Brightness Variations Research Articles

Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear

Nowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system through infrared thermography and a convolutional neural network (CNN). During the experiment, we tested different conditions in the motor and the gearbox. The induction motor was operated in four conditions, in a healthy state, with one broken bar, a damaged bearing, and misalignment, while the gearbox was operated in three conditions with healthy gears, 50% wear, and 75% wear. The motor failures and gear wear were induced by different machining operations. Data augmentation was then performed using basic transformations such as mirror image and brightness variation. Ablation tests were also carried out, and a convolutional neural network with a basic architecture was proposed; the performance indicators show a precision of 98.53%, accuracy of 98.54%, recall of 98.65%, and F1-Score of 98.55%. The system obtained confirms that through the use of infrared thermography and deep learning, it is possible to identify faults at different points of an electromechanical system.

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.

An efficient method for image denoising based on a new nonlinear wavelet thresholding function

Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the wavelet denoising method works in the transform domain, where the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones.Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of the image and noise components. Another thing: Studies have proven that the thresholding function has an effective role in obtaining better quality of the denoised image. To address these disadvantages, in this paper, we propose an efficient method for image denoising in wavelets domain. This method is based on a new nonlinear wavelet thresholding function, that is characterized by main mathematical properties and a shape parameter. The theoretical study of this new method proves that we can overcome the drawbacks of classical thresholding methods and by freely adjusting the shape parameter, we achieve a compromise between Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to classical thresholding methods in terms of the visual quality of the denoised image.

Examining the brightness variability, accretion disk, and evolutionary stage of the binary OGLE-LMC-ECL-14413

Several intermediate-mass close binary systems exhibit photometric cycles longer than their orbital periods, potentially due to changes in their accretion disks. Past studies indicate that analyzing historical light curves can provide valuable insights into disk evolution and track variations in mass transfer rates within these systems. Our study aims to elucidate both short-term and long-term variations in the light curve of the eclipsing system with a particular focus on the unusual reversals in eclipse depth. We aim to clarify the role of the accretion disk in these fluctuations, especially in long-cycle changes spanning hundreds of days. Additionally, we seek to determine the evolutionary stage of the system and gain insights into the internal structure of its stellar components. We analyzed photometric time series from the Optical Gravitational Lensing Experiment (OGLE) project in the $I$ and $V$ bands, and from the MAssive Compact Halo Objects (MACHO) project in the $B_ M $ and $R_ M $ bands, covering a period of 30.85 years. Using light curve data from 27 epochs, we constructed models of the accretion disk. An optimized simplex algorithm was employed to solve the inverse problem, deriving the best-fit parameters for the stars, orbit, and disk. We also utilized the Modules for Experiments in Stellar Astrophysics ( MESA ) software to assess the evolutionary stage of the binary system, investigating the progenitors and potential future developments. We found an orbital period of 38.15917 pm 0.00054 days and a long-term cycle of approximately 780 days. Temperature, mass, radius, and surface gravity values were determined for both stars. The photometric orbital cycle and the long-term cycle are consistent with a disk containing variable physical properties, including two shock regions. The disk encircles the more massive star and the system brightness variations align with the long-term cycle at orbital phase 0.25. Our mass transfer rate calculations correspond to these brightness changes. MESA simulations indicate weak magnetic fields in the donor star's subsurface, which are insufficient to influence mass transfer rates significantly.

Superflare on a rapidly-rotating solar-type star captured in X-rays

In this work, we studied X-ray source SRGe J021932.4−040154 (SRGe J021932), which we associated with a single X-ray active star of spectral class G2V-G4V and the rotational period Prot<9.3 days. Additional analysis of TESS light-curves allowed for the rotational period estimation of 3.2±0.5 days. SRGe J021932 was observed with the SRG/eROSITA during eUDS survey in 2019 in a much dimmer state compared to the XMM-Newton catalogue 4XMM-DR12. Detailed analysis revealed that the archival XMM-Newton observations captured the source during a flaring event in 2017. The XMM-Newton light curve demonstrates a strong flare described with the Gaussian rise and exponential decay, typical for stellar flares, characterized by timescales of ∼400 s and ∼1300 s, respectively. The spectral analysis of the quiescent state reveals ∼10 MK plasma at luminosity of (1.4±0.4)×1029ergs−1 (0.3−4.5 keV). The spectrum of the flare is characterized by temperature of ∼40 MK and luminosity (5.5±0.6)×1030ergs−1. The total energy emitted during the flare ∼1.7×1034 erg exceeds the canonical threshold of 1033 erg, allowing us to classify the observed event as a superflare on a rapidly-rotating solar-type star. Additionally, we present the upper limit on the surface starspot area based on the brightness variations and consider the hypothesis of the object being a binary system with G-type and M-type stars, suggested by two independent estimations of radial velocity variations from APOGEE-2 and Gaia.

A NEW LASER SYSTEM WITH AUTOMATED CONTROL OF COAGULATION DEPTH: TOWARDS THE QUESTION OF COMPARABILITY OF BRIGHTNESS AND DEPTH OF LASER BURNS

Background. The development of laser systems with automated control of retinal coagulation depth will make it possible to obtain reproducible laser burns of a given brightness on the ocular fundus during treatment. Nevertheless, the problem of correspondence of the brightness of the formed coagulates fixed by video capture to the depth of the formed coagulates in the retinal thickness remains poorly studied.Objective. To study the correspondence between the brightness of the formed burns recorded by video capture and the depth of retinal coagulation during dose-dependent laser photocoagulation using an automated laser system based on the feedback principle.Materials and methods. A prototype of an automated system for laser photocoagulation of the retina, consisting of a 0.81 µm diode laser and a video capture module integrated into the optical system of the slit lamp, as well as software that controls laser exposure using the feedback principle. Studies were performed on rabbit eyes (n=20). At spot diameter 200 µm, power 100, 140, 180, 220, 260 and 300 mW and planned coagulation levels 5, 10, 20, 30, 40, 50 and 70%, the actual brightness of the obtained coagulates (%), the correspondence between planned and actual coagulate brightness were studied. The depth of coagulation (% of neuroretina thickness) and its correlation with the actual brightness of coagulates were studied at OCT.Results. With a planned brightness of 5-10% (grade 1 according to L’Esperance), the actual brightness of the burns was significantly higher, the depth of coagulation on OCT was 20 to 30% of the retinal thickness. At the planned brightness of 20-40% and 5-70% (grade 2 and 3 according to L’Esperance), the actual burns brightness was not significantly different throughout the power range, and the depth of coagulation by OCT was 35-50% and 80-120%, respectively. The coefficient of variation of actual brightness and coagulation depth did not exceed 15% for the whole range of planned brightness and used power, except for cases of planned brightness of 5-10% and power of 220 mW and more. A significant correlation was found between actual brightness and coagulation depth (R=0.96, p=0.001).Conclusion. During laser coagulation of the retina using a new automated laser system based on the feedback principle, homogeneous laser coagulates are automatically formed, and there is a significant correlation between the depth of neuroretinal coagulation and the brightness of burns recorded by video capture.

Swift Observations of Mrk 421 in Selected Epochs. IV. Physical Implications of X-Ray Flaring Activity and Features of Relativistic Magnetic Reconnection in 2018 April–2023 December

We present the spectral and timing results obtained during the intense observations of Mrk 421 by the Swift-based ultraviolet to X-ray instruments during 2018 April–2023 December. The source showed various strengths of X-ray flaring activity, exceeding a level of 3.5 × 10−9 erg cm−2 s−1 during the strongest 0.3–10 keV flares. Our study identifies a number of intraday brightness variability, including 61 instances that occurred within 1 ks exposures that are consistent with the shock-in-jet scenario and accompanied by significant, fast spectral changes. The source exhibited extreme spectral properties with dominance of the log-parabolic distributions of photons with energy and the frequent occurrence of hard photon indices in the 0.3–10 keV and 0.3–300 GeV bands, with the peak of synchrotron spectral energy distribution E p detected at the energies beyond 29 keV for the first time. The source showed very fast transitions of log-parabolic-to-power-law spectra, most plausibly caused by turbulence-driven relativistic magnetic reconnection. Our spectral results also demonstrate the importance of the first-order Fermi mechanism within the magnetic field of different confinement efficiencies, stochastic acceleration, transitions in the turbulence spectrum, and hadronic cascades. The X-ray, UV, and γ-ray fluxes showed a lognormal variability, which hints at the imprint of accretion disk instabilities on the blazar jet, as well as the possible presence of hadronic cascades. The UV and γ-ray variabilities demonstrated weak correlations with the X-ray flaring activity, which is not consistent with simple synchrotron self-Compton models and requires more complex particle acceleration and emission scenarios.

Unveiling Illumination Variations During a Lunar Eclipse: Multi-Wavelength Spaceborne Observations of the January 21, 2019 Event

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang’E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth’s umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy.

Machine learning-based classification for Single Photon Space Debris Light Curves

The growing number of man-made debris in Earth’s orbit poses a threat to active satellite missions due to the risk of collision. Characterizing unknown debris is, therefore, of high interest. Light Curves (LCs) are temporal variations of object brightness and have been shown to contain information such as shape, attitude, and rotational state. Since 2015, the Satellite Laser Ranging (SLR) group of Space Research Institute (IWF) Graz has been building a space debris LC catalogue. The LCs are captured on a Single Photon basis, which sets them apart from CCD-based measurements. In recent years, Machine Learning (ML) models have emerged as a viable technique for analysing LCs. This work aims to classify Single Photon Space Debris LCs using the ML framework. We have explored LC classification using k-Nearest Neighbour (k-NN), Random Forest (RDF), XGBoost (XGB), and Convolutional Neural Network (CNN) classifiers in order to assess the difference in performance between traditional and deep models. Instead of performing classification on the direct LC data, we extracted features from the data first using an automated pipeline. We apply our models on three tasks, which are classifying individual objects, objects grouped into families according to origin, and grouping into general types. We successfully classified Space Debris LCs captured on Single Photon basis, obtaining accuracies as high as 90.7%. Further, our experiments show that the classifiers provide better classification accuracy with automated extracted features than other methods.

SynCOM: An Empirical Model for High-resolution Simulations of Transient Solar Wind Flows

The Synthetic Corona Outflow Model (SynCOM), an empirical model, simulates the solar corona’s dynamics to match high-resolution observations, providing a useful resource for testing velocity measurement algorithms. SynCOM generates synthetic images depicting radial variability in total brightness and includes stochastic elements for plasma outflows and instrumental noise. It employs a predefined probability distribution for flow velocity and an adjustable signal-to-noise ratio to evaluate different data analysis methods for coronal flows. By adjusting parameters to match specific coronal and instrumental conditions, SynCOM offers a platform to assess these methods for determining coronal velocity and acceleration. Validating these measurements would help us to understand the origins of the solar wind and support missions such as the Polarimeter to Unify the Corona and Heliosphere (PUNCH). In this study, we demonstrate how SynCOM can be employed to assess the precision and performance of two different flow-tracking methods. By providing a ground truth based on observational data, we highlight the importance of SynCOM in confirming observational standards for detecting coronal flows.

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 min, 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 overcoming the 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.

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.

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.