The slitless spectroscopy mode of the NISP onboard Euclid has enabled efficient spectroscopy of objects within a large FoV. We present a large and homogeneous sample of bright quasars identified from the Euclid Quick Data Release (Q1) by combining high-purity candidate selections from Gaia and WISE with the NISP spectra. Through visual inspection of the Euclid spectra of these quasar candidates, we identify approximately 3500 quasars with reliable redshifts at $0

Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the intergalactic medium. Yet, it remains unclear how feedback, through its connection to key galaxy properties, shapes the radial gas density profile in the host halo. We tackle this question using suites of the EAGLE, IllustrisTNG, and Simba cosmological hydrodynamical simulations, which span a variety of feedback models. We develop a random forest algorithm that predicts the radial gas density profile within haloes from the total halo mass and five global properties of the central galaxy: gas and stellar mass; star formation rate; mass and accretion rate of the central black hole (BH). The algorithm reproduces the simulated gas density profiles with an average accuracy of $\sim$80-90% over the halo mass range 10^{9.5} \, \mathrm{M}_{\odot} < M_{\rm 200c} < 10^{15} \, \mathrm{M}_{\odot} and redshift interval $0

We present PolySwyft, a novel, non-amortised simulation-based inference framework that unites the strengths of nested sampling (NS) and neural ratio estimation (NRE) to tackle challenging posterior distributions when the likelihood is intractable but a forward simulator is available. By nesting rounds of NRE within the exploration of NS, and employing a principled KL-divergence criterion to adaptively terminate sampling, PolySwyft achieves faster convergence on complex, multimodal targets while rigorously preserving Bayesian validity. On a suite of toy problems with analytically known posteriors of a dim(theta,D)=(5,100) multivariate Gaussian and multivariate correlated Gaussian mixture model, we demonstrate that PolySwyft recovers all modes and credible regions with fewer simulator calls than swyft's TNRE. As a real-world application, we infer cosmological parameters dim(theta,D)=(6,111) from CMB power spectra using CosmoPower. PolySwyft is released as open-source software, offering a flexible toolkit for efficient, accurate inference across the astrophysical sciences and beyond.

We investigate the microlensing detectability of extraterrestrial technosignatures originating from Dyson sphere \textendash like structures, such as Dyson Swarms surrounding primordial black holes (PBHs). These hypothetical swarms consist of stochastically varying, partially opaque structures that could modulate standard microlensing light curves through time-dependent transmission effects. We introduce a probabilistic framework that includes a stochastic transmission model governed by variable optical depth and random gap distributions. We perform a parameter scan and generate heatmaps of the optical transit duration. We study the infrared excess radiation and peak emission wavelength as complementary observational signatures. Additionally, we define and analyze the effective optical depth and the anomalous microlensing event rate for these stochastic structures. Our findings provide a new avenue for searching for extraterrestrial advanced civilizations by extending microlensing studies to include artificial, dynamic modulation signatures.

The launch of the James Webb Space Telescope (JWST) has delivered high-quality atmospheric observations and expanded the known chemical inventory of exoplanetary atmospheres, opening new avenues for atmospheric chemistry modeling to interpret these data. Here, we present XODIAC, a fast, GPU-accelerated, one-dimensional photochemical model with a built-in equilibrium chemistry solver, an updated thermochemical database, and three chemical reaction networks. This framework enables comparative atmospheric chemistry studies, including the newly developed XODIAC-2025 network, a state-of-the-art C-H-O-N-P-S-Metals network, linking 594 species through 7,720 reactions. The other two are existing, publicly available C-H-O-N-S and C-H-O-N-S-Metals networks, from the established photochemical models VULCAN and ARGO, respectively, which are commonly used in the community. The XODIAC model has been rigorously benchmarked on the well-studied hot Jupiter HD 189733 b, with results compared against these two models. Benchmarking shows excellent agreement and demonstrates that, when the same chemical network and initial conditions are used, the numerical scheme for solving atmospheric chemistry does not significantly affect the results. We also revisited the atmospheric chemistry of HD 189733 b and performed a comparative analysis across the three networks. Sulfur chemistry shows the least variation across networks, carbon chemistry shows slightly more, and phosphorus chemistry varies the most, primarily due to the introduction of unique PHO and PN pathways comprising 390 reactions in the XODIAC-2025 network. These findings highlight XODIAC's capability to advance exoplanetary atmospheric chemistry and provide a robust framework for comparative exoplanetology.

Fast Radio Bursts (FRBs) are bright millisecond radio pulses. Their origin is still unknown in the field of astronomy. A notable distinction among FRBs is that some sources repeat, while others appear to be non-repeating events. Interestingly, repeating FRBs tend to exhibit broader temporal widths and narrower spectral bandwidths compared to non-repeat events, suggesting they may arise from different physical mechanisms. However, current radio telescopes have limited coverage and sensitivity, which hinders a complete survey with continuous long-term monitoring. This issue makes it difficult to confirm repeat activity and potentially leads to misclassification of repeaters as non-repeaters; these are referred to as repeater candidates. To address this, machine learning techniques have emerged as a useful tool for classifying distinct FRB types in previous studies. In this study, we utilize the CHIME/FRB baseband catalog with three orders of magnitude better time resolution than the intensity catalog. Measured fluences are available in the baseband catalog, while only upper limits are reported in the intensity catalog. We apply machine learning to the baseband catalog to evaluate classification outcomes. We identify 15 repeater candidates among 122 non-repeating FRBs in the baseband catalog. Additionally, our classification identifies 31 sources previously categorized as repeater candidates as non-repeaters, highlighting a significant difference from the prior work. Of these repeater candidates, 14 overlap with previous findings, while 1 is newly identified in this work. Notably, one of our candidates was confirmed as a repeater by CHIME/FRB. Follow-up observations for the 14 candidates are highly encouraged.

Stochastic gravitational-wave background (SGWB) poses significant challenges for data analysis and parameter inference in future space-based gravitational-wave missions, such as LISA and Taiji, as it appears as an additional stochastic component along with instrumental noise. Previous studies have developed various approaches to distinguish the SGWB from instrumental noise, often under simplified assumptions such as static or equal-arm configurations. However, in realistic scenarios, time-varying arm-lengths introduce additional complexities that require careful modeling. In this work, we investigate the impact of template construction on SGWB parameter estimation under realistic orbital configurations. Using the simulated SGWB signals and dominant instrumental noise sources, we compare three template strategies: time-averaged template constructed from segmented data, equal-arm template, and a template treating the arm-lengths as a free parameter. Our results show that the time-averaged template yield improves parameter estimation accuracy under time-varying arm-lengths, whereas introducing the effective arm-length as a free parameter increases estimation uncertainty. These findings highlight the importance of realistic template construction for high-precision SGWB analysis in future space-based missions.

Interstellar objects such as 1I/'Oumuamua and 2I/Borisov offer a unique window into the formation and evolution of other star systems, yet the tracking and analysis of their trajectories remain limited to specialized research institutions. Existing interstellar and solar system datasets are large, complex, and difficult to navigate, reducing accessibility for developers, researchers, and enthusiasts. To address this, we present The Interstellar Signature: a computational framework for open-source interstellar tracking, implemented through a web-based platform. Interstellar Signature bridges raw astronomical data and an intuitive, developer-friendly interface. The framework integrates live data streams from public repositories and APIs with physics-based simulation methods to model and visualize the motion of interstellar and solar system objects in real time. The platform supports interactive visualizations, comparative orbital analysis, and modular tools that allow users to explore and extend the system for research, experimentation, or development. As an open-source project, the framework encourages collaboration and hands-on engagement with complex datasets. It exists within NexusCosmos, an ecosystem envisioned as a "Linux for the space race," aimed at democratizing access to space science tools and data. By transforming large datasets into visual, interactive, and customizable simulations, Interstellar Signature expands participation in interstellar research and observation. Future extensions will add AI-driven trajectory prediction, anomaly detection, and advanced visualization. By combining open-source accessibility with computational rigor, this framework lowers the barrier to interstellar analysis and serves as a step toward bridging professional astronomy and public scientific engagement.

Short-period exoplanets may exhibit orbital precession driven by several different processes, including tidal interactions with their host stars and secular interactions with additional planets. This motion manifests as periodic shifts in the timing between transits which may be detectable via high-precision and long-baseline transit- and occultation-timing measurements. Detecting precession and attributing it to a particular process may constrain the tidal responses of planets and point to the presence of otherwise undetected perturbers. However, over relatively short timescales, orbital decay driven by the same tidal interactions can induce transit-timing signals similar to the precession signal, and distinguishing between the two processes requires robust assessment of the model statistics. In this context, occultation observations can help distinguish the two signals, but determining the precision and scheduling of observations sufficient to meaningfully contribute can be complicated. In this study, we expand on earlier work focused on searches for tidal decay to map out simple metrics that facilitate detection of precession and how to distinguish it from tidal decay. We discuss properties for a short-period exoplanet system that can maximize the likelihood for detecting such signals and prospects for contributions from citizen-science observations.

We describe a new version (numbered 3.1) of NASA's Meteoroid Engineering Model (MEM) in which we extend the model to handle locations that lie more than a few degrees in latitude off the ecliptic plane. We provide our algorithms for computing the spatial density and directionality of meteoroids far from the ecliptic and discuss their applications. In particular, we demonstrate how correct modeling of the out-of-ecliptic environment is critical for accurately assessing the risk posed by meteoroids to solar observation missions such as Solaris.

Ultracool dwarfs consist of lowest-mass stars and brown dwarfs. Their interior is fully convective, different from that of the partly-convective Sun-like stars. Magnetic field generation process beneath the surface of ultracool dwarfs is still poorly understood and controversial. To increase samples of active ultracool dwarfs significantly, we have identified 962 ultracool dwarfs in the latest LAMOST data release, DR11. We also simulate the Chinese Space Station Survey Telescope (CSST) low-resolution slitless spectra by degrading the LAMOST spectra. A semi-supervised machine learning approach with an autoencoder model is built to identify ultracool dwarfs with the simulated CSST spectra, which demonstrates the capability of the CSST all-sky slitless spectroscopic survey on the detection of ultracool dwarfs. Magnetic activity of the ultracool dwarfs is investigated by using the H$\alpha$ line emission as a proxy. The rotational periods of 82 ultracool dwarfs are derived based on the Kepler/K2 light curves. We also derive the activity-rotation relation of the ultracool dwarfs, which is saturated around a Rossby number of 0.12.

Conventional ground-based optical telescopes, even those with large apertures, primarily observe stars, close binaries, and multiple systems as unresolved point sources through photometric measurements. Spectroscopy can identify multiple stellar components within a system, but both techniques are fundamentally limited in resolving stellar surfaces and providing direct angular separations. Although photometric and spectroscopic observations yield critical information on magnitudes/flux, metallicities, and orbital properties, complementary high-angular-resolution methods are required to constrain additional system characteristics, including angular orbital parameters, model-independent distances, radii, and stellar masses. The limitations of these two methods arise due to the Diffraction Limit of the telescopes and atmospheric turbulence. Speckle Interferometry (SI) is a clever and affordable method for ground-based telescopes to work around atmospheric turbulence. In this work, we utilize the speckle images obtained by the 3.6 m DOT and demonstrate the capability of SI to resolve binary systems, measure their orbital separations, and determine their position angles. For systems with faint companions where conventional analysis fails, we employ Bayesian inference to model speckle patterns and estimate orbital parameters with high precision. These results establish the effective methodology for using a medium-sized, 4-m class telescope like the DOT as a high-resolution stellar interferometer and demonstrate the potential of speckle interferometry as a powerful technique to advance optical interferometric studies within Indian astronomy.

The detection of gravitational waves from compact binary coalescences has provided significant insights into our Universe, and the discovery of new and unique gravitational wave candidates from independent searches remains an ongoing field of research. In this work, we built a hybrid search pipeline that combines matched filtering and deep learning to identify stellar-mass binary black hole candidates from detector strain data. We first present results from a targeted injection study to benchmark the sensitivity of our method and compare it with existing search pipelines. We demonstrate that our hybrid approach has comparable sensitivity for injections with a source-frame chirp mass greater than 25$\,$M$_{\odot}$, and below this threshold our sensitivity drops off for signals with a network SNR less than 15. We also observe that our search method can identify a significant population of unique candidates. Furthermore, we conduct an offline search for gravitational wave candidates in the third observing run of the LIGO-Virgo-KAGRA Collaboration (LVK), yielding 31 candidates previously reported by the LVK with a probability of astrophysical origin $p_{\rm astro}\geq0.5$. We identify two other candidates: one previously reported only in a search conducted by the Institute for Advanced Study, and one previously unreported promising new candidate with a $p_{\rm astro}$ of 0.63. This unique candidate has a high chirp mass and a high probability that the primary black hole is an intermediate-mass black hole.

Solar flares and coronal mass ejections, originating from solar active regions (ARs), are the primary drivers of space weather and can disrupt technological systems. Forecasting efforts heavily rely on photospheric magnetic field data from the Space-weather HMI Active Region Patch (SHARPs) data products. However, the crucial energy release occurs higher in the solar corona. Radio observations from instruments like the RATAN-600 telescope directly probe this region, but their scientific use has been hindered by a lack of standardized and accessible data products. To address this gap, we have developed the Ratan Active Region Patches (RARPs) database, a new public resource of multi-frequency radio spectra for solar ARs. Generated using RATANSunPy software, RARPs provides the first standardized radio counterpart to magnetic field archives. The database contains over 160,000 calibrated AR observations from 2009 to 2025, each including 3-18 GHz spectra and rich metadata. We demonstrate the scientific utility of this database by using machine learning to forecast solar flares. The radio spectra are first compressed into low-dimensional embedded features using an autoencoder, which are then used as predictors in baseline logistic regression classifiers. We compare the predictive power of these embedded RARPs features with that of the 18 SHARPs magnetic field parameters provided in the SHARPs data product headers. Our results show that while SHARPs data provides superior flare discrimination, the radio signatures in RARPs possess clear predictive potential and, for M-class and above flares, yield lower Brier Scores and positive Brier Skill Scores relative to SHARPs, indicating more accurate probabilistic forecasts for these events. This establishes radio data as a valuable and complementary information source.

Disk and halo stars are generally classified using several conventional methods, such as the Toomre diagram, sharp cuts in metallicity ([Fe/H]), vertical distance ($\left|Z\right|$) from the Galactic plane, or thresholds on the orbital circularity parameter ($\epsilon$). However, all these methods rely on hard selection cuts, which either contaminate samples when relaxed or exclude genuine members when applied too strictly, leading to uncertain and biased classifications. We develop a flexible and reliable approach to classify disk and halo stars in galaxies by applying fuzzy set theory, which can overcome the limitations of traditional hard-cut selection methods. As a case study, we analyze one of the Milky Way/M31-like galaxies in the TNG50 catalogue. We consider multiple stellar properties as fuzzy variables and characterize their variations between disk and halo stars to construct the respective membership functions. These functions are then combined to assign each star a membership degree corresponding to its galactic component. Our fuzzy set approach provides a more realistic distinction between the disk and the halo stars. This method effectively reduces contamination and recovers genuine members that are often excluded by rigid selection criteria. The fuzzy set theory framework offers a robust alternative to conventional hard-cut methods, enabling more accurate and physically meaningful separation of stellar populations in galaxies.

A number of simulations have seen the emergence of strongly-toroidally-magnetized accretion disks from interstellar medium inflows. Recently, Guo et al. 2025 (G25) studied an idealized test problem of toroidally-magnetized disks in isothermal ideal MHD with an Eulerian static-mesh method, and argued the midplane behavior changes qualitatively (with a significant loss of toroidal magnetic flux) when the the thermal scale-length is resolved (\Delta x < H_{\rm thermal}). We rerun the G25 test problem with two Lagrangian methods: meshless finite-mass, and meshless finite-volume. We show that Lagrangian methods reproduce the high-resolution ($\Delta x \ll H_{\rm thermal}$) Eulerian G25 results. At low resolution ($\Delta x \gg H_{\rm thermal}$), behaviors differ: Lagrangian methods still lose flux and evolve 'as close as possible' to the converged solution, while Eulerian methods show no evolution. We argue this difference in convergence behavior is related to the ability of Lagrangian codes to follow flows to an arbitrarily thin midplane layer, analogous to the well-studied difference in Jeans fragmentation problems. This and results from other higher-resolution simulations and different codes suggest that the sustained midplane toroidal fields seen in recent Lagrangian multi-scale, multi-physics simulations cannot be a numerical resolution effect, and some physical difference between those simulations and the G25 test problem explains their different behaviors.

Inferring the number of distinct components contributing to an observation, while simultaneously estimating their parameters, remains a long-standing challenge across signal processing, astrophysics, and neuroscience. Classical trans-dimensional Bayesian methods such as Reversible Jump Markov Chain Monte Carlo (RJMCMC) provide asymptotically exact inference but can be computationally expensive. Instead, modern deep learning provides a faster alternative to inference but typically assume fixed component counts, sidestepping the core challenge of trans-dimensionality. To address this, we introduce SlotFlow, a deep learning architecture for trans-dimensional amortized inference. The architecture processes time-series observations, which we represent jointly in the frequency and time domains through parallel encoders. A classifier produces a distribution over component counts K, and its MAP estimate specifies the number of slots instantiated. Each slot is parameterized by a shared conditional normalizing flow trained via permutation-invariant Hungarian matching. On sinusoidal decomposition with up to 10 overlapping components and Gaussian noise, SlotFlow achieves 99.85% cardinality accuracy and well-calibrated parameter posteriors, with systematic biases well below one posterior standard deviation. Direct comparison with RJMCMC shows close agreement in amplitude and phase, with Wasserstein distances W_2 < 0.01 and < 0.03, indicating that shared global context captures inter-component structure despite a factorized posterior. Frequency posteriors remain centered but exhibit 2-3x broader intervals, consistent with an encoder bottleneck in retaining long-baseline phase coherence. The method delivers a $\sim 10^6\times$ speedup over RJMCMC, suggesting applicability to time-critical workflows in gravitational-wave astronomy, neural spike sorting, and object-centric vision.