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In this work, we have carried out a systematic analysis of the Very Large Array Sky Survey (VLASS) quick look catalogs together with Gaia DR3 to identify the optical counterparts of 3 GHz radio emitters within 500 pc to obtain a homogeneous statistical sample of stellar radio sources. We have identified distinct populations of 3 GHz emitters across the Gaia DR3 color–magnitude diagram. We also present candidate sources (transient, highly variable, or background artifacts) that can be confirmed by follow-up observations. A majority of the detected sources constitute main-sequence G-, K-, and M-type stars, including ultracool dwarfs. Pinning down the origin of radio emission from these populations can help us gain further insights into the origin of stellar and planetary magnetic fields. By analyzing the variation of brightness temperature of the sources with their spectral type, we have tentatively associated possible emission mechanisms with different object types. We inspected the correlation between quiescent radio and X-ray emission for our sample, which can provide crucial insights into the current understanding of the Gudel–Benz relationship, which is essential for modeling steady radio emission and coronal heating. This VLASS-Gaia DR3 analysis acts as a pilot study for follow-up observations at multiple wavelengths to better understand stellar structure, model flaring activities, and detect radio emission caused by star–planet interactions.

Giant planets exhibit diverse orbital properties, hinting at their distinct formation and dynamic histories. In this paper, using Gaia Data Release 3 (DR3), we investigate if and how the orbital properties of Jupiters are linked to their host star properties, particularly their metallicity and age. We obtain metallicities for main-sequence stars of spectral type F, G, and K, hosting hot, warm, and cold Jupiters with varying eccentricities. We compute the velocity dispersions of the host stars of these three groups using kinematic information from Gaia DR3 and obtain average ages using a velocity dispersion–age relation. We find that the host stars of hot Jupiters are relatively metal rich ([Fe/H] = 0.18 ± 0.13) and young (median age of 3.97 ± 0.51 Gyr) compared to the host stars of cold Jupiters in nearly circular orbits, which are relatively metal poor (0.03 ± 0.18) and older (median age of 6.07 ± 0.79 Gyr). The host stars of cold Jupiters in high-eccentricity orbits, on the other hand, show metallicities similar to those of the hosts of hot Jupiters, but are older, on average (median age of 6.25 ± 0.92 Gyr). The similarity in metallicity between the hosts of hot Jupiters and the hosts of cold Jupiters in high-eccentricity orbits supports high-eccentricity migration as the potential origin of hot Jupiters, with the latter serving as the progenitors of hot Jupiters. However, the average age difference between them suggests that the older hot Jupiters may have been engulfed by their host star over timescales ∼ 6 Gyr. This allows us to estimate the value of stellar tidal quality factor, ${Q}_{* }^{{\prime} }\sim {10}^{6\pm 1}$ .

Coherent radio emission with properties similar to planetary auroral signals has been reported from GJ 1151, a quiescent, slow-rotating mid-M star, by the LOFAR Two-meter (120–170 MHz) Sky Survey. The observed LOFAR emission is fairly bright at 0.89 mJy with 64% circular polarization, and the emission characteristics are consistent with the interaction between an Earth-sized planet with an orbital period of 1–5 days and the magnetic field of the host star. However, no short-period planet has been detected around GJ 1151. To confirm the reported radio emission caused by the putative planet around GJ 1151 and to investigate the nature of this emission, we carried out upgraded Giant Metrewave Radio Telescope observations of GJ 1151 at 150, 218, and 400 MHz over 33 hr across ten epochs. No emission was detected at any frequency. While at 150 and 218 MHz, nondetection could be due to the low sensitivity of our observations, at 400 MHz, the rms sensitivities achieved were sufficient to detect the emission observed with LOFAR at ∼20 σ level. Our findings suggest that the radio emission is highly time variable, likely influenced by the star-planet system’s phase and the host star’s magnetic field. Additional observations below 170 MHz, at more frequent epochs (as the periodicity of the emission is unknown), especially during periods of high stellar magnetic field strength, are needed to confirm the emission.

We have recently been witnessing that our society is starting to heal from the impacts of COVID-19. The economic, social and cultural impacts of a pandemic cannot be ignored and we should be properly equipped to deal with similar situations in future. Recently, Monkeypox has been concerning the international health community with its lethal impacts for a probable pandemic. In such situations, having appropriate protocols and methodologies to deal with the outbreak efficiently is of paramount interest to the world. Early diagnosis and treatment stand as the only viable option to tackle such problems. To this end, in this paper, we propose an ensemble learning-based framework to detect the presence of the Monkeypox virus from skin lesion images. We first consider three pre-trained base learners, namely Inception V3, Xception and DenseNet169 to fine-tune on a target Monkeypox dataset. Further, we extract probabilities from these deep models to feed into the ensemble framework. To combine the outcomes, we propose a Beta function-based normalization scheme of probabilities to learn an efficient aggregation of complementary information obtained from the base learners followed by the sum rule-based ensemble. The framework is extensively evaluated on a publicly available Monkeypox skin lesion dataset using a five-fold cross-validation setup to evaluate its effectiveness. The model achieves an average of 93.39%, 88.91%, 96.78% and 92.35% accuracy, precision, recall and F1 scores, respectively. The supporting source codes are presented in https://github.com/BihanBanerjee/MonkeyPox.

The GAIA space mission is impacting astronomy in many significant ways by providing a uniform, homogeneous, and precise data set for over 1 billion stars and other celestial objects in the Milky Way and beyond. Exoplanet science has greatly benefited from the unprecedented accuracy of the stellar parameters obtained from GAIA. In this study, we combine photometric, astrometric, and spectroscopic data from the most recent Gaia DR3 to examine the kinematic and chemical age proxies for a large sample of 2611 exoplanets hosting stars whose parameters have been determined uniformly. Using spectroscopic data from the Radial Velocity Spectrometer on board GAIA, we show that stars hosting massive planets are metal-rich and α -poor in comparison to stars hosting small planets. The kinematic analysis of the sample reveals that stellar systems with small planets and those with giant planets differ in key aspects of galactic space velocity and orbital parameters, which are indicative of age. We find that the galactic orbital parameters have a statistically significant difference of 0.06 kpc for Z _max and 0.03 for eccentricity, respectively. Furthermore, we estimated the stellar ages of the sample using the MIST-MESA isochrone models. The ages and their proxies for the planet-hosting stars indicate that the hosts of giant planetary systems are younger when compared to the population of stars harboring small planets. These age trends are also consistent with the chemical evolution of the galaxy and the formation of giant planets from the core-accretion process.