Fit Residuals Research Articles

ZTF SN Ia DR2: Study of Type Ia supernova light-curve fits

Type Ia supernova (SN Ia) cosmology relies on the estimation of light-curve parameters to derive precision distances, which are used to infer cosmological parameters such as $H_0$, $ and $w$. The empirical SALT2 light-curve modeling that relies on only two parameters, a stretch $x_1$ and a color $c$, has been used by the community for almost two decades. We study the ability of the SALT2 model to fit the nearly 3000 cosmology-grade SN Ia light curves from the second release of the Zwicky Transient Facility (ZTF) cosmology science working group. While the ZTF data were not used to train SALT2, the algorithm models the ZTF SN Ia optical light curves remarkably well, except for light-curve points prior to $-10$ d from maximum, where the training critically lacks data. We find that the light-curve fitting is robust against the considered choice of phase range, but we show that the $ $ d range is optimal in terms of statistics and accuracy. We do not detect any significant features in the light-curve fit residuals that could be connected to the host environment. Potential systematic uncertainties associated tp population differences related to the SN Ia host properties might thus not be accountable for by the inclusion of addition of light-curve parameters. However, a small but significant inconsistency between residuals of blue and red SN Ia strongly suggests the existence of a phase-dependent color term, with potential implications for the use of SNe Ia in precision cosmology. We thus encourage further work in this area to explore this possibility, and we emphasize that SN Ia cosmology must include a SALT2 retraining to accurately model the light curves and avoid biasing the derivation of cosmological parameters.

A Sub-Channel Spatial Homogeneity-Based Channel Estimation Method for Underwater Optical Densely Arrayed MIMO Systems

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing Nt sub-channels of K-sparse to 2Nt irrespective of CIR residual compensation.

The structure of massive star-forming galaxies from JWST and ALMA: Dusty, high-redshift disc galaxies

We present an analysis of the NIRCam and MIRI morphological and structural properties of 80 massive ($ (M_ M_ odot )$\,=\,11.2\,pm \,0.1) dusty star-forming galaxies at $, identified as sub-millimetre galaxies (SMGs) by ALMA, which have been observed as part of the PRIMER project. To compare the structure of these massive, active galaxies to more typical, less actively star-forming galaxies, we defined two comparison samples. The first of 850 field galaxies matched in specific star formation rate and redshift and the second of 80 field galaxies matched in stellar mass . From the visual classification of the SMGs, we have identified 20\,pm \,5<!PCT!> as candidate late-stage major mergers, a further 40\,pm \,10<!PCT!> as potential minor mergers, and 40\,pm \,10<!PCT!> that have comparatively undisturbed disc-like morphologies, with no obvious massive neighbours on lesssim \,20\,--\,30\,kpc (projected) scales. These rates are comparable to those for the field samples and indicate that the majority of the sub-millimetre-detected galaxies are not late-stage major mergers, but have interaction rates similar to the general field population at $z$\,sim \,2--3. Through a multi-wavelength morphological analysis, using parametric and non-parametric techniques, we establish that SMGs have comparable near-infrared, mass-normalised sizes to the less active population, $ F444W $\,=\,2.7\,pm \,0.2\,kpc versus $ F444W $\,=\,3.1\,pm \,0.1\,kpc, but exhibit lower S\'ersic indices, consistent with bulge-less discs: F444W $\,=\,1.1\,pm \,0.1, compared to F444W $\,=\,1.9\,pm \,0.1 for the less active field galaxies and $n_ F444W $\,=\,2.8\,pm \,0.2 for the most massive field galaxies . The SMGs exhibit greater single-S\'ersic fit residuals and their morphologies are more structured at 2 relative to 4 when compared to the field galaxies. This appears to be caused by significant structured dust content in the SMGs and we find evidence for dust reddening as the origin of the morphological differences by identifying a strong correlation between the F200W$-$F444W pixel colour and the 870 surface brightness using high-resolution ALMA observations. We conclude that SMGs and both massive and less massive star-forming galaxies at the same epochs share a common disc-like structure, but the weaker bulge components (and potentially lower black hole masses) of the SMGs result in their gas discs being less stable. Consequently, the combination of high gas masses and instabilities triggered either secularly or by minor external perturbations results in higher levels of activity (and dust content) in SMGs compared to typical star-forming galaxies.

How to evaluate local fit (residuals) in large structural equation models.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntaxand output files are freely available to readers.

Extrinsic parameters optimization for fringe projection system based on standard components

Fringe projection profilometry (FPP) is a wildly used three-dimension reconstruction method, where the reconstruction accuracy is closely related to the calibration precision. However, various sources of error in the calibration process can result in inaccurate calibration results. In particular, the calibration results of extrinsic parameters are more likely to deviate from the true values, which in turn leads to low-frequency errors in the reconstruction results. In order to obtain the extrinsic parameters closer to the true values, a simple but effective method is proposed in this paper. The proposed method utilizes the standard plane and the dumbbell bat, which are commonly used in measurements, to optimize the extrinsic parameters. First, the standard plane and dumbbell bat are reconstructed using the initial calibration results to transfer the errors in the extrinsic parameters to the plane fitting residuals, as well as to the fitting residuals for the radius and the center distance of dumbbell bat. The residuals and errors are then minimized using nonlinear optimization, resulting in the more accurate extrinsic parameters. Experimental results demonstrate that the proposed method can effectively compensate the calibration error of the extrinsic parameters. The root mean square (RMS) error of planar reconstruction is reduced from 0.0291 mm to 0.0106 mm, which is reduced by about 63.6 %. The RMS error of spherical reconstruction is reduced from 0.0358 mm to 0.0166 mm, which is about 53.6 %. The measurement accuracy of the FPP system is further improved.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Adaptive analysis of the heteroscedastic multivariate regression modeling

Multivariate regression is a widely used technique for large-scale statistical applications. In this paper, we propose a novel method for the multivariate regression model, called multivariate weighted lasso (MWL). We consider the heteroscedasticity in the noise matrix and introduce an adjustable weight to calibrate the fitting residuals of different responses. The proposed method shows an advantage in complex multivariate regressions with heteroscedasticity. To implement the procedure, an efficient algorithm is provided based on the multivariate coordinate descent. We provide theoretical guarantees for the proposed method. Numerical simulations and financial applications are conducted using the proposed and other existing methods. The results indicate that the proposed method performs well in both variable selection and coefficient estimation.

AGNfitter-rx : Modeling the radio-to-X-ray spectral energy distributions of AGNs

We present new advancements in the modeling of the spectral energy distributions (SEDs) of active galaxies by introducing the radio-to-X-ray fitting capabilities of the publicly available Bayesian code AGNfitter . The new code release, called AGNfitter-rx models the broad-band photometry covering the radio, infrared (IR), optical, ultraviolet (UV), and X-ray bands consistently using a combination of theoretical and semi-empirical models of the active galactic nucleus (AGN) and host-galaxy emission. This framework enables the detailed characterization of four physical components of the active nuclei, namely the accretion disk, the hot dusty torus, the relativistic jets and core radio emission, and the hot corona, and can be used to model three components within the host galaxy: stellar populations, cold dust, and the radio emission from the star-forming regions. Applying AGNfitter-rx to a diverse sample of 36 AGN SEDs at $z 0.7$ from the AGN SED ATLAS, we investigated and compared the performance of state-of-the-art torus and accretion disk emission models in terms of fit quality and inferred physical parameters. We find that clumpy torus models that include polar winds and semi-empirical accretion disk templates including emission-line features significantly increase the fit quality in 67<!PCT!> of the sources by reducing by $2 fit residuals in the $1.5-5 1mm m$ and $0.7 1mm m$ regimes. We demonstrate that, by applying AGNfitter-rx to photometric data, we are able to estimate the inclination and opening angles of the torus, consistent with spectroscopic classifications within the AGN unified model, as well as black hole masses congruent with virial estimates based on Halpha . We investigate wavelength-dependent AGN fractions across the spectrum for Type 1 and Type 2 AGNs, finding dominant AGN fractions in radio, mid-infrared, and X-ray bands, which are in agreement with the findings from empirical methods for AGN selection. The wavelength coverage and the flexibility for the inclusion of state-of-the-art theoretical models make AGNfitter-rx a unique tool for the further development of SED modeling for AGNs in present and future radio-to-X-ray galaxy surveys.

Initial evaluation of measurement properties of the Work Environment Impact Questionnaire (WEIQ) - using Rasch analysis

BackgroundTo provide both preventive and rehabilitative conditions in a workplace, one must understand how employees experience work demands. Such an understanding can be obtained from each individual with valid and quality-assured questionnaires. The Work Environment Impact Questionnaire (WEIQ) is a new questionnaire for measuring employees’ self-perceived work ability in relation to their specific workplace environment. The purpose of this study was to assess the measurement properties in terms of construct validity of the WEIQ.MethodsA cross-sectional survey study was conducted with 288 respondents from three different workplaces involving assisted living personnel, vocational rehabilitation personnel and personnel at a research institute. The measurement properties of the WEIQ were assessed according to Rasch Measurement Theory (RMT), including assessment of item-to-sample targeting, threshold ordering, item fit statistics, unidimensionality and reliability.ResultsItem fit, i.e., fit residuals, item characteristic curves (ICC) and chi square values, were all satisfactory, and no disordered thresholds were present after collapsing the lowest response categories. However, issues with local dependent (LD) item correlations was present in 7.6% cases, four items showed statistically significant differential item functioning (DIF), where 11% of the respondents had person fit residuals outside the recommended range of ± 2.5 and the t-test for unidimensionality did not meet the criterion of 5%. Scale-to-sample targeting and reliability (0.92) were good. LD could be resolved with testlets and at the same time maintaining fit and improving dimensionality, but then the reliability decreased to 0.82.ConclusionsThis study provides an initial validation of the WEIQ to be used for assessing employees’ self-perceived work ability. Most measurement properties were acceptable, but further exploration of LD, DIF and unidimensionality in additional work settings and with larger sample sizes is warranted.Trial registrationNot applicable.

GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?

The J-difference edited γ-aminobutyric acid (GABA) signal is contaminated by other co-edited signals-the largest of which originates from co-edited macromolecules (MMs)-and is consequently often reported as "GABA+." MM signals are broader and less well-characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus-recommended alternative to parameterized modeling; however, they are relatively under-studied in the context of edited MRS. To address this limitation in the literature, we have acquired GABA-edited MEGA-PRESS data with pre-inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single-Gaussian parameterization, mean experimental MM spectrum and new multi-Gaussian parameterization were compared in a three-way analysis of a public MEGA-PRESS dataset of 61 healthy participants. Both the experimental MMs and the multi-Gaussian parameterization exhibited reduced fit residuals compared to the single-Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single-Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi-Gaussian parameterization (58%), compared to the single-Gaussian approach (50%). Our results indicate that single-Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra.

A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements

This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).

Astrometric detection of a Neptune-mass candidate planet in the nearest M-dwarf binary system GJ65 with VLTI/GRAVITY

The detection of low-mass planets orbiting the nearest stars is a central stake of exoplanetary science, as they can be directly characterized much more easily than their distant counterparts. Here, we present the results of our long-term astrometric observations of the nearest binary M-dwarf Gliese 65 AB (GJ65), located at a distance of only 2.67 pc. We monitored the relative astrometry of the two components from 2016 to 2023 with the VLTI/GRAVITY interferometric instrument. We derived highly accurate orbital parameters for the stellar system, along with the dynamical masses of the two red dwarfs. The GRAVITY measurements exhibit a mean accuracy per epoch of 50−60 ms in 1.5 h of observing time using the 1.8 m Auxiliary Telescopes. The residuals of the two-body orbital fit enable us to search for the presence of companions orbiting one of the two stars (S-type orbit) through the reflex motion they imprint on the differential A–B astrometry. We detected a Neptune-mass candidate companion with an orbital period of p = 156 ± 1 d and a mass of mp = 36 ± 7 M⊕. The best-fit orbit is within the dynamical stability region of the stellar pair. It has a low eccentricity, e = 0.1 − 0.3, and the planetary orbit plane has a moderate-to-high inclination of i > 30° with respect to the stellar pair, with further observations required to confirm these values. These observations demonstrate the capability of interferometric astrometry to reach microarcsecond accuracy in the narrow-angle regime for planet detection by reflex motion from the ground. This capability offers new perspectives and potential synergies with Gaia in the pursuit of low-mass exoplanets in the solar neighborhood.

Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: Testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model

The β-correction to the Hartmann-Tran (HT) profile, recently introduced to model the spectral shape of a molecular transition strongly affected by the Dicke narrowing effect (Konefal et al., JQSRT 242 (2020) 106,784), the HT profile (Ngo et al., JQSRT 129 (2013) 89), and the speed dependent (complex) hard collision model (SDcHC, in which the velocity changing collision rate is characterized by a complex number) are tested using spectra of two rovibrational lines of HCl. The R(5) and R(9) lines of the fundamental band of HCl broadened by He, Ar and SF6 have been recorded with a difference-frequency laser spectrometer for total pressures ranging from 12 to 930 mbar. These lines, measured in large pressure ranges with different collision-partners provide a meaningful test of the above-mentioned line-shape models. The results confirm that non-Voigt effects are significant for HCl broadened by Ar and SF6 and mainly due to the large influence of the Dicke narrowing effect. For HCl-SF6 and HCl-Ar, especially for the R(9) line, using the β-correction together with the HT profile (and with the speed-dependent hard collision model, SDHC) significantly improves the fit residuals while it has no effect on (or tends to deteriorate) the quality of the fit for HCl-He and for the R(5) line of HCl-SF6, for which the influence of velocity changing collisions is smaller. Except for HCl-He, the SDcHC model leads to better quality of fit compared to the HT profile. The results also show that numerical correlations between refined line-shape parameters of the HT profile are important and can lead to ill-determined parameters while they are more properly determined with the SDcHC model.

Research on Indoor Environment Prediction of Pig House Based on OTDBO-TCN-GRU Algorithm.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO-TCN-GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO-TCN-GRU model, OTDBO-TCN-GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO-TCN-GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO-TCN-GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO-TCN-GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator.

For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance-covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables.

Analysis of BDS inter-satellite link ranging performance

The Ka-band inter-satellite link (ISL) is a key technology of BeiDou Global Navigation Satellite System (BDS-3) that can improve the system performance. Based on 30 days of ISL observations, we analyze the BDS-3 ISL ranging performance and the related influencing factors, such as satellite manufacturer, satellite orbit type and link type. The fitting residuals of the ISL geometry-free observations are used to characterize the measurement noise, and the post-fit ISL residuals from precise orbit determination (POD) and precise clock estimation (PCE) are used to further validate the ranging performance. The fitting residuals follow a normal distribution, and the average root mean square (RMS) value for all 421 links is approximately 2.5 cm. The satellite manufacturers, China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM), have an obvious impact on the ISL measurement noise. The RMS of the ISL fitting residuals for the links established by the two CAST satellites is only 1.45 cm, while it can reach 3.34 cm for the links established by the two SECM satellites. This correlation between ISL ranging performance and the satellite manufacturer is also found in the POD and PCE post-fit ISL residuals. The RMS of PCE residuals is about 3.1 cm, while the POD residuals are larger with the RMS of 5.5 cm due to the effect of orbital errors, especially for the Inclined Geosynchronous Orbit (IGSO) and Geostationary Orbit (GEO) satellites. Overall, the ranging accuracy of the BDS-3 ISL is better than 5 cm according to the above analyses. Furthermore, the BDS-3 satellite orbit and clock results are evaluated by internal consistency check and external comparison to analyze the application of ISL observations.

Spectral Fit Residuals as an Indicator to Increase Model Complexity

Spectral fitting of X-ray data usually involves minimizing statistics like the chi-square and the Cash statistic. Here we discuss their limitations and introduce two measures based on the cumulative sum (CuSum) of model residuals to evaluate whether model complexity could be increased: the percentage of bins exceeding a nominal threshold in a CuSum array (pctCuSum), and the excess area under the CuSum compared to the nominal (p area). We demonstrate their use with an application to a Chandra ACIS spectral fit.

A vibration correction system for cold atom gravimeter

Cold atom gravimeters are used to measure the absolute value of the gravitational acceleration. To mitigate the influence of seismic noise on the mirror, a vibration correction system is used. A commercial seismometer CMG-3ESPC is employed to measure the mirror vibration. The transfer function between the mirror vibration and the output of the seismometer is regarded as a proportional element with a time delay. The best proportional element and the best time delay are obtained by minimizing the standard deviation of the fitting residuals of the interference fringe with vibration correction. This system not only exhibits excellent environmental adaptability but also eliminates the need for rough estimation of local gravitational acceleration during the vibration correction process. The system was built on the homemade cold atom gravimeter to carry out experiments in the laboratory. Compared with the measurement results without vibration correction, the standard deviation of the gravity measurement results was reduced from Gal to Gal, resulting in a significant reduction by a factor of 28.7. Meanwhile, its sensitivity was significantly improved from 330.8 to 18.0 by performing vibration correction. In the future, this system is expected to carry out comprehensive verification experiments in more complex noisy environments.

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

The sensitivities of two periodograms are compared for weak signal planet detection in transit surveys: the widely used Box Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (S/N) and a false alarm probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. We find that the TCF periodogram applied to ARIMA fit residuals with the S/N detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present due to heteroscedastic noise and false period detection. Application of these methods to TESS light curves with known small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.

Automatic and accurate compensation for phase aberrations in digital holographic microscopy based on iteratively reweighted least squares fitting

Phase aberrations must be precisely compensated for phase-contrast imaging in digital holographic microscopy. In this paper, an automatic and accurate phase aberration compensation method is proposed based on iteratively reweighted least squares fitting. Instead of selecting the known-to-be-flat region with manual operation or image segmentation, this method starts from conventional least squares fitting in the whole image and attributes lower weights to the pixels with higher fitting residuals in an iterative reweighting procedure, during which the influence of specimen and noise is automatically reduced. Both phase curvature and high-order aberrations could be corrected without extra operation. Numerical simulation demonstrates that the proposed method is more accurate than the state-of-the-art numerical methods. Experimental result proves that this method is robust and fast enough for dynamic imaging of living cells.