skip to main content

Title: Calibration method for an extended depth-of-field microscopic structured light system
This paper presents a calibration method for a microscopic structured light system with an extended depth of field (DOF). We first employed the focal sweep technique to achieve large enough depth measurement range, and then developed a computational framework to alleviate the impact of phase errors caused by the standard off-the-shelf calibration target (black circles with a white background). Specifically, we developed a polynomial interpolation algorithm to correct phase errors near the black circles to obtain more accurate phase maps for projector feature points determination. Experimental results indicate that the proposed method can achieve a measurement accuracy of approximately 1.0 μ m for a measurement volume of approximately 2,500 μ m (W) × 2,000 μ m (H) × 500 μ m (D).  more » « less
Award ID(s):
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Optics Express
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    We present new near-infrared VLTI/GRAVITY interferometric spectra that spatially resolve the broad Br γ emission line in the nucleus of the active galaxy IRAS 09149−6206. We use these data to measure the size of the broad line region (BLR) and estimate the mass of the central black hole. Using an improved phase calibration method that reduces the differential phase uncertainty to 0.05° per baseline across the spectrum, we detect a differential phase signal that reaches a maximum of ∼0.5° between the line and continuum. This represents an offset of ∼120  μ as (0.14 pc) between the BLR and the centroid of the hot dust distribution traced by the 2.3 μ m continuum. The offset is well within the dust sublimation region, which matches the measured ∼0.6 mas (0.7 pc) diameter of the continuum. A clear velocity gradient, almost perpendicular to the offset, is traced by the reconstructed photocentres of the spectral channels of the Br γ line. We infer the radius of the BLR to be ∼65  μ as (0.075 pc), which is consistent with the radius–luminosity relation of nearby active galactic nuclei derived based on the time lag of the H β line from reverberation mapping campaigns. Our dynamical modelling indicates the black hole mass is ∼1 × 10 8   M ⊙ , which is a little below, but consistent with, the standard M BH – σ * relation. 
    more » « less
  2. Abstract Sensitive dispersive readouts of single-electron devices (“gate reflectometry”) rely on one-port radio-frequency (RF) reflectometry to read out the state of the sensor. A standard practice in reflectometry measurements is to design an impedance transformer to match the impedance of the load to the characteristic impedance of the transmission line and thus obtain the best sensitivity and signal-to-noise ratio. This is particularly important for measuring large impedances, typical for dispersive readouts of single-electron devices because even a small mismatch will cause a strong signal degradation. When performing RF measurements, a calibration and error correction of the measurement apparatus must be performed in order to remove errors caused by unavoidable non-idealities of the measurement system. Lack of calibration makes optimizing a matching network difficult and ambiguous, and it also prevents a direct quantitative comparison between measurements taken of different devices or on different systems. We propose and demonstrate a simple straightforward method to design and optimize a pi matching network for readouts of devices with large impedance, $$Z \ge 1\hbox {M}\Omega$$ Z ≥ 1 M Ω . It is based on a single low temperature calibrated measurement of an unadjusted network composed of a single L-section followed by a simple calculation to determine a value of the “balancing” capacitor needed to achieve matching conditions for a pi network. We demonstrate that the proposed calibration/error correction technique can be directly applied at low temperature using inexpensive calibration standards. Using proper modeling of the matching networks adjusted for low temperature operation the measurement system can be easily optimized to achieve the best conditions for energy transfer and targeted bandwidth, and can be used for quantitative measurements of the device impedance. In this work we use gate reflectometry to readout the signal generated by arrays of parallel-connected Al-AlOx single-electron boxes. Such arrays can be used as a fast nanoscale voltage sensor for scanning probe applications. We perform measurements of sensitivity and bandwidth for various settings of the matching network connected to arrays and obtain strong agreement with the simulations. 
    more » « less
  3. Abstract

    Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single‐platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrievedRecovary with the structural characteristics of the cloud and the Sun‐view geometry. Recently, it has been shown that the bias in MODISRecan be estimated by fusing MODIS data with data from Terra's Multi‐angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional‐scale, bias‐corrected, monthly‐meanRe1.6,Re2.1, andRe3.7values retrieved from the 1.6, 2.1, and 3.7 μm MODIS spectral channels. Our results reveal that monthly‐mean bias inRe2.1exhibits large regional dependency, ranging from at least ~1 to 10μm (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias‐corrected monthly‐meanRe2.1ranges from 4 to 17μm, compared to 10 to 25 μm for uncorrectedRe2.1, with estimated uncertainties of 0.1 to 1.8 μm. The bias‐corrected monthly‐meanRe3.7andRe2.1show difference of approximately +0.6 μm in the coastal marine stratocumulus regions and down to approximately −2μm in the cumuliform cloud regions, compared to uncorrected values of about −1 to −6 μm, respectively. Bias‐correctedRevalues compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of globalRedistributions from MODISReproducts and may further impact studies, which use the original MODISReproducts to study, for example, aerosol‐cloud interactions and cloud microphysical parameterization.

    more » « less

    Velocity offsets in the broad Balmer lines of quasars and their temporal variations serve as indirect evidence for bound supermassive black hole binaries (SBHBs) at sub-parsec separations. In this work, we test the SBHB hypothesis for 14 quasars with double-peaked broad emission lines using their long-term (14–41 yr) radial velocity curves. We improve on the previous work by (i) using elliptical instead of circular orbits for the SBHBs, (ii) adopting a statistical model for radial velocity jitter, (iii) employing a Markov chain Monte Carlo method to explore the orbital parameter space efficiently and build posterior distributions of physical parameters, and (iv) incorporating new observations. We determine empirically that jitter comprises approximately Gaussian distributed fluctuations about the smooth radial velocity curves that are larger than the measurement errors by factors of a few. We initially treat jitter by enlarging the effective error bars and then verify this approach via a variety of Gaussian process models for it. We find lower mass limits for the hypothesized SBHBs in the range 108–1011 M⊙. For seven objects, the SBHB scenario appears unlikely based on goodness-of-fit tests. For two additional objects, the minimum SBHB masses are unreasonably large (>1010 M⊙), strongly disfavouring the SBHB scenario. Using constraints on the orbital inclination angle (which requires some assumptions) makes the minimum masses of four more objects unreasonably large. We also cite physical and observational arguments against the SBHB hypothesis for nine objects. We conclude that the SBHB explanation is not the favoured explanation of double-peaked broad emission lines.

    more » « less
  5. Abstract

    Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

    more » « less