Brightness Variations Research Articles

A molecular mechanism for bright color variation in parrots.

Parrots produce stunning plumage colors through unique pigments called psittacofulvins. However, the mechanism underlying their ability to generate a spectrum of vibrant yellows, reds, and greens remains enigmatic. We uncover a unifying chemical basis for a wide range of parrot plumage colors, which result from the selective deposition of red aldehyde- and yellow carboxyl-containing psittacofulvin molecules in developing feathers. Through genetic mapping, biochemical assays, and single-cell genomics, we identified a critical player in this process, the aldehyde dehydrogenase ALDH3A2, which oxidizes aldehyde psittacofulvins into carboxyl forms in late-differentiating keratinocytes during feather development. The simplicity of the underlying molecular mechanism, in which a single enzyme influences the balance of red and yellow pigments, offers an explanation for the exceptional evolutionary lability of parrot coloration.

Automated production of batched unclonable micro-patterns anti-counterfeiting labels with strong robustness and rapid recognition speed

Anti-counterfeiting technologies are indeed crucial for information security and protecting product authenticity. Traditional anti-counterfeiting methods have their limitations due to their clonable nature. Exploring new technologies, particularly those based on pixel-level textures, is a promising avenue to address the clonable issue due to high encoding capacity. However, research in this field is still in its early stages. This work introduces a new fluorescent anti-counterfeiting label technology with four key characteristics: efficient laser etching, high-throughput fabrication and segmentation, robustness aided by data augmentation, and an exceptionally high recognition speed. Specifically, the etching achieves a speed of 1,200 labels/3s, the high-throughput procedures yield a rate of 2,400 labels/4 mins, and the total count is about 5.2 × 104 labels. The number of labels is further augmented to about 5.2 × 106 by implementing arbitrary rotation and brightness variation to enhance the robustness in the recognition procedure. We divide these labels into 44 categories based on differences in patterns. Utilizing machine learning methods, we have achieved a total recognition (including extraction and search process) time per label averaging 421.96 ms without classification, and 40.13 ms with classification. Specifically, the search process with classification is nearly fiftieth times shorter than the non-classification method, reaching 8.52 ms in average. The overall recognition time is much faster than previous works, and achieves an accuracy of over 98.7%. This work significantly increases the practical significance of pixel-level anti-counterfeiting labels.

Gaussian in the Dark: Real‐Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting

Abstract3D Gaussian Splatting has recently emerged as a powerful representation that can synthesize remarkable novel views using consistent multi‐view images as input. However, we notice that images captured in dark environments where the scenes are not fully illuminated can exhibit considerable brightness variations and multi‐view inconsistency, which poses great challenges to 3D Gaussian Splatting and severely degrades its performance. To tackle this problem, we propose Gaussian‐DK. Observing that inconsistencies are mainly caused by camera imaging, we represent a consistent radiance field of the physical world using a set of anisotropic 3D Gaussians, and design a camera response module to compensate for multi‐view inconsistencies. We also introduce a step‐based gradient scaling strategy to constrain Gaussians near the camera, which turn out to be floaters, from splitting and cloning. Experiments on our proposed benchmark dataset demonstrate that Gaussian‐DK produces high‐quality renderings without ghosting and floater artifacts and significantly outperforms existing methods. Furthermore, we can also synthesize light‐up images by controlling exposure levels that clearly show details in shadow areas.

A Search for Collisions and Planet–Disk Interactions in the Beta Pictoris Disk with 26 Years of High-precision HST/STIS Imaging

β Pictoris's well-studied debris disk and two known giant planets, in combination with the stability of the Hubble Space Telescope’s Space Telescope Imaging Spectrograph (HST/STIS) (and now also JWST), offers a unique opportunity to test planet–disk interaction models and to observe recent planetesimal collisions. We present HST/STIS coronagraphic imaging from two new epochs of data taken between 2021 and 2023, complementing earlier data taken in 1997 and 2012. This data set enables a temporal comparison with the longest baseline and highest precision of any debris disk to date, with sensitivity to variations in temporal surface brightness of sub-percent levels in the midplane of the disk. While no localized changes in surface brightness are detected, which would be indicative of a recent planetesimal collision, there is a tentative brightening of the southeast side of the disk over the past decade. We link the constraints on surface brightness variations to dynamical models of the planetary system’s evolution and to the collisional history of planetesimals. Using a coupled collisional model and injection/recovery framework, we estimate sensitivity to expanding collisional debris down to a Ceres mass per progenitor in the most sensitive regions of the disk midplane. These results demonstrate the capabilities of long-baseline, temporal studies with HST (and also soon with JWST) for constraining the physical processes occurring within debris disks.

Deep coupled registration and segmentation of multimodal whole-brain images.

Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: Registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcomingthe bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. The code is available at https://github.com/tingtingup/DCRS. Supplementary data are available at Bioinformaticsonline.

Discovery of persistent quasi-periodic oscillations in accreting white dwarfs: a new link to X-ray binaries

ABSTRACT Almost all accreting black hole and neutron star (NS) X-ray binary systems (XRBs) exhibit prominent brightness variations on a few characteristic time-scales and their harmonics. These quasi-periodic oscillations (QPOs) are thought to be associated with the precession of a warped accretion disc, but the physical mechanism that generates the precessing warp remains uncertain. Relativistic frame dragging (Lense–Thirring precession) is one promising candidate, but a misaligned magnetic field is an alternative, especially for NS XRBs. Here, we report the discovery of five accreting white dwarf systems (AWDs) that display strong optical QPOs with characteristic frequencies and harmonic structures that suggest they are the counterpart of the QPOs seen in XRBs. Since AWDs are firmly in the classical (non-relativistic) regime, Lense–Thirring precession cannot account for these QPOs. By contrast, a weak magnetic field associated with the white dwarf can drive disc warping and precession in these systems, similar to what has been proposed for NS XRBs. Our observations confirm that magnetically driven warping is a viable mechanism for generating QPOs in disc-accreting astrophysical systems, certainly in AWDs and possibly also in NS XRBs. Additionally, they establish a new way to estimate magnetic field strengths, even in relatively weak-field systems where other methods are not available.

BGFlow: Brightness-guided normalizing flow for low-light image enhancement

Low-light image enhancement poses significant challenges due to its ill-posed nature. Recently, deep learning-based methods have attempted to establish a unified mapping relationship between normal-light images and their low-light versions but frequently struggle to capture the intricate variations in brightness conditions. As a result, these methods often suffer from overexposure, underexposure, amplified noise, and distorted colors. To tackle these issues, we propose a brightness-guided normalizing flow framework, dubbed BGFlow, for low-light image enhancement. Specifically, we recognize that low-frequency sub-bands in the wavelet domain carry significant brightness information. To effectively capture the intricate variations in brightness within an image, we design a transformer-based multi-scale wavelet-domain encoder to extract brightness information from the multi-scale features of the low-frequency sub-bands. The extracted brightness feature maps, at different scales, are then injected into the brightness-guided affine coupling layer to guide the training of the conditional normalizing flow module. Extensive experimental evaluations demonstrate the superiority of BGFlow over existing deep learning-based approaches in both qualitative and quantitative assessments. Moreover, we also showcase the exceptional performance of BGFlow on the underwater image enhancement task.

Finding The Relationship between Pulsation Motion and Hα Emission Lines on γ Cas using Photometric and Spectroscopic Data

Abstract Pulsating stars show variability in brightness due to changes in their volume. There are two types of pulsation/oscillation: radial and non-radial. Radial oscillation keeps the symmetrical shape of the star, while non-radial oscillation does not. We study a pulsating star: γ Cas, classified as a B0.5 IVe variable star because of its pulsations and also an emission star due to the emission lines in its spectra. When a star pulsates, its atmosphere expands and contracts, potentially leading to the formation of shock waves. Observational features such as discontinuities in the radial velocity curve and emission lines support this phenomenon. In this study, we analyse the relationship between pulsation motion and the H α emission lines of γ Cas using photometric and spectroscopic data. Photometric data is obtained from the BRIght Target Explorer (BRITE) catalog, while spectroscopic data comes from the Be Star Spectra (BeSS) catalog. Both datasets cover the same observational dates: August 28 to October 26, 2015. Confirming the pulsation period of γ Cas at 1.21 days, we also discover an added pulsation period of 10 days from photometric data. To confirm this, we examine spectroscopic data from three specific dates: August 28, September 7, and 27, 2015. Notably, the relative flux of the H α emission lines continue to increase, leading us to conclude that these lines are indeed caused by the pulsation of γ Cas.

Superluminal Proper Motion in the X-Ray Jet of Centaurus A

The structure of the jet in Cen A is likely better revealed in X-rays than in the radio band, which is usually used to investigate jet proper motions. In this paper, we analyze Chandra Advanced CCD Imaging Spectrometer observations of Cen A from 2000 to 2022 and develop an algorithm for systematically fitting the proper motions of its X-ray jet knots. Most of the knots had an apparent proper motion below the detection limit. However, one knot at a transverse distance of 520 pc had an apparent superluminal proper motion of 2.7 ± 0.4c. This constrains the inclination of the jet to be i < 41° ± 6° and the velocity of this knot to be β > 0.94 ± 0.02. This agrees well with the inclination measured in the inner jet by the Event Horizon Telescope but contradicts previous estimates based on jet and counterjet brightness. It also disagrees with the proper motion of the corresponding radio knot, of 0.8 ± 0.1c, which further indicates that the X-ray and radio bands trace distinct structures in the jet. There are four prominent X-ray jet knots closer to the nucleus, but only one of these is inconsistent with being stationary. A few jet knots also have a significant proper-motion component in the nonradial direction. This component is typically larger closer to the center of the jet. We also detect brightness and morphology variations at a transverse distance of 100 pc from the nucleus.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

A Detailed Study of Jupiter’s Great Red Spot over a 90-day Oscillation Cycle

Jupiter’s Great Red Spot (GRS) is known to exhibit oscillations in its westward drift with a 90-day period. The GRS was observed with the Hubble Space Telescope on eight dates over a single oscillation cycle in 2023 December to 2024 March to search for correlations in its physical characteristics over that time. Measured longitudinal positions are consistent with a 90-day oscillation in drift, but no corresponding oscillation is found in latitude. We find that the GRS size and shape also oscillate with a 90-day period, having a larger width and aspect ratio when it is at its slowest absolute drift (minimum date-to-date longitude change). The GRS’s UV and methane gas absorption-band brightness variations over this cycle were small, but the core exhibited a small increase in UV brightness in phase with the width oscillation; it is brightest when the GRS is largest. The high-velocity red collar also exhibited color changes, but out of phase with the other oscillations. Maximum interior velocities over the cycle were about 20 m s−1 larger than minimum velocities, slightly larger than the mean uncertainty of 13 m s−1, but velocity variability did not follow a simple sinusoidal pattern as did other parameters such as longitude width or drift. Relative vorticity values were compared with aspect ratios and show that the GRS does not currently follow the Kida relation.

Connecting the Low to High Corona: Propagating Disturbances as Tracers of the Near-Sun Solar Wind

We revisit a quiet 14 day period of solar minimum during 2008 January and track substreamer propagating disturbances (PDs) from low heights in STEREO/EUVI to the extended corona through STEREO/COR1 and into STEREO/COR2 along nonradial paths that trace the structure of the underlying streamers. Using our recently developed method for generating nonradial height–time profiles of outward PDs (OPDs) and inward PDs (IPDs), we obtained their velocities along the radial and position angle directions. Our analysis of 417 unique OPDs revealed two classes: slow and fast OPDs. Slow OPDs form preferentially at ≈1.6 R ⊙ closer to the streamer boundaries, with asymmetric occurrence rates, and show speeds of 16.4−8.4+26.6kms−1 at 1.5 R ⊙ and accelerate up to 200.1−57.9+71.1kms−1 at 7.5 R ⊙. Fast OPDs form preferentially at ≈1.6 R ⊙ and at ≈3.0 R ⊙ both at the streamer boundaries and slightly more often within them. They show speeds of 87.8−24.8+59.1kms−1 at 1.5 R ⊙ up to 197.8−46.7+61.8kms−1 at 7.5 R ⊙. IPDs are observed forming at ≈1.8 R ⊙ with speeds of tens of kilometers per second, mostly concentrated in the aftermath of a coronal mass ejection eruption. We present an example in which we show that periodic brightness variations related to OPDs remained in the range of 98 to 128 minutes, down to ≈2.0 R ⊙, well within the field of view of COR1. The velocity profiles of slow OPDs for a heliocentric height below 3.0 R ⊙ show good agreement with speeds more closely related to the bulk solar wind obtained via interplanetary scintillation.

An international film dosimetry intercomparison to establish a multi-center audit framework.

In 2021, a Technical Meeting was hosted by the International Atomic Energy Agency (IAEA) where it was recommended that a standardized method for assessing the accuracy of film dose calculations should be established. To design an audit that evaluates the accuracy of film dosimetry processes. To propose a framework for identifying out-of-tolerance results and to perform an international pilot study to test the audit design. Six members of an international Dosimetry Audit Network (DAN) developed an audit for radiochromic film dosimetry. A single host center provided the materials to each participating DAN member to conduct the audits. Materials included: (1) a set of two irradiated audit films (10 Sq: 10cm × 10cm, 15 Sq: 15cm × 15cm), (2) a reference calibration film set, and (3) a blank sheet of film. The participants were blinded to the dose and tasked with producing dose maps using their standard film dosimetry process. Average Region-Of-Interest (ROI: 2cm × 2cm) dose was measured from the dose maps and compared to the known dose. In the audit, all participants used their local scanning and software protocols. Film calibration was performed in two distinct ways: (1) using a calibration film set which was provided by the host center and (2) using a calibration film set which was locally irradiated. Several variations of the audit were also performed to examine how scanning and software processing can affect film dosimetry results. In the first variation of the audit (VariantA), a set of film scans was processed using five different software solutions. In the second variation of the audit (VariantB), all films were scanned on the same scanner and processed using two in-house software solutions. Taking one film scan from each participant, the standard deviations (1σ) (SD) in the dose returned from the host calibration and returned from the local calibration were±7.2% and±6.5% respectively, with variations from -12.4% to 12.9% of the known dose. The larger dose variations in the data set were attributed to the corrections applied for variations in scanner brightness during processing and incorrectly assigned calibration doses. When the raw image data set was processed by an expert user of each software solution (VariantA) the SDs were±2.7% and±3.7% for in-house and vendor-based software, respectively. When the films were scanned on a single scanner and processed with the two in-house software solutions (VariantB) the results had a SD of±2.3%. An audit has been designed and tested for radiotherapy film dosimetry at an international level. A framework for diagnosing issues within a film dosimetry process has been proposed that could be used to audit centers that use film as a dosimeter. Incorporating quality assurance throughout the film process is important in obtaining accurate and consistent film dosimetry. A better understanding of vendor-based software systems is necessary for users to process accurate and consistent film dosimetry.

Distribution of cool starspots on the surface of the red dwarf V647 Her

Photometric observations of the star V647 Her (M3.5V) obtained in 2022 at the 1.25 m telescope of the Crimean Astrophysical Observatory are analyzed. The presence of a low-amplitude variability in brightness of the star with a period of 20.69 d, found from observations in 2019, is confirmed; it is shown that as the brightness decreases, the star becomes redder. The observed nature of photometric variability is due to the presence of cool spots on the surface of the star and manifestation of rotational brightness modulation with a full amplitude of no more than 0m.05. We have performed a comparison of the photometric results obtained in 2019, 2022 and 2004. The zones of starspot concentrations in different epochs were determined from the analysis of phase curves. The distribution of spots has been maintained for 40–100 days. Starspot parameters were estimated in the framework of the zonal model. The temperature of the spots is 2700–2800 K. The area they occupied in 2004 is 15% of the total surface area of the star. According to the 2019 and 2022 data, it increases to 30%. The difference between the spottedness of the hemispheres caused by their seasonal redistribution is less than 2%.

A new method for estimating nanoparticle deposition coverage from a set of weak-contrast SEM images

Imaging nanomaterials in hybrid systems is critical to understanding the structure and functionality of these systems. However, current technologies such as scanning electron microscopy (SEM) may obtain high resolution/contrast images at the cost of damaging or contaminating the sample. For example, to prevent the charging of organic substrate/matrix, a very thin layer of metal is coated on the surface, which will permanently contaminate the sample and eliminate the possibility of reusing it for following processes. Conversely, examining the sample without any modifications, in pursuit of high-fidelity digital images of its unperturbed state, can come at the cost of low-quality images that are challenging to process. Here, a solution is proposed for the case where no brightness threshold is available to reliably judge whether a region is covered with nanomaterials. The method examines local brightness variability to detect nanomaterial deposits. Very good agreement with manually obtained values of the coverage is observed, and a strong case is made for the method's automatability. Although the developed methodology is showcased in the context of SEM images of Polydimethylsiloxane (PDMS) substrates on which silicone dioxide (SiO2) nanoparticles are assembled, the underlying concepts may be extended to situations where straightforward brightness thresholding is not viable.

Wavelet optical flow velocimetry of a scramjet combustor using high-speed frame-straddling focusing schlieren images

Scramjet combustors feature ultra-high-speed turbulent reacting flows with high temperatures and intense luminescence. Measurement of velocity fields under such extreme conditions presents great challenges. The present work demonstrated a seedless velocimetry approach using focusing schlieren images (FSIs) of high spatiotemporal resolution in a scramjet engine. Two fuel mass flow rates (Case1 and Case2) were investigated with corresponding global equivalence ratios of 0.27 and 0.13, respectively. The FSIs enabled by the employment of a high-speed pulsed LED light source are characterized by an effective exposure of 100 ns, and a 500-ns frame-straddling time interval with full resolution of 1280 × 800 pixels recorded at 76 kHz. The 100-ns exposure allows for capturing of transient high-speed flow motion without blurring, and the 500-ns time interval ensures an appropriate spatiotemporal correlation between subsequent schlieren images for high-speed reacting flows. A wavelet-based optical flow velocimetry (wOFV) algorithm was developed and applied to the FSIs. In contrast to the correlation-based algorithms widely employed in PIV for distinct particles, the wOFV algorithm suits better FSIs with continuous variation in brightness. The maximal velocities in the main duct of the scramjet combustor were measured to be approximately 550 m s-1 and 1100 m s-1 for Case 1 and Case2, suggestively corresponding to subsonic and supersonic combustion modes, respectively. The measured velocity inside the cavity is generally below 200 m s-1 for both cases. Recirculation regions and their dynamic motions inside the cavity were well resolved. In summary, the development of the novel velocimetry approach holds great potential for applications in extreme flow conditions. Novelty and Significance Statement: Present work demonstrates a seedless velocimetry approach based on high-speed frame-straddling focusing schlieren imaging coupled with the novel wavelet optical flow velocimetry (wOFV) algorithm. Velocity field measurement realized in a scramjet combustor with high spatiotemporal resolution (1280 × 800 pixels at 38 kHz) for the first time, showing great abilities to accommodate wide velocity range and to resolve dynamic flow characteristics with potentials for broader future applications.

Redshift-dependent galaxy formation efficiency at z = 5 − 13 in the FirstLight Simulations

Context. Some models of the formation of first galaxies predict low masses and faint objects at extremely high redshifts, z ≃ 9 − 15. However, the first observations of this epoch indicate a higher-than-expected number of bright (sometimes massive) galaxies. Aims. Numerical simulations can help to elucidate the mild evolution of the bright end of the UV luminosity function and they can provide the link between the evolution of bright galaxies and variations of the galaxy formation efficiency across different redshifts. Methods. We use the FirstLight database of 377 zoom-in cosmological simulations of a volume- and mass-complete sample of galaxies. Mock luminosities are estimated by a dust model constrained by observations of the β–MUV relation at z = 6 − 9. Results. FirstLight contains a high number of bright galaxies, MUV ≤ −20, consistent with current data at z = 6 − 13. The evolution of the UV cosmic density is driven by the evolution of the galaxy efficiency and the relation between MUV and halo mass. The efficiency of galaxy formation increases significantly with mass and redshift. At a fixed mass, galactic halos at extremely high redshifts convert gas into stars at a higher rate than at lower redshifts. The high gas densities in these galaxies enable high efficiencies. Our simulations predict higher number densities of massive galaxies, M* ≃ 109 M⊙, than other models with constant efficiency. Conclusions. Cosmological simulations of galaxy formation with detailed models of star formation and feedback can reproduce the different regimes of galaxy formation across cosmic history.

Siam-AUnet: An end-to-end infrared and visible image fusion network based on gray histogram

Infrared and visible image fusion aim to leverage the respective advantages of both modalities to extract richer information from a scene. In environments with ample lighting, it is imperative to preserve the characteristics of visible images and salvage details lost due to overexposure. Conversely, in dark environments, a preference is typically inclined towards the utilization of infrared imagery. However, current methodologies are unable to adaptively modulate their tendency towards either modality, thereby resulting in suboptimal fusion quality. A gray histogram visually represents the dynamic range of image distribution, revealing the contrast and degree of brightness variations. Drawing from this insight, in this paper, we propose a novel image fusion method called Siam-AUnet for the first time. This methodology transforms the distribution of luminance histograms extracted from visible images into weights during the fusion of the two modalities. We adopt a Siamese network architecture and a multi-scale attention mechanism to focus more effectively on key features while reducing the number of parameters. Extensive experiments on different datasets and comparisons with other methods have demonstrated the superiority of this fusion approach. By utilizing the characteristics of the histogram, Siam-AUnet can adaptively tend towards the modality with clearer texture details. Qualitative and quantitative experimental results on the MSRS test dataset demonstrate that Siam-AUnet achieves optimal performance in terms of EN, SSIM, and MS_SSIM, while exhibiting suboptimal performance in metrics such as SCD, VIF, and Qqbf. In well-lit conditions, it can preserve the detailed features of visible images and the salient features of infrared images; while under adverse lighting conditions, it emphasizes the infrared modality features more strongly. Our code will be publicly available at https://github.com/xkangKK/Siam-AUnet.

Seasonal Variation of Saturn's Lyα Brightness

We examine Saturn’s nonauroral (dayglow) emissions at Lyα observed by the Cassini/Ultraviolet Imaging Spectrograph (UVIS) instrument from 2003 until 2017, to constrain meridional and seasonal trends in the upper atmosphere. We separate viewing geometry effects from trends driven by atmospheric properties, by applying a multivariate regression to the observed emissions. The Lyα dayglow brightnesses depend on the incident solar flux, solar incidence angle, emission angle, and observed latitude. The emissions across latitudes and seasons show a strong dependence with solar incidence angle, typical of resonantly scattered solar flux and consistent with no internal source such as electroglow. We observe a bulge in Lyα brightnesses that shifts with the summer season from the southern to the northern hemisphere. We estimate atomic hydrogen optical depths above the methane homopause level for dayside disk observations (2004–2016) by comparing observed Lyα emissions to a radiative transfer model. We model emissions from resonantly scattered solar flux and a smaller but significant contribution by scattered photons from the interplanetary hydrogen (IPH) background. During the northern summer, inferred hydrogen optical depths steeply decrease with latitude toward the winter hemisphere from a northern hemisphere bulge, as predicted by a 2D seasonal photochemical model. The southern hemisphere mirrors this trend during its summer. However, inferred optical depths show substantially more temporal variation between 2004 and 2016 than predicted by the photochemical model. We benchmark our brightness values by comparing observed IPH Lyα emissions from Cassini/UVIS in 2006 with a model of the IPH emissions. Cassini/UVIS observations agree well with the modeled IPH background.

Seeing the Unseen: A Method to Detect Unresolved Rings in Protoplanetary Disks

While high-resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations reveal a wealth of substructure in protoplanetary disks, they remain incapable of resolving the types of small-scale dust structures predicted, for example, by numerical simulations of the streaming instability. In this article, we propose a method to find evidence for unresolved, optically thick dusty rings in protoplanetary disks. We demonstrate that, in presence of unresolved rings, the brightness of an inclined disk exhibits a distinctive emission peak at the minor axis. Furthermore, the azimuthal brightness depends on both the geometry of the rings and the dust optical properties; we can therefore use the azimuthal brightness variations to both detect unresolved rings and probe their properties. By analyzing the azimuthal brightness in the test case of ringlike substructures formed by streaming instability, we show that the resulting peak is likely detectable by ALMA for typical disk parameters. Moreover, we present an analytic model that not only qualitatively but also quantitatively reproduces the peak found in the simulations, validating its applicability to infer the presence of unresolved rings in observations and characterize their optical properties and shape. This will contribute to the identification of disk regions where streaming instability (and thus planet formation) is occurring.