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We consider the problem of recovering an unknown k-factor, hidden in a weighted random graph. For k = 1 this is the planted matching problem, while the k = 2 case is closely related to the planted traveling salesman problem. The inference problem is solved by exploiting the information arising from the use of two different distributions for the weights on the edges inside and outside the planted sub-graph. We argue that, in the large size limit, a phase transition can appear between a full and a partial recovery phase as function of the signal-to-noise ratio. We give a criterion for the location of the transition. [ABSTRACT FROM AUTHOR]

This study introduces the HYPERSCINT research platform (HYPERSCINT-RP100, Medscint Inc., Quebec, Canada), the first commercially available scintillation dosimetry platform capable of multi-point dosimetry through the hyperspectral approach. Optic and dosimetric performances of the system were investigated through comparison with another commercially available solution, the Ocean Optics QE65Pro spectrometer. The optical characterization was accomplished by measuring the linearity of the signal as a function of integration time, photon detection efficiency and spectral resolution for both systems under the same conditions. Dosimetric performances were then evaluated with a 3-point plastic scintillator detector (mPSD) in terms of signal to noise ratio (SNR) and signal to background ratio (SBR) associated with each scintillator. The latter were subsequently compared with those found in the literature for the Exradin W1, a single-point plastic scintillator detector. Finally, various beam measurements were realized with the HYPERSCINT platform to evaluate its ability to perform clinical photon beam dosimetry. Both systems were found to be comparable in terms of linearity of the signal as a function of the intensity. Although the QE65Pro possesses a higher spectral resolution, the detection efficiency of the HYPERSCINT is up to 1000 time greater. Dosimetric measurements shows that the latter also offers a better SNR and SBR, surpassing even the SNR of the Exradin W1 single-point PSD. While doses ranging from 1 to 600 cGy were accurately measured within 2.1% of the predicted dose using the HYPERSCINT platform coupled to the mPSD, the Ocean optics spectrometer shows discrepancies up to 86% under 50cGy. Similarly, depth dose, full width at half maximum region of the beam profile and output factors were all accurately measured within 2.3% of the predicted dose using the HYPERSCINT platform and exhibit an average difference of 0.5%, 1.6% and 0.6%, respectively. [ABSTRACT FROM AUTHOR]

Multi-layer imaging (MLI) devices improve the detective quantum efficiency (DQE) while maintaining the spatial resolution of conventional mega-voltage (MV) x-ray detectors for applications in radiotherapy. To date, only MLIs with identical detector layers have been explored. However, it may be possible to instead use different scintillation materials in each layer to improve the final image quality. To this end, we developed and validated a method for optimally combining the individual images from each layer of MLI devices that are built with heterogeneous layers. Two configurations were modeled within the GATE Monte Carlo package by stacking different layers of a terbium doped gadolinium oxysulfide Gd2O2S:Tb (GOS) phosphor and a LKH-5 glass scintillator. Detector response was characterized in terms of the modulation transfer function (MTF), normalized noise power spectrum (NNPS) and DQE. Spatial frequency-dependent weighting factors were then analytically derived for each layer such that the total DQE of the summed combination image would be maximized across all spatial modes. The final image is obtained as the weighted sum of the sub-images from each layer. Optimal weighting factors that maximize the DQE were found to be the quotient of MTF and NNPS of each layer in the heterogeneous MLI detector. Results validated the improvement of the DQE across the entire frequency domain. For the LKH-5 slab configuration, DQE(0) increases between 2%–3% (absolute), while the corresponding improvement for the LKH-5 pixelated configuration was 7%. The performance of the weighting method was quantitatively evaluated with respect to spatial resolution, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated planar images of phantoms at 2.5 and 6 MV. The line pair phantom acquisition exhibited a twofold increase in CNR and SNR, however MTF was degraded at spatial frequencies greater than 0.2 lp mm−1. For the Las Vegas phantom, the weighting improved the CNR by around 30% depending on the contrast region while the SNR values are higher by a factor of 2.5. These results indicate that the imaging performance of MLI systems can be enhanced using the proposed frequency-dependent weighting scheme. The CNR and SNR of the weighted combined image are improved across all spatial scales independent of the detector combination or photon beam energy. [ABSTRACT FROM AUTHOR]

A robust algorithm based on cross-correlations and lucky imaging reliably allows the correction of spectrally diffused datasets. This step enables the resolution-limited analysis of the emission fine structure of semiconductor quantum dots (QDs). Bright and dark excitonic transitions are resolved with optimum signal-to-noise ratio, allowing for a precise determination of the angular direction of linear polarization of the different lines. The angular phases between polarization directions are intrinsically connected to the orientations of emission dipoles. This fact provides a tool for accurate numerical computation of the azimuth and polar angle of the QD with respect to the optical axis. Our in-situ characterization of QD fine structure and orientation represents a precise and non-invasive method without requiring specialized equipment beyond a standard luminescence setup. In this way, important information is provided whenever efficient coupling of a quantum emitter to the electromagnetic field is targeted by various nano- and micro-optic strategies. [ABSTRACT FROM AUTHOR]

Between 30% and 50% of white dwarfs (WDs) show heavy elements in their atmospheres. This pollution is thought to arise from the accretion of planetesimals perturbed by outer planet(s) to within the WD's tidal disruption radius. A small fraction of these WDs show either emission or absorption from circumstellar (C-S) gas. The abundances of metals in the photospheres of WDs with C-S gas are mostly similar to the bulk composition of the Earth. The C-S component arises from gas produced through collisions and/or the sublimation of disintegrating planetesimals. High-resolution spectroscopic observations of WD 1124−293 reveal photospheric and C-S absorption of Ca in multiple transitions. Here, we present high signal-to-noise ratio spectra, an updated WD atmosphere analysis, and a self-consistent model of its C-S gas. We constrain the abundances of Ca, Mg, and Fe in the photosphere of WD 1124−293, and find agreement with the abundances of these three species in the C-S gas. We find the location of the C-S gas is ∼100 white dwarf radii, the C-S and photospheric compositions are thus far consistent, the gas is not isothermal, and the amount of C-S Ca has not changed in two decades. We also demonstrate how to use Cloudy to model C-S gas viewed in absorption around polluted WDs. Modeling the abundances of gas around polluted WDs with Cloudy provides a new method to measure the composition of exoplanetesimals and will allow a direct comparison to the composition of rocky bodies in the solar system. [ABSTRACT FROM AUTHOR]

We report a neutronics study of a blanket mock-up using a discharge-type compact fusion neutron source. Deuterium–deuterium fusion neutrons were irradiated to the mock-ups composed of tritium breeder and neutron reflector/moderator. The tritium production rate (TPR) per source neutron was measured by a single-crystal diamond detector with a 6Li-enriched lithium fluoride film convertor after the calibration process. Despite the low neutron yield, energetic alpha and triton particles via 6Li(n, t)α neutron capture as well as 12C via elastic scattering were successfully detected by the SDD with high signal to noise ratios. The TPRs were experimentally evaluated with errors of 8.4%–8.5% at the 1σ level at the positions with high thermal neutron fluxes where the errors were dominantly introduced by uncertainties in the monitoring of the neutron production rate. The calculated to experimental (C/E) values of TPR were evaluated to be 0.91–1.27 (FENDL-2.1) and 0.94–1.28 (FENDL-3.1). As the neutron source can generate 14 MeV neutrons using a mixed gas of deuterium and tritium, this approach provides more opportunities for blanket neutronics experiments. [ABSTRACT FROM AUTHOR]

Objective. Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task. Approach. We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome. Main results. Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature. Significance. The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice. [ABSTRACT FROM AUTHOR]

Biological cells estimate concentration gradients of signaling molecules with a precision that is limited not only by sensing noise, but additionally by the cell's own stochastic motion. We ask for the theoretical limits of gradient estimation in the presence of both motility and sensing noise. We introduce a minimal model of a stationary chemotactic agent in the plane subject to rotational diffusion with rotational diffusion coefficient D. The agent uses Bayesian estimation to optimally infer the gradient direction relative to itself from noisy concentration measurements. Meanwhile, this direction changes on a time-scale 1/D. We show that the optimal effective measurement time, which characterizes the time interval over which past gradient measurements should be averaged to reduce sensing noise, does not scale with the rotational diffusion time 1/D, but with the square root (rD)−1/2, where r is a rate of information gain defined as a signal-to-noise ratio normalized per unit time. This result for gradient sensing parallels a recent result by Mora et al (2019 Phys. Rev. Lett.) for sensing absolute concentration in time-varying environments. [ABSTRACT FROM AUTHOR]

In the first paper of this series, we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada–France–Hawaii Telescope. This paper expands upon this notion by developing an artificial neural network to estimate the line ratios of strong emission lines present in the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000 synthetic spectra using line ratios taken from the Mexican Million Model database replicating H ii regions. Residual analysis of the network on the test set reveals the network's ability to apply tight constraints to the line ratios. We verified the network's efficacy by constructing an activation map, checking the [ N ii ] doublet fixed ratio, and applying a standard k-fold cross-correlation. Additionally, we apply the network to SITELLE observations of M33; the residuals between the algorithm's estimates and values calculated using standard fitting methods show general agreement. Moreover, the neural network reduces the computational costs by two orders of magnitude. Although standard fitting routines do consistently well depending on the signal-to-noise ratio of the spectral features, the neural network can also excels at predictions in the low signal-to-noise regime within the controlled environment of the training set as well as on observed data when the source spectral properties are well constrained by models. These results reinforce the power of machine learning in spectral analysis. [ABSTRACT FROM AUTHOR]

Imaging surveys of CO and other molecular transition lines are fundamental to measuring the large-scale distribution of molecular gas in the Milky Way. Due to finite angular resolution and sensitivity, however, observational effects are inevitable in the surveys, but few studies are available on the extent of uncertainties involved. The purpose of this work is to investigate the dependence of observations on angular resolution (beam sizes), sensitivity (noise levels), distances, and molecular tracers. To this end, we use high-quality CO images of a large-scale region (25.°8 < l < 49.°7 and ∣b∣ < 5°) mapped by the Milky Way Imaging Scroll Painting (MWISP) survey as a benchmark to simulate observations with larger beam sizes and higher noise levels, deriving corresponding beam filling and sensitivity clip factors. The sensitivity clip factor is defined to be the completeness of observed flux. Taking the entire image as a whole object, we found that 12CO has the largest beam filling and sensitivity clip factors and C18O has the lowest. For molecular cloud samples extracted from images, the beam filling factor can be described by a characteristic size, l1/4 = 0.762 (in beam size), at which the beam filling factor is approximately 1/4. The sensitivity clip factor shows a similar relationship but is more correlated with the mean voxel signal-to-noise ratio of molecular clouds. This result may serve as a practical reference on beam filling and sensitivity clip factors in further analyses of the MWISP data and other observations. [ABSTRACT FROM AUTHOR]

Black-holes are known to span at least 9 orders of magnitude in mass: from the stellar-mass objects observed by the Laser Interferometer Gravitational-Wave Observatory Scientific Collaboration and Virgo Collaboration, to supermassive black-holes like the one observed by the Event Horizon Telescope at the heart of M87. Regardless of the mass scale, all of these objects are expected to form binaries and eventually emit observable gravitational radiation, with more massive objects emitting at ever lower gravitational-wave frequencies. We present the tool, gwent , for modeling the sensitivities of current and future generations of gravitational wave detectors across the entire gravitational-wave spectrum of coalescing black-hole binaries (BHBs). We provide methods to generate sensitivity curves for pulsar timing arrays (PTAs) using a novel realistic PTA sensitivity curve generator (Hazboun, Romano and Smith 2019 Phys. Rev. D 100 104028), space-based interferometers using adaptive models that can represent a wide range of proposed detector designs (Amaro-Seoane et al 2017 arXiv:1702.00786), and ground-based interferometers using realistic noise models that can reproduce current (Abbott et al 2016 Phys. Rev. Lett. 116 061102), second, and third generation designs (Hild et al 2011 Class. Quantum Grav. 28 094013), as well as novel variations of the essential design parameters. To model the signal from BHBs at any mass scale, we use phenomenological waveforms capable of modeling the inspiral, merger, and ringdown for sources with varying mass ratios and spins (Khan et al 2016 Phys. Rev. D 93 044007; Husa et al 2016 Phys. Rev. D 93 044006). Using this adaptable framework, we produce signal-to-noise ratios (SNR) for the combination of any modeled parameter, associated with either the detector or the source. By allowing variation across each detector and source parameter, we can pinpoint the most important factors to determining the optimal performance for particular instrument designs. The adaptability of our detector and signal models can easily be extended to new detector designs and other models of gravitational wave signals. [ABSTRACT FROM AUTHOR]

Optical fibre backscatter reflectometry is an important technique for Structural Health Monitoring (SHM). In recent years, increasing the intensity of backscattered light in backscatter reflectometry has shown the advantage of improving the signal detection in shape sensing and temperature detection due to the increase of signal to noise ratio and this approach could potentially be used to improve the performance of an SHM system. Doping nanoparticles (NPs) is a method to increase the intensity of backscattered light in distributed fibre optic sensing. The increased intensity of light backscattered by the NPs needs to be investigated to design suitable optical sensing fibres with NPs for backscatter reflectometry. In this work NPs were added to refractive index matching liquid and tested with commercial NP suspensions experimentally between the tips of two optical fibres. An estimate of the intensity of backscattered light from the NPs in this structure was performed by simulation to give a better understanding of the expected levels of intensities of scattered light from NPs in this distributed fibre optic sensing configuration. We present analytical models based on Mie theory and the Monte Carlo Method. Simulated results are presented, for a broad bandwidth Gaussian spectra shape incident light with a central wavelength around 1550 nm, to match the experimental conditions in this work. The novelty is in developing this model for scattered light by NPs at optical fibre interfaces and the evaluation of the possibility of detection by the calculated scattered intensity levels. [ABSTRACT FROM AUTHOR]

Objective. High-resolution serosal recordings provide detailed information about the bioelectrical conduction patterns in the gastrointestinal (GI) tract. However, equivalent knowledge about the electrical activity through the GI tract wall remains largely unknown. This study aims to capture and quantify the bioelectrical activity across the wall of the GI tract. Approach. A needle-based microelectrode array was used to measure the bioelectrical activity across the GI wall in vivo. Quantitative and qualitative evaluations of transmural slow wave characteristics were carried out in comparison to the serosal slow wave features, through which the period, amplitude, and SNR metrics were quantified and statistically compared. Main results. Identical periods of 4.7 ± 0.3 s with amplitudes of 0.17 ± 0.04 mV versus 0.31 ± 0.1 mV and signal to noise ratios of 5.5 ± 1.3 dB versus 14.4 ± 1.1 dB were observed for transmural and serosal layers, respectively. Four different slow wave morphologies were observed across the transmural layers of the GI wall. Similar amplitudes were observed for all morphology types, and Type 1 and Type 2 were of the highest prevalence, dominating the outer and inner layers. Type 2 was exclusive to the middle layer while Type 4 was primarily observed in the middle layer as well. Significance. This study demonstrates the validity of new methodologies for measuring transmural slow wave activation in the GI wall and can now be applied to investigate the source and origin of GI dysrhythmias leading to dysmotility, and to validate novel therapeutics for GI health and disease. [ABSTRACT FROM AUTHOR]

Image saturation is a challenging problem for three-dimensional shape measurement of objects with shiny surfaces using fringe projection profilometry technology. This paper presents a new method based on projection of multiple fringe patterns, providing an effective solution to the problem. First, a set of phase shifted sinusoidal fringe patterns with different intensity levels are projected to the object of interest, and the reflections are captured by the camera. With the captured images a set of masks are created, which are able to pick up the unsaturated data from the captured images. The unsaturated data are then combined based on maximal Signal-to-noise Ratio (SNR) scenario, and the combined images are used to retrieve the phase information. Comparing to existing methods, the proposed technique is advantageous by improved measurement accuracy due to the improved SNR. The performance of the proposed method is verified by the experiments. [ABSTRACT FROM AUTHOR]

Recently, most cross-domain fault diagnosis methods focus on single source domain adaptation. However, it is usually possible to obtain multiple labeled source domains in real industrial scenarios. The question of how to use multiple source domains to extract common domain-invariant features and obtain satisfactory diagnosis results is a difficult one. This paper proposes a novel adversarial domain adaptation with a classifier alignment method (ADACL) to address the issue of multiple source domain adaptation. The main elements of ADACL consist of a universal feature extractor, multiple classifiers and a domain discriminator. The parameters of the main elements are simultaneously updated via a cross-entropy loss, a domain distribution alignment loss and a domain classifier alignment loss. Under the framework of multiple loss cooperative learning, not only is the distribution discrepancy among all domains minimized, but so is the prediction discrepancy of target domain data among all classifiers. Two experimental cases on two source domains and three source domains verify that the ADACL can remarkably enhance the cross-domain diagnostic performance under diverse operating conditions. In addition, the diagnostic performance of different methods is extensively evaluated under noisy environments with a different signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) on Earth today has biogenic and anthropogenic sources. During the Covid-19 pandemic, observations of global NO2 emissions have shown a significant decrease in urban areas. Drawing upon this example of NO2 as an industrial byproduct, we use a one-dimensional photochemical model and synthetic spectral generator to assess the detectability of NO2 as an atmospheric technosignature on exoplanets. We consider cases of an Earth-like planet around Sun-like, K-dwarf, and M-dwarf stars. We find that NO2 concentrations increase on planets around cooler stars because there are fewer short-wavelength photons that can photolyze NO2. In cloud-free results, present Earth-level NO2 on an Earth-like planet around a Sun-like star at 10 pc can be detected with signal-to-noise ratio ∼5 within ∼400 hr with a 15 m LUVOIR-like telescope when observed in the 0.2–0.7 μm range where NO2 has a strong absorption. However, clouds and aerosols can reduce the detectability and could mimic the NO2 feature. Historically, global NO2 levels were 3× higher, indicating the capability of detecting a civilization at the stage where Earth's civilization was 40 yr ago. Transit and direct imaging observations to detect infrared spectral signatures of NO2 on habitable planets around M-dwarfs would need several hundred hours of observation time, both due to weaker NO2 absorption in this region and because of masking features by dominant H2O and CO2 bands in the infrared part of the spectrum. Non-detection at these levels could be used to place upper limits on the prevalence of NO2 as a technosignature. [ABSTRACT FROM AUTHOR]

Sample selection is a necessary preparation for weak lensing measurement. It is well-known that selection itself may introduce bias to the measured shear signal. Using image simulation and the Fourier_Quad shear measurement pipeline, we quantify the selection bias in various commonly used selection criteria (signal-to-noise ratio, magnitude, etc.). We propose a new selection criterion defined in the power spectrum of the galaxy image. This new selection criterion has a low selection bias, and it is particularly convenient for shear measurement pipelines based on Fourier transformation. [ABSTRACT FROM AUTHOR]

We analyzed archival molecular line data of pre-main-sequence (PMS) stars in the Lupus and Taurus star-forming regions obtained with ALMA surveys with an integration time of a few minutes per source. We stacked the data of 13CO and C18O (J = 2–1 and 3–2) and CN (N = 3–2, J = 7/2–5/2) lines to enhance the signal-to-noise ratios and measured the stellar masses of 45 out of 67 PMS stars from the Keplerian rotation in their circumstellar disks. The measured dynamical stellar masses were compared to the stellar masses estimated from the spectroscopic measurements with seven different stellar evolutionary models. We found that the magnetic model of Feiden provides the best estimate of the stellar masses in the mass range of 0.6 M⊙ ≤ M⋆ ≤ 1.3 M⊙ with a deviation of <0.7σ from the dynamical masses, while all the other models underestimate the stellar masses in this mass range by 20%–40%. In the mass range of <0.6 M⊙, the stellar masses estimated with the magnetic model of Feiden have a larger deviation (>2σ) from the dynamical masses, and other, nonmagnetic stellar evolutionary models of Siess et al., Baraffe et al., and Feiden show better agreement with the dynamical masses with the deviations of 1.4σ–1.6σ. Our results show the mass dependence of the accuracy of these stellar evolutionary models. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing measurements based on photometry are limited by shape noise: the variance in the unknown unlensed orientations of the source galaxies. If the source is a disk galaxy with a well-ordered velocity field, however, velocity field data can support simultaneous inference of the shear, inclination, and position angle, virtually eliminating shape noise. We use the Fisher information matrix formalism to forecast the precision of this method in the idealized case of a perfectly ordered velocity field defined on an infinitesimally thin disk. For nearly face-on targets, one shear component, γ×, can be constrained to , where I0 is the signal-to-noise ratio of the central intensity pixel and npix is the number of pixels across a diameter enclosing 80% of the light. This precision degrades with inclination angle by a factor of 3 by i = 50°. The uncertainty on the other shear component, γ+, is about 1.5 (7) times larger than the γ× uncertainty for targets at i = 10° (50°). For an arbitrary galaxy position angle on the sky, these forecasts apply not to γ+ and γ× as defined on the sky but to two eigenvectors in (γ+, γ×, μ) space, where μ is the magnification. We also forecast the potential of less expensive partial observations of the velocity field, such as slit spectroscopy. We conclude by outlining some ways in which real galaxies depart from our idealized model and thus create random or systematic uncertainties not captured here. In particular, our forecast γ× precision is currently limited only by the data quality rather than scatter in galaxy properties because the relevant type of scatter has yet to be measured. [ABSTRACT FROM AUTHOR]

Positron emission tomography-magnetic resonance (PET-MR) scanners could improve radiotherapy planning through combining PET and MR functional imaging. This depends on acquiring high quality and quantitatively accurate images in the radiotherapy position. This study evaluated PET-MR image quality using a flat couch and coil bridge for pelvic radiotherapy. MR and PET image quality phantoms were imaged in three setups: phantom on the PET-MR couch with anterior coil on top (diagnostic), phantom on a flat couch with coil on top (couch), and phantom on the flat couch with coil on a coil bridge (radiotherapy). PET images were also acquired in each setup without the anterior coil. PET attenuation correction of the flat couch and coil bridge were generated using kilovoltage computed tomography (CT) images and of the anterior coil using megavoltage CT images. MR image quality was substantially affected, with MR signal to noise ratio (SNR) relative to the diagnostic setup of 89% ± 2% (mean ± standard error of the mean, couch) and 54% ± 1% (radiotherapy), likely due to the increased distance between the patient and receive coils. The reduction impacted the low-contrast detectability score: 23 ± 1 (diagnostic), 19.7 ± 0.3 (couch) and 15 ± 1 (radiotherapy). All other MR metrics agreed within one standard error. PET quantitative accuracy was also affected, with measured activity with anterior coil being different to diagnostic without anterior coil by −16.7% ± 0.2% (couch) and −17.7 ± 0.1% (radiotherapy), without attenuation correction modification. Including the couch and coil bridge attenuation correction reduced this difference to −7.5% ± 0.1%, and including the anterior coil reduced this to −2.7% ± 0.1%. This was better than the diagnostic setup with anterior coil (difference −8.3% ± 0.2%). This translated into greater PET SNR performance for the fully corrected radiotherapy setup compared to diagnostic with coil. However contrast recovery was unchanged by the modified attenuation correction, with the diagnostic setup remaining ∼2% better. Quantitative PET in the radiotherapy setup is possible if appropriate attenuation correction is used. Pelvic radiotherapy PET-MR imaging protocols will need to consider the impact on PET-MR image quality. [ABSTRACT FROM AUTHOR]

The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies have been proposed, both in the temporal and spatial domain, which substantially improve the signal to noise ratio of the reconstructed dynamic images. However, although most filtering approaches are fairly successful in reducing the spatio-temporal inter-voxel variability, they may also average out or completely eradicate the critically important temporal signature of a transient neurotransmitter activation response that may be present in a non-steady state dynamic PET study. In this work, we explore an approach towards temporal denoising of non-steady state dynamic PET images using an artificial neural network, which was trained to identify the temporal profile of a time-activity curve, while preserving any potential activation response. We evaluated the performance of a feed-forward perceptron neural network to improve the signal to noise ratio of dynamic [11C]raclopride activation studies and compared it with the widely used highly constrained back projection (HYPR) filter. Results on both simulated Geant4 Application for Tomographic Emission data of a realistic rat brain phantom and experimental animal data of a freely moving animal study showed that the proposed neural network can efficiently improve the noise characteristics of dynamic data in the temporal domain, while it can lead to a more reliable estimation of voxel-wise activation response in target region. In addition, improvements in signal-to-noise ratio achieved by denoising the dynamic data using the proposed neural network led to improved accuracy and precision of the estimated model parameters of the lp-ntPET model, compared to the HYPR filter. The performance of the proposed denoising approach strongly depends on the amount of noise in the dynamic PET data, with higher noise leading to substantially higher variability in the estimated parameters of the activation response. Overall, the feed-forward network led to a similar performance as the HYPR filter in terms of spatial denoising, but led to notable improvements in terms of temporal denoising, which in turn improved the estimation activation parameters. [ABSTRACT FROM AUTHOR]

We report the discovery of a 10 comoving megaparsec (cMpc)-scale structure traced by massive submillimeter galaxies (SMGs) at z ∼ 4.6. These galaxies are selected from an emission line search of ALMA Band 7 observations targeting 184 luminous submillimeter sources (S850μm ≥ 6.2 mJy) across 1.6 degrees2 in the COSMOS field. We identify four [C ii ] emitting SMGs and two probable [C ii ] emitting SMG candidates at z = 4.60–4.64 with velocity-integrated signal-to-noise ratio of S/N > 8. Four of the six emitters are near-infrared blank SMGs. After excluding one SMG whose emission line is falling at the edge of the spectral window, all galaxies show clear velocity gradients along the major axes that are consistent with rotating gas disks. The estimated rotation velocities of the disks are 330–550 km s−1 and the inferred host dark-matter halo masses are ∼2–8 × 1012M⊙. From their estimated halo masses and [C ii ] luminosity function, we suggest that these galaxies have a high (50%–100%) duty cycle and high (∼0.1) baryon conversion efficiency (SFR relative to baryon accretion rate), and that they contribute ≃2% to the total star formation rate density at z = 4.6. These SMGs are concentrated within just 0.3% of the full survey volume, suggesting they are strongly clustered. The extent of this structure and the individual halo masses suggest that these SMGs will likely evolve into members of a ∼1015M⊙ cluster at z = 0. This survey reveals a synchronized dusty starburst in massive halos at z > 4, which could be driven by mergers or fed by smooth gas accretion. [ABSTRACT FROM AUTHOR]

Accurate and efficient determination of stellar atmospheric parameters (T eff , logg, [Fe/H]) is essential for large-scale sky survey to conduct Galactic archeology and stellar evolution history. This paper proposes a novel data-driven model based on statistical features and Catboost algorithm (SCDD). The model extracts the statistical features of the spectra by windows, and constructs a nonlinear mapping based on the Catboost algorithm between the spectral features and the stellar parameters. After being trained using LAMOST DR6 spectral data set, the SCDD showed excellent results in the estimation of the stellar parameters. In the condition of that the g-band signal-to-noise ratio (S/Ng) is higher than 100, the root mean square errors (RMSEs) of Teff, logg, and [Fe/H] are 36 K, 0.077 dex and 0.037 dex, respectively. Compared with the StarNet and Cannon2 models, SCDD performs better in mean absolute error and RMSE, which proves its good fitting ability. In addition, this paper compares the parameters estimated by the SCDD with those of APOGEE. The results are in good agreement, which shows that the model we proposed is reliable. [ABSTRACT FROM AUTHOR]

Direct exoplanet spectroscopy aims to measure the spectrum of an exoplanet while simultaneously minimizing the light collected from its host star. Isolating the planet light from the starlight improves the signal-to-noise ratio (S/N) per spectral channel when noise due to the star dominates, which may enable new studies of the exoplanet atmosphere with unprecedented detail at high spectral resolution (>30,000). However, the optimal instrument design depends on the flux level from the planet and star compared to the noise due to other sources, such as detector noise and thermal background. Here we present the design, fabrication, and laboratory demonstration of specially-designed optics to improve the S/N in two potential regimes in direct exoplanet spectroscopy with adaptive optics instruments. The first is a pair of beam-shaping lenses that increase the planet signal by improving the coupling efficiency into a single-mode fiber at the known position of the planet. The second is a grayscale apodizer that reduces the diffracted starlight for planets at small angular separations from their host star. The former especially increases S/N when dominated by detector noise or thermal background, while the latter helps reduce stellar noise. We show good agreement between the theoretical and experimental point spread functions in each case and predict the exposure time reduction (∼33%) that each set of optics provides in simulated observations of 51 Eridani b using the Keck Planet Imager and Characterizer instrument at W. M. Keck Observatory. [ABSTRACT FROM AUTHOR]

Most of today's pump–probe experiments, which rely on two-dimensional detectors, suffer from low read-out rates that prevent the implementation of fast lock-in techniques to increase the signal-to-noise ratio. The first cameras running at true kHz-frame rates are available at large-scale facilities, but require sophisticated data management strategies. Here we present a scheme for high-speed spatial encoding of modulated pump–probe signals using slow area detectors at full repetition rate of a laser system without an increase in data rate or change of the sample environment. Towards that end we block the probe light in front of the detector alternatingly with two inverted masks at the same frequency as the signal is modulated, e.g. the chopping frequency of the pump light. Modulation frequencies up to 500 Hz are demonstrated using a commercial mechanical chopper and have been applied to a time-resolved Faraday microscopy experiment probing all-optical magnetic switching of a GdFe-alloy with femtosecond temporal resolution. We believe that our concept bridges the gap between today's slow area detectors and the upcoming generation of true kHz-frame-rate cameras. [ABSTRACT FROM AUTHOR]

As an emerging field in the discipline of optics, plasmonics and metasurfaces have been demonstrated to enable a new degree of freedom to manipulate light for arbitrary beam steering, spectral splitting as well as precise wavefront shaping. However, it has been mostly studied in parallel with the field of diffractive optics, and awaits the unveiling of how the hybridizations between plasmonic effect and diffraction effect interact and impact. Here, we have theoretically proposed a new type of polarization-insensitive meta-grating structure across the broadband visible regime. The structure design combines the width gradient (critical resonant length) from a trapezoid-nanoantenna with the height gradient from a blazed grating profile. The hybridized meta-grating creates both plasmonic effect and grating effect, which enables all the optical incident photons to be directed to the same orientation regardless of the light polarization. As we know, both metasurfaces and diffractive optical elements (such as gratings) are, more often than not, quite sensitive to the incident light polarization. Moreover, if placing our meta-grating on a flexible/stretchable substrate (such as polydimethylsiloxane), the outgoing angle can be effectively adjusted by tuning the period or density of meta-grating arrays. Such meta-grating architectures can be potentially manufactured by existing photolithography and nanoimprint techniques, and can easily find a wide range of practical polarization-insensitive applications, including broadband deflector and emitter, tunable display and imaging device, high signal-to-noise ratio spectrometer, polarization-insensitive plasmonic coupler, etc. [ABSTRACT FROM AUTHOR]

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training data without considering dose differences. However, the radiation dose difference has a great impact on the ultimate results, and lower doses increase the difficulty of restoration. Moreover, there is increasing demand to design and estimate acceptable scanning doses for patients in clinical practice, necessitating dose-aware networks embedded with adaptive dose estimation. In this paper, we consider these dose differences of input LDCT images and propose an adaptive dose-aware network. First, considering a large dose distribution range for simulation convenience, we coarsely define five dose levels in advance as lowest, lower, mild, higher and highest radiation dose levels. Instead of directly building the end-to-end mapping function between LDCT images and high-dose CT counterparts, the dose level is primarily estimated in the first stage. In the second stage, the adaptively learned low-dose level is used to guide the image restoration process as the pattern of prior information through the channel feature transform. We conduct experiments on a simulated dataset based on original high dose parts of American Association of Physicists in Medicine challenge datasets from the Mayo Clinic. Ablation studies validate the effectiveness of the dose-level estimation, and the experimental results show that our method is superior to several other DL-based methods. Specifically, our method provides obviously better performance in terms of the peak signal-to-noise ratio and visual quality reflected in subjective scores. Due to the dual-stage process, our method may suffer limitations under more parameters and coarse dose-level definitions, and thus, further improvements in clinical practical applications with different CT equipment vendors are planned in future work. [ABSTRACT FROM AUTHOR]

We demonstrate theoretically and experimentally a method to break the Rayleigh limit of an imaging system by modulating only the aperture of the imaging optics. By fully utilizing the useful information extracted from an image sequence, a superresolution image with high-frequency components beyond the cutoff frequency of the imaging system is reconstructed. Resolution enhancement of 2 times is achieved experimentally. Simulation results show that the method can still work with the signal-to-noise ratio of ~ 10 for the acquired images. [ABSTRACT FROM AUTHOR]

Phase modulation is a crucial step when the frequency-based wavefront optimization technique is exploited to measure the optical transmission matrix (TM) of a scattering medium. We report a simple but powerful method, direct digital frequency synthesis (DDS) technology to modulate the phase front of the laser and measure the TM. By judiciously modulating the phase front of a He–Ne laser beam, we experimentally generate a high quality focus at any targeted location through a 2 mm thick 120 grit ground glass diffuser, which is commercially used in laser display and laser holographic display for improving brightness uniformity and reducing speckle. The signal to noise ratio (SNR) of the clear round focus is ∼ 50 and the size is about 44 μm. Our study will open up new avenues for enhancing light energy delivery to the optical engine in laser TV to lower the power consumption, phase compensation to reduce the speckle noise, and controlling the lasing threshold in random lasers. [ABSTRACT FROM AUTHOR]

Objective. Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts in the reconstructed image due to the inhomogeneous distribution of noise levels in low-dose sinograms. Approach. To tackle this challenge, this paper proposes SDBDNet, an end-to-end dual-domain self-supervised method for LDCT denoising. With the network designed based on the properties of inhomogeneous noise in low-dose sinograms and the principle of moderate sinogram-domain denoising, SDBDNet achieves effective denoising in dual domains without introducing blurring artifacts. Specifically, we split the sinogram into two subsets based on the positions of detector cells to generate paired training data with high similarity and independent noise. These sub-sinograms are then restored to their original size using 1D interpolation and learning-based correction. To achieve adaptive and moderate smoothing in the sinogram domain, we integrate Dropblock, a type of convolution layer with regularization, into SDBDNet, and set a weighted average between the denoised sinograms and their noisy counterparts, leading to a well-balanced dual-domain approach. Main results. Numerical experiments show that our method outperforms popular non-learning and self-supervised learning methods, demonstrating its effectiveness and superior performance. Significance. While introducing a novel high-performance dual-domain self-supervised LDCT denoising method, this paper also emphasizes and verifies the importance of appropriate sinogram-domain denoising in dual-domain methods, which might inspire future work., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. Positron emission tomography (PET) is essential for non-invasive imaging of metabolic processes in healthcare applications. However, the use of radiolabeled tracers exposes patients to ionizing radiation, raising concerns about carcinogenic potential, and warranting efforts to minimize doses without sacrificing diagnostic quality. Approach. In this work, we present a novel neural network architecture, PETformer, designed for denoising ultra-low-dose PET images without requiring structural priors such as computed tomography (CT) or magnetic resonance imaging. The architecture utilizes a U-net backbone, synergistically combining multi-headed transposed attention blocks with kernel-basis attention and channel attention mechanisms for both short- and long-range dependencies and enhanced feature extraction. PETformer is trained and validated on a dataset of 317 patients imaged on a total-body uEXPLORER PET/CT scanner. Main results. Quantitative evaluations using structural similarity index measure and liver signal-to-noise ratio showed PETformer's significant superiority over other established denoising algorithms across different dose-reduction factors. Significance. Its ability to identify and recover intrinsic anatomical details from background noise with dose reductions as low as 2% and its capacity in maintaining high target-to-background ratios while preserving the integrity of uptake values of small lesions enables PET-only fast and accurate disease diagnosis. Furthermore, PETformer exhibits computational efficiency with only 37 M trainable parameters, making it well-suited for commercial integration., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous. Furthermore, due to the decoupling of speckle denoising from the subsequent DL classification, investing intensive time in parameter tuning is inevitable to attain the optimal denoising parameters for various datasets and DL models. Pre-denoising will also add extra complexity to the classification task and make it no longer end-to-end. Approach . In this work, we propose a multi-scale high-frequency-based feature augmentation (MSHFFA) module that couples feature augmentation and speckle noise suppression with specific DL models, preserving an end-to-end fashion. In MSHFFA, the input US image is first decomposed to multi-scale low-frequency and high-frequency components (LFC and HFC) with discrete wavelet transform. Then, multi-scale augmentation maps are obtained by computing the correlation between LFC and HFC. Last, the original DL model features are augmented with multi-scale augmentation maps. Main results . On two public US datasets, all six renowned DL models exhibited enhanced F1-scores compared with their original versions (by 1.31%-8.17% on the POCUS dataset and 0.46%-3.89% on the BLU dataset) after using the MSHFFA module, with only approximately 1% increase in model parameter count. Significance . The proposed MSHFFA has broad applicability and commendable efficiency and thus can be used to enhance the performance of DL-aided US diagnosis. The codes are available athttps://github.com/ResonWang/MSHFFA., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM). Approach. First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods. Significance. Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . Most methods for partial volume correction (PVC) of positron emission tomography (PET) data employ anatomical segmentation of images into regions of interest. This approach is not optimal for exploratory functional imaging beyond regional hypotheses. Here, we describe a novel method for unbiased voxel-wise PVC. Approach. B-spline basis functions were combined with geometric transfer matrices to enable a method (bsGTM) that provides PVC or alternatively provides smoothing with minimal regional crosstalk. The efficacy of the proposed method was evaluated using Monte Carlo simulations, human PET data, and murine functional PET data. Main results. In simulations, bsGTM provided recovery of partial volume signal loss comparable to iterative deconvolution, while demonstrating superior resilience to noise. In a real murine PET dataset, bsGTM yielded much higher sensitivity for detecting amphetamine-induced reduction of [ 11 C]raclopride binding potential. In human PET data, bsGTM smoothing enabled increased signal-to-noise ratios with less degradation of binding potentials relative to Gaussian convolution or non-local means. Significance. bsGTM offers improved performance for PVC relative to iterative deconvolution, the current method of choice for voxel-wise PVC, especially in the common PET regime of low signal-to-noise ratio. The new method provides an anatomically unbiased way to compensate partial volume errors in cases where anatomical segmentation is unavailable or of questionable relevance or accuracy., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM). Approach . DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020). Main results . For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively. Significance . This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications., (Creative Commons Attribution license.)

Objective. Time-of-flight (TOF) capability and high sensitivity are essential for brain-dedicated positron emission tomography (PET) imaging, as they improve the contrast and the signal-to-noise ratio (SNR) enabling a precise localization of functional mechanisms in the different brain regions. Approach. We present a new brain PET system with transverse and axial field-of-view (FOV) of 320 mm and 255 mm, respectively. The system head is an array of 6 × 6 detection elements, each consisting of a 3.9 × 3.9 × 20 mm 3 lutetium-yttrium oxyorthosilicate crystal coupled with a 3.93 × 3.93 mm 2 SiPM. The SiPMs analog signals are individually digitized using the multi-voltage threshold (MVT) technology, employing a 1:1:1 coupling configuration. Main results. The brain PET system exhibits a TOF resolution of 249 ps at 5.3 kBq ml -1 , an average sensitivity of 22.1 cps kBq -1 , and a noise equivalent count rate (NECR) peak of 150.9 kcps at 8.36 kBq ml -1 . Furthermore, the mini-Derenzo phantom study demonstrated the system's ability to distinguish rods with a diameter of 2.0 mm. Moreover, incorporating the TOF reconstruction algorithm in an image quality phantom study optimizes the background variability, resulting in reductions ranging from 44% (37 mm) to 75% (10 mm) with comparable contrast. In the human brain imaging study, the SNR improved by a factor of 1.7 with the inclusion of TOF, increasing from 27.07 to 46.05. Time-dynamic human brain imaging was performed, showing the distinctive traits of cortex and thalamus uptake, as well as of the arterial and venous flow with 2 s per time frame. Significance. The system exhibited a good TOF capability, which is coupled with the high sensitivity and count rate performance based on the MVT digital sampling technique. The developed TOF-enabled brain PET system opens the possibility of precise kinetic brain PET imaging, towards new quantitative predictive brain diagnostics., (© 2024 Institute of Physics and Engineering in Medicine.)

Using photometry collected with the Zwicky Transient Facility, we are conducting an ongoing survey for binary systems with short orbital periods (with the goal of identifying new gravitational-wave sources detectable by the upcoming Laser Interferometer Space Antenna (LISA). We present a sample of 15 binary systems discovered thus far, with orbital periods ranging from 6.91 to 56.35 minutes. Of the 15 systems, seven are eclipsing systems that do not show signs of significant mass transfer. Additionally, we have discovered two AM Canum Venaticorum systems and six systems exhibiting primarily ellipsoidal variations in their lightcurves. We present follow-up spectroscopy and high-speed photometry confirming the nature of these systems, estimates of their LISA signal-to-noise ratios, and a discussion of their physical characteristics. [ABSTRACT FROM AUTHOR]

Recent developments of x-ray free electron lasers and pulsed electron sources have enabled ultrafast scattering to become an increasingly powerful tool for exploring molecular dynamics. This article describes our recent experimental and methodological advances in ultrafast gas-phase x-ray scattering experiments at the LCLS. A re-designed short-pathlength windowless diffractometer is coupled with careful optimization of sample density and independent normalization of x-ray intensity fluctuations to provide gas-phase scattering patterns with exceptionally high signal-to-noise ratios. These advances, coupled with careful geometry optimization and data treatment, provide both ground- and excited-state signals in excellent agreement with high level ab initio total scattering patterns. [ABSTRACT FROM AUTHOR]

This work studies the adverse effects, as regards noise, of immersing in water an integrating line piezoelectric detector devoted to optoacoustic imaging. We found that the sensor, in conjunction with the acoustic coupling medium (water), behaves as a resonant dielectric antenna. This phenomenon limits the performance of the system because it efficiently captures unwanted electromagnetic signals. The requirement of good acoustic coupling between the water and the sensor precluded the use of a standard metallic shielding enclosure. Therefore, we resorted to a silver-paint-based electrical shield deposited on the detector. This easy-to-implement and low-cost solution significantly increases the signal to noise ratio and does not degrade the acoustic performance. The noise reduction allows the use of a better transimpedance amplifier with higher gain and bandwidth; thus achieving a very sensitive, low-noise detection system. [ABSTRACT FROM AUTHOR]

We introduce an example of thermodynamic uncertainty relation (TUR) for systems modeled by a generalised Langevin dynamics with memory, determining the motion of a micro-bead driven in a complex fluid. Contrary to TURs typically discussed in the previous years, our observables and the entropy production rate are one-time variables. The bound to the signal-to-noise ratio (SNR) of such state-dependent observables can only in some cases be mapped to the entropy production rate. For example, this is true for steady states and for a subclass of Markovian systems. In fact, the presence of memory in the system complicates the thermodynamic interpretation of the uncertainty relation. [ABSTRACT FROM AUTHOR]

We present a new constraint on the Lyman continuum (LyC) escape fraction at. We obtain deep, high sensitivity far-UV imaging with the Advanced Camera for Surveys Solar Blind Channel on the Hubble Space Telescope, targeting 11 star-forming galaxies at 1.2 < z < 1.4. The galaxies are selected from the 3D-HST survey to have high Hα equivalent width (EW) with an EW > 190 Å, low stellar mass (M* < 1010M⊙), and U-band magnitude of U < 24.2. These criteria identify young, low metallicity star-bursting populations similar to the early star-forming galaxies believed to have reionized the universe. We do not detect any LyC signal (with a signal-to-noise ratio > 3) in the individual galaxies or in the stack in the far-UV images. We place 3σ limits on the relative escape fraction of individual galaxies to be and a stacked 3σ limit of. Measuring various galaxy properties, including stellar mass, dust attenuation, and star formation rate, we show that our measured values fall within the broad range of values covered by the confirmed LyC emitters from the literature. In particular, we compare the distribution of Hα and [O III ] EWs of confirmed LyC emitters and non-detections, including the galaxies in this study. Finally, we discuss if a dichotomy seen in the distribution of Hα EWs can perhaps distinguish the LyC emitters from the non-detections. [ABSTRACT FROM AUTHOR]

We present a large high-resolution study of the distribution and evolution of C iv absorbers, including the weakest population with equivalent widths. By searching 369 high-resolution, high signal-to-noise ratio spectra of quasars at from Keck/HIRES and VLT/UVES, we find 1268 C iv absorbers with Å (our ∼50% completeness limit) at redshifts. A Schechter function describes the observed equivalent width distribution with a transition from power-law to exponential decline at. The power-law slope α rises by ∼7%, and the transition equivalent width W⋆ falls by ∼20% from to 3.6. We find that the comoving redshift path density, , of Å absorbers rises by ∼1.8 times from z ≃ 4.0 to 1.3, while the Å rises by a factor of ∼8.5. We quantify the observed evolution by a model in which decreases linearly with increasing redshift. The model suggests that populations with larger thresholds evolve faster with redshift and appear later in the universe. The cosmological Technicolor Dawn simulations at z = 3–5 overproduce the observed abundance of absorbers with while yielding better agreement at higher. Our empirical linear model successfully describes C iv evolution in the simulations and the observed evolution of Å C iv for the past. Combining our measurements with the literature gives us a picture of C iv absorbing structures becoming more numerous and/or larger in physical size over the last of cosmic time (z ∼ 6–0). [ABSTRACT FROM AUTHOR]

This study presents a comprehensive chemodynamical analysis of LAMOST J1109+0754, a bright (V = 12.8), extremely metal-poor ([Fe/H] = −3.17) star, with a strong r-process enhancement ([Eu/Fe] = +0.94 ± 0.12). Our results are based on the 7D measurements supplied by Gaia and the chemical composition derived from a high-resolution (R ∼ 110,000), high signal-to-noise ratio (optical spectrum obtained by the 2.4 m Automated Planet Finder Telescope at Lick Observatory. We obtain chemical abundances of 31 elements (from lithium to thorium). The abundance ratios ([X/Fe]) of the light elements (Z ≤ 30) suggest a massive Population III progenitor in the 13.4–29.5 M⊙ mass range. The heavy-element (30 < Z ≤ 90) abundance pattern of J1109+075 agrees extremely well with the scaled-solar r-process signature. We have developed a novel approach to trace the kinematic history and orbital evolution of J1109+0754 with a cOsmologically deRIved timE-varyiNg Galactic poTential (the ORIENT) constructed from snapshots of a simulated Milky Way analog taken from the Illustris-TNG simulation. The orbital evolution within this Milky Way–like galaxy, along with the chemical abundance pattern, implies that J1109+0754 likely originated in a low-mass dwarf galaxy located ∼60 kpc from the center of the Galaxy, which was accreted ∼6–7 Gyr ago, and that the star now belongs to the outer-halo population. [ABSTRACT FROM AUTHOR]

In this work we model the noise properties of a computed radiography (CR) mammography system by adding an extra degree of freedom to a well-established noise model, and derive a variance-stabilizing transform (VST) to convert the signal-dependent noise into approximately signal-independent. The proposed model relies on a quadratic variance function, which considers fixed-pattern (structural), quantum and electronic noise. It also accounts for the spatial-dependency of the noise by assuming a space-variant quantum coefficient. The proposed noise model was compared against two alternative models commonly found in the literature. The first alternative model ignores the spatial-variability of the quantum noise, and the second model assumes negligible structural noise. We also derive a VST to convert noisy observations contaminated by the proposed noise model into observations with approximately Gaussian noise and constant variance equals to one. Finally, we estimated a look-up table that can be used as an inverse transform in denoising applications. A phantom study was conducted to validate the noise model, VST and inverse VST. The results show that the space-variant signal-dependent quadratic noise model is appropriate to describe noise in this CR mammography system (errors< 2.0% in terms of signal-to-noise ratio). The two alternative noise models were outperformed by the proposed model (errors as high as 14.7% and 9.4%). The designed VST was able to stabilize the noise so that it has variance approximately equal to one (errors< 4.1%), while the two alternative models achieved errors as high as 26.9% and 18.0%, respectively. Finally, the proposed inverse transform was capable of returning the signal to the original signal range with virtually no bias. [ABSTRACT FROM AUTHOR]

X-ray CT polymer gel dosimetry (PGD) remains a promising tool for three dimensional verification of high-dose treatment deliveries such as non-coplanar stereotactic irradiations. Recent demonstrations have shown a proof-of-principle application of linac-integrated cone beam CT-imaged (LI-CBCT) PGDs for 3D dose verification. LI-CBCT offers advantages over previous CT based PGD, including close to real-time imaging of the irradiated dosimeter, as well as the ability to maintain the dosimeter in the same physical location for irradiation and imaging, thereby eliminating spatial errors due to dosimeter re-positioning for read-out that may occur for other systems. However the dosimetric characteristics of a LI-CBCT PGD system remain to be established. The work herein determines the dosimetric properties and critical parameters needed to perform cone beam PGD. In particular, we show that imaging the dosimeter 20–30 min post irradiation offers excellent recovery of maximum polymerization yield (90%), averaging with as few as 10 image averages can provide ∼90% gamma pass rates (3%, 3 mm) as compared to treatment planning, and that eliminating outlier averaging points can improve the precision and signal to noise ratio of resultant images. In summary, with appropriate methodology LI-CBCT PGD can provide dosimetric data capable of verification of complex high dose radiation deliveries in three dimensions and may find use in commissioning and validation of novel complex treatments. [ABSTRACT FROM AUTHOR]

This work compared the impact of x-ray tube performance and automatic dose rate control (ADRC) parameter selection on system imaging efficiency of two Siemens angiography systems: a Siemens Megalix x-ray tube installed on an Artis Zee system (denoted 'MEGALIX') and a newer generation Gigalix x-ray tube installed on an Artis Q (denoted 'GIGALIX'). A method was used that accounted for two potential sources of bias in this comparison: differences in radiation output between the x-ray tubes and differences between the x-ray detectors on the two systems. First, ADRC x-ray factors (tube voltage, tube current, pulse length, focus size, spectral prefilter) and radiation output were recorded as a function of poly(methyl) methacrylate (PMMA) thickness on the MEGALIX unit. These factors were then applied manually on the GIGALIX system and incident air kerma rate (IAKR) and signal difference to noise ratio (SDNR) were measured. Second, the ADRC on the GIGALIX system was used to give the x-ray factors and both IAKR and SDNR relevant to the GIGALIX based system directly. This method enabled the SDNR to be measured from images acquired on the same x-ray detector. SDNR and IAKR were measured on both systems using a PMMA phantom covering thicknesses from 6 cm to 40 cm. A small 0.3 mm iron insert was used to measure SDNR, which was then multiplied by modulation transfer function based weighting factors for focal spot blurring and motion blurring. These factors were evaluated for an object motion of 25 mm s−1 and at a spatial frequency of 1.4 mm−1 in the object plane, relevant to interventional cardiology, giving a spatial frequency dependent SDNR(u). In the second phase of the study, a technical figure of merit (FOM) was used to express imaging performance of both systems, calculated as SDNR2(u)/IAKR. Averaged over all phantom thicknesses, the FOM of the GIGALIX-based system was 42% and 73% higher compared to that of the MEGALIX based system, for fluoroscopy and acquisition mode respectively. The results indicate that increased x-ray tube power and smaller foci can improve overall system efficiency and reduce doses. [ABSTRACT FROM AUTHOR]

We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T1-mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remove the streaking artefacts from under-sampled T1 mapping images. A total of 2090 T1-weighted images for 33 patients (55 ± 15 years, 19 males) and five healthy subjects (30 ± 14 years, 2 males) were used for training and testing the network. The images were acquired using radial slice interleaved T1 mapping sequence (STONE) and retrospectively under-sampled to achieve acceleration rate of 4 (corresponding to 48 spokes). The dataset was split into training and testing subsets with 23 subjects (60%) and 15 subjects (40%), respectively. For generating voxel-wise T1 maps, a two-parameter fitting model was used. Network performance was evaluated using normalized mean square error (NMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics. The proposed network allowed fast (<0.3 s/image) removal of the artefact from all T1-weighted testing images and the corresponding T1 maps with PSNR = 64.3 ± 1.02, NMSE = 0.2 ± 0.09 and SSIM = 0.9 ± 0.3 × 10−4. There was no statistically significant difference between the measured T1 maps for both per-subject (reference: 1085 ± 37 ms, CNN: 1088 ± 37 ms, p = 0.4) and per-segment (reference: 1084 ± 48 ms, CNN: 1083 ± 58 ms, p = 0.9) analyses. In summary, deep CNN allows fast and reliable removal of streaking artefact from under-sampled radial T1 mapping images. Our results show that the highly non-linear operations of deep CNN processing of T1 mapping images do not impact accurate reconstruction of myocardial T1 maps. [ABSTRACT FROM AUTHOR]

We present a method by which multi-harmonic signals acquired during a normal tapping mode (amplitude modulated) AFM scan of a sample in air or vacuum with standard microcantilevers can be used to map quantitatively the local mechanical properties of the sample such as elastic modulus, adhesion, and indentation. The approach is based on the observation that during the tapping mode operation in air or vacuum, the 0th and 2nd harmonic signals of microcantilever vibration are encountered under most operating conditions and can be mapped with sufficient signal to noise ratio. By measuring the amplitude and phase of the driven harmonic as well as the 0th and 2nd harmonic observables, we find analytical/semi-analytical formulas that relate these multi-harmonic observables to local mechanical properties for several classical tip-sample interaction models. Least squares estimation of the local mechanical properties is performed pixel by pixel. The method is validated through computations as well as experimental data acquired on a polymer blend made of Polystyrene and Polyolefin elastomer. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to explore the use of a logarithmic amplifier to improve the spatial resolution (RES) of a low-cost electrical impedance tomography (EIT) system. In an EIT system, the measured signal has a large dynamic range from µV to mV, which requires high-RES (analog to digital conversion) cards. The logarithmic amplifier reduces the dynamic range by expanding lower values and compressing higher values, thereby improving the sensitivity and at the same time preventing the signal from saturation. In addition, a low-RES analog to digital conversion (ADC) cards can be used, making the system cost effective. This work evaluates the performance of a logarithmic amplifier and a linear amplifier used for signal conditioning in a low-cost EIT system. Approach: Two EIT systems based on a linear amplifier and logarithmic amplifier were designed. Phantom experiments were carried out with very small amounts of current injection. The signal-to-noise ratio (SNR), image quality, minimum detectable size and minimum detectable conductivity change were obtained. Main results: The logarithmic amplifier-based EIT system increased the average SNR by 4 dB. It also showed improvement in the RES and contrast-to-noise ratio of the images. The minimum size detectable by the logarithmic amplifier-based system was of radius 0.25 cm in a tank of radius 11 cm and the minimum change in conductivity detectable was 11%. Significance: Logarithmic amplifier-based signal conditioning is a promising technique for improving the spatial RES of a low-cost EIT system that has a low-RES ADC. [ABSTRACT FROM AUTHOR]

We describe the first spectroscopic tomographic (spectrotomographic) weak-lensing measurement for a galaxy cluster based only on background galaxies with spectroscopically determined redshifts. We use the massive cluster A2029 to demonstrate the power of combining spectroscopy and lensing to obtain accurate masses and to overcome biases from contamination and photometric redshift errors. We detect the shear signal from the cluster at > 3.9σ significance. The shear signal scales with source redshift in a way that is consistent with the angular diameter distance ratio variation in a ΛCDM universe. Furthermore, the amplitude of the measured signal is consistent with the X-ray mass. Upcoming spectroscopic instruments such as the Prime Focus Spectrograph on Subaru will permit spectrotomographic weak-lensing measurements with a signal-to-noise ratio comparable with current photometric-redshift-based weak-lensing measurements for hundreds of galaxy clusters. Thus, spectrotomography may enable sensitive cosmological constraints that complement and are independent of other measurement techniques. [ABSTRACT FROM AUTHOR]