Estimation Of Systematic Errors Research Articles

UltraStat: Ultrafast Spectroscopy beyond the Fourier Limit Using Bayesian Inference.

The discrete Fourier transform (dFT) plays a central role in many ultrafast experiments, allowing the recovery of spectroscopic observables from time-domain measurements. In resonant experiments when population relaxation and coherence components of the signal coexist, the dFT is usually preceded by multiexponential fitting to remove the large population term. However, this procedure results in errors in both the recovered decay rates and the line shapes of the coherence spectral components. While other methods such as linear prediction singular value decomposition fit both terms simultaneously, they are limited to specific models that may not represent the true signal. These methods do not allow for systematic noise analysis or error estimation and require a priori knowledge of the signal rank. Here, we describe a general approach to parameter estimation in ultrafast spectroscopy─UltraStat─grounded in Bayesian analysis without the limitations set by Fourier theory. Using simulated, but realistic data, we demonstrate in a statistical sense how UltraStat provides accurate parameter estimation in the presence of many experimental constraints: noise, signal truncation, limited photon budget, and nonuniform sampling. UltraStat provides superior resolution compared to the dFT, up to an order of magnitude in cases where the line shapes are well-approximated. In these cases, we establish that primarily noise, not sampling, limits spectral resolution. Moreover, we show that subsampling may reduce the number of acquired points by 90% compared to the Nyquist-Shannon criteria. UltraStat greatly improves parameter estimation by providing statistically bound spectral and dynamics analysis, pushing the limits of ultrafast science.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Tidal heating as a discriminator for horizons in equatorial eccentric extreme mass ratio inspirals

Tidal heating in a binary black hole system is driven by the absorption of energy and angular momentum by the black hole’s horizon. Previous works have shown that this phenomenon becomes particularly significant during the late stages of an extreme mass ratio inspiral (EMRI) into a rapidly spinning massive black hole, a key focus for future low-frequency gravitational-wave observations by (for instance) the Laser Interferometer Space Antenna mission. Past analyses have largely focused on quasicircular inspiral geometry, with some of the most detailed studies looking at equatorial cases. Though useful for illustrating the physical principles, this limit is not very realistic astrophysically, since the population of EMRI events is expected to arise from compact objects scattered onto relativistic orbits in galactic centers through many-body events. In this work, we extend those results by studying the importance of tidal heating in equatorial EMRIs with generic eccentricities. Our results suggest that accurate modeling of tidal heating is crucial to prevent significant dephasing and systematic errors in EMRI parameter estimation. We examine a phenomenological model for EMRIs around exotic compact objects by parametrizing deviations from the black hole (BH) picture in terms of the fraction of radiation absorbed compared to the BH case. Based on a mismatch calculation, we find that reflectivities as small as |R|2∼O(10−5) are distinguishable from the BH case, irrespective of the value of the eccentricity. We stress, however, that this finding should be corroborated by future parameter estimation studies. Published by the American Physical Society 2024

A MASH simulation of the photoexcited dynamics of cyclobutanone.

In response to a community prediction challenge, we simulate the nonadiabatic dynamics of cyclobutanone using the mapping approach to surface hopping (MASH). We consider the first 500fs of relaxation following photoexcitation to the S2 state and predict the corresponding time-resolved electron-diffraction signal that will be measured by the planned experiment. 397 abinitio trajectories were obtained on the fly with state-averaged complete active space self-consistent field using a (12,11) active space. To obtain an estimate of the potential systematic error, 198 of the trajectories were calculated using an aug-cc-pVDZ basis set and 199 with a 6-31+G* basis set. MASH is a recently proposed independent trajectory method for simulating nonadiabatic dynamics, originally derived for two-state problems. As there are three relevant electronic states in this system, we used a newly developed multi-state generalization of MASH for the simulation: the uncoupled spheres multi-state MASH method (unSMASH). This study, therefore, serves both as an investigation of the photodissociation dynamics of cyclobutanone, and also as a demonstration of the applicability of unSMASH to abinitio simulations. In line with previous experimental studies, we observe that the simulated dynamics is dominated by three sets of dissociation products, C3H6 + CO, C2H4 + C2H2O, and C2H4 + CH2 + CO, and we interpret our predicted electron-diffraction signal in terms of the key features of the associated dissociation pathways.

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform.

Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R2 = 0.98), faba bean (R2 = 0.96), and wheat (R2 = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R2 in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R2 from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.

A Novel Sea Target Tracking Algorithm for Multiple Unmanned Aerial Vehicles Considering Attitude Error in Low-Precision Geodetic Coordinate Environments

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

Comparison of the accuracy of commercial two-point and multi-echo Dixon MRI for quantification of fat in liver, paravertebral muscles, and vertebral bone marrow

PurposeExcess fat accumulation contributes significantly to metabolic dysfunction and diseases. This study aims to systematically compare the accuracy of commercially available Dixon techniques for quantification of fat fraction in liver, skeletal musculature, and vertebral bone marrow (BM) of healthy individuals, investigating biases and sex-specific influences. Method100 healthy White individuals (50 women) underwent abdominal MRI using two-point and multi-echo Dixon sequences. Fat fraction (FF), proton density fat fraction (PDFF) and T2* values were calculated for liver, paravertebral muscles (PVM) and vertebral BM (Th8–L5). Agreement and systematic deviations were assessed using linear correlation and Bland-Altman plots. ResultsHigh correlations between FF and PDFF were observed in liver (r = 0.98 for women; r = 0.96 for men), PVM (r = 0.92 for women; r = 0.93 for men) and BM (r = 0.97 for women; r = 0.95 for men). Relative deviations between FF and PDFF in liver (18.92 % for women; 13.32 % for men) and PVM (1.96 % for women; 11.62 % for men) were not significant. Relative deviations in BM were significant (38.13 % for women; 27.62 % for men). Bias correction using linear models reduced discrepancies. T2* times were significantly shorter in BM (8.72 ms for women; 7.26 ms for men) compared to PVM (13.45 ms for women; 13.62 ms for men) and liver (29.47 ms for women; 26.35 ms for men). ConclusionWhile no significant differences were observed for liver and PVM, systematic errors in BM FF estimation using two-point Dixon imaging were observed. These discrepancies – mainly resulting from organ-specific T2* times – have to be considered when applying two-point Dixon approaches for assessment of fat content. As suitable correction tools, linear models could provide added value in large-scale epidemiological cohort studies. Sex-specific differences in T2* should be considered.

The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and Its Implications for Structure Growth

We present new measurements of cosmic microwave background (CMB) lensing over 9400 deg2 of the sky. These lensing measurements are derived from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) CMB data set, which consists of five seasons of ACT CMB temperature and polarization observations. We determine the amplitude of the CMB lensing power spectrum at 2.3% precision (43σ significance) using a novel pipeline that minimizes sensitivity to foregrounds and to noise properties. To ensure that our results are robust, we analyze an extensive set of null tests, consistency tests, and systematic error estimates and employ a blinded analysis framework. Our CMB lensing power spectrum measurement provides constraints on the amplitude of cosmic structure that do not depend on Planck or galaxy survey data, thus giving independent information about large-scale structure growth and potential tensions in structure measurements. The baseline spectrum is well fit by a lensing amplitude of A lens = 1.013 ± 0.023 relative to the Planck 2018 CMB power spectra best-fit ΛCDM model and A lens = 1.005 ± 0.023 relative to the ACT DR4 + WMAP best-fit model. From our lensing power spectrum measurement, we derive constraints on the parameter combination S8CMBL≡σ8Ωm/0.30.25 of S8CMBL=0.818±0.022 from ACT DR6 CMB lensing alone and S8CMBL=0.813±0.018 when combining ACT DR6 and Planck NPIPE CMB lensing power spectra. These results are in excellent agreement with ΛCDM model constraints from Planck or ACT DR4 + WMAP CMB power spectrum measurements. Our lensing measurements from redshifts z ∼ 0.5–5 are thus fully consistent with ΛCDM structure growth predictions based on CMB anisotropies probing primarily z ∼ 1100. We find no evidence for a suppression of the amplitude of cosmic structure at low redshifts.

Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors

Background and purposeRespiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41–58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH. Materials and methodsThe study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors. ResultsLung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of −0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %. ConclusionsThe presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.

Bias and Variance in Multiparty Election Polls

Abstract Recent polling failures highlight that election polls are prone to biases that the margin of error customarily reported with polls does not capture. However, such systematic errors are difficult to assess against the background noise of sampling variance. Shirani-Mehr et al. (2018) developed a hierarchical Bayesian model to disentangle random and systematic errors in poll estimates of two-party vote shares at the election level. The method can inform realistic assessments of poll accuracy. We adapt the model to multiparty elections and improve its temporal flexibility. We then estimate bias and variance in 5,240 German national election polls, 1994–2021. Our analysis suggests that the average absolute election-day bias per party was about 1.5 percentage points, ranging from 0.9 for the Greens to 3.2 for the Christian Democrats. The estimated variance is, on average, about twice as large as that implied by usual margins of error. We find little evidence of house or mode effects. Common biases indicate industry effects due to similar methodological problems. The Supplementary Material provides additional results for 1,751 regional election polls.

Inferring nonlinear fractional diffusion processes from single trajectories

We present a method to infer the arbitrary space-dependent drift and diffusion of a nonlinear stochastic model driven by multiplicative fractional Gaussian noise from a single trajectory. Our method, fractional Onsager-Machlup optimisation (fOMo), introduces a maximum likelihood estimator by minimising a field-theoretic action which we construct from the observed time series. We successfully test fOMo for a wide range of Hurst exponents using artificial data with strong nonlinearities, and apply it to a data set of daily mean temperatures. We further highlight the significant systematic estimation errors when ignoring non-Markovianity, underlining the need for nonlinear fractional inference methods when studying real-world long-range (anti-)correlated systems.

An active piezoelectric damping vibration control system for the sting used in wind tunnel

In wind tunnel tests, the cantilever sting supporting system often suffers from low-frequency and large-amplitude resonance due to its inherent low structural damping characteristic, resulting in the degradation of data quality and structural safety. To improve wind tunnel testing safety and data accuracy, this paper is dedicated to establish an active vibration control system using piezoelectric stack actuators. A novel methodology of vibration monitoring based on modal transformation, which uses measured strain and a Strain-to-Displacement Transformation (SDT) matrix to reconstruct dynamic displacement field, is proposed herein. Meanwhile, strain sensor positions are optimized by an improved Particle Swarm Optimization (PSO) algorithm to reduce systematic estimation errors of this method. Furthermore, a Back-Propagated Neutral Network (BPNN) is established to implement a self-adaptive control strategy. A series of verification tests are performed to demonstrate the validity of the proposed system. Experimental results indicate that the relative Root Mean Square Error (RMSE) between estimated vibration displacement and measured vibration displacement is less than 3%, and a vibration attenuation of over 14 dB/Hz is achieved in ground tests, proving the superiority of this intelligent active vibration suppression system.

Correcting systematic errors in diffraction data with modern scaling algorithms.

X-ray diffraction enables the routine determination of the atomic structure ofmaterials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.

Evaluation of error characteristics of derived TEC with IRI-2016

The total electron content (TEC) derived from the Global Navigation Satellite System (GNSS) has been widely used to study the ionosphere. The evaluation of the derivation method is usually based on the fitting residuals, which are the difference between the estimate and observation. However, there are few studies on the statistical characteristics of fitting residuals. The characteristics of fitting residuals and estimation errors are studied with international reference ionosphere (IRI-2016) and GNSS observations. First, the systematic estimation errors caused by mathematical modeling and related assumptions are discussed with IRI-2016. The errors are 1 TECU and 0.5 TECU for slant TEC (STEC) and vertical TEC (vTEC), respectively. The fitting residuals of STEC observations (ΔSTECr) are studied by different noise models, including independent and identically distributed (IID) Gaussian noise, independent and non identical distributed (INID) Gaussian noise and INID Laplace noise. The statistical analysis shows that the distribution of ΔSTECr for the whole observations of all receivers is Gaussian when the noise is IID Gaussian, while it is Laplace for INID Gaussian or Laplace noise. The ΔSTECr reflects the statistical characteristic of noise. The estimation error of STEC (ΔSTEC), which is the difference between the estimate (STECe) and the model STEC (STEC) is discussed with IRI-2016. The distribution of ΔSTEC is neither Gaussian nor Laplace, and STECe is a biased estimate of STEC. The distribution of estimation error of vTEC (ΔvTEC) is neither Gaussian nor Laplace. Finally, the ΔSTECr is studied with GNSS observations. The distribution of ΔSTECr is more consistent with Laplace than Gaussian, indicating that the noise of the GNSS observations should not be modeled as IID Gaussian distribution.

A new method for accurate alignment and calibration of strapdown INS

A new method is proposed for the coarse and fine alignments of the strapdown inertial navigation system (INS) along with the estimation of the INS systematic errors. This is achieved by utilizing an accurate external positioning system such as the global positioning system (GPS) and external gravity disturbances obtained from the Earth gravity models (EGMs) during the INS initialization time. First, a new method for the transformation of the INS acceleration from the sensor frame to the inertial frame using gyro output data is disclosed. Then, using the new method of the transformation, new techniques are developed for the alignment and calibration of the INS using minimizing the variations of the residuals of the gravity disturbances during the back-and-forth rotational motions. The common and conventional initializations cannot be started from any initial orientation, and cannot yield the accurate and actual INS errors. In contrast, under the alignment condition, the accurate and actual estimation of all the INS systematic errors is possible using the developed fine alignment and calibration which can guarantee the accurate autonomous strapdown INS positioning for a long period of time even during high dynamic motions. This is while the estimated error values of the INS by the conventional Kalman filter is unrealistic. In the field of autonomous positioning, a horizontal position with an error less than 200 m and 500 m was achieved during low and high dynamic motions, respectively, after the fine alignment and calibration, for the two hours of the INS positioning for a flight distance about 880 km. The employed INS was a high-performance strapdown INS, the Honeywell LASEREF III.

Five-year in-orbit background of Insight-HXMT

We present the five-year in-orbit background evolution of Insight-HXMT since the launch, as well as the effects of the background model in data analysis. The backgrounds of the three main payloads, i.e., low-energy telescope, medium-energy telescope, and high-energy telescope, are described. The evolution of the background over time is obtained by simply comparing the background in every year during the in-orbit operation of Insight-HXMT. The major observational characteristics of the Insight-HXMT in-orbit background are presented, including the light curve, spectrum, geographical distribution, and long-term evolution. The systematic error in background estimation is investigated for every year. The observational characteristics of the five-year in-orbit background are consistent with our knowledge of the satellite design and the space environment, and the background model is still valid for the latest observations of Insight-HXMT.

A Disturbance Estimation Approach to Self-Calibration of Gimbal Resolver- to-Digital Conversion System

High-precision position measurement of the gimbal servo system is indispensable to accurate torque output of the control moment gyroscope (CMG). This article addresses the systematic error of the resolver caused by the nonideal output signals of the resolver in the gimbal resolver-to-digital (R/D) conversion system. Due to the limitation of the structure and installation space of the CMG, it is impractical to install additional high-precision sensors to calibrate the raw angular position. Thus, this article proposes a new self-calibration method based on the discrete extended state observer (DESO) to compensate the systematic error of the resolver. The systematic error of the resolver is observed through iterating the resolver systematic estimation error and the compensation table is generated by the output of the DESO. Then, the steady-state error and dynamic performance of the DESO is analyzed and simulated. Finally, the proposed self-calibration method is verified by experiments, which show that the angular position control accuracy is significantly improved, which enhances the speed precision of the gimbal servo system.

Satellite Constellation Avoidance with the Rubin Observatory Legacy Survey of Space and Time

We investigate a novel satellite avoidance strategy to mitigate the impact of large commercial satellite constellations in low-Earth orbit on the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We simulate the orbits of currently planned Starlink and OneWeb constellations (∼40,000 satellites) to test how effectively an upgraded Rubin scheduler algorithm can avoid them, and assess how the overall survey is affected. Given a reasonably accurate satellite orbit forecast, we find it is possible to adjust the scheduler algorithm to effectively avoid some satellites. Overall, sacrificing 10% of LSST observing time to avoid satellites reduces the fraction of LSST visits with streaks by a factor of 2. Whether such a mitigation will be required depends on the overall impact of streaks on science, which is not yet well quantified. This is due to a lack of adequate information about satellite brightness distributions as well as the impact of glints and low surface brightness residuals on alert purity and systematic errors in cosmological parameter estimation. A significant increase in the number of satellites or their brightness during Rubin Operations may make implementing this satellite avoidance strategy worthwhile.

Assessing the model waveform accuracy of gravitational waves

With the improvement in sensitivity of gravitational wave (GW) detectors and the increasing diversity of GW sources, there is a strong need for accurate GW waveform models for data analysis. While the current model accuracy assessments require waveforms generated by numerical relativity (NR) simulations as the ``true waveforms,'' in this paper we propose an assessment approach that does not require NR simulations, which enables us to assess model accuracy everywhere in the parameter space. By measuring the difference between two waveform models, we derive a necessary condition for a pair of waveform models to both be accurate, for a particular set of parameters. We then apply this method to the parameter estimation samples of the Gravitational-Wave Transient Catalogs GWTC-3 and GWTC-2.1, and find that the waveform accuracy for high signal-to-noise ratio events in some cases fails our assessment criterion. Based on analysis of real events' posterior samples, we discuss the correlation between our quantified accuracy assessments and systematic errors in parameter estimation. We find that waveform models that perform worse in our assessment are more likely to give inconsistent estimations. We also investigate waveform accuracy in different parameter regions, and find that the accuracy degrades as the spin effects go up, the mass ratio deviates from one, or the orbital plane is nearly aligned with the line of sight. Furthermore, we make predictions of waveform accuracy requirements for future detectors and find that the accuracy of current waveform models should be improved by at least 3 orders of magnitude, which is consistent with previous works.

Belief Distortions and Macroeconomic Fluctuations

This paper combines a data-rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (“belief distortions”) embedded in survey responses. We find sizable distortions even for professional forecasters, with all respondent-types overweighting the implicit judgmental component of their forecasts relative to what can be learned from publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with belief distortions evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgment and improve predictive accuracy. (JEL C45, D83, E23, E27, E31, E32, E37)