Time-domain Astronomy Research Articles

Insight HXMT and GECAM joint observation of time domain astronomy from cosmic outbursts

Abstract The field of time-domain astronomy focuses on the observation of temporal variations in celestial phenomena, in particular gamma-ray bursts (GRBs). It emphasized the importance of monitoring transient events and phenomena, which occurred on different time scales, ranging from milliseconds to years. This paper also explained the properties of X-rays and Gamma-rays, their sources and the significance of studying them for understanding the dynamic nature of the universe. The collaboration between the Insight HXMT and GECAM missions was designed to detect and study high-energy astronomical events, such as those associated with black holes and neutron stars, and to provide for rapid data transmission and dissemination of astronomical alerts. By combining observations from the two instruments, researchers can gain a more comprehensive understanding of cosmic phenomena and explore the fundamental physics of the universe.

Improving Power Spectrum Estimation Using Multitapering: Efficient Asteroseismic Analyses for Understanding Stars, the Milky Way, and Beyond

Asteroseismic time series data have imprints of stellar oscillation modes, whose detection and characterization through time series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb–Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the nonuniform fast Fourier transform (mtNUFFT) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of 3.97 ± 0.52 Gyr. PBjam allows the improvement of age precision relative to the 4.27 ± 0.75 Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available at https://github.com/aaryapatil/tapify.

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

In-field Phasing at the Upgraded GMRT

In time-domain radio astronomy with arrays, voltages from individual antennas are added together with proper delay and fringe correction to form the beam in real time. In order to achieve the correct phased addition of antenna voltages, one has to also correct for the ionospheric and instrumental gains. Conventionally this is done using observations of a calibrator source located near to the target field. This scheme is suboptimal since it does not correct for the variation of the gains with time and position in the sky. Further, since the ionospheric phase variation is typically most rapid at the longest baselines, the most distant antennas are often excluded while forming the beam. We present here a different methodology (“in-field phasing”), in which the gains are obtained in real-time using a model of the intensity distribution in the target field, which overcomes all of these drawbacks. We present observations with the upgraded Giant Metrewave Radio Telescope (uGMRT) which demonstrates that in-field phasing does lead to a significant improvement in sensitivity. We also show, using observations of the millisecond pulsar J1120−3618 that this in turn leads to a significant improvement of measurements of the Dispersion Measure and Time of Arrival. Finally, we present test observations of the GMRT discovered eclipsing black widow pulsar J1544+4937 showing that in-field phasing leads to improvement in the measurement of the cut-off frequency of the eclipse.

Building spectral templates and reconstructing parameters for core-collapse supernovae with CASTOR

ABSTRACT The future of time-domain optical astronomy relies on the development of techniques and software capable of handling a rising amount of data and gradually complementing, or replacing if necessary, real observations. Next-generation surveys, like the Large Synoptic Survey Telescope, will open the door to the new era of optical astrophysics, creating, at the same time, a deficiency in spectroscopic data necessary to confirm the nature of each event and to fully recover the parametric space. In this framework, we developed Core collApse Supernovae parameTers estimatOR (CASTOR), a novel software for data analysis. CASTOR combines Gaussian process and other machine-learning techniques to build time-series templates of synthetic spectra and to estimate parameters of core-collapse supernovae (CCSNe) for which only multiband photometry is available. Techniques to build templates are fully data driven and non-parametric through empirical and robust models, and rely on the direct comparison with a training set of 111 CCSNe from the literature. Furthermore, CASTOR employees the real photometric data and the reconstructed synthetic spectra of an event to estimate parameters that belong to the supernova ejecta, to the stellar progenitor and to the event itself, in a rapid and user-friendly framework. In this work, we provide a demonstration of how CASTOR works, studying available data from SN 2015ap and comparing our results with those available in literature.

X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

A Surprising Excess of Radio Emission in Extremely Stable Quasars: A Unique Clue to Jet Launching?

Quasars are generally divided into jetted radio-loud and nonjetted radio-quiet ones, but why only 10% of quasars are radio-loud has been puzzling for decades. Other than jet-induced phenomena, black hole mass, or Eddington ratio, a prominent difference between jetted and nonjetted quasars has scarcely been detected. Here we show a unique distinction between them, and the mystery of jet launching could be disclosed by a prominent excess of radio emission in extremely stable quasars (ESQs; i.e., type 1 quasars with extremely weak variability in UV/optical over 10 yr). Specifically, we find that >25% of the ESQs are detected by the FIRST/VLASS radio survey, while only ∼6%–8% of the control sample, matched in redshift, luminosity, and Eddington ratio, are radio-detected. The excess of radio detection in ESQs has a significance of 4.4σ (99.9995%) and predominantly occurs at intermediate radio loudness with R ∼ 10–60. The radio detection fraction of ESQs also tends to increase in the ESQ samples selected with more stringent thresholds. Our results are in contrast to the common view that radio-loud quasars are likely more variable in UV/optical due to jet contribution. New clues/challenges posed by our findings highlight the importance of extensive follow-up observations to probe the nature of jets in ESQs and theoretical studies on the link between jet launching and ESQs. Moreover, our results make ESQs, an essential population that has never been explored, unique targets in the burgeoning era of time domain astronomy, like their opposite counterparts of quasars exhibiting extreme variability or changing-look features.

The role of magnetar transient activity in time-domain and multimessenger astronomy

Time-domain and multimessenger astronomy (TDAMM) involves the study of transient and time-variable phenomena across various wavelengths and messengers. The Astro2020 Decadal Survey has identified TDAMM as the top priority for NASA in this decade, emphasizing its crucial role in advancing our understanding of the universe and driving new discoveries in astrophysics. The TDAMM community has come together to provide further guidance to funding agencies, aiming to define a clear path toward optimizing scientific returns in this research domain. This encompasses not only astronomy but also fundamental physics, offering insights into properties of gravity, the formation of heavy elements, the equation of state of dense matter, and quantum effects associated with extreme magnetic fields. Magnetars, neutron stars with the strongest magnetic fields in the universe, play a critical role in this context. We aim to underscore the significance of magnetars in TDAMM, highlighting the necessity of ensuring observational continuity, addressing current limitations, and outlining essential requirements to expand our knowledge in this field.

Design and implementation of a prototype of Antarctic optical telescope array for time domain astronomy based on drift-scan CCD technology

Design and implementation of a prototype of Antarctic optical telescope array for time domain astronomy based on drift-scan CCD technology

The Host Galaxy Fluxes of Active Galaxy Nuclei Are Generally Overestimated by the Flux Variation Gradient Method

In terms of the variable nature of normal active galaxy nuclei (AGN) and luminous quasars, a so-called flux variation gradient (FVG) method has been widely utilized to estimate the underlying non-variable host galaxy fluxes. The FVG method assumes an invariable AGN color, but this assumption has been questioned by the intrinsic color variation of quasars and local Seyfert galaxies. Here, using an up-to-date thermal fluctuation model to simulate multi-wavelength AGN variability, we theoretically demonstrate that the FVG method generally overestimates the host galaxy flux; that is, it is more significant for brighter AGN/quasars. Furthermore, we observationally confirm that the FVG method indeed overestimates the host galaxy flux by comparing it to that estimated through other independent methods. We thus caution that applying the FVG method should be performed carefully in the era of time-domain astronomy.

Comprehensive characterization and simulation of the point spread function of lobster eye X-ray micro pore optics

Lobster eye micro pore optics has been extensively researched, developed, and experimented with over the past few decades since its proposal by Angel in 1979. The lobster eye X-ray telescope represents the next generation of X-ray time-domain astronomy. This study presents a novel semi-analytical numerical model for simulating and studying the optical performance of micro pore X-ray optics based on geometric physical models, channel errors, and Fresnel reflectance. The numerical method accurately and rapidly models the X-ray point-spread function through millions of square microchannels. Soft X-ray testing validates the semi-analytical numerical model, which simulates common channel errors in lobster eye optics manufacturing. The X-ray results demonstrated that tilt and shear channel errors significantly impacted the point spread function, which is consistent with simulation outcomes and has a significant guiding value for the subsequent process improvement.

LCGCT: A light curve generator in customisable-time-bin based on time-series database

In the era of time-domain astronomy, scientists often need to generate light curves with varying time-bin. However, an increase in time resolution typically leads to a substantial increase in data transmission. To enhance the data processing efficiency in time-domain astronomy, we propose a novel time-series data model for storing time-series observation data, and we construct the LCGCT, a tool designed to produce light curves with customisable time bins. To validate our approach, we utilise the 7-year MAXI/GSC (Gas Slit Camera of the Monitor of All-sky X-ray Image) X-ray source catalogue, incorporating its 24-h binned light curves for a comparative analysis with our approach. The results obtained confirm the accuracy and effectiveness of our proposed approach. Subsequently, we compare the storage capacity and query performance of LCGCT with a PostgreSQL-based implementation, and results show that LCGCT conserves 75% of the storage space and achieves three times the query speed. Owing to its noteworthy storage and query performance, our proposed time-series data model-based LCGCT can be used in time-domain astronomical projects with high time resolution.

Light curve classification with DistClassiPy: A new distance-based classifier

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at https://pypi.org/project/distclassipy/.

Thai National Radio Astronomy Observatory Project and a future South-East Asian VLBI Network

Abstract NARIT initiated a national flagship project in 2017 for development of radio astronomy and geodesy in Thailand. In this project, a 40-m Thai National Radio Telescope (TNRT) and a 13-m VLBI Global Observing System radio telescope are constructed in Chiang Mai. The 40-m TNRT is the largest telescope for radio astronomy in South-East Asia. Its flexible operation with a wide-frequency coverage 0.3-115 GHz will allow us to uniquely contribute to the time-domain astronomy as well as carry out unbiased surveys for a wide variety of research fields, which were published in a white paper. Within the framework of collaboration with VLBI arrays in the world, TNRT will drastically improve the imaging quality and performances based on its unique geographical location. Through commissioning of the L-band system, the 1st Call for Proposals of the 40-m TNRT in the L-band1 has been internationally announced on 10th October 2023, TST 10 am. Future vision for establishment of forthcoming regional VLBI networks based on TNRT is also introduced: Thai National VLBI Array and South-East Asian VLBI Network in collaboration with Indonesia, Malaysia, and Vietnam.

Recovered supernova Ia rate from simulated LSST images

Aims. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods. We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results. We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Redback: a Bayesian inference software package for electromagnetic transients

ABSTRACT Fulfilling the rich promise of rapid advances in time-domain astronomy is only possible through confronting our observations with physical models and extracting the parameters that best describe what we see. Here, we introduce redback; a Bayesian inference software package for electromagnetic transients. redback provides an object-orientated python interface to over 12 different samplers and over 100 different models for kilonovae, supernovae, gamma-ray burst afterglows, tidal disruption events, engine-driven transients among other explosive transients. The models range in complexity from simple analytical and semi-analytical models to surrogates built upon numerical simulations accelerated via machine learning. redback also provides a simple interface for downloading and processing data from various catalogues such as Swift and FINK. The software can also serve as an engine to simulate transients for telescopes such as the Zwicky Transient Facility and Vera Rubin with realistic cadences, limiting magnitudes, and sky coverage or a hypothetical user-constructed survey or a generic transient for target-of-opportunity observations with different telescopes. As a demonstration of its capabilities, we show how redback can be used to jointly fit the spectrum and photometry of a kilonova, enabling a more powerful, holistic probe into the properties of a transient. We also showcase general examples of how redback can be used as a tool to simulate transients for realistic surveys, fit models to real, simulated, or private data, multimessenger inference with gravitational waves, and serve as an end-to-end software toolkit for parameter estimation and interpreting the nature of electromagnetic transients.

An online framework for fitting fast transient light curves

ABSTRACT The identification of extragalactic fast optical transients (eFOTs) as potential multimessenger sources is one of the main challenges in time-domain astronomy. However, recent developments have allowed for probes of rapidly evolving transients. With the increasing number of alert streams from optical time-domain surveys, the next paradigm is building technologies to rapidly identify the most interesting transients for follow-up. One effort to make this possible is the fitting of objects to a variety of eFOT light curve models such as kilonovae and γ-ray burst (GRB) afterglows. In this work, we describe a new framework designed to efficiently fit transients to light curve models and flag them for further follow-up. We describe the pipeline’s workflow and a handful of performance metrics, including the nominal sampling time for each model. We highlight as examples ZTF20abwysqy, the shortest long gamma-ray burst discovered to date, and ZTF21abotose, a core-collapse supernova initially identified as a potential kilonova candidate.

A proposal for a fast infrared bursts detector

The gravitational wave GW170817 from a binary neutron star merger and the simultaneous electromagnetic detection of the GRB170717A by Fermi Gamma-Ray Space Telescope, opened a new era in the multi-messenger astronomy. Furthermore, the GRBs (Gamma-Ray Bursts) and the mysterious FRBs (Fast Radio Bursts) have sparked interest in the development of new detectors and telescopes dedicated to the time-domain astronomy across the electromagnetic spectrum. Time-domain astronomy aims to acquire fast astronomical bursts in temporal range between a few seconds down to 1 ns. Fast InfraRed Bursts (FIRB's) have been relatively understudied, often due to the lack of appropriate tools for observation and analysis. In this scientific scenario, the present contribution proposes a new detection system for ground-based reflecting telescopes working in the mid-infrared (mid-IR) range to search for astronomical FIRB's. Experience developed in the diagnostics for lepton circular accelerators can be used to design temporal devices for astronomy. Longitudinal diagnostic instruments acquire bunch-by-bunch particle shifts in the direction of flight, that is equivalent to temporal. Transverse device integrates the beam signal in the horizontal and vertical coordinates, as standard telescopes. The proposed instrument aims to work in temporal mode. Feasibility study tests have been carried out at SINBAD, the infrared beam line of DAFNE, the e+e- collider of INFN. SINBAD releases pulsed infrared synchrotron light with 2.7 ns separation. The front-end detector system has been evaluated to detect temporal fast infrared signals with 2–12 μm wavelengths and 1 ns rise times. The present contribute aims to be a step toward a feasibility study report.

Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention

Nowadays, astronomy has entered the era of Time-Domain Astronomy, and the study of the time-varying light curves of various types of objects is of great significance in revealing the physical properties and evolutionary history of celestial bodies. The Ground-based Wide Angle Cameras telescope, on which this paper is based, has observed more than 10 million light curves, and the detection of anomalies in the light curves can be used to rapidly detect transient rare phenomena such as microgravity lensing events from the massive data. However, the traditional statistically based anomaly detection methods cannot realize the fast processing of massive data. In this paper, we propose a Discrete Wavelet (DW)-Gate Recurrent Unit-Attention (GRU-Attention) light curve warning model. Wavelet transform has good effect on data noise reduction processing and feature extraction, which can provide richer and more stable input features for a neural network, and the neural network can provide more flexible and powerful output model for wavelet transform. Comparison experiments show an average improvement of 61% compared to the previous pure long-short-term memory unit (LSTM) model, and an average improvement of 53.5% compared to the previous GRU model. The efficiency and accuracy of anomaly detection in previous paper work are not good enough, the method proposed in this paper possesses higher efficiency and accuracy, which incorporates the Attention mechanism to find out the key parts of the light curve that determine the anomalies. These parts are assigned higher weights, and in the actual anomaly detection, the star is detected with 83.35% anomalies on average, and the DW-GRU-Attention model is compared with the DW-LSTM model, and the detection result f1 is improved by 5.75% on average, while having less training time, thus providing valuable information and guidance for astronomical observation and research.

A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning

Identifying and classifying variable stars is essential to time-domain astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) acquired a large amount of spectral data. However, there is no corresponding variable source-related information in the data, constraining LAMOST data utilization for scientific research. In this study, we systematically investigated variable source classification methods for LAMOST data. We constructed a 10-class classification model using three mainstream machine-learning methods. Through performance comparison, we chose the LightGBM and XGBoost models. We further identified variable source candidates in the r band in LAMOST DR9 and obtained 281,514 variable source candidates with probabilities greater than 95%. Subsequently, we filtered out the sources of periodic variable sources using the generalized Lomb–Scargle periodogram and classified these periodic variable sources using the classification model. Finally, we propose a reliable periodic variable star catalog containing 176,337 stars with specific types.