Residual Scatter Research Articles

How Many Elements Matter?

Abstract Some studies of stars’ multielement abundance distributions suggest at least 5–7 significant dimensions, but others show that many elemental abundances can be predicted to high accuracy from [Fe/H] and [Mg/Fe] (or [Fe/H] and age) alone. We show that both propositions can be, and are, simultaneously true. We adopt a machine-learning technique known as normalizing flow to reconstruct the probability distribution of Milky Way disk stars in the space of 15 elemental abundances measured by APOGEE. Conditioning on T eff and log g minimizes the differential systematics. After further conditioning on [Fe/H] and [Mg/Fe], the residual scatter for most abundances is σ [X/H] ≲ 0.02 dex, consistent with APOGEE’s reported statistical uncertainties of ∼0.01–0.015 dex and intrinsic scatter of 0.01–0.02 dex. Despite the small scatter, residual abundances display clear correlations between elements, which we show are too large to be explained by measurement uncertainties or by the finite sampling noise. We must condition on at least seven elements to reduce the correlations to a level consistent with the observational uncertainties. Our results demonstrate that cross-element correlations are a much more sensitive probe of a hidden structure than dispersion, and they can be measured precisely in a large sample even if the star-by-star measurement noise is comparable to the intrinsic scatter. We conclude that many elements have an independent story to tell, even for the mundane disk stars and elements produced by the core-collapse and Type Ia supernovae. The only way to learn these lessons is to measure the abundances directly, and not merely infer them.

A new method to measure the spectra of transiting exoplanet atmospheres using multi-object spectroscopy

ABSTRACT Traditionally, ground-based spectrophotometric observations probing transiting exoplanet atmospheres have employed a linear map between comparison and target star light curves (e.g. via differential spectrophotometry) to correct for systematics contaminating the transit signal. As an alternative to this conventional method, we introduce a new Gaussian Processes (GP) regression-based method to analyse ground-based spectrophotometric data. Our new method allows for a generalized non-linear mapping between the target transit light curves and the time-series used to detrend them. This represents an improvement compared to previous studies because the target and comparison star fluxes are affected by different telluric and instrumental systematics, which are complex and non-linear. We apply our method to six Gemini/GMOS transits of the warm (Teq = 990 K) Neptune HAT-P-26b. We obtain on average ∼20 per cent better transit depth precision and residual scatter on the white light curve compared to the conventional method when using the comparison star light curve as a GP regressor and ∼20 per cent worse when explicitly not using the comparison star. Ultimately, with only a cost of 30 per cent precision on the transmission spectra, our method overcomes the necessity of using comparison stars in the instrument field of view, which has been one of the limiting factors for ground-based observations of the atmospheres of exoplanets transiting bright stars. We obtain a flat transmission spectrum for HAT-P-26b in the range of 490–900 nm that can be explained by the presence of a grey opacity cloud deck, and indications of transit timing variations, both of which are consistent with previous measurements.

FROG: A global machine-learning temperature calibration for branched GDGTs in soils and peats

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental settings. Nevertheless, it was previously shown that other parameters than temperature and pH, such as soil moisture, thermal regime or vegetation can also influence the relative distribution of brGDGTs in soils and peats. This can explain a large part of the residual scatter in the global brGDGT calibrations with mean annual air temperature (MAAT) and pH in these settings. Despite improvements in brGDGT analytical methods and development of refined models, the root-mean-square error (RMSE) associated with global calibrations between brGDGT distribution and MAAT in soils and peats remains high (∼5 °C). The aim of the present study was to develop a new global terrestrial brGDGT temperature calibration from a worldwide extended dataset (i.e. 775 soil and peat samples, i.e. 112 samples added to the previously available global calibration) using a machine learning algorithm. Statistical analyses highlighted five clusters with different effects of potential confounding factors in addition to MAAT on the relative abundances of brGDGTs. The results also revealed the limitations of using a single index and a simple linear regression model to capture the response of brGDGTs to temperature changes. A new improved calibration based on a random forest algorithm was thus proposed, the so-called random Forest Regression for PaleOMAAT using brGDGTs (FROG). This multi-factorial and non-parametric model allows to overcome the use of a single index, and to be more representative of the environmental complexity by taking into account the non-linear relationships between MAAT and the relative abundances of the individual brGDGTs. The FROG model represents a refined brGDGT temperature calibration (R2 = 0.8; RMSE = 4.01 °C) for soils and peats, more robust and accurate than previous global soil calibrations while being proposed on an extended dataset. This novel improved calibration was further applied and validated on two paleo archives covering the last 110 kyr and the Pliocene, respectively.

Radial-velocity Precision of ESPRESSO Through the Analysis of the Solar Twin HIP 11915

Abstract Different stellar phenomena affect radial velocities (RVs), causing variations large enough to make it difficult to identify planet signals from the stellar variability. RV variations caused by stellar oscillations and granulation can be reduced through some methods, but the impact of rotationally modulated magnetic activity on RV, due to stellar active regions, is harder to correct. New instrumentation promises an improvement in precision of one order of magnitude, from about 1 m s−1 to about 10 cm s−1. In this context, we report our first results from 24 spectroscopic Echelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observations (ESPRESSO)/Very-Large Telescope observations of the solar twin star HIP 11915, spread over 60 nights. We used a Gaussian Process approach and found for HIP 11915 a RV residual rms scatter of about 20 cm s−1, representing an upper limit for the performance of ESPRESSO.

A test of constancy of dark matter halo surface density and radial acceleration relation in relaxed galaxy groups

The dark matter halo surface density, given by the product of the dark matter core radius ($r_c$) and core density ($\rho_c$) has been shown to be a constant for a wide range of isolated galaxy systems. Here, we carry out a test of this {\em ansatz} using a sample of 17 relaxed galaxy groups observed using Chandra and XMM-Newton, as an extension of our previous analysis with galaxy clusters. We find that $\rho_c \propto r_c^{-1.35^{+0.16}_{-0.17}}$, with an intrinsic scatter of about 27.3%, which is about 1.5 times larger than that seen for galaxy clusters. Our results thereby indicate that the surface density is discrepant with respect to scale invariance by about 2$\sigma$, and its value is about four times greater than that for galaxies. Therefore, the elevated values of the halo surface density for groups and clusters indicate that the surface density cannot be a universal constant for all dark matter dominated systems. Furthermore, we also implement a test of the radial acceleration relation for this group sample. We find that the residual scatter in the radial acceleration relation is about 0.32 dex and a factor of three larger than that obtained using galaxy clusters. The acceleration scale which we obtain is in-between that seen for galaxies and clusters.

A test of Radial Acceleration Relation for the Giles et al Chandra cluster sample

We carry out a test of the radial acceleration relation (RAR) for a sample of 10 dynamically relaxed and cool-core galaxy clusters imaged by the Chandra X-ray telescope, which was studied in Giles et al. For this sample, we observe that the best-fit RAR shows a very tight residual scatter equal to 0.09 dex. We obtain an acceleration scale of $1.59 \times 10^{-9} m/s^2$, which is about an order of magnitude higher than that obtained for galaxies. Furthermore, the best-fit RAR parameters differ from those estimated from some of the previously analyzed cluster samples, which indicates that the acceleration scale found from the RAR could be of an emergent nature, instead of a fundamental universal scale.

Projected Rotational Velocities and Fundamental Properties of Low-mass Pre-main-sequence Stars in the Taurus–Auriga Star-forming Region

Abstract The projected stellar rotational velocity ( ) is critical for our understanding of processes related to the evolution of angular momentum in pre-main-sequence stars. We present measurements of high-resolution infrared and optical spectroscopy for 70 pre-main-sequence stars in the Taurus–Auriga star-forming region, in addition to effective temperatures measured from line-depth ratios, as well as stellar rotation periods determined from optical photometry. From the literature, we identified the stars in our sample that show evidence of residing in circumstellar disks or multiple systems. The comparison of infrared measurements calculated using two techniques shows a residual scatter of ∼1.8 km s−1, defining a typical error floor for the of pre-main-sequence stars from infrared spectra. A comparison of the distributions of stars with and without companions shows that binaries/multiples typically have a higher measured , which may be caused by contamination by companion lines, shorter disk lifetimes in binary systems, or tidal interactions in hierarchical triples. A comparison of optical and infrared values shows no significant difference regardless of whether the star has a disk or not, indicating that CO contamination from the disk does not impact measurements above the typical ∼1.8 km s−1 error floor of our measurements. Finally, we observe a lack of a correlation between the , presence of a disk, and H-R diagram position, which indicates a complex interplay between stellar rotation and evolution of pre-main-sequence stars.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography.

We have been investigating two-dimensional (2D) antiscatter grids (2D ASGs) to reduce scatter fluence and improve image quality in cone beam computed tomography (CBCT). In this work, two different aspects of 2D ASGs, their scatter rejection and correction capability, were investigated in CBCT experiments. To correct residual scatter transmitted through the 2D ASG, it was used as a scatter measurement device with a novel method: grid-based scatter sampling. Three focused 2D ASG prototypes with grid ratios of 8, 12, and 16 were developed for linac-mounted offset detector CBCT geometry. In the first phase, 2D ASGs were used as a scatter rejection device, and the effect of grid ratio on CT number accuracy and contrast-to-noise ratio (CNR) evaluated in CBCT images. In the second phase, a grid-based scatter sampling method which exploits the signal modulation characteristics of the 2D ASG's septal shadows to measure and correct residual scatter transmitted through the grid was implemented. To evaluate CT number accuracy, the percent change in CT numbers was measured by changing the phantom from head to pelvis size and configuration. When 2D ASG was used as a scatter rejection device, CT number accuracy increased and the CT number variation due to change in phantom dimensions was reduced from 23% to 2-6%. A grid ratio of 16 yielded the lowest CT number variation. All three 2D ASGs yielded improvement in CNR, up to a factor of two in pelvis-sized phantoms. When 2D ASG prototypes were used for both scatter rejection and correction, CT number variations were reduced further, to 1.3-2.6%. In comparisons with a clinical CBCT system and a high-performance radiographic ASG, 2D ASG provided higher CT number accuracy under the same imaging conditions. When 2D ASG is used solely as a scatter rejection device, substantial improvement in CT number accuracy can be achieved by increasing the grid ratio. Two-dimensional ASGs also provided significant CNR improvement even at lower grid ratios. Two-dimensional ASGs used in conjunction with the grid-based scatter sampling method provided further improvement in CT number accuracy, irrespective of the grid ratio, while preserving 2D ASGs' capacity to improve CNR. The combined effect of scatter rejection and residual scatter correction by 2D ASG may accelerate implementation of new techniques in CBCT that require high quantitative accuracy, such as radiotherapy dose calculation and dual energy CBCT.

It’s Dust: Solving the Mysteries of the Intrinsic Scatter and Host-galaxy Dependence of Standardized Type Ia Supernova Brightnesses

Abstract The use of Type Ia supernovae (SNe Ia) as cosmological tools has motivated significant effort to understand what drives the intrinsic scatter of SN Ia distance modulus residuals after standardization, characterize the distribution of SN Ia colors, and explain why properties of the host galaxies of the SNe correlate with SN Ia distance modulus residuals. We use a compiled sample of ∼1450 spectroscopically confirmed photometric light curves of SNe Ia and propose a solution to these three problems simultaneously that also explains an empirical 11σ detection of the dependence of Hubble residual scatter on SN Ia color. We introduce a physical model of color where intrinsic SN Ia colors with a relatively weak correlation with luminosity are combined with extrinsic dust-like colors (E(B − V)) with a wide range of extinction parameter values (R V ). This model captures the observed trends of Hubble residual scatter and indicates that the dominant component of SN Ia intrinsic scatter is variation in R V . We also find that the recovered E(B − V) and R V distributions differ based on global host-galaxy stellar mass, and this explains the observed correlation (γ) between mass and Hubble residuals seen in past analyses, as well as an observed 4.5σ dependence of γ on SN Ia color. This finding removes any need to ascribe different intrinsic luminosities to different progenitor systems. Finally, we measure biases in the equation of state of dark energy (w) up to ∣Δw∣ = 0.04 by replacing previous models of SN color with our dust-based model; this bias is larger than any systematic uncertainty in previous SN Ia cosmological analyses.

A unified scatter rejection and correction method for cone beam computed tomography.

Scattered radiation is a major cause of image quality degradation in flat panel detector-based cone beam CT (CBCT). While recently introduced 2D antiscatter grids reject the majority of scatter fluence, the small percentage of scatter fluence still transmitted to the detector remains a major challenge for implementation of quantitative imaging techniques such as dual energy imaging in CBCT. Additionally, this residual scatter is also a major source of grid-induced artifacts, which impedes implementation of 2D grids in CBCT. We therefore present a new method to achieve both robust scatter rejection and residual scatter correction using a 2D antiscatter grid; in doing so, we expand the role of 2D grids from mere scatter rejection devices to scatter measurement devices. In our method, the radiopaque septa of the 2D grid emulate a micro array of beam-stops placed on the detector which introduce spatially periodic septal shadows. By selecting sufficiently thin grid septa, the primary intensity can be reduced while preserving the uniformity of scatter intensity. This enables us to correlate the modulated pixel signal intensity in septal shadows with local scatter intensity. Our method then exploits this correlation to measure and remove residual scatter intensity from projections. No assumptions are made about the object being imaged. We refer to this as Grid-based Scatter Sampling (GSS). In this work, we evaluate the principle of signal modulation with grid septa, the accuracy of scatter estimates, and the effect of the GSS method on image quality using simulations and measurements. We also implement the GSS method experimentally using a 2D grid prototype. Our results demonstrate that the GSS method increased CT number accuracy and reduced image artifacts associated with scatter. With 2D grid and residual scatter correction, HU nonuniformity was reduced from 65HU to 30HU in pelvis sized phantoms, and HU variations due to change in phantom size were reduced from 59HU to 20HU, when compared to use of only a 2D grid. With residual scatter correction via GSS method, grid-induced ring artifacts were suppressed, leading to a 41% reduction in noise. The shape of the modulation transfer function (MTF) was preserved before and after suppression of ring artifacts. Our grid-based scatter sampling method enables utilization of a 2D grid as a scatter measurement and correction device. This method significantly improves quantitative accuracy in CBCT, further reducing the image quality gap between CBCT and multi-detector CT. By correcting residual scatter with the proposed method, grid-induced line artifacts in projections and associated ring artifacts in CBCT images were also suppressed with no compromise of spatial resolution.

Yet another test of Radial Acceleration Relation for galaxy clusters

We carry out a test of the radial acceleration relation (RAR) for galaxy clusters from two different catalogs compiled in literature, as an independent cross-check of two recent analyses, which reached opposite conclusions. The datasets we considered include a Chandra sample of 12 clusters and the X-COP sample of 12 clusters. For both the samples, we find that the residual scatter is small (0.11-0.14 dex), although the best-fit values for the Chandra sample have large error bars. Therefore, we argue that at least one of these cluster samples (X-COP) obeys the radial acceleration relation. However, since the best-fit parameters are discrepant with each other as well as the previous estimates, we argue that the RAR is not universal. For both the catalogs, the acceleration scale, which we obtain is about an order of magnitude larger than that obtained for galaxies, and is agreement with both the recent estimates.

EXPRES. II. Searching for Planets around Active Stars: A Case Study of HD 101501

Abstract By controlling instrumental errors to below 10 cm s−1, the EXtreme PREcision Spectrograph (EXPRES) allows for a more insightful study of photospheric velocities that can mask weak Keplerian signals. Gaussian processes (GP) have become a standard tool for modeling correlated noise in radial velocity data sets. While GPs are constrained and motivated by physical properties of the star, in some cases they are still flexible enough to absorb unresolved Keplerian signals. We apply GP regression to EXPRES radial velocity measurements of the 3.5 Gyr old chromospherically active Sun-like star, HD 101501. We obtain tight constraints on the stellar rotation period and the evolution of spot distributions using 28 seasons of ground-based photometry, as well as recent Transiting Exoplanet Survey Satellite data. Light-curve inversion was carried out on both photometry data sets to reveal the spot distribution and spot evolution timescales on the star. We find that the >5 m s−1 rms radial velocity variations in HD 101501 are well modeled with a GP stellar activity model without planets, yielding a residual rms scatter of 45 cm s−1. We carry out simulations, injecting and recovering signals with the GP framework, to demonstrate that high-cadence observations are required to use GPs most efficiently to detect low-mass planets around active stars like HD 101501. Sparse sampling prevents GPs from learning the correlated noise structure and can allow it to absorb prospective Keplerian signals. We quantify the moderate to high-cadence monitoring that provides the necessary information to disentangle photospheric features using GPs and to detect planets around active stars.

Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series

AbstractThe present study investigated the uncertainty associated with Climatol's adjustment algorithm applied to daily minimum and maximum air temperature. The uncertainty quantification was performed based on several numerical experiments and the benchmark data that were created in the framework of the INDECIS project. Using a complex approach, the uncertainty was evaluated at different levels of detail (day‐to‐day evaluation through formalism of random functions and six statistical metrics) and time resolution (daily and yearly). However, only the main source of potential residual errors was considered, namely station signals introduced into a raw data set to be homogenized/adjusted. Other influencing factors, such as the averaged correlation between a candidate and references, were removed from the analysis or kept almost unchanged. According to our calculations, the Climatol's adjustment uncertainty, evaluated on the daily scale, varies over time. The width of the residual errors distribution in summer months is substantially less compared with wintertime. The slight seasonality is also observed in the means of the residual errors. The uncertainty evaluation based on the statistical metrics usually neglect such seasonal non‐stationarity of the residual errors providing only assessments averaged over time. On the other hand, the metrics provide detailed information regarding both types of the residual errors, systematic and scatter. The metrics values confirmed good capability of the Climatol software to remove the systematic errors related to jumps in the means, while the scatter errors are removed from the raw time series less efficiently. On the yearly scale, the uncertainty evaluation was performed for the yearly temperature data and several climate extreme indices. Both types of errors are removed well in the yearly time series of the air temperature and the extreme indices. The metrics values showed a significant reduction of the Climatol's adjustment uncertainty. A substantial decrease of the linear trend errors in the yearly time series can also be observed.

Measurement of PET Quantitative Bias In Vivo.

Quantitative imaging biomarkers are widely used in PET for both research and clinical applications, yet bias in the underlying image data has not been well characterized. In the absence of a readily available reference standard for in vivo quantification, bias in PET images has been inferred using physical phantoms, even though arrangements of this sort provide only a poor approximation of the imaging environment in real patient examinations. In this study, we used data acquired from patient volunteers to assess PET quantitative bias in vivo. Image-derived radioactivity concentrations in the descending aorta were compared with blood samples counted on a calibrated γ-counter. Methods: Ten patients with prostate cancer were studied using 2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid PET/CT. For each patient, 3 whole-body PET/CT image series were acquired after a single administration of the radiotracer: shortly after injection as well as approximately 1 and 4 h later. Venous blood samples were obtained at 8 time points over an 8-h period, and whole blood was counted on a NaI γ-counter. A 10-mm-diameter, 20-mm-long cylindric volume of interest was positioned in the descending thoracic aorta to estimate the PET-derived radioactivity concentration in blood. A triexponential function was fit to the γ-counter blood data and used to estimate the radioactivity concentration at the time of each PET acquisition. Results: The PET-derived and γ-counter-derived radioactivity concentrations were linearly related, with an R2 of 0.985, over a range of relevant radioactivity concentrations. The mean difference between the PET and γ-counter data was 4.8% ± 8.6%, with the PET measurements tending to be greater. Conclusion: Human image data acquired on a conventional whole-body PET/CT system with a typical clinical protocol differed by an average of around 5% from blood samples counted on a calibrated γ-counter. This bias may be partly attributable to residual uncorrected scatter or attenuation correction error. These data offer an opportunity for the assessment of PET bias in vivo and provide additional support for the use of quantitative imaging biomarkers.

Dynamical mass inference of galaxy clusters with neural flows

ABSTRACT We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, that is, the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning (ML) approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a lognormal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate- to high-mass range. This is an improvement by nearly a factor of 4 relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed ML approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.

Evaluation of Ground-Motion Models for USGS Seismic Hazard Models Using Near-Source Instrumental Ground-Motion Recordings of the Ridgecrest, California, Earthquake Sequence

ABSTRACTWe evaluated the performance of 12 ground-motion models (GMMs) for earthquakes in the tectonically active shallow crustal region of southern California using instrumental ground-motion observations from the 2019 Ridgecrest, California, earthquake sequence (Mw 4.0–7.1). The sequence was well recorded by the Southern California Seismic Network and rapid response portable aftershock monitoring stations. Ground-motion recordings of this size and proximity are rare, valuable, and independent of GMM development, allowing us to evaluate the predictive powers of GMMs. We first compute total residuals and compare the probability density functions, means, and standard deviations of the observed and predicted ground motions. Next we use the total residuals as inputs to the probabilistic scoring method (log-likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) logic trees. We also explore GMM performance for a range of earthquake magnitudes, wave propagation distances, and site characteristics. We find that the Next Generation Attenuation West-2 (NGAW2) active crust GMMs perform well for the 2019 Ridgecrest, California, earthquake sequence and thus validate their use in the 2018 USGS NSHM. However, significant ground-motion residual scatter remains unmodeled by NGAW2 GMMs due to complexities such as local site amplification and source directivity. Results from this study will inform logic-tree weights for updates to the USGS National NSHM. Results from this study support the use of nonergodic GMMs that can account for regional attenuation and site variations to minimize epistemic uncertainty in USGS NSHMs.

Simultaneous scatter rejection and correction method using 2D antiscatter grids for CBCT.

While two-dimensional antiscatter grids (2D grid) reduce scatter intensity substantially in Cone Beam Computed Tomography (CBCT), a small fraction of scattered radiation is transmitted through the 2D grid to the detector. Residual scatter limits the accuracy of CT numbers and interferes with the correction of grid's septal shadows, or footprint, in projections. If grid's septal shadows are not adequately suppressed in projections, it will lead to ring artifacts in CBCT images. In this work, we present a new method to correct residual scatter transmitted through the grid by employing the 2D grid itself as a residual scatter measurement device. Our method, referred as grid-based scatter sampling (GSS), exploits the spatial modulation of primary x-ray fluence by 2D grid's septal shadows. The shape of the signal modulation pattern varies as a function of residual scatter intensity registered by detector pixels. Such a variation in signal pattern was employed to measure residual scatter intensity in each projection, and subsequently, residual scatter was subtracted from each projection. To validate the GSS method, CBCT imaging experiments were conducted using a 2D antiscatter grid prototype in a linac mounted CBCT system. The effect of GSS method on the ring artifact reduction was quantified by measuring noise in CBCT images. In addition, the nonuniformity of Hounsfield Units (HU) and HU accuracy was measured in both head and pelvis-sized phantoms. In qualitative evaluations, GSS method successfully reduced ring artifacts caused by 2D grid's footprint. Image noise reduced by 23% due to reduction of ring artifacts. HU nonuniformity in water-equivalent sections was reduced from 20 HU to 10 HU, and streak artifacts between high density inserts were reduced. The phantom size dependent variations in HU was also reduced after application of GSS method. Without GSS method, HU of density inserts reduced by 9% on the average when phantom size was increased from head to pelvis. With GSS method, HU values reduced only by 5% under the same conditions. In summary, GSS method complements the 2D grid's scatter suppression performance, by correcting the scatter transmitted through the grid. This approach does not require additional scatter-measurement hardware, such as beam-stop arrays, since the grid itself is employed as the scatter measurement device. By suppressing residual scatter in projections, our proposed method successfully reduced artifacts caused by 2D grid's footprint, and further improved CT number accuracy.

Loose Ends for the Exomoon Candidate Host Kepler-1625b

Abstract The claim of an exomoon candidate in the Kepler-1625b system has generated substantial discussion regarding possible alternative explanations for the purported signal. In this work, we examine these possibilities in detail. First, the effect of more flexible trend models is explored, and we show that sufficiently flexible models are capable of attenuating the signal—although this is an expected byproduct of invoking such models. We also explore trend models using x- and y-centroid positions, and show that there is no data-driven impetus to adopt such models over temporal ones. We quantify the probability that the 500 ppm moon-like dip could be caused by a Neptune-sized transiting planet to be <0.75%. We show that neither autocorrelation, Gaussian processes, nor a Lomb–Scargle periodogram are able to recover a stellar rotation period, demonstrating that K1625 is a quiet star with periodic behavior <200 ppm. Through injection and recovery tests, we find that the star does not exhibit a tendency to introduce false-positive dip-like features above that of pure Gaussian noise. Finally, we address a recent reanalysis by Kreidberg et al. and show that the difference in conclusions is not from differing systematics models but rather the reduction itself. We show that their reduction exhibits, in comparison to the original analysis: (i) slightly higher intraorbit and post-fit residual scatter, (ii) ≃900 ppm larger flux offset at the visit change, (iii) ≃2 times larger y-centroid variations, and (iv) ≃3.5 times stronger flux-centroid correlation coefficient. These points could be explained by larger systematics in their reduction, potentially impacting their conclusions.

The mass–metallicity and the fundamental metallicity relation revisited on a fully Te-based abundance scale for galaxies

ABSTRACT The relationships between stellar mass, gas-phase metallicity and star-formation rate (i.e. the mass–metallicity, MZR, and the fundamental metallicity relation, FMR) in the local Universe are revisited by fully anchoring the metallicity determination for SDSS galaxies on the Te abundance scale defined exploiting the strong-line metallicity calibrations presented by Curti et al. Self-consistent metallicity measurements allow a more unbiased assessment of the scaling relations involving M, Z and SFR, which provide powerful constraints for the chemical evolution models. We parametrize the MZR with a new functional form that allows us to better characterize the turnover mass. The slope and saturation metallicity are in good agreement with previous determinations of the MZR based on the Te method, while showing significantly lower normalization compared to those based on photoionization models. The Z–SFR dependence at fixed stellar mass is also investigated, being particularly evident for highly star-forming galaxies, where the scatter in metallicity is reduced up to a factor of ${\sim}30{{\ \rm per\ cent}}$. A new parametrization of the FMR is given by explicitly introducing the SFR dependence of the turnover mass into the MZR. The residual scatter in metallicity for the global galaxy population around the new FMR is 0.054 dex. The new FMR presented in this work represents a useful local benchmark to compare theoretical predictions and observational studies (of both local and high-redshift galaxies) whose metallicity measurements are tied to the abundance scale defined by the Te method, hence allowing proper assessment of its evolution with cosmic time.