Small Uncertainties Research Articles

Development of neutron self-indication thermometry at J-PARC

ABSTRACT Determining temperatures with small uncertainties is important for neutron thermometry, required for both fundamental science and industrial applications. Conventionally, neutrons transmitted through a sample in shielded materials are detected. The sample temperature is then derived by analyzing the energy dependence of the transmission neutrons at resonances influenced by the Doppler-broadening effect. However, reducing the temperature-determination uncertainty is extremely hard with the conventional method, because it is determined only by the small changes at the resonances, i.e. temperature-sensitive components, compared to the primary neutrons. Therefore, we propose a new thermometry named neutron self-indication thermometry (NSIT), which combines the Doppler-broadening effect with a self-indication technique that irradiates the sample and an indicator containing the same nuclide as the sample. The NSIT can enhance temperature sensitivity by measuring prompt gamma-rays to indirectly obtain the temperature-sensitive components at resonances by employing the same resonance twice. The temperature sensitivity and uncertainty of the NSIT were compared with those of the conventional method by varying the sample temperatures from 23.0°C to 492.6°C. The results demonstrated that the NSIT was approximately 1.6 times more sensitive and had lower uncertainty in determining temperature. These findings highlight the potential of the NSIT as an effective alternative to remote thermometry.

Cross-sections for formation of 73Ga through natGe(n, x)73Ga reaction induced by d-T neutrons

The formation of 73Ga through natGe(n,x)73Ga reaction induced by d-T neutrons was investigated using cross-sections measured by four characteristic gamma-rays employing the weighted average method. The neutron energy and uncertainty were determined by the Q equation of reaction 3H(d,n)4He combined with the solid angle of the sample. The gamma spectrum of the activated sample was obtained by an off-line technique using a high-resolution, high-purity germanium detector (HPGe). The contribution of nuclear reactions 76Ge(n,α)73Zn, 73Ge(n,p)73Ga, and 74Ge(n,d*)73Ga to residual nuclei 73Ga was analyzed and the cross-sections of reaction 73Ge(n,p)73Ga were determined with a small uncertainty. The TALYS-1.96 package was utilized for performing the theoretical calculation of the cross section of 76Ge(n,α)73Zn, 73Ge(n,p)73Ga, and 74Ge(n,d*)73Ga reactions. The results of the theoretical calculation of nuclear reaction 73Ge(n,p)73Ga were compared with the experimental measurements and the evaluation results provided by the database.

Flare-accelerated Electrons in the Kappa Distribution from X-Ray Spectra with the Warm-target Model

Abstract X-ray observations provide important and valuable insights into the acceleration and propagation of nonthermal electrons during solar flares. Improved X-ray spectral analysis requires a deeper understanding of the dynamics of energetic electrons. Previous studies have demonstrated that the dynamics of accelerated electrons with a few thermal speeds are more complex than those with significantly higher speeds. To better describe the energetic electrons after injection, a model considering energy diffusion and thermalization effects in flare conditions (the warm-target model) has recently been developed for spectral analysis of hard X-rays. This model has demonstrated how the low-energy cutoff, which can hardly be constrained in cold-target modeling, can be determined. However, the power-law form may not be the most suitable representation of injected electrons. The kappa distribution, which is proposed as a physical consequence of electron acceleration, has been applied successfully in RHESSI spectral analysis. In this study, we employ the kappa-form injected electrons in the warm-target model to analyze two M-class flares, observed by RHESSI and the Spectrometer/Telescope for Imaging X-rays, respectively. The best-fit results show that the kappa-form energetic electron spectrum generates lower nonthermal energy than the power-law form when producing a similar photon spectrum in the fit range. We also demonstrated that the fit parameters associated with the kappa-form electron spectrum can be well determined with small uncertainty. Further, the kappa distribution, which covers the entire electron energy range, enables the determination of key electron properties such as total electron number density and average energy at the flare site, providing valuable information on electron acceleration processes.

Electron density measurement of an ionospheric-like plasma using an impedance probe.

In this work, an impedance probe was designed to measure ionospheric low-density plasma. The probe is made of short cylindrical dipoles with an antenna 20cm in length and 0.25cm in diameter, which can operate in the frequency range of 0-100 MHz. Combined with a vector network analyzer, the performance of the probe was tested in laboratory-created ionospheric-like plasma, and the results were compared with those of the Langmuir probe measurements. The densities measured by the two methods show a consistent trend, and the impedance probe data show much smaller uncertainties, which suggests that the impedance probe can achieve high-precision measurements. Furthermore, by fitting the antenna phase data, the electron-neutral collision frequency can also be obtained. Therefore, the impedance probe provides a feasible method for exploring low-density partially ionized plasma and can be expanded to actual ionospheric exploration in the future.

Analysis of three-body decays B→D(V→)PP under the factorization-assisted topological-amplitude approach

Motivated by the accumulated experimental results on three-body charmed B decays with resonance contributions in , LHCb, and Belle (II), we systematically analyze B(s)→D(s)(V→)P1P2 decays with V representing a vector resonance (ρ,K*,ω, or ϕ) and P1,2 as a light pseudoscalar meson (pion or kaon). The intermediate subprocesses B(s)→D(s)V are calculated with the factorization-assisted topological-amplitude (FAT) approach and the intermediate resonant states V described by the relativistic Breit-Wigner distribution successively decay to P1P2 via strong interaction. Taking all lowest resonance states (ρ,K*,ω,ϕ) into account, we calculate the branching fractions of these decay modes as well as the Breit-Wigner-tail effects for B(s)→D(s)(ρ,ω→)KK. Our results agree with the data by , LHCb, and Belle (II). Among the predictions that are still not observed, there are some branching ratios of order 10−6–10−4 which are hopeful to be measured by LHCb and Belle II. Our approach and the perturbative QCD approach (PQCD) adopt the compatible theme to deal with the resonance contributions. What is more, our data for the intermediate two-body charmed B-meson decays in FAT approach are more precise. As a result, our results for branching fractions have smaller uncertainties, especially for color-suppressed emission diagram dominated modes. Published by the American Physical Society 2024

Propagation of hybrid uncertainty by synthesizing B-spline chaos and augmented change of probability measure

Acquiring engineering data is frequently expensive, resulting in sparse data that may lead to a lack of knowledge for design and analysis. Thus, it is not always feasible to precisely determine the probability density functions (PDFs) of uncertain model parameters. Under such circumstances that involve simultaneous aleatory and epistemic uncertainties, repeated uncertainty propagation (UP) analysis is generally required. In this paper, a novel approach for hybrid UP is proposed by integrating B-spline chaos and augmented change of probability measure (aCOM) for meeting different goals. The B-spline chaos is adopted to represent the complicated computational model as a function of an arbitrary input random variable, while the aCOM is employed to reconstruct the PDF of the model output when the input PDF is changed due to epistemic uncertainty. In the case of small epistemic uncertainty, hybrid UP can be achieved directly by changing the assigned probabilities of existing sample results. While in the case of large epistemic uncertainty, additional samples from an augmenting PDF are generated. The proposed method is compatible with both cases. The numerical algorithm of the proposed method is presented and illustrated by four benchmark problems. Further, the accuracy and efficiency of the proposed method are substantiated by four numerical examples compared with analytical solutions or Monte Carlo simulations. An attempt to enhance the proposed method with the aid of active subspace methods to handle high-dimensional problems is also discussed in this work. The limitations and potential improvements of the proposed approach are outlined as well.

Accurate and Model-independent Radius Determination of Single FGK and M Dwarfs Using Gaia DR3 Data

Measuring fundamental stellar parameters is key to fully comprehending the evolution of stars. However, current theoretical models overpredict effective temperatures, and underpredict radii, compared to observations of K and M dwarfs (radius inflation problem). In this work, we have developed a model-independent method to infer precise radii of single FGK and M dwarfs using Gaia DR3 parallaxes and photometry, and used it to study the radius inflation problem. We calibrated nine surface brightness–color relations for the three Gaia magnitudes and colors using a sample of stars with angular diameter measurements. We achieved an accuracy of 4% in our angular diameter estimations, which Gaia’s parallaxes allow us to convert to physical radii. We validated our method by comparing our radius measurements with literature samples and the Gaia DR3 catalog, which confirmed the accuracy of our method and revealed systematic offsets in the Gaia measurements. Moreover, we used a sample with measured Hα equivalent width (Hα EW), a magnetic activity indicator, to study the radius inflation problem. We demonstrated that active stars have larger radii than inactive stars, showing that radius inflation is correlated with magnetic activity. We found a correlation between the radius inflation of active stars and Hα EW for the mass bin 0.5 < M[M ⊙] ≤ 0.6, but we found no correlation for lower masses. This could be due to lack of precision in our radius estimation or a physical reason. Radius measurements with smaller uncertainties are necessary to distinguish between the two scenarios.

Identification of an optimal ground-based validation site for FLEX and quantification of uncertainties using airborne HyPlant data - A case study in Italy

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

Probabilistic Attention Based on Gaussian Processes for Deep Multiple Instance Learning.

Multiple instance learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although recent deep learning MIL approaches have obtained state-of-the-art results, they are fully deterministic and do not provide uncertainty estimations for the predictions. In this work, we introduce the attention Gaussian process (AGP) model, a novel probabilistic attention mechanism based on Gaussian processes (GPs) for deep MIL. AGP provides accurate bag-level predictions as well as instance-level explainability and can be trained end-to-end. Moreover, its probabilistic nature guarantees robustness to overfit on small datasets and uncertainty estimations for the predictions. The latter is especially important in medical applications, where decisions have a direct impact on the patient's health. The proposed model is validated experimentally as follows. First, its behavior is illustrated in two synthetic MIL experiments based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is evaluated in three different real-world cancer detection experiments. AGP outperforms state-of-the-art MIL approaches, including deterministic deep learning ones. It shows a strong performance even on a small dataset with less than 100 labels and generalizes better than competing methods on an external test set. Moreover, we experimentally show that predictive uncertainty correlates with the risk of wrong predictions, and therefore it is a good indicator of reliability in practice. Our code is publicly available.

Experimental uncertainty evaluation by measuring a micro gear standard using focus variation

Abstract Micro gears play an increasingly important role in various industrial applications, and the minimization of their deviations is challenging for metrology and manufacturing. A promising method is the focus variation technology, which enables areal measurements of micro gears. Practice-related standards are used to determine measurement uncertainties by comparison with calibration values. In this work, the external micro gear standard of the Physikalisch-Technische Bundesanstalt (PTB) is used to evaluate experimental measurement uncertainties of a focus variation coordinate measurement system for the first time. The traceable standard with modules between 0.1 and 1 mm is calibrated using micro tactile coordinate measurements. Optical and tactile measurements are then compared. As a result, small expanded measurement uncertainties of less than 4 µm are achieved.

Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.

Uncertainty analysis on long-term runoff projection from the Budyko framework and a conceptual hydrological model

ABSTRACT Runoff projections are subject to uncertainties related to model structure and parameters. This study aims to analyze uncertainties in long-term runoff estimations from an empirical (Budyko framework) and a conceptual hydrological model (MHD-INPE). Results indicate that both MHD-INPE and Budyko estimations tend to overestimate long-term runoff during years of recurring droughts. Pareto front solutions in MHD-INPE exhibited small uncertainties in long-term runoff estimations regarding parameter calibration (bias between 5 and 7%); differences were observed in low (bellow 5% variation) and high (bellow 10% variation) daily runoff. Related to model structure uncertainties, both models follow similar patterns and performance for a qualitative analysis. Budyko's future projections tend to exceed MHD-INPE's during high precipitation estimates, where at 2000 mm yearly precipitation the estimated runoff from Budyko tends to be 100 mm greater than the hydrological model. Under arid conditions Budyko tends to estimate smaller runoff than MHD-INPE due to variations in soil moisture and water storage not properly represented in Budyko's parameter. Although uncertainties were identified related to model complexity and calibrated parameters, higher uncertainties were identified as related to the climate models. Therefore, the Budyko method is a viable alternative for first-order analysis of long-term impacts.

Effects of input data accuracy, catchment threshold areas and calibration algorithms on model uncertainty reduction

AbstractLow resolution of input data and equifinality in model calibration can lead to inaccuracy and insufficient reflection of spatial differences, thereby increasing model errors. However, the impact of input data accuracy, catchment threshold area, and calibration algorithm on model uncertainty reduction has not yet been well understood. The sequential uncertainty fitting version 2 (SUFI‐2) that is linked with the Soil and Water Assessment Tool (SWAT) in the package called SWAT Calibration Uncertainty Programs (SWAT‐CUP) was introduced to quantify the effects of different input data resolutions on parameter sensitivity and model uncertainty in the Jinghe River watershed, and the effects of different sub‐basin delineations and other two calibration algorithms on model uncertainty were also comparatively analysed. (i) USLE_C, EPCO, ALPHA_BNK, and CN2 are the most sensitive parameters among all SWAT projects. When the change of digital elevation model (DEM) resolution is small, the sensitivity of parameters does not change obviously. When the DEM resolution changes significantly, BIOMIX, LAT_SED, USLE_K, and CH_N1 become highly sensitive parameters by replacing OV_N, SMTMP, SURLAG, and USLE_P. However, the change in land use resolution has little impact on parameter sensitivity, with only a slight change in the sensitivity ranking of specific parameters. (ii) Model uncertainty responded to changes in the resolution of DEM more than land use. Most of the runoff simulations had smaller uncertainties (P factor, R factor, percentage of bias [PBIAS]) than sediment. High resolution DEM data reduced model uncertainty, but the models with 2000 m DEM resolution also achieved small uncertainty. Small catchment threshold area leads to high uncertainty of the model, and large catchment threshold areas decrease the model uncertainty. The model has relatively good simulation effects in runoff and sediment when the catchment threshold area was 2000 km2. (iii) The SWAT model has different simulation deviations and uncertainties in different calibration algorithms, the SUFI‐2 and generalized likelihood uncertainty estimation (GLUE) algorithms show better applicability than particle swarm optimization (PSO). The NSE indicators of the three algorithms are in the following order: SUFI‐2 &gt; GLUE &gt; PSO for runoff, and GLUE &gt; SUFI‐2 &gt; PSO for sediment. This study helps us understand the cause, knowledge of which moves from the particular to the general by the comprehension of essence, power, and nature in reducing model uncertainty.

Third density and acoustic virial coefficients of helium isotopologues from abinitio calculations.

Improved two-body and three-body potentials for helium have been used to calculate from first principles the third density and acoustic virial coefficients for both 4He and 3He. For the third density virial coefficient C(T), uncertainties have been reduced by a factor of 4-5 compared to the previous state of the art; the accuracy of first-principles C(T) now exceeds that of the best experiments by more than two orders of magnitude. The range of calculations has been extended to temperatures as low as 0.5K. For the third acoustic virial coefficient γa(T), we applied the Schlessinger point method, which can calculate γa and its uncertainty based on the C(T) data, overcoming some limitations of direct path-integral calculation. The resulting γa are calculated at temperatures down to 0.5K; they are consistent with available experimental data but have much smaller uncertainties. The first-principles data presented here will enable improvement of primary temperature and pressure metrology based on gas properties.

Assessment of alternative fluid calibration to estimate traceable liquefied hydrogen flow measurement uncertainty

The usage of liquefied hydrogen (LH2) is growing fast as the EU aims to be climate-neutral by 2050. Therefore, reliable and consistent measurements of LH2 amounts (as determined by flow meters) will become increasingly important for custody transfers. Reliability and consistency of measurements is established from calibration. Due to the lack of facilities being able to calibrate flow meters directly with LH2 as calibration fluid, the question arises whether the measurement error of the calibration performed on a different fluid and different process conditions are representative for the errors when measuring LH2. An on-site calibration of two turbine meters measuring LH2 flow was performed. A Coriolis mass flow meter calibrated on water was used as a reference in conjunction with a dedicated flow meter model, which was validated by a directly SI-traceable calibration on liquefied natural gas. LH2 flow rates were within 1000 kg/h and 3000 kg/h, which are relevant to hydrogen transport by LH2 trailers. The analysis of the measurement data reveal errors on totalized mass ranging from 2.0 % to 2.9 % with a total calibration uncertainty of 1.3 % (k=2), where the main uncertainty source was identified as the on-site density estimate necessary to convert the volume flow measurements into mass flow measurements. The results indicate the need for reliable and accurate temperature measurements (the latter allowing for an accurate density estimate), so that volume based and mass based amount measurements can be compared with smaller uncertainties.

Super-correlation consistent composite approach (s-ccCA) for the late 3d and 4d transition metals: Impact of higher-order excitations on thermochemical prediction

Resonant 2 Photon Ionization (R2PI) spectroscopy and its ability to achieve thermochemical energies with unprecedented accuracy and extraordinarily small uncertainties has enabled the emergence of a new level of ab initio composite methodologies. The super correlation consistent Composite Approach (s-ccCA) includes coupled cluster terms with up to pentuple excitations has been used to achieve excellent agreement with R2PI for 3d and 4d transition metal thermochemistry. For system size increases, a continued fraction approximant has been examined to address the CCSDTQP contribution.

Re-determination of R(13C/12C) for Vienna Peedee belemnite (VPDB).

The isotope ratio for the internationally agreed but virtual zero-point of the carbon isotope-delta scale, Vienna Peedee belemnite (VPDB), plays a critical role in linking carbon isotope delta values to the SI. It is also a quantity used for various data processing procedures including '17O correction', clumped isotope analysis and conversion of carbon isotope delta values into other expressions of isotopic composition. A value for RVPDB(13C/12C) with small uncertainty is therefore desirable to facilitate these procedures. The value of RVPDB(13C/12C) was determined by errors-in-variables regression of isotope delta values traceable to VPDB measured by isotope ratio mass spectrometry against isotope ratios traceable to the SI by use of gravimetric mixtures of 12C- and 13C-enriched d-glucose measured by multicollector inductively coupled plasma mass spectrometry. A value of RVPDB(13C/12C) = 0.0111105 ± 0.0000042 (expanded uncertainty, k = 2) was obtained. The new value for RVPDB(13C/12C) agrees very well with the consensus values calculated from previous measurement results proposed by Kaiser and by ourselves, as well as recent determinations independent of mass spectrometry. The expanded uncertainty of 0.4‰ when expressed as an isotope delta value is a tenfold improvement over the previous best measurement of the isotopic composition of carbon.

NONLINEAR DYNAMICS METHODS APPLICATIONS TO PRODUCT LIFECYCLE MANAGEMENT

The research being carried out is aimed at applying nonlinear dynamics methods in product life cycle management to improve communication between processes and achieve environmental sustainability. The purpose of the article is to create a nonlinear mathematical model for analysing product lifecycle management. Chaotic nonlinear dynamical processes of product lifecycle are deterministic but have the property that even small uncertainties in initial conditions lead to exponentially growing errors in the management that limit long-term forecasts. The emergence of chaotic behaviour in processes of product lifecycle was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and the Surrogate model of chaotic Lorenz waterwheel. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed efficiency and effectiveness of product life cycle process development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting product life cycle process dynamics. The research results are valuable for companies seeking to reduce their environmental impact and implement circular economy principles.

Steady but model dependent Arctic amplification of the forced temperature response in 21st century CMIP6 projections

We examine sources of uncertainty in projections of Arctic amplification (AA) using the CMIP6 multi-model (MM) ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 MM mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.

Electron and photon efficiencies in LHC Run 2 with the ATLAS experiment

Precision measurements of electron reconstruction, identification, and isolation efficiencies and photon identification efficiencies are presented. They use the full Run 2 data sample collected by the ATLAS experiment in pp collisions at a centre-of-mass energy of 13 TeV during the years 2015–2018, corresponding to an integrated luminosity of 139 fb−1. The measured electron identification efficiencies have uncertainties that are around 30%–50% smaller than the previous Run 2 results due to an improved methodology and the inclusion of more data. A better pile-up subtraction method leads to electron isolation efficiencies that are more independent of the amount of pile-up activity. Updated photon identification efficiencies are also presented, using the full Run 2 data. When compared to the previous measurement, a 30%–40% smaller uncertainty is observed on the photon identification efficiencies, thanks to the increased amount of available data.