Absolute Calibration Error Research Articles

Predictive model for intraoperative decision-making in video-assisted thoracoscopic lobectomy: optimizing chest drain placement for high-risk patients.

For years, it has been the common and widely accepted practice in thoracic surgery to place apical and basal drains after a lobectomy to completely drain the pleural cavity. With the development of thoracoscopic technology, it became apparent that the use of a single chest tube provided the same clinical results. However, sometimes tension pneumothorax occurs with the need for additional pleural drainage. The aim of this study was to develop a prognostic model to identify high risk patients intraoperatively and to insert additional pleural drainage to prevent the development of pneumothorax after video-assisted thoracoscopic surgery (VATS) lobectomy. This was a retrospective multicenter study of patients (registry data was analyzed) who underwent VATS lobectomy via a standardized multiport approach between 2014 and 2022. In all cases, a single drain was used postoperatively. We used a machine learning algorithm and data synthesis to expand patient selection according to Riley's method. A total of 418 cases were analyzed in this study. After determining the prognostically significant factors, we performed a binary logistic regression analysis using reverse step-by-step inclusion of variables according to the Akaike information criterion. After validation of the model by bootstrap (400 iterations) and with the original dataset, a nomogram with a specific point distribution for each risk factor was created. The rate of tension pneumothorax was 4.53% (n=66). The most significant variables associated with the need for additional drainage were adhesions, intraoperative lung suturing, fused interlobar fissure, enlarged intrapulmonary lymph nodes, chronic obstructive pulmonary disease (P<0.001). The C-index of the model was 0.957, the mean absolute calibration error was 0.6%, and the slope of the calibration curve was 0.959. A score of 26 points indicated a 95% risk of postoperative tension pneumothorax. The nomogram achieved good predictive performance for tension pneumothorax after minimally invasive lobectomy. High-risk patients could be identified, and additional drainage may be placed intraoperatively to reduce the risk of lung collapse in the postoperative period.

Predicting cardiovascular disease risk using photoplethysmography and deep learning.

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS's C-statistic (71.1%, 95% CI 69.9-72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

A Clinical Model to Predict the Occurrence of Select High-risk Infections in the First Year Following Heart Transplantation.

Invasive infection remains a dangerous complication of heart transplantation (HT). No objectively defined set of clinical risk factors has been established to reliably predict infection in HT. The aim of this study was to develop a clinical prediction model for use at 1 mo post-HT to predict serious infection by 1 y. A retrospective cohort study of HT recipients (2000-2018) was performed. The composite endpoint included cytomegalovirus (CMV), herpes simplex or varicella zoster virus infection, blood stream infection, invasive fungal, or nocardial infection occurring 1 mo to 1 y post-HT. A least absolute shrinkage and selection operator regression model was constructed using 10 candidate variables. A concordance statistic, calibration curve, and mean calibration error were calculated. A scoring system was derived for ease of clinical application. Three hundred seventy-five patients were analyzed; 93 patients experienced an outcome event. All variables remained in the final model: aged 55 y or above, history of diabetes, need for renal replacement therapy in first month, CMV risk derived from donor and recipient serology, use of induction and/or early lymphodepleting therapy in the first month, use of trimethoprim-sulfamethoxazole prophylaxis at 1 mo, lymphocyte count under 0.75 × 103cells/µL at 1 mo, and inpatient status at 1 mo. Good discrimination (C-index 0.80) and calibration (mean absolute calibration error 3.6%) were demonstrated. This model synthesizes multiple highly relevant clinical parameters, available at 1 mo post-HT, into a unified, objective, and clinically useful prediction tool for occurrence of serious infection by 1 y post-HT.

Towards a Consistent Wind Data Record for the CFOSAT Scatterometer

Since launch, the Ku-band rotating fan-beam scatterometer onboard the China–France Oceanography Satellite (CFOSAT) has provided valuable sea surface wind measurements for more than four years. The performance of CFOSAT scatterometer (CSCAT)-derived wind vectors is generally good in terms of root-mean-square error, while the absolute calibration error remains an issue in the current CSCAT product. In this paper, the temporal variation in CSCAT winds is overviewed by analyzing the collocated CSCAT and numerical weather prediction (NWP) model winds. Then, the reasons for the inconsistency of CSCAT-retrieved winds are discussed. The results show that the imperfect calibration of radar backscatter coefficients is likely the main problem of CSCAT wind processing. Consequently, a running-window-based (i.e., weekly) ocean calibration is proposed to evaluate the consistency of CSCAT radar backscatters, and in turn, to recalibrate CSCAT backscattering measurements before the reprocessing of CSCAT wind data. Although the proposed method is not feasible for the near-real-time processing of CSCAT data, it significantly mitigates the temporal variations in CSCAT wind speed bias, resulting in a more consistent CSCAT wind data record that may be beneficial to meteorological quantitative applications.

EHR foundation models improve robustness in the presence of temporal distribution shift

Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global patterns that can improve the robustness of task-specific models. The objective was to evaluate the utility of EHR foundation models in improving the in-distribution (ID) and out-of-distribution (OOD) performance of clinical prediction models. Transformer- and gated recurrent unit-based foundation models were pretrained on EHR of up to 1.8 M patients (382 M coded events) collected within pre-determined year groups (e.g., 2009–2012) and were subsequently used to construct patient representations for patients admitted to inpatient units. These representations were used to train logistic regression models to predict hospital mortality, long length of stay, 30-day readmission, and ICU admission. We compared our EHR foundation models with baseline logistic regression models learned on count-based representations (count-LR) in ID and OOD year groups. Performance was measured using area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve, and absolute calibration error. Both transformer and recurrent-based foundation models generally showed better ID and OOD discrimination relative to count-LR and often exhibited less decay in tasks where there is observable degradation of discrimination performance (average AUROC decay of 3% for transformer-based foundation model vs. 7% for count-LR after 5–9 years). In addition, the performance and robustness of transformer-based foundation models continued to improve as pretraining set size increased. These results suggest that pretraining EHR foundation models at scale is a useful approach for developing clinical prediction models that perform well in the presence of temporal distribution shift.

A novel bi-planar calibration method for digital templating in total hip arthroplasty

In total hip arthroplasty and reconstructive orthopedic surgery, pre-operative digital templating is essential for surgical treatment optimization, risk management, and quality control. Calibration is performed before templating to address magnification effects. Conventional methods including fixed calibration factors, individual marker-based calibration and dual-scale marker methods are not reliable. A novel bi-planar calibration method is described aiming to reduce the error below clinical significance. The bi-planar calibration method requires two conventional orthogonal radiographs and a standard radiopaque marker ball. An algorithm computes the hip plane height parallel to the detector in the antero-posterior radiograph. Foreseeable errors (i.e., patient rotation and misplaced markers or lateral offset) are considered in a correction algorithm. Potential effects of errors are quantified in a standard model. Influence of rotation in lateral radiographs and lateral offset of marker on the calibration factor are quantified. Without correction, patient rotation in the lateral radiograph of 30° results in absolute calibration error of 2.2% with 0 mm offset and 6.5% with 60 mm lateral offset. The error is below the threshold of 1.5% for rotation less than 26° with 0 mm offset and 10° with 60 mm offset. The method is supposed to be reliable in precisely predicting the hip plane and thereby the calibration factor. It may be superior to other methods available. In theory, the method allows correction of clinically relevant rotation of at least 30° and marker displacement without impacting the computed calibration factor.

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine

Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. The objective was to characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness. In this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008–2010, 2011–2013, 2014–2016 and 2017–2019). Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008–2010 (ERM[08–10]) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008–2016 and evaluated them on 2017–2019. UDA experiment leveraged unlabelled samples from 2017 to 2019 for unsupervised distribution matching. DG and UDA models were compared to ERM[08–16] models trained using 2008–2016. Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies. In the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080–0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM[08–10] applied to 2017–2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008–2010. When compared with ERM[08–16], DG and UDA experiments failed to produce more robust models (range of AUROC difference, − 0.003 to 0.050). In conclusion, DG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.

Field Evaluation of Column CO2 Retrievals From Intensity‐Modulated Continuous‐Wave Differential Absorption Lidar Measurements During the ACT‐America Campaign

AbstractWe present an evaluation of airborne intensity‐modulated continuous‐wave (IM‐CW) lidar measurements of atmospheric column CO2 mole fractions during the Atmospheric Carbon and Transport–America (ACT‐America) project. This lidar system transmits online and offline wavelengths simultaneously on the 1.57111‐μm CO2 absorption line, with each modulated wavelength using orthogonal swept frequency waveforms. After the spectral characteristics of this system were calibrated through short‐path measurements, we used the HITRAN spectroscopic database to calculate the average‐column CO2 mole fraction (XCO2) from the lidar‐measured optical depths. Using in situ measurements of meteorological parameters and CO2 concentrations for calibration data, we demonstrate that our lidar CO2 measurements were consistent from season to season and had an absolute calibration error (standard deviation) of 0.80 ppm when compared to XCO2 values calculated from in situ measurements. By using a 10‐s or longer moving average, a precision of 1 ppm or better was obtained. The estimated CO2 measurement precision for 0.1‐, 1‐, 10‐, and 60‐s averages was determined to be 3.4, 1.2, 0.43, and 0.26 ppm, respectively. These correspond to measurement signal‐to‐noise ratios of 120, 330, 950, and 1,600, respectively. The drift in XCO2 over 1‐hr of flight time was found to be below 0.1 ppm. These analyses demonstrate that the measurement stability, precision, and accuracy are all well below the thresholds needed to study synoptic‐scale variations in atmospheric XCO2.

Dosimetric impact of failing to apply correction factors to ion recombination in percentage depth dose measurements and the volume-averaging effect in flattening filter-free beams

PurposeTo examine whether it is essential to apply correction factors for ion recombination (kS) to percentage depth dose (PDD) measurements and to the volume-averaging effect (kvol) to ensure accurate absolute dose calibration for flattening filter-free (FFF) beams for the most commonly used ionization chambers. MethodsWe surveyed medical physicists worldwide (n = 159) to identify the five most common ionization chamber combinations used for absolute and relative reference dosimetry of FFF beams. We then assessed the overall absolute dose calibration error for FFF beams of the Artiste Siemens and TrueBeam Varian linear accelerators resulting from failing to apply correction factors kS in the PDD(10) and the volume-averaging effect (kvol) to such chamber combinations. ResultsAll the chamber combinations examined—the Farmer PTW 30013 ionization chamber used for absolute dosimetry, and the PTW 31010, PTW 30013, IBA CC04, IBA CC13, and PTW 31021 ionization chambers used for PDD curves measurements—showed non-negligible errors (≥0.5%). The largest error (1.6%) was found for the combination of the Farmer PTW 30013 chamber with the IBA CC13 chamber, which was the most widely used chamber combination in our survey. ConclusionsBased on our findings, we strongly recommend assessing the impact of failing to apply correction factors kS in the PDD(10) and kvol prior to using any chamber type for FFF beam reference dosimetry purposes.

Mantis: an all-sky visible-to-near-infrared hyper-angular spectropolarimeter.

In this paper, we introduce and present first results from Mantis, a pushbroom type spectropolarimeter recently acquired by the Naval Research Laboratory and built by Polaris Sensor Technologies, Inc. The instrument is designed for high spatial and spectral resolution polarimetric imaging of downwelling skylight. Linear Stokes vectors are acquired over the spectral range of 382-1017 nm, with ≈0.64nm channel spacing, and each line scan consists of 2226 pixels over a 72° field of view (0.75 mrad instantaneous). Measurement of the full sky dome is achieved through the use of a high-precision motorized pan-tilt unit and systematic scanning. An automated Sun shade allows for data collection in the main solar plane without saturation of the focal plane. The uncertainty in the degree of linear polarization varies between 0.07% and 0.5%, depending on incidence angle and wavelength. The total radiometric uncertainty is 2.07% to 2.5%, of which 2% is absolute calibration error. Preliminary data analysis reveals the instrument has a large potential for remote sensing applications.

Thermal properties of large main-belt asteroids observed byHerschelPACS

Non-resolved thermal infrared observations enable studies of thermal and physical properties of asteroids via thermo-physical models provided the shape and rotational properties of the target are well determined. We used calibration-programmeHerschelPACS data (70, 100, 160μm) and state-of-the-art shape models derived from adaptive-optics observations and/or optical light curves to constrain for the first time the thermal inertia of twelve large main-belt asteroids. We also modelled previously well-characterised targets such as (1) Ceres or (4) Vesta as they constitute important benchmarks. Using the scale as a free parameter, most targets required a re-scaling ~5% consistent with what would be expected given the absolute calibration error bars. This constitutes a good cross-validation of the scaled shape models, although some targets required larger re-scaling to reproduce the IR data. We obtained low thermal inertias typical of large main belt asteroids studied before, which continues to give support to the notion that these surfaces are covered by fine-grained insulating regolith. Although the wavelengths at which PACS observed are longwards of the emission peak for main-belt asteroids, they proved to be extremely valuable to constrain size and thermal inertia and not too sensitive to surface roughness. Finally, we also propose a graphical approach to help examine how different values of the exponent used for scaling the thermal inertia as a function of heliocentric distance (i.e. temperature) affect our interpretation of the results.

J-PLUS: photometric calibration of large-area multi-filter surveys with stellar and white dwarf loci

Aims. We present the photometric calibration of the 12 optical passbands observed by the Javalambre Photometric Local Universe Survey (J-PLUS). Methods. The proposed calibration method has four steps: (i) definition of a high-quality set of calibration stars using Gaia information and available 3D dust maps; (ii) anchoring of the J-PLUS gri passbands to the Pan-STARRS photometric solution, accounting for the variation in the calibration with the position of the sources on the CCD; (iii) homogenization of the photometry in the other nine J-PLUS filters using the dust de-reddened instrumental stellar locus in (𝒳 − r) versus (g − i) colours, where 𝒳 is the filter to calibrate. The zero point variation along the CCD in these filters was estimated with the distance to the stellar locus. Finally, (iv) the absolute colour calibration was obtained with the white dwarf locus. We performed a joint Bayesian modelling of 11 J-PLUS colour–colour diagrams using the theoretical white dwarf locus as reference. This provides the needed offsets to transform instrumental magnitudes to calibrated magnitudes outside the atmosphere. Results. The uncertainty of the J-PLUS photometric calibration, estimated from duplicated objects observed in adjacent pointings and accounting for the absolute colour and flux calibration errors, are ∼19 mmag in u, J0378, and J0395; ∼11 mmag in J0410 and J0430; and ∼8 mmag in g, J0515, r, J0660, i, J0861, and z. Conclusions. We present an optimized calibration method for the large-area multi-filter J-PLUS project, reaching 1–2% accuracy within an area of 1022 square degrees without the need for long observing calibration campaigns or constant atmospheric monitoring. The proposed method will be adapted for the photometric calibration of J-PAS, that will observe several thousand square degrees with 56 narrow optical filters.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers

BackgroundHerd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation.MethodsArtificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model’s ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error.ResultsAfter data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%.ConclusionBecause the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows.

High-precision method of binocular camera calibration with a distortion model.

A high-precision camera calibration method for binocular stereo vision system based on a multi-view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e-6 and 1.2e-6 for the focal length and the principal point, respectively, under zero-mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e-6 over the intrinsic parameters. The proposed method is convenient, cost-efficient, highly precise, and simple to carry out.

Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8

It has been known and documented that the Thermal Infrared Sensor (TIRS) on-board Landsat 8 suffers from a significant stray light problem (Reuter et al., 2015; Montanaro et al., 2014a). The issue appears both as a non-uniform banding artifact across Earth scenes and as a varying absolute radiometric calibration error. A correction algorithm proposed by Montanaro et al. (2015) demonstrated great potential towards removing most of the stray light effects from TIRS image data. It has since been refined and will be implemented operationally into the Landsat Product Generation System in early 2017. The algorithm is trained using near-coincident thermal data (i.e., Terra/MODIS) to develop per-detector functional relationships between incident out-of-field radiance and additional (stray light) signal on the TIRS detectors. Once trained, the functional relationships are used to estimate and remove the stray light signal on a per-detector basis from a scene of interest.The details of the operational stray light correction algorithm are presented here along with validation studies that demonstrate the effectiveness of the algorithm in removing the stray light artifacts over a stressing range of Landsat/TIRS scene conditions. Results show that the magnitude of the banding artifact is reduced by half on average over the current (uncorrected) product and that the absolute radiometric error is reduced to approximately 0.5% in both spectral bands on average (well below the 2% requirement). All studies presented here indicate that the implementation of the stray light algorithm will lead to greatly improved performance of the TIRS instrument, for both spectral bands.

Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) onboard Landsat 8 was tasked with continuing thermal band measurements of Earth as part of the Landsat program. From first light in early 2013, there were obvious indications, such as nonuniform banding and varying absolute calibration errors, that stray light was contaminating the thermal image data collected from the instrument. Stray light in this case refers to unwanted radiance from outside the field-of-view entering the optical system and being recorded by the focal plane. Standard calibration techniques used to flat-field and radiometrically correct the data were not sufficient to adjust the image products to within the accuracy that the Landsat community has come to expect. The development of an operational technique to remove the effects of the stray light in the TIRS data has become a high priority. A methodology is presented that makes use of a stray light optical model developed for the instrument along with knowledge of the out-of-field area surrounding the TIRS earth scene. Two versions of the algorithm are proposed in which one method utilizes near-coincident image data from an external sensor while another novel method is proposed that makes use of TIRS image data itself without the need for external data. Preliminary results of the algorithm indicate that banding artifacts due to stray light are significantly reduced when the methods are applied. Additionally, initial absolute calibration error estimates of over 9K are reduced to within 2K when applying the correction methods. Although both variations of the proposed algorithm have significantly reduced the stray light effects, the fact that the latter method utilizing TIRS image data itself does not rely on any external data is a significant advantage toward the development of an operational stray light correction solution. Ongoing work is focused on operationalizing the algorithm and identifying and quantifying potential sources of error when applying the method.

ERRATUM: “THE HERSCHEL INVENTORY OF THE AGENTS OF GALAXY EVOLUTION (HERITAGE) IN THE MAGELLANIC CLOUDS, A HERSCHEL OPEN TIME KEY PROGRAM” (2013, AJ, 146, 62)

We provide a corrected Table 4 that lists the total galaxy fluxes for the HERTIAGE bands and corresponding Figure 19 which plots these fluxes in comparison to prior measurements These corrected fluxes differ by up to 41% to what we reported in the original paper. These revised fluxes utilize a more appropriate subtraction of the Milky Way Foreground Cirrus emission which contaminates especially the PACS 100 and 160 micron bands. The subtraction process uses the HI 21 cm emission to develop a model for the MW cirrus dust emission. In addition, the better subtraction process corrected for an over subtraction of the background in the SPIRE images of the SMC that occurred during the original data processing. The need for these better foreground subtractions was realized while working on an analysis of the dust masses and gas-to-dust ratios in the LMC and SMC reported by Gordon et al. (2014) and Roman-Duval et al. (2014). After the subtraction has been done, the fluxes were derived by simply summing up all the pixels in the image. The errors we quote for the fluxes reflect the absolute flux calibration errors for extended sources which are approx.10% for PACS and approx. 8% for SPIRE. In the revised Figure 19, we confirm that these corrected global fluxes remain within the range of prior global measurements for both the LMC and SMC. Indeed, the shape of the corrected spectral energy distributions appears better aligned with prior measurements.

Smiling for the Delayed Volatility Swaps

We present a variance drift-adjusted version of the Heston model which leads to a significant improvement of the market volatility surface fitting (compared with Heston). The numerical example we performed with recent market data shows a significant reduction of the average absolute calibration error (calibration on 12 dates ranging from September 19 to October 17, 2011 for the FOREX underlying EURUSD). Our model has two additional parameters compared with the Heston model, can be implemented very easily, and was initially introduced for volatility derivative pricing purposes. The main idea behind our model is to take into account some past history of the variance process in its (risk-neutral) diffusion. Using a change of time method for continuous local martingales, we derive a closed formula for the Brockhaus and Long approximation of the volatility swap price in this model. We also consider dynamic hedging of volatility swaps using a portfolio of variance swaps.