM) fungi. In this work, we selected five tree species associated with AM (Juglans mandshurica Maxim. and Cunninghamia lanceolata (Lamb.) Hook.) or ECM (Picea koraiensis Nakai, Quercus mongolica Fischer ex Turcz. and Larix kaempferi (Lamb.) Carriere). We grew tree seedlings inside of cores lined with different mesh sizes to investigate how roots, hyphae and exudates influence soil carbon (C) and nitrogen (N) mineralization via the rhizosphere priming effect, using a 13 C natural abundance approach and a 15 N pool dilution method, concurrently. We found that tree seedlings significantly accelerated soil C decomposition by on average 78%, i.e., positive priming, compared to unplanted control pots. AM-associated trees induced 2.1 times greater soil C decomposition than ECM-associated trees across all mesh sizes. In contrast, gross N mineralization did not differ between tree-mycorrhizal associations. Compared to ECM counterparts, AM-associated trees had higher C- and lower N-degrading enzyme activities. Consequently, AM-associated trees induced a significantly higher C:N ratio of SOM decomposition than their ECM counterparts, which could be associated with the differences in soil enzyme activities for C and N degradation. Further, for both AM- and ECM-associated trees, we found no significant influences of mesh size on soil C decomposition, suggesting that the rhizosphere priming effect of mycorrhizal symbiosis was predominantly driven by root exudates. We conclude that SOM decomposition caused by AM-associated trees may have a higher C:N ratio than that by ECM-associated trees mainly due to differences in microbial enzyme investment. Our findings imply that tree-mycorrhizal associations are capable of modulating soil biogeochemical cycling via the rhizosphere priming effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗