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• This algorithm attains attitude inversion with less data, confirming accuracy and noise resilience. The attitude of space debris holds significant implications for active debris removal and collision warnings. This study constructs a comprehensive photometry dataset for the target, utilizing angle transformation relationships and drawing upon the CZ-4C photometric data tested in the laboratory. The photometry in the dataset with noise and translation simulates the telescope observed one. We use the genetic algorithm to search for attitude parameters of the photometry that matches the simulated photometry in the dataset under the constraint of attitude angle rules. The derived and preset values of attitude parameters exhibit a strong alignment, applicable to light curves with an amplitude of about 2.5 magnitudes and noise levels up to 1.0 magnitudes. This suggests that the algorithm's accuracy is validated against the preset values, demonstrating its robust resistance to noise. Furthermore, the algorithm proposed in this study for deriving attitude from photometry requires a small amount of photometry data. This study provides a promising algorithm for future attitude analyses from telescope-observed photometry. [ABSTRACT FROM AUTHOR]

Knowledge of the maneuver axis of actively controlled satellites and the rotation axes of uncontrolled satellites is critical for maintaining Space Situational Awareness (SSA) and predicting satellite motions. Estimates of the spin rate and spin-axis of rotating satellites have been shown to be retrievable via passively collected time series of satellite brightness, otherwise known as light curves. Retrieval of satellite spin state parameters from light curves is accomplished by application of a physically derived relationship, the “Epoch Method," that explains the difference between the apparent spin rate and the inertial spin rate using the known relative motion between the observation telescope, the satellite, and the sun. There are two major challenges for retrieving the inertial spin rate from relative spin rate measurements operationally via the Epoch Method. One challenge is that satellites can have complex rotation states in the sense that they rotate about multiple axes with varying angular velocities. A second challenge is that the apparent difference between the inertial and relative spin rate is a direct function of the observer-to-solar geometry, meaning that geographical sites must be tasked optimally in order to maximize their ability to collect useful measurements for spin state retrieval. In order to overcome these challenges we derive an information metric that can both (1) explain the rotation axis that was maximally observable for a collected light curve from a geographic site, and (2) be utilized to task a diverse network of geographic sites for collecting maximally useful light curve measurements for monitoring the spin axis of an uncontrolled satellite. This is accomplished by deriving the observability of the inertial spin axis information according to the Fisher Information Matrix (FIM). We assume that the state vector that is being tracked is the inertial spin axis and that the measurement is an apparent spin rate measurement. We then derive a sensitivity matrix that utilizes the physical theory of the Epoch Method in deriving the partial derivatives of the measurement with respect to the state vector. We present several examples of how this metric can be applied towards tasking satellite networks across the continental United States for monitoring the inertial spin axis of Low Earth Orbit (LEO) and Geostationary (GEO) satellites. [ABSTRACT FROM AUTHOR]

Predicting the future impact of climate change on Stipa tenacissima steppes is of paramount importance to prevent desertification. From this perspective, we selected seven populations, covering a rainfall range from 100 to 600 mm/year at the sites of seed collection, and we grew the seedlings at the same site. Gas exchange measurements of CO2, light response curves, chlorophyll content and isotopic analysis were investigated. Thala and Valencia populations displayed a contrasted gas exchange response. Both populations were subjected to A-Ci curve analysis. No significant difference was detected between these two different populations in the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax). Stipa tenacissima exhibited a variable response in relation to different light intensities. Photosynthesis and chlorophyll fluorescence parameters proved to be significantly different among populations in response to light intensities. All populations exhibited similar chlorophyll content. The isotopic analysis highlighted a different carbon and nitrogen isotopic composition (δ13C and δ15N) and carbon content © among populations. Mesic populations presented a higher gas exchange activity and light response. The xeric populations performed a photosynthesis adjustment along with minimizing chlorophyll content. Under climate change, S. tenacissima limits productivity, which may lead to desertification. [ABSTRACT FROM AUTHOR]

A structural re-modeling of the thylakoid system, including granum size and regularity, occurs in chlorophyll-deficient wheat mutants affected by photosynthetic membrane over-reduction. In the chloroplast of land plants, the thylakoid system is defined by appressed grana stacks and unstacked stroma lamellae. This study focuses on the variations of the grana organization occurring in outdoor-grown wheat mutants characterized by low chlorophyll content and a tendency for photosynthetic membrane over-reduction. Triticum aestivum ANK-32A and Triticum durum ANDW-7B were compared to their corresponding WT lines, NS67 and LD222, respectively. Electron micrographs of chloroplasts were used to calculate grana ultrastructural parameters. Photosynthetic parameters were obtained by modulated chlorophyll fluorescence and applying Light Curves (LC) and Rapid Light Curves (RLC) protocols. For each photosynthetic parameter, the difference Δ(RLC-LC) was calculated to evaluate the flexible response to light in the examined lines. In the mutants, fewer and smaller disks formed grana stacks characterized by a marked increase in lateral and cross-sectional irregularity, both negatively correlated with the number of layers per granum. A relationship was found between membrane over-reduction and granum structural irregularity. The possible acclimative significance of a greater proportion of stroma-exposed grana domains in relieving the excess electron pressure on PSI is discussed. [ABSTRACT FROM AUTHOR]

The real-time light curve classification of transients is helpful in searching for rare transients. We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow's architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve. We also use the TLW algorithm to classify ZTF real transients. The real-time classification results for ZTF transients show that the accuracy of the TLW algorithm is higher than the other six algorithms. [ABSTRACT FROM AUTHOR]

Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper proposes a new meteor detection algorithm for the GWAC system on the base of the solely mini-GWAC system data algorithm. The new algorithm consists of the following key steps: (1) to compare differences between adjacent frames, applying block-based image binarization thresholds, and incorporating median filtering to reduce noise; (2) to adopt the probabilistic Hough transform (PHT) to identify moving objects and cluster them based on the origin moment of the line segments, while assessing the credibility of clustering; (3) to introduce the so-called maximum disappearance frame for moving objects in the tracking algorithm, enhancing the ability to track multi-frame moving objects. The utilization of the line segment inclination angle of the moving object as the direction of movement facilitates the tracking of multiple moving objects, thereby reducing the probability of mistakenly selecting single-frame moving objects; (4) to leverage the light curves of single-frame moving objects to select meteors to enhance the accuracy of meteor detection. Comparative experiments demonstrate that our proposed algorithm processes each frame image in just 0.39 s, achieving an accuracy of 89.8% in the dataset of 5856 adjacent frames. The experimental results indicate that the algorithm achieved an accuracy of 90.27% when applied in the meteor detection of the image data captured by the GWAC system from Dec. 10th to 19th in 2019 and 2021, obtaining excellent detection results. [ABSTRACT FROM AUTHOR]

The circular medium Earth orbits with a period of about half a day in the inclination range from 50° to 70° are used by various global navigation satellite systems (GNSS), such as GLONASS, GPS, Beidou, Galileo. GNSS operating orbits are one of the important areas in near-Earth space. The information about the space debris (SD) existing in this region and its characteristics is important for risk assessments and mitigation. We report the results of photometrical observations of SD objects in the vicinity of GNSS orbits obtained with the 1.6-meter AZT-33IK telescope of ISTP SB RAS Sayan Solar Observatory in 2018–2023. We show how SD objects existing in this region are distributed relative to GNSS objects. We derive time and phase dependences of the apparent brightness of all measured SD objects. Folded light curves are constructed, rotation periods and their dynamics are determined. The results of modeling the light curve inherent for several SD objects from the GNSS orbital are presented. We suggest a possible space object shape and parameters of proper rotation, which correspond to the observed light curve.

The main belt asteroids 4747, 5255, 11411, 15433, and 17866 were studied at the Baldone Astrophysical Observatory in the time span range 2018–2022. The obtained light curve data together with published Minor Planet Center data are analyzed with Fourier series, Lomb-Scargle periodogram, and phase dispersion minimization methods. A plan of analysis is given. The results computed from different observatories’ data are compared, and mean-weighted periods are obtained.

In this work, the short period binary system named (CN Andromeda) have been chosen to be analyze with B,V light curves using the PyWD2015-Qt5 program .The physical and geometrical parameters have been gained and contrasted with previous results of workers. Also, the absolute parameters have been established with results that are acceptable with the results of other workers. With good accuracy, the bolometric magnitude and Roche lobe radius have been calculated. .The present result shows that the selected system is over contact. Spot solution parameters for the analysed binary system have been evaluated. The observed light curves exhibit asymmetries and oddities caused by the system.

Light curves are plots of brightness measured over time. In the field of Space Situational Awareness (SSA), light curves of Resident Space Objects (RSOs) can be utilized to infer information about an RSO such as the type of object, its attitude, and its shape. Light curves of RSOs in geostationary orbit (GEO) have been a main research focus for many years due to the availability of long time series data spanning hours. Given that a large portion of RSOs are in low Earth orbit (LEO), it is of great importance to study trends in LEO light curves as well. The challenge with LEO light curves is that they tend to be short, typically no longer than a few minutes, which makes them difficult to analyze with typical time series techniques. This study presents a novel approach to observational LEO light curve classification. We extract features from light curves using a wavelet scattering transformation which is used as an input for a machine learning classifier. We performed light curve classification using both a conventional machine learning approach, namely a support vector machine (SVM), and a deep learning technique, long short-term memory (LSTM), to compare the results. LSTM outperforms SVM for LEO light curve classification with a 92% accuracy. This proves the viability of RSO classification by object type and spin rate from real LEO light curves. [ABSTRACT FROM AUTHOR]

In this article research, drawing and analyzing the light curve of short period binary (SPB) star CN Andromeda (CN And) with B, V, R, and I. Light curve fitting and results for the trajectory parameters of the CN And were obtained and compared with those obtained by previous works. The author will determine the size, luminosity and masse of the Individual star as well as the orbital size and orientation of the binary system. Also calculate the bolometric magnitude, reflection coefficient and absolute parameters for short period binary star. These coefficients are needed when analyzing light curves for eclipsing binary stars analyzed to facilitate getting numerical values for their relative and absolute characteristics. Some parameters are also fixed or calculated during light curve modeling. The graph of the dimmer binary luminosity in one complete orbital period is known as light curve. Furthermore, calculating the bolometric magnitude, reflection coefficients, armpit, and absolute parameters for CN, and these terms are very important for understanding the properties of CN And. [ABSTRACT FROM AUTHOR]

The real-time light curve classification of transients is helpful in searching for rare transients. We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow’s architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve. We also use the TLW algorithm to classify ZTF real transients. The real-time classification results for ZTF transients show that the accuracy of the TLW algorithm is higher than the other six algorithms.

Solar oblateness is a key parameter that provides a strong constraint for understanding the variations in total solar irradiance as well as the differential rotation of the Sun. Furthermore, it takes part in the evaluation of General Relativity theory. In this paper, we propose a procedure to measure solar flattening based on modeling the light curve during a solar eclipse observed from the ground or from Earth orbit. We apply this procedure to synthetic data corresponding to the solar eclipse observed from Lakeland (Queensland, North Australia) on November 13, 2012. The results show that accurate measurements of the solar equator-to-polar radius difference can reach 1 km when based on the current DE430 ephemeris and the LRO DEM data (equivalent to 1.4 × 10 − 6 in solar oblateness). [ABSTRACT FROM AUTHOR]

• A new Non-Linearity Index enables comparison of different transformation models. • In Attitude Determination, the NLI shows the measurement model is highly non-linear. • The new Adaptive Gaussian Mixtures UKF for AD is a contender to the Particle Filter. • Generalization of the Confidence Interval typical of a Gaussian distribution. The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state. [ABSTRACT FROM AUTHOR]

This research work was carried out to analyze the X-ray flares in Blazar Mkn 421 using data from XMM-Newton observation (Observation ID: 0658801301) that lasted for 8 hours. EPIC/pn data was used for statistical analysis of the light and spectrum curve in energy ranges from 0.3 to 10.0 keV. The best fit model for the spectrum was found to be the additive model (log-parabola + black-body). The measured integrated flux in the energy range of 0.3-2 keV is 5.015x10-10 ergs cm-2 s-1 with an associated uncertainty range of (5.010 – 5.020) x 10-10 ergs cm-2 s-1 at a 90% confidence level. Similarly, the measured integrated flux in the energy range of 2-10 keV is 2.398 x 10-10 ergs cm-2 s-1 with an associated uncertainty range of (2.392 – 2.405) x 10-10 ergs cm-2 s-1 at a 90% confidence level. The fractional variability of 0.444 is relatively high, which indicates that the source is variable. The fall in count per second per energy around 2 keV in the spectrum curve suggests that the intensity of X-rays decreases in a power law manner after a strong flare at this energy. The count-rate distribution of the observation analyzed is best described by a normal distribution which suggests that the emission mechanism in the blazar is governed by additive processes.

Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper proposes a new meteor detection algorithm for the GWAC system on the base of the solely mini-GWAC system data algorithm. The new algorithm consists of the following key steps: (1) to compare differences between adjacent frames, applying block-based image binarization thresholds, and incorporating median filtering to reduce noise; (2) to adopt the probabilistic Hough transform (PHT) to identify moving objects and cluster them based on the origin moment of the line segments, while assessing the credibility of clustering; (3) to introduce the so-called maximum disappearance frame for moving objects in the tracking algorithm, enhancing the ability to track multi-frame moving objects. The utilization of the line segment inclination angle of the moving object as the direction of movement facilitates the tracking of multiple moving objects, thereby reducing the probability of mistakenly selecting single-frame moving objects; (4) to leverage the light curves of single-frame moving objects to select meteors to enhance the accuracy of meteor detection. Comparative experiments demonstrate that our proposed algorithm processes each frame image in just 0.39 s, achieving an accuracy of 89.8% in the dataset of 5856 adjacent frames. The experimental results indicate that the algorithm achieved an accuracy of 90.27% when applied in the meteor detection of the image data captured by the GWAC system from Dec. 10th to 19th in 2019 and 2021, obtaining excellent detection results.

Light curves are plots of brightness measured over time. In the field of Space Situational Awareness (SSA), light curves of Resident Space Objects (RSOs) can be utilized to infer information about an RSO such as the type of object, its attitude, and its shape. Light curves of RSOs in geostationary orbit (GEO) have been a main research focus for many years due to the availability of long time series data spanning hours. Given that a large portion of RSOs are in low Earth orbit (LEO), it is of great importance to study trends in LEO light curves as well. The challenge with LEO light curves is that they tend to be short, typically no longer than a few minutes, which makes them difficult to analyze with typical time series techniques. This study presents a novel approach to observational LEO light curve classification. We extract features from light curves using a wavelet scattering transformation which is used as an input for a machine learning classifier. We performed light curve classification using both a conventional machine learning approach, namely a support vector machine (SVM), and a deep learning technique, long short-term memory (LSTM), to compare the results. LSTM outperforms SVM for LEO light curve classification with a 92% accuracy. This proves the viability of RSO classification by object type and spin rate from real LEO light curves.

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAEt) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAEt). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAEt embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the F1 score in the task of classifying exoplanets. Moreover, the S-VRAEt model increases all the advantages of VRAEt, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.

The photometric study of the short period contact binary V415 Gem are performed for the first time. We discovered that it is a W-subtype shallow contact binary with a mass ratio of q = 2.297 and a contact degree of f = 3.6%. The light curves of the target exhibit an O'Connell effect, we attached a cool star-spot on its primary component for better fitting results. At the same time, the third light was found during our analysis by using the 2015 version of the Wilson–Devinney (W − D) code. And the average luminosity contribution of the third light is 44% of the total light. Besides, the O − C curve of V415 Gem exhibits a cyclic variation, which is due to the light-travel time effect caused by the presence of a third component with an amplitude of 0.0033 days and a period of 11.5 years. The mass of the third body is estimated to be M 3 ∼ 1. 08 M ⊙. • The light curves of V415 Gem in B, V, R and I-bands are obtained and analyzed. • We found that V415 Gem is a W-subtype shallow contact binary with a third body. • The orbital period of the third body is 11.5 years and its minimum mass is about 0.13M ⊙. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the problem of clustering 1318 new variable stars found in the Milky way. Our recent work distinguishes these stars based on their light curves which are univariate series of brightness from the stars observed at discrete time points. This work proposes a new approach to look at these discrete series as continuous curves over time by transforming them into functional data. Then, functional principal component analysis is performed using these functional light curves. Clustering based on the significant functional principal components reveals two distinct groups of eclipsing binaries with consistency and superiority compared to our previous results. This method is established as a new powerful light curve-based classifier, where implementation of a simple clustering algorithm is effective enough to uncover the true clusters based merely on the first few relevant functional principal components. Simultaneously we discard the noise from the data study involving the higher order functional principal components. Thus the suggested method is very useful for clustering big light curve data sets which is also verified by our simulation study. [ABSTRACT FROM AUTHOR]

Premise: Wetland plants regularly experience physiological stresses resulting from inundation; however, plant responses to the interacting effects of water level and inundation duration are not fully understood. Methods: We conducted a mesocosm experiment on two wetland species, sawgrass (Cladium jamaicense) and muhly grass (Muhlenbergia filipes), that co‐dominate many freshwater wetlands in the Florida Everglades. We tracked photosynthesis, respiration, and growth at water levels of −10 (control), 10 (shallow), and 35 cm (deep) with reference to soil surface over 6 months. Results: The response of photosynthesis to inundation was nonlinear. Specifically, photosynthetic capacity (Amax) declined by 25% in sawgrass and by 70% in muhly grass after 1–2 months of inundation. After 4 months, Amax of muhly grass in the deep‐water treatment declined to near zero. Inundated sawgrass maintained similar leaf respiration and growth rates as the control, whereas inundated muhly grass suppressed both respiration and growth. At the end of the experiment, sawgrass had similar nonstructural carbohydrate pools in all treatments. By contrast, muhly grass in the deep‐water treatment had largely depleted sugar reserves but maintained a similar starch pool as the control, which is critical for post‐stress recovery. Conclusions: Overall, the two species exhibited nonlinear and contrasting patterns of carbon uptake and use under inundation stress, which ultimately defines their strategies of surviving regularly flooded habitats. The results suggest that a future scenario with more intensive inundation, due to the water management and climate change, may weaken the dominance of muhly grass in many freshwater wetlands of the Everglades. [ABSTRACT FROM AUTHOR]

In this work, the short period system named (ET Leo) have been chosen to be analyzed with B , V and R light curves with the Wilson-Devinney code and the results of orbital parameters of the system were obtained and compared with those obtained from previous studies. The present results indicated that the selected system is a contact system and the absolute parameters of the components have been determined with suitable accuracy, for which the physical parameters have been obtained in a uniform way. Spot solution parameters have been evaluated for the binary system under investigation. The system shows asymmetries and peculiarities in the observed light curves.

The experiments were conducted on tomato plants to study the interactive responses of water levels (W70%: 70% of water consumption and W90%: 90% water consumption) and nitrogen rates (N100%: 100% of recommended and N80%:80% of N1.0) under two straw mulching conditions (NS; no straw introduced and WS: with buried straw layer) on growth and physiological parameters for two fruit growing years to assess the interactive responses of fertigation under buried straw layer on the changes in plant fresh and dry biomass, roots biomass, photosynthesis rate (PN), stomatal conductance (GS) and chlorophyll fluorescence of tomato plants. Buried straw layer was proved to improve plant biomass, photosynthesis rate and other physiological traits such as chlorophyll contents (Chl), maximum electron transport rate (ETRmax), maximum quantum yield (FV/FM) and GS under the lower fertilizer (N80%) and irrigation levels (W70%). However, increasing fertilizer and irrigation level decreased these parameters significantly (p

We have carried out a systematic analysis of the light curves of gamma-ray bursts (GRBs) detected in the SPI-ACS experiment onboard the INTEGRAL observatory to search for extended emission. The emission occasionally recorded after the prompt active phase of a GRB in the form of an emission that is longer than the active phase and less intense is called the extended one. Out of the 739 brightest GRBs recorded from 2002 to 2017, extended emission has been detected in of the individual light curves; its maximum duration reaches 10 000 s. Two different types of extended emission have been revealed. One of them is an additional component of the light curve, which is described by a power law (PL) with an index of close to the PL index of the afterglow in the optical and X-ray bands. The second type can be described by a steeper PL decay of the light curve typical of the active burst phase. Extended emission has also been found in the combined light curve of long GRBs in the individual curves of which no extended emission has been detected. The PL index of the extended emission in the combined light curve is . It is most likely associated with the superposition of light curves at the active phase; its total duration is 800 s. [ABSTRACT FROM AUTHOR]

We present the first photometric observations and light curve modelling of the discovered systems GSC 01870-00458 and USNO-A2.0-0975 04721840. Our modelling was carried out using a recent Windows interface version of Wilson and Devinney code based on model atmospheres provided by Kurucz. The accepted models revealed absolute and physical parameters that can be used to study the evolutionary states of systems. The parameters show that primary component is more massive and hotter than the secondary component for both systems, and spectral types of the system components were adopted. Locations of both systems on theoretical mass-luminosity and mass-radius curves revealed a good fit for components of both systems except for the secondary component of the system GSC 01870-00458. [ABSTRACT FROM AUTHOR]

We report the results of multicolor optical observations of asteroid (3552) Don Quixote carried out at the Zeiss-1000 telescope of the Sanglokh International Astronomical Observatory of the Institute of Astrophysics of the Academy of Sciences of the Republic of Tajikistan in July 2018. The apparent and absolute magnitudes of the asteroid were determined in the VRI bands. The analysis of the light curves of the asteroid showed that the brightness substantially varied during the observations—from 11.50 ± 0.10 to 13.10 ± 0.18 of absolute it magnitudes. Such a considerable change in the magnitude is indicative of an outburst the asteroid, which means that we detected activity typical for comets. According to our observations, the color index (V‒R) corresponds to the values for nuclei of extinct short-period comets and D-type asteroids. The comet-like orbit, the low albedo, the color index, and detected activity suggest that the asteroid is very likely the nucleus of an extinct comet. According to the observations performed ten days after the outburst, the mean value of the effective diameter of the asteroid is 18.5 ± 2.5 km; and this estimate agrees well with the data available, which means that the outburst had terminated by that time. We suppose that the dust ejection and, consequently, the outburst resulted from a collision of asteroid 3552 with a small object or from the micrometeoroids bombardment of its surface. [ABSTRACT FROM AUTHOR]

The Clark-type electrode can be used to assess the rates of photosynthesis by detecting changes in O 2 concentration in a culture. This chapter describes a method for a liquid phase measurement of light and dissolved inorganic carbon-dependent photosynthesis using the model green alga Chlamydomonas reinhardtii. The technique can be used to evaluate the presence or efficiency of carbon-concentrating mechanisms., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.

The conventional detection of micrometeoroid and orbital debris (MMOD) impacts on satellites is based on in-situ sensing or direct, visual inspection of the retrieved surface elements exposed to the particle flux and thus requires active in-orbit operations. We propose Quanta Photogrammetry (QPM) as an optical method for remote detection of surface structural anomalies of passive satellites by measuring solar photon flux reflected off the satellite surface towards the ground detection system.The hypertemporal light curves of the Experimental Geodetic Satellite - Ajisai (NORAD 16908, altitude of 1490 km) are collected with the state-of-the-art photon counting system designed and operated by the Graz Satellite Laser Ranging Observatory (Austria). QPM utilizes the inertial attitude model of the satellite to project the high-rate photometric samples onto the spacecraft body-fixed frame. The single-photon light curves collected from Oct. 2015 until Jan. 2018 are used to map reflectivity of 149 mirror panels on-board Ajisai (approx. size of 20 × 20 cm each) and reveal relatively small, irregularly shaped and spot-like anomalies that can indicate surface degradation due to the long-term environmental interactions and MMOD hypervelocity impacts. • Quanta Photogrammetry remotely detects reflective anomalies of Ajisai mirrors. • The high-resolution photograms of Ajisai mirrors reveal location of the anomalies. • Detected anomalies are most likely caused by the aging effects or MMOD impacts. [ABSTRACT FROM AUTHOR]

We consider prospects for investigating asteroids with the multicolor photometric method at the Robophot telescope—a robotic telescope of the Dall–Kirkham system with a 0.6-m primary mirror and a focal length of 4.2 m. It is equipped with a three-channel photometer with the g', r', and i' filters of the SDSS photometric system, and the field of view is 20′. The use of a three-channel photometer in observations of small bodies will make it possible to construct multicolor light curves without phase delays. Diagrams of the r' – i' and g' – r' color indices will provide the means of classifying the C- and S-type asteroids. From the high-accuracy light curves, the rotation parameters of asteroids can be determined, for which it is necessary to take the Yarkovsky effect into account correctly. The results of multicolor photometric observations at the Robophot telescope will allow for solving complex scientific problems related to the studies of the physical and dynamic evolution of small bodies in the Solar System. [ABSTRACT FROM AUTHOR]

Shape and attitude of resident space objects directly affect the orbit propagation via drag and solar radiation pressure. Obtaining information beyond an object state is integral to identifying an object, aid in tracing its origin and its capabilities. For objects that have a significant distance to the observer, only non-resolved imaging is available, which does not reveal any details of the object. So-called non-resolved light curve measurements, i.e. brightness measurements over time, can be used to determine the shape of convex space objects using an inversion scheme. The inversion process starts by first determining the Extended Gaussian Image and then solving Minkowski problem to obtain the closed shape result. In this paper, the effect of measurement noise on the shape inversion and the influence of the measurement geometry is investigated. Despite the presence of measurement noise, in a new methodology, expanding upon established inversion techniques, almost perfect inversion results can be obtained in a two-step process: first, an initial light curve is used for shape hypothesis creation, and then, a second (potentially very short) light curve is used for selection of the best hypothesis. Results are shown for two standard shapes of a cuboid and a house shape. [ABSTRACT FROM AUTHOR]

• Description of the space debris data acquisition with standard astronomical optical telescopes. • Astrometric reduction, data validation and orbit improvement for space debris objects. • Photometric data acquisition, light curve construction and processing, measurement error propagation. • Public catalogue of space debris light curves with electronic link. The Faculty of Mathematics, Physics and Informatics of Comenius University in Bratislava, Slovakia (FMPI) operates its own 0.7-m Newtonian telescope (AGO70) dedicated to the space surveillance tracking and research, with an emphasis on space debris. The observation planning focuses on objects on geosynchronous (GEO), eccentric (GTO and Molniya) and global navigation satellite system (GNSS) orbits. To verify the system's capabilities, we conducted an observation campaign in 2017, 2018 and 2019 focused on astrometric and photometric measurements. In last two years we have built up a light curve catalogue of space debris which is now freely available for the scientific community. We report periodic signals extracted from more than 285 light curves of 226 individual objects. We constructed phase diagrams for 153 light curves for which we obtained apparent amplitudes. [ABSTRACT FROM AUTHOR]

In this paper we describe how to observe a planetary transit using a small aperture telescope, as well as the strategies used to select the object, make the observations, the treatment and the data analysis. As an example, we observed the planetary transit on the WASP 76 star and estimated the transit time, the radius, the orbit semi major-axis and the orbital inclination of the planet. The results show that telescopes with modest instrumental configuration can be used to obtain satisfactory data about exoplanets that transit bright stars. Based on this result, we propose that it is possible to do observational astronomy with small-aperture telescopes, such as the instruments that exist in the different small-observatories installed in universities and a research centers in Brazil, to provide scientific literacy for undergraduate students and from high school students. [ABSTRACT FROM AUTHOR]

The asteroid (13553) Masaakikoyama was observed with the ZA-320M and MTM-500M telescopes of the Pulkovo observatory in August and September 2018. Its axial rotation period was estimated based on the observed sections of the light curve (and additional observational data provided by B.D. Warner) at 97.2 ± 0.3 h. This estimation was made difficult by the fact that the period is commensurable with a day. The light curve has certain features suggestive of tumbling rotation and a complex shape. The orbit of (13553) Masaakikoyama was improved using the obtained astrometric positions of the asteroid. [ABSTRACT FROM AUTHOR]

The experiments were conducted on tomato plants to study the interactive responses of water levels (W70%: 70% of water consumption and W90%: 90% water consumption) and nitrogen rates (N100%: 100% of recommended and N80%:80% of N1.0) under two straw mulching conditions (NS; no straw introduced and WS: with buried straw layer) on growth and physiological parameters for two fruit growing years to assess the interactive responses of fertigation under buried straw layer on the changes in plant fresh and dry biomass, roots biomass, photosynthesis rate (PN), stomatal conductance (GS) and chlorophyll fluorescence of tomato plants. Buried straw layer was proved to improve plant biomass, photosynthesis rate and other physiological traits such as chlorophyll contents (Chl), maximum electron transport rate (ETRmax), maximum quantum yield (FV/FM) and GS under the lower fertilizer (N80%) and irrigation levels (W70%). However, increasing fertilizer and irrigation level decreased these parameters significantly (p <.05 to p <.001) under buried straw layer. Conversely, increased fertilizer (N100%) and irrigation (W90%) levels increased these parameters significantly (p <.05 to p <.001) under no straw condition. The overall findings revealed that buried straw layer could relieve stress developed by limited irrigation water and fertilizer and benefit plant growth, physiological parameters and fruit yield of tomato plant. [ABSTRACT FROM AUTHOR]