Time-domain Astronomy Research Articles

Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.

A Very-High-Energy Gamma-Ray View of the Transient Sky

The development of the latest generation of Imaging Atmospheric Cherenkov Telescopes (IACTs) over recent decades has led to the discovery of new extreme astrophysical phenomena in the very-high-energy (VHE, E > 100 GeV) gamma-ray regime. Time-domain and multi-messenger astronomy are inevitably connected to the physics of transient VHE emitters, which show unexpected (and mostly unpredictable) flaring or exploding episodes at different timescales. These transients often share the physical processes responsible for the production of the gamma-ray emission, through cosmic-ray acceleration, magnetic reconnection, jet production and/or outflows, and shocks interactions. In this review, we present an up-to-date overview of the VHE transients field, spanning from novae to supernovae, neutrino counterparts or fast radio bursts, among others, and we outline the expectations for future facilities.

AstroSA: An astronomical observation scheduler assessment framework in python

Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (AstroSA), implemented as a Python package. The AstroSA offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, AstroSA includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.

TT-HEALpix: A New Data Indexing Strategy for Efficient Cross-match of Large-scale Astronomical Catalogs

Cross-matching is an indispensable operation in the data preparation, analysis, and research processes of multi-band astronomy and time-domain astronomy. Multi-catalog time-series data reconstruction is an important part of time-domain astronomy. In the large-scale distributed reconstruction process, boundary problems have always affected the accuracy of time-series data. To optimize these boundary problems and improve data precision, this paper proposes a new hybrid astronomical data indexing method called Translated Transformation based HEALPix Dual Index (TT-HEALPix). Under the reasonable Healpix division level, by translation transformation, the two indexes before and after the transformation form a unique pseudo-hybrid index strategy, which not only retains the advantages of the hybrid index scheme suitable for large-scale parallel computing, but also compensates for its shortage of high omission at the block boundary position. Based on TT-HEALPix, this paper completes the multi-catalog time-series reconstruction process on the Spark platform and compares it with the HEALPix+HTM hybrid indexing strategy. The experiments demonstrate that TT-HEALPix has significant advantages over the traditional HEALPix+HTM hybrid indexing method in terms of data accuracy and cross-matching efficiency. At level 9 of the Healpix index, TT-HEALPix achieves a 6%–19% improvement in cross-matching efficiency in a distributed environment compared to HEALPix+HTM. In terms of data accuracy, for the AST3-II dataset at level 9, TT-HEALPix has 62.2% accuracy improvement over HEALPix and 45.5% improvement over HEALPix+HTM. In conclusion, the proposed novel indexing strategy, TT-HEALPix, is better suited to the efficiency and accuracy requirements of cross-match.

Science with a Small Two-Band UV-Photometry Mission II: Observations of Stars and Stellar Systems

AbstractWe outline the impact of a small two-band UV-photometry satellite mission on the field of stellar physics, magnetospheres of stars, binaries, stellar clusters, interstellar matter, and exoplanets. On specific examples of different types of stars and stellar systems, we discuss particular requirements for such a satellite mission in terms of specific mission parameters such as bandpass, precision, cadence, and mission duration. We show that such a mission may provide crucial data not only for hot stars that emit most of their light in UV, but also for cool stars, where UV traces their activity. This is important, for instance, for exoplanetary studies, because the level of stellar activity influences habitability. While the main asset of the two-band UV mission rests in time-domain astronomy, an example of open clusters proves that such a mission would be important also for the study of stellar populations. Properties of the interstellar dust are best explored when combining optical and IR information with observations in UV.It is well known that dust absorbs UV radiation efficiently. Consequently, we outline how such a UV mission can be used to detect eclipses of sufficiently hot stars by various dusty objects and study disks, rings, clouds, disintegrating exoplanets or exoasteroids. Furthermore, UV radiation can be used to study the cooling of neutron stars providing information about the extreme states of matter in the interiors of neutron stars and used for mapping heated spots on their surfaces.

The dynamic universe: realizing the potential of classical time domain and multimessenger astrophysics

In parallel with the multi-messenger revolution, major advances in time-domain astronomy across multiple science disciplines relevant to astrophysics are becoming more urgent to address. Aside from electromagnetic observations of gravitational wave events and explosive counterparts, there are a number of “classical” astrophysical areas that require new thinking for proper exploration in the time domain. How NASA, NSF, ESA, and ESO consider the 2020 USA Decadal Survey within the astronomy community, as well as the worldwide call to support and expand time domain and multi-messenger astrophysics, it is crucial that all areas of astrophysics, including stellar, galactic, Solar System, and exoplanetary science participate in the discussion, and that it not be made into an exclusive preserve of cosmological, high-energy, explosive and transient science. Time domain astronomy is used to explore many aspects of astrophysics–particularly concerning ground- and space-based mission science goals of exploring how the Universe works, understanding how did we get here, and are we alone. Time domain studies are already built into the core operations of many currently operating and future space telescopes (e.g., Roman, PLATO) as well as current and planned large areal ground-based surveys (e.g., Rubin). Time-domain observations designed for one scientific purpose, also lead to great discoveries in many other science areas. The recent advent of user-friendly hardware, software, observational approaches, and online data infrastructure has also helped make time domain observations especially suitable and appealing for citizen science projects. We provide a review of the current state of TDAMM alerts and observational protocols, revealing a wide array of software and applications, much of which is incompatible. Any conversation regarding TDAMM astrophysics should include all aspects of the field, including those aspects seen as classical applications.

High-speed readout system of X-ray CMOS image sensor for time domain astronomy

We developed an FPGA-based high-speed readout system for a complementary metal-oxide-semiconductor (CMOS) image sensor to observe soft X-ray transients in future satellite missions, such as HiZ-GUNDAM. Our previous research revealed that the CMOS image sensor has low-energy X-ray detection capability (0.4–4 keV) and strong radiation tolerance, which satisfies the requirements of the HiZ-GUNDAM mission. However, CMOS sensors typically have small pixel sizes (e.g., ∼10 µm), resulting in large volumes of image data. GSENSE400BSI has 2048×2048 pixels, producing 6 Mbyte per frame. These large volumes of observed raw image data cannot be stored in a satellite bus system with a limited storage size. Therefore, only X-ray photon events must be extracted from the raw image data. Furthermore, the readout time of CMOS image sensors is approximately ten times faster than that of typical X-ray CCDs, requiring faster event extraction on a timescale of ∼0.1 s. To address these issues, we have developed an FPGA-based image signal processing system capable of high-speed X-ray event extraction onboard without storing raw image data. The developed compact system enabled mounting on a CubeSat mission, facilitating early in-orbit operation demonstration. Here, we present the design and results of the performance evaluation tests of the proposed FPGA-based readout system. Utilizing X-ray irradiation experiments, the results of the X-ray event extraction with the onboard and offline processing methods were consistent, validating the functionality of the proposed system.

Surface Passivation by Quantum Exclusion: On the Quantum Efficiency and Stability of Delta-Doped CCDs and CMOS Image Sensors in Space.

Radiation-induced damage and instabilities in back-illuminated silicon detectors have proved to be challenging in multiple NASA and commercial applications. In this paper, we develop a model of detector quantum efficiency (QE) as a function of Si-SiO2 interface and oxide trap densities to analyze the performance of silicon detectors and explore the requirements for stable, radiation-hardened surface passivation. By analyzing QE data acquired before, during, and after, exposure to damaging UV radiation, we explore the physical and chemical mechanisms underlying UV-induced surface damage, variable surface charge, QE, and stability in ion-implanted and delta-doped detectors. Delta-doped CCD and CMOS image sensors are shown to be uniquely hardened against surface damage caused by ionizing radiation, enabling the stability and photometric accuracy required by NASA for exoplanet science and time domain astronomy.

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes.

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey’s Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a “bagging-like” ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).

Performance evaluation of an Ultra-Fast IR Detector for Astronomy Transients

In this work, we present the design of an ultra-fast IR (InfraRed) detector for the search of astronomical transients. Time-domain astronomy is a field in continuous growth in the last years, with interest in detectors with nanosecond performance, in order to observe potential fast transients with the time scale of GRBs (Gamma Ray Bursts) and FRBs (Fast Radio Bursts). An ultra-fast temporal detector based on HgCdTe photoconductors is under development in Italy, at the Frascati National Laboratory of the National Institute of Nuclear Physics, by tests at SINBAD, the DAFNE infrared beamline. The detector will be used for mid-IR observations together with a ground-based reflecting telescope or launched on a scientific balloon.

Robotic Astronomy

Abstract Robotic telesopes are essential in studying time domain astronomy. Astronomer-in-Charge at the Liverpool Telescope Chris Copperwheat expounds its benefits, and gives us a preview of coming interactions with the Liverpool Telescope's planned successor.

Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11

Aims. We present a variability-, color-, and morphology-based classifier designed to identify multiple classes of transients and persistently variable and non-variable sources from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST Chilean AGN/Galaxy Evolution Survey (ChANGES). That being said, it also serves as a more general time-domain astronomy study. Methods. The model uses nine colors computed from CatWISE and Pan-STARRS1 (PS1), a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band, since ZTF DR11 treats the light curves observed in a particular combination of field, filter, and charge-coupled device (CCD) quadrant independently. We used a hierarchical local classifier per parent node approach-where each node is composed of a balanced random forest model. We adopted a taxonomy with 17 classes: non-variable stars, non-variable galaxies, three transients (SNIa, SN-other, and CV/Nova), five classes of stochastic variables (lowz-AGN, midz-AGN, highz-AGN, Blazar, and YSO), and seven classes of periodic variables (LPV, EA, EB/EW, DSCT, RRL, CEP, and Periodic-other). Results. The macro-averaged precision, recall, and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes (lowz-AGN, midz-AGN, highz-AGN, and Blazar) into one single class, its precision-recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. This demonstrates the good performance of the model in classifying AGN candidates. We applied the model to all the sources in the ZTF/4MOST overlapping sky (−28 ≤ Dec ≤ 8.5), avoiding ZTF fields that cover the Galactic bulge (|gal_b| ≤ 9 and gal_l ≤ 50). This area includes 86 576 577 light curves in the g band and 140 409 824 in the r band with 20 or more observations and with an average magnitude in the corresponding band lower than 20.5. Only 0.73% of the g-band light curves and 2.62% of the r-band light curves were classified as stochastic, periodic, or transient with high probability (Pinit ≥ 0.9). Even though the metrics obtained for the two models are similar, we find that, in general, more reliable results are obtained when using the g-band model. With it, we identified 384 242 AGN candidates (including low-, mid-, and high-redshift AGN and Blazars), 287 156 of which have Pinit ≥ 0.9.

A simulation framework for telescope array and its application in distributed reinforcement learning-based scheduling of telescope arrays

Time-domain astronomy necessitates continuous observation of celestial objects across the entire sky, with specific observation depth and cadence requirements. Telescope arrays, comprised of numerous wide-field optical telescopes, have emerged as a novel observational instrument for time-domain astronomy. However, the observation capabilities of ground-based optical telescope arrays are often constrained by various dynamic factors such as clouds, satellites, and sky background. To meet the observation requirements, active scheduling of telescopes within a telescope array is essential, leveraging telemetry data from the environment. However, due to the complexity and cost of telescope arrays, it is impractical to directly design or test algorithms using a physical telescope array. In this paper, we propose a framework for simulating telescope arrays that incorporates the majority of real observation effects. Building upon this framework, we further introduce a scheduler based on a distributed reinforcement learning framework to optimize the observation strategy of telescopes within the array. The scheduler is trained and evaluated using the simulator. Results demonstrate that the distributed control framework-based scheduler enhances the observation efficiency of the telescope array.

Automated SpectroPhotometric Image REDuction (ASPIRED)

We provide a suite of public open-source spectral data-reduction software to rapidly obtain scientific products from all forms of long-slit-like spectroscopic observations. Automated SpectroPhotometric REDuction (ASPIRED) is a Python-based spectral data-reduction toolkit. It is designed to be a general toolkit with high flexibility for users to refine and optimize their data-reduction routines for the individual characteristics of their instruments. The default configuration is suitable for low-resolution long-slit spectrometers and provides a quick-look quality output. However, for repeatable science-ready reduced spectral data, some moderate one-time effort is necessary to modify the configuration. Fine-tuning and additional (pre)processing may be required to extend the reduction to systems with more complex setups. It is important to emphasize that although only a few parameters need updating, ensuring their correctness and suitability for generalization to the instrument can take time due to factors such as instrument stability. We compare some example spectra reduced with ASPIRED to published data processed with iraf-based and STARLINK-based pipelines, and find no loss in the quality of the final product. The Python-based, iraf-free ASPIRED can significantly ease the effort of an astronomer in constructing their own data-reduction workflow, enabling simpler solutions to data-reduction automation. This availability of near-real-time, science-ready data will allow adaptive observing strategies, particularly important in, but not limited to, time-domain astronomy.

Gaussian Process Regression for Astronomical Time Series

The past two decades have seen a major expansion in the availability, size, and precision of time-domain data sets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity, and comparative robustness, Gaussian processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such data sets. In this review, we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modeling choices involved in GP regression. We then review applications of GPs to time-domain data sets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code; discuss the problem of computational cost and scalability; and give a snapshot of the current ecosystem of open-source GP software packages. In summary: ▪GP regression is a conceptually simple but statistically principled and powerful tool for the analysis of astronomical time series.▪It is already widely used in some subfields, such as exoplanets, and gaining traction in many others, such as optical transients.▪Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time-domain astronomy for many years to come.

The orbital period versus absolute magnitude relationship of intermediate polars: implications for low states and outbursts

ABSTRACT Recent advances in time-domain astronomy have led to fresh observational insights into intermediate polars, a subtype of magnetic cataclysmic variables generally accreting via a partial accretion disc. These new discoveries include detections of superhumps, low states, and outbursts. However, these studies have largely relied on relative photometry. Here, we tabulate the absolute G magnitudes of confirmed intermediate polars, plot them against their orbital periods, and compare the results to similar studies of dwarf novae during quiescence and in outburst. This exercise suggests the presence of two distinct luminosity classes of intermediate polars, with practical and physical implications for the studies of low states and outbursts. In particular, we point out that two of the optically luminous systems showing short outbursts are also seen to exhibit superhumps, suggesting that they may be caused by the same underlying mechanism.

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Time-domain astronomy is an active research area now, which requires frequent observations of the whole sky to capture celestial objects with temporal variations. In the optical band, several telescopes in different locations could form a distributed telescope array to capture images of celestial objects continuously. However, there are millions of celestial objects to observe each night, and only limited telescopes could be used for observation. Besides, the observation capacity of these telescopes would be affected by different effects, such as the sky background or the seeing condition. It would be necessary to develop an algorithm to optimize the observation strategy of telescope arrays according to scientific requirements. In this paper, we propose a novel framework that includes a digital simulation environment and a deep reinforcement learning algorithm to optimize observation strategy of telescope arrays. Our framework could obtain effective observation strategies given predefined observation requirements and observation environment information. To test the performance of our algorithm, we simulate a scenario that uses distributed telescope arrays to observe space debris. Results show that our algorithm could obtain better results in both discovery and tracking of space debris. The framework proposed in this paper could be used as an effective strategy optimization framework for distributed telescope arrays, such as the Sitian project or the TIDO project.

Predicting Stellar Mass Accretion: An Optimized Echo State Network Approach in Time Series Modeling

Modeling the dynamics of the formation and evolution of protostellar disks as well as the history of stellar mass accretion typically involve the numerical solution of complex systems of coupled differential equations. The resulting mass accretion history of protostars is known to be highly episodic due to recurrent instabilities and also exhibits short timescale flickering. By leveraging the strong predictive abilities of neural networks, we extract some of the critical temporal dynamics experienced during the mass accretion including periods of instability. Particularly, we utilize a novel form of the Echo-State Neural Network (ESN), which has been shown to efficiently deal with data having inherent nonlinearity. We introduce the use of Optimized-ESN (Opt-ESN) to make model-independent time series forecasting of mass accretion rate in the evolution of protostellar disks. We apply the network to multiple hydrodynamic simulations with different initial conditions and exhibiting a variety of temporal dynamics to demonstrate the predictability of the Opt-ESN model. The model is trained on simulation data of $\sim 1-2$ Myr, and achieves predictions with a low normalized mean square error ($\sim 10^{-5}$ to $10^{-3}$) for forecasts ranging between 100 and 3800 yr. This result shows the promise of the application of machine learning based models to time-domain astronomy.

A huge-amplitude white-light superflare on a L0 brown dwarf discovered by GWAC survey

ABSTRACT White-light superflares from ultra-cool stars are thought to be resulted from magnetic reconnection, but the magnetic dynamics in a fully convective star is not clear yet. In this paper, we report a stellar superflare detected with the ground wide angle camera (GWAC), along with rapid follow-ups with the F60A, Xinglong 2.16-m, and LCOGT telescopes. The effective temperature of the counterpart is estimated to be 2200 ± 50 K by the BT-Settl model, corresponding to a spectral type of L0. The R-band light curve can be modelled as a sum of three exponential decay components, where the impulsive component contributes a fraction of 23 per cent of the total energy, while the gradual and the shallower decay phases emit 42 per cent and 35 per cent of the total energy, respectively. The strong and variable Balmer narrow emission lines indicate the large amplitude flare is resulted from magnetic activity. The bolometric energy released is about 6.4 × 1033 erg, equivalent to an energy release in a duration of 143.7 h at its quiescent level. The amplitude of Δ R = −8.6 mag (or Δ V = −11.2 mag), placing it one of the highest amplitudes of any ultra-cool star recorded with excellent temporal resolution. We argue that a stellar flare with such rapidly decaying and huge amplitude at distances greater than 1 kpc may be false positive in searching for counterparts of catastrophic events such as gravitational wave events or gamma-ray bursts, which are valuable in time-domain astronomy and should be given more attention.