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We study the representative arm identification (RAI) problem in the multi-armed bandits (MAB) framework, wherein we have a collection of arms, each associated with an unknown reward distribution. An underlying instance is defined by a partitioning of the arms into clusters of predefined sizes, such that for any $j > i$, all arms in cluster $i$ have a larger mean reward than those in cluster $j$. The goal in RAI is to reliably identify a certain prespecified number of arms from each cluster, while using as few arm pulls as possible. The RAI problem covers as special cases several well-studied MAB problems such as identifying the best arm or any $M$ out of the top $K$, as well as both full and coarse ranking. We start by providing an instance-dependent lower bound on the sample complexity of any feasible algorithm for this setting. We then propose two algorithms, based on the idea of confidence intervals, and provide high probability upper bounds on their sample complexity, which orderwise match the lower bound. Finally, we do an empirical comparison of both algorithms along with an LUCB-type alternative on both synthetic and real-world datasets, and demonstrate the superior performance of our proposed schemes in most cases., Comment: We analyse a clustered multi-armed bandit formulation, where the learning objective is to identify representative arms from each cluster, in a fixed confidence setting

Redshift estimation and the classification of gamma-ray AGNs represent crucial challenges in the field of gamma-ray astronomy. Recent efforts have been made to tackle these problems using traditional machine learning methods. However, the simplicity of existing algorithms, combined with their basic implementations, underscores an opportunity and a need for further advancement in this area. Our approach begins by implementing a Bayesian model for redshift estimation, which can account for uncertainty while providing predictions with the desired confidence level. Subsequently, we address the classification problem by leveraging intelligent initialization techniques and employing soft voting. Additionally, we explore several potential self-supervised algorithms in their conventional form. Lastly, in addition to generating predictions for data with missing outputs, we ensure that the theoretical assertions put forth by both algorithms mutually reinforce each other., Comment: Extended Abstract accepted NeurIPS ML4PS workshop 2023. Complete manuscripts can be found at arXiv:2309.12846 and arXiv:2310.06095

NASA's all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as \texttt{TRICERATOPS}, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61$^{+0.09} _{-0.10}$ R$_\oplus$), TOI-771b (1.42$^{+0.11} _{-0.09}$ R$_\oplus$), TOI-871b (1.66$^{+0.11} _{-0.11}$ R$_\oplus$), TOI-1467b (1.83$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-1739b (1.69$^{+0.10} _{-0.08}$ R$_\oplus$), TOI-2068b (1.82$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-4559b (1.42$^{+0.13} _{-0.11}$ R$_\oplus$), and TOI-5799b (1.62$^{+0.19} _{-0.13}$ R$_\oplus$). Among all these planets, six of them fall within the region known as 'keystone planets,' which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune., Comment: Accepted: Publications of the Astronomical Society of Australia

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray emitting sources, among which a significant number of objects have been identified as active galactic nuclei (AGN). The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialisation techniques and self-supervised learning for classifying blazars into BL Lacertae objects (BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be classified as FSRQs., Comment: Accepted at MNRAS, Comments are appreciated

With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active galactic nuclei (AGN), which are a class of supermassive black hole systems known for their intense multi-wavelength emissions and violent variability. Determining the redshifts of AGNs is essential for understanding their distances, which, in turn, sheds light on our current understanding of the structure of the nearby universe. However, the task involves a number of challenges such as the need for meticulous follow-up observations across multiple wavelengths and astronomical facilities. In this study, we employ a simple yet effective deep learning model with a single hidden layer having $64$ neurons and a dropout of 0.25 in the hidden layer, on a sample of AGNs with known redshifts from the latest AGN catalog, 4LAC-DR3, obtained from Fermi-LAT. We utilized their spectral, spatial, and temporal properties to robustly predict the redshifts of AGNs as well quantify their associated uncertainties, by modifying the model using two different variational inference methods. We achieve a correlation coefficient of 0.784 on the test set from the frequentist model and 0.777 and 0.778 from both the variants of variational inference, and, when used to make predictions on the samples with unknown redshifts, we achieve mean predictions of 0.421, 0.415 and 0.393, with standard deviations of 0.258, 0.246 and 0.207 from the models, respectively., Comment: Accepted: MNRAS

We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve high-accuracy results. We have also compiled a dataset of binary and exoplanet stars for training and validation by cross-matching observations from multiple space-based telescopes with catalogs of known binary and exoplanet stars. The application of wavelet transformation on the light curves has reduced the number of data points and improved the training time. Our algorithm has shown exceptional performance, with a test accuracy of 81.17%. This method can be applied to large datasets from current and future space-based telescopes, providing an efficient and accurate way of classifying stars., Comment: In Review: MNRAS

NASA's Transiting Exoplanet Survey Satellite (TESS) is an all-sky survey mission designed to find transiting exoplanets orbiting nearby bright stars. It has identified more than 329 transiting exoplanets, and almost 6,000 candidates remain unvalidated. In this manuscript, we discuss the findings from the ongoing VaTEST (Validation of Transiting Exoplanets using Statistical Tools) project, which aims to validate new exoplanets for further characterization. We validated 11 new exoplanets by examining the light curves of 24 candidates using the LATTE and TESS-Plot tools and computing the False Positive Probabilities using the statistical validation tool TRICERATOPS. These include planets suitable for atmospheric characterization using transmission spectroscopy (TOI-2194b), emission spectroscopy (TOI-3082b and TOI-5704b) and for both transmission and emission spectroscopy (TOI-672b, TOI- 1694b, and TOI-2443b); One super-Earth (TOI-2194b) orbiting a bright (V = 8.42 mag), metal-poor ([Fe/H] = -0.3720 $\pm$ 0.1) star; one short-period Neptune-like planet (TOI-5704) in the Hot Neptune Desert. In total, we validated 1 super-Earth, 7 sub-Neptunes, 1 Neptune-like, and 2 sub-Saturn or super-Neptune-like exoplanets. Additionally, we identify five likely planet candidates (TOI-323, TOI- 1180, TOI-2200, TOI-2408 and TOI-3913) which can be further studied to establish their planetary nature., Comment: Accepted for Publication in Astronomical Journal, 28 Pages, 7 Figures

In today's era, a tremendous amount of data is generated by different observatories and manual classification of data is something which is practically impossible. Hence, to classify and categorize the objects there are multiple machine and deep learning techniques used. However, these predictions are overconfident and won't be able to identify if the data actually belongs to the trained class. To solve this major problem of overconfidence, in this study we propose a novel Bayesian Neural Network which randomly samples weights from a distribution as opposed to the fixed weight vector considered in the frequentist approach. The study involves the classification of Stars and AGNs observed by XMM Newton. However, for testing purposes, we consider CV, Pulsars, ULX, and LMX along with Stars and AGNs which the algorithm refuses to predict with higher accuracy as opposed to the frequentist approaches wherein these objects are predicted as either Stars or AGNs. The proposed algorithm is one of the first instances wherein the use of Bayesian Neural Networks is done in observational astronomy. Additionally, we also make our algorithm to identify stars and AGNs in the whole XMM-Newton DR11 catalogue. The algorithm almost identifies 62807 data points as AGNs and 88107 data points as Stars with enough confidence. In all other cases, the algorithm refuses to make predictions due to high uncertainty and hence reduces the error rate., Comment: 4 pages

In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer-based citizen science project called Galaxy Zoo was introduced, which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. For the last few years, many algorithms have been proposed which happen to do a phenomenal job in classifying galaxies into multiple classes. But all these algorithms tend to classify galaxies into less than six classes. However, after considering the minute information which we know about galaxies, it is necessary to classify galaxies into more than eight classes. In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. Great care is given to disc edge and disc face galaxies, distinguishing between a variety of substructures and minute features which are associated with each class. The proposed model consists of convolution layers to extract features making this method fully automatic. The achieved test accuracy is 84.73 per cent which happens to be promising after considering such minute details in classes. Along with convolution layers, the proposed model has three more layers responsible for classification, which makes the algorithm consume less time., Comment: Published in MNRAS

We present a first systematic time series study of a sample of blazars observed by the Transiting Exoplanet Survey Satellite $\textit{TESS}$ spacecraft. By cross matching the positions of the sources in the TESS observations with those from Roma-BZCAT, 29 blazars including both BL Lacerate objects and flat-spectrum radio quasars were identified. The observation lengths of the 79 light curves of the sources, across all sectors on which the targets of interest have been observed by $\textit{TESS}$, range between 21.25 and 28.2 days. The light curves were analyzed using various methods of time series analysis. The results show that the sources exhibit significant variability with fractional variability spanning between 1.41% and 53.84%. The blazar flux distributions were studied by applying normal and lognormal probability density function models. The results indicate that optical flux histogram of the sources are consistent with normal probability density function with most of them following bi-modal distribution as opposed to uni-modal distribution. This suggests that the days-timescale optical variability is contributed either by two different emission zones or two distinct states of short-term activity in blazars. Power spectral density analysis was performed by using the power spectral response method and the true power spectra of unevenly sampled light curves were estimated. The power spectral slopes of the light curves ranged from 1.7 to 3.2., Comment: Accepted to MNRAS

Solar plages, which are bright regions on the Sun's surface, are an important indicator of solar activity. In this study, we propose an automated algorithm for identifying solar plages in Ca K wavelength solar data obtained from the Kodaikanal Solar Observatory. The algorithm successfully annotates all visually identifiable plages in an image and outputs the corresponding calculated plage index. We perform a time series analysis of the plage index (rolling mean) across multiple solar cycles to test the algorithm's reliability and robustness. The results show a strong correlation between the calculated plage index and those reported in a previous study. The correlation coefficients obtained for all the solar cycles are higher than 0.90, indicating the reliability of the model. We also suggest that adjusting the hyperparameters appropriately for a specific image using our web-based app can increase the model's efficiency. The algorithm has been deployed on the Streamlit Community Cloud platform, where users can upload images and customize the hyperparameters for desired results. The input data used in this study is freely available from the KSO data archive, and the code and the generated data are publicly available on our GitHub repository. Our proposed algorithm provides an efficient and reliable method for identifying solar plages, which can aid the study of solar activity and its impact on the Earth's climate, technology, and space weather., Comment: Accepted: RAS Techniques and Instruments

Frequency band of 30 to 300 GHz, which is termed as mm wave band offers 100 times bandwidth as compared to sub 6 GHz band. In this study, we propose a novel design which acts as a near unity absorber at 258 GHz or 0.258 THz in Y polarization and 292 GHz or 0.292 THz in X polarization. The study also includes effect of change in parameters on resonance frequency in both X and Y polarization which allows us to use same design with modified parameters to get near unity absorptivity in frequency of 205 to 345 GHz. Further, we also discuss on higher frequency mode which particularly appears in Y polarization and on changing the inner square dimension of the design.

Frequency Band of 0.5 THz to 1.1 THz (WR1.5 and WR1) is one of the promising bands when it comes to 6G. In this paper, we propose a novel metamaterial absorber suitable to be used at lower WR1 frequencies in TE mode. Alternatively, the design can be used as a strong absorber at lower Terahertz frequencies. In addition, when used in TM mode the absorber works as a perfect absorber at higher WR1 band.