Time domain surveys produced a large amount of light curves. Besides some hot topics of searching for exoplanets, two kinds of targets provide us many physical information which are variables and transients. In this talk, I will discuss two machine-learning applications on the Kepler dataset, including flare detection and variable classification. Stellar flares offer invaluable insights into stellar magnetic activity and exoplanetary environments. First, I will introduce FCN4Flare, a deep-learning approach using fully convolutional networks for precise point-to-point flare prediction regardless of light curve length. Applying FCN4Flare to Kepler and LAMOST, we compile a catalog of 30,285 high-confidence flares across 1426 stars. Flare energies are estimated and stellar/exoplanet properties are analyzed, identifying pronounced activity for an M-dwarf hosting a habitable zone planet. This work demonstrates new scientific discovery ability in analyzing photometric time-series data. In the second work, I will show you a so-called RLNet which is based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The experiment classified about 150,000 Kepler light curves from Quarter 1 into 11 categories. The results show that RLNet outperforms other methods in all metrics. Finally, I will outlook the big model application in the time sequence analysis.

Since the successful discovery of neutrinos, their origin is still a mystery until the association with TXS 0506+056. More messengers are achievable to study the intrinsic mechanism of extreme phenomena. Recently, Fermi-LAT Collaboration published the fourth catalogue of active galactic nuclei (4LAC), together with Data Release 2 later, including totally 3148 sources. The majority of these objects are blazars, which are classified into flat-spectrum radio quasars (FSRQs), BL Lac-type objects (BLLs), and blazars of uncertain type (BCUs) according to their optical observation feature. The BCUs take up to 38.2 per cent of total, whose classification is quite challenging and manpower consuming. However with the remarkable advances of technology, deep learning has been widely applied in astronomy. In this work, we take the advantage of 11 machine learning algorithms plus the convolutional neural network (CNN)-based deep learning algorithm to classify BCUs based on 10 parameters and the broad-band spectral energy distribution of each object obtained with VOU-BLAZARS . On average, this method has impressive performance, reaching above 95 per cent of balanced accuracy for the training sample, best among the studies so far. We correlate the IceCube neutrinos and blazars in 4LAC, found a few possible associations. With the machine learning prediction, we later elaborate the association of these BCUs with neutrinos samples and find out most of the BCUs associated with neutrinos are with low synchrotron peak frequency, which may be due to the energy distribution of high-energy particles. We propose neutrinos might be another feature for objects classification in the future.

The 4m International Liquid Mirror Telescope (ILMT) is the first telescope dedicated to optical survey in India. It uses a continuously spinning bowl of mercury as the primary reflector. This makes it the only operational liquid mercury telescope in the world. The FoV of the telescope is 22’ with a limiting magnitude of ~22 in the g' band. The ILMT acquires up to 15 GB of data on any given night of observation resulting in close to 30 science frames. One of the primary science goals with the ILMT is to discover new optical transients and variables. An automated transient detection pipeline has been developed to discover and perform broad classification of transients and variable sources lying in the ILMT field. The pipeline hosts an image subtraction algorithm that performs image subtraction of acquired science frames to give difference frames. A convolutional neural network (CNN) based classifier detects real sources in the difference image by classifying all the sources in the difference image as real and artefact. Another CNN classifier classifies these real sources into three broad categories. Finally, out of the detected real sources, the known asteroids and other cataloged solar system objects are automatically identified and can be rejected at later stages. The pipeline was tested on acquired ILMT data which led to the detection of several asteroids and variable stars. Given the high limiting magnitude of the telescope, the pipeline is expected to discover several transient and variable objects and phenomenon including supernovae, AGNs, blazars, events like episodic outbursts in pre-main sequence T Tauri stars and Herbig Ae/Be stars, post-main sequence variable stars like RR Lyrae, Cepheid Variables, Luminous Blue Variables (LBVs) and other known and unknown sources of variability. The discovered transients and variable objects will be shared with the larger scientific community through a dedicated alert stream.

Cross-matching of various information sources is a powerful tool that helps to not only enrich and augment the contents of individual ones, but also to discover new and unique objects. In astronomy, cross-matching of catalogues is a standard tool for getting broader information on the objects by combining their data from the surveys performed at different wavelengths, and it allows to solve number of tasks like studying various populations of astronomical objects or investigating the properties of interstellar medium. However, the analysis of objects present in just one catalogue and missing the counterparts in all others is also a promising method that may lead to the discovery of both transients and objects with extreme color characteristics. Here we report on our preliminary search for objects that manifest only in ultraviolet observations by comparing the data from GALEX catalogue with several other surveys in different wavelength ranges. We describe the selection of representative sky surveys for this task and give the details on the process of their cross-matching and filtering of the results. We also discuss the possible nature of several outstanding objects detected during the analysis, and discuss the potential output of a larger-scale investigation we are planning based on the experience gained during this initial study.

Although novae, which are the sudden visual brightening triggered by runaway thermonuclear burning on the surface of an accreting white dwarf, are fairly common and bright event, multiple fundamental questions about them, like the discrepancy in observed and theoretical ejecta mass, or the detailed pathway of developing clumps, rings, polar caps, and jets from spherical ejecta, remain unanswered. Despite their astronomical significance as nearby laboratory for the study of nuclear burning and accretion phenomena, multiple such discrepancies suggest surprising limits to our physical understanding of these events. In this talk, I will describe how multifrequency radio observations can potentially play a crucial role in addressing some of these puzzling issues particularly focusing on the low radio frequency observations of novae with the GMRT and results from those observations.

In this talk, I will present the longitudinal advantage of the Indian telescope to perform critical time domain observations of transients such as GRBs and Supernovae. The explosion and progenitor properties derived from high-cadence photometric and spectroscopic observations will be discussed. Also, I will also talk about the first light results of the zenith sky survey from the 4m International Liquid Mirror Telescope (ILMT).

The late-type stellar sources show various transient phenomena such as eclipses, pulsation, inhomogeneities, and flares. For our study, a sample of A-F type CP stars were selected from Bernhard et al. (2020) who have studied the rotational properties of magnetic chemically peculiar stars using ground-based photometric data taken from ASAS-3, KELT, and MASCARA. In this talk, I will present the time-resolved TESS space photometry and spectroscopic analysis of a set of stars. The parameters derived from our analysis will be used to model aiming to probe their internal structure and evolutionary phases.

SNAD (SuperNova Anomaly Detection) is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys using machine learning algorithms. SNAD is based on machine learning techniques and aims to discover and classify various astronomical phenomena. We give an overview of the SNAD project and present the superluminous supernova (SLSN) candidates discovered in the archived data. We also discuss the SNAD project as a contribution in classifying new transients.

Multicolour photometric observations, conducted with instruments in both the Northern and Southern hemispheres in the optical band, along with a comprehensive study, are presented for GRB 181201A and its afterglow. The optical observations began approximately 0.5 days after the burst and lasted almost continuously for about 24 days. After an eight-month hiatus, observations were resumed using the SALT telescope at the South African Astronomical Observatory (SAAO). This enabled us to determine the contribution of the host galaxy to the measured fluxes and to estimate the associated extinction. The comprehensive coverage of the light curve was made possible through the coordinated efforts of a network of nine telescopes worldwide. Convincing evidence of an incipient supernova explosion at the location of the burst source was obtained at the end of the first series of observations. Thus, GRB 181201A became yet another event that confirmed the association of gamma-ray bursts with supernovae. Thirty such events based on photometric observations of burst afterglows were known before it. A comparison of the supernova-induced excess emission in the light curve of the afterglow from GRB 181201A with other events has allowed some of the supernova parameters to be determined.

Based on the collected multiwavelength data, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog, selecting groups of blazars with similar properties. Using machine learning methods, we have constructed an independent classification of the blazars, reflecting the continuous nature of transition from BL Lac-type blazars to radio quasars with a flat spectrum (FSRQs). We compared our clusters with the known Roma-BZCAT classification as well as with other known blazar types: high synchrotron peakers (HSPs) and TeV blazars. The clustering algorithms divided both BL Lac-type objects and FSRQs into two subclasses along with a separate group of mixed BL Lacs and FSRQs. The group of HSP blazars stands out on the overall distribution.

Recent observations of very-high-energy (VHE, ≳ 100 GeV) late-time emission from gamma-ray bursts (GRBs) 180720B and 190829A by H.E.S.S. have challenged our understanding of the GRB environment and the particle energy requirements. We interpret these observations to be the result of external Compton (EC) radiation, considering the blastwave evolution in both the wind and interstellar medium scenarios. We present multiwavelength light curves and energy spectra, ranging from optical to VHE, of these GRBs. We also include the synchrotron and synchrotron self-Compton (SSC) radiation mechanisms, and corrected the EC and SSC models for γ-ray attenuation by absorption of photons through their interaction with the extragalactic background light (EBL). For a given set of fixed model parameters we found that for GRB 180720B the EC gives a satisfactory fit, whereas for GRB 190829A the SSC dominates the fit. For both GRBs a wind environment is preferred over constant density inter-stellar medium, with the Cosmic Microwave Background as the external radiation field. However, with more data and an effective optimisation tool we can find a more robust fit of the model, implying better constraints on GRB physics of late-time emission at these extreme energies.

Ultra-Luminous X-ray sources (ULXs) are extragalactic point-like sources that exhibit an extraordinarily high luminosity in the X-ray (Lx > 1039 erg/s), exceeding the Eddington limit of neutron stars and stellar-mass black holes. This work presents analysis of the optical-to-X-ray spectral index (αox) within the context of ULXs associated with OB-type stars. αox serves as a pivotal parameter to understand the intricate interplay between X-ray and UV emissions, shedding light on the underlying accretion processes. The study investigates the impact of metallicity on αox. Notably, it observes a direct correlation between metallicity and the number of ultra-luminous X-ray sources hosting OB-type stars. The slope of αox vs. UV emission at lower metallicity is comparable to the observational data. Furthermore, the study highlights significant distinctions between ULXs with black hole and neutron primaries, particularly all the B-type stars are associated with ULXs with black holes, displaying distinct αox trends based on metallicity. This work also uncovers a strong anti-correlation between αox and beaming factor b in systems with b < 1.0, providing insights into the impact of beaming on αox values.