We describe $\texttt{stistools.barycentric_correction}$, a new Python utility for calculating barycentric timing corrections for HST/STIS observations. This tool replaces the deprecated $\texttt{ this http URL }$ IRAF function that was previously used for HST barycentric corrections. Our new utility uses $\texttt{astropy}$ for conversion between time formats and standards and introduces a new way to calculate HST's position through JPL Horizons, replacing the need to download separate HST orbital ephemeris files. Here, we describe the methods used in the new utility, the tests that were carried out to verify its accuracy, and explain some of the complexities involved in determining light travel times to accuracies down to a millisecond for HST. We also summarize the current understanding of the absolute accuracy of STIS time stamps.

We quantify an unusual flux-dependent systematic which is periodic in raw counts in flight data from the James Webb Space Telescope's Near Infrared Imager and Slitless Spectrograph (JWST/NIRISS), used extensively for exoplanet imaging and spectroscopy. Originally discovered in the aperture masking interferometry (AMI) mode, it also manifests in the Single Object Slitless Spectroscopy (SOSS) mode with the same dominant period of 1024 in raw analog-to-digital units (ADU). The likely cause of the signal is an analog-to-digital converter (ADC) integral nonlinearity (INL) in which case it will apply to all observations taken with the NIRISS instrument. Fortunately, it is straightforward to correct the data in postprocessing. The periodic INL is shown to be flux-dependent, increasing in amplitude with higher pixel counts on the detector. We derive a model of this periodic INL by fitting a combination of a polynomial and sinusoid multiplied with the residuals of ramp fits to the uncalibrated data and find an amplitude of 125ppm, up to a 2.5-count shift for a pixel with 20,000ADU. We apply this model to correct the well-studied NIRISS SOSS Program ERS1366 dataset of WASP-39b and reduce the data into a transmission spectrum. We find that our corrected transmission spectrum removes the INL systematic from the uncorrected spectrum at the 30ppm level across both orders, and also corrects a 55ppm offset between Order 1 and Order 2. We recommend a larger scale data-driven calibration of the periodic INL and the adoption of the outcome into NIRISS data pipelines.

The second solar spectrum is the spectrum of the Stokes parameter Q (linear polarization) close to the solar limb. It is made of a few polarized lines with Q/I of about 1% (such as CaI, SrI, or BaII), but most lines exhibit weaker polarization. This paper presents processing of unpublished observations made in 2008 with the Meudon solar tower spectropolarimeter, which are of interest for weak and turbulent unresolved magnetic field measurements in the quiet Sun, through the Hanle effect.

Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from a machine learning perspective. We cover the basic concepts of time series (stationarity, autocorrelation, seasonality), classical statistical models (autoregressive, moving average, ARIMA, exponential smoothing, state-space models), and modern machine learning approaches. In particular, we discuss how traditional statistical methods lay the groundwork, and then explore machine learning methods for time series, including feature-based regression, tree-based ensemble methods, hidden Markov models, Gaussian processes, and deep learning models (recurrent neural networks, convolutional networks, transformers). Throughout, we illustrate with examples drawn from multiple domains (e.g. astronomy, weather forecasting, finance) to emphasize common principles. The goal is to equip readers with both the theoretical understanding and practical context to apply machine learning techniques for time series analysis in their research.

Imaging Atmospheric Cherenkov Telescopes (IACTs) are the leading instruments for very-high-energy (VHE) gamma-ray astronomy, covering the range from hundreds of GeV to hundreds of TeV. This chapter reviews the critical role of machine learning in reconstructing the physical properties of particles detected by IACTs. We introduce the IACT technique and its central challenge: distinguishing rare gamma-ray showers from the overwhelming cosmic-ray background. We detail the standard reconstruction pipeline, from image cleaning and Hillas parameterization to stereoscopic observation, and frame event reconstruction as a supervised learning problem encompassing particle classification and energy/direction regression. Two frontiers of innovation are then explored: the exploitation of the temporal dimension of shower images through novel timing-based features that enhance background rejection at low energies, and the application of advanced ensemble methods (gradient boosting, stacking) that surpass baseline Random Forests, notably in mitigating systematic energy bias. Finally, we discuss performance metrics and provide an outlook on next-generation approaches dominated by deep learning, including Convolutional and Graph Neural Networks.

Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes. Although population-synthesis models predict distinct planetary populations, establishing a quantitative connection between observed exoplanets and synthetic populations remains challenging. We investigate the intrinsic organisation of close-in exoplanets using physically motivated dynamical parameters and connect the resulting populations to pebble-accretion formation pathways. A two-stage Gaussian mixture model (GMM) is applied to an observed sample of close-in exoplanets, performing unsupervised probabilistic clustering in a feature space dominated by dynamical descriptors of planet-star interactions. The resulting clusters are mapped onto a pebble-accretion synthetic population within a statistically motivated three-dimensional parameter space. Formation-related quantities, including gas availability, gas fraction, and ice-rock mass ratio, are then used to interpret the mapped populations. We identify statistically supported sub-populations without imposing predefined classification boundaries, including very-massive gas giants, hot giants, warm-Jupiter-dominated systems, and lower-mass giants. The mapped synthetic populations reveal systematic differences in formation timing, gas accretion, and solid growth histories. In particular, very-massive gas giants are preferentially associated with earlier formation epochs than hot-giant and warm-Jupiter-dominated populations. These results demonstrate that physically motivated machine-learning approaches can provide a statistically robust framework for linking observed exoplanet populations to theoretical planet formation pathways.

We are developing a compact three-element Wide Field Corrector (WFC) with spherical and aspherical lenses for the 2.34 m Vainu Bappu Telescope (VBT) to enhance its field coverage for imaging and spectroscopic applications. The design consists of three optical elements, with at least one spherical lens movable to serve as an Atmospheric Dispersion Corrector (ADC), while the aspherical elements remain fixed to maintain optical stability. We are currently testing two design configurations, one with two spherical lenses and one aspherical lens, and another with two aspherical lenses and one spherical lens. The ADC is designed to correct atmospheric dispersion for zenith angles ranging from 0 degree to 60 degree. The system is optimized to operate over a wavelength range of 0.4 {\mu}m to 0.9 {\mu}m, targeting an effective field of view of about 0.5 degree. Considering the limited mechanical space available at the VBT prime focus, the design emphasizes compactness, ease of alignment, and manufacturability. The system achieves a mean D80 better than 0.3 arcsec and 0.23 arcsec for Design 1 and Design 2, respectively, at zenith, and maintains a mean D80 within 0.57 arcsec and 0.45 arcsec up to a zenith angle of 60 degree after atmospheric dispersion correction. Atmospheric dispersion at higher zenith angles (up to 60 degrees) is corrected using a movable lens element, enabling the system to preserve high image quality across the field.

Gravitational-wave interferometers exhibit a wide variety of short-duration non-Gaussian transients, commonly referred to as glitches, that complicate the detection of astrophysical signals, bias parameter estimation, and detector characterisation. Existing machine-learning approaches classify glitch morphologies but do not provide a complete mechanism to segment and extract these disturbances from the strain data. We introduce a wavelet-based, saliency-guided framework for the supervised extraction of transient noise. Candidates are first pre-tagged using Uniform Manifold Approximation and Projection, which is also used as a diagnostic of the learned representations. A traditional learning model operating on Continuous Wavelet Transform spectrograms then identifies relevant time-frequency regions through saliency maps. These saliency patterns are transferred to an invertible multiresolution representation via the Discrete Wavelet Transform, where adaptive coefficient masking enables exact reconstruction of both glitch-only and glitch-suppressed waveforms. We demonstrate effective extraction across several representative glitch families, including 'Whistle' and 'Scattered-Light' transients, and show robustness in challenging regimes such as low signal-to-noise events and partially overlapping structures, where classical thresholding or band-limited filtering methods typically fail or introduce leakage. The proposed framework offers an interpretable and computationally efficient approach to transient-noise extraction, establishing a foundation for scalable applications to larger glitch catalogs and future observing runs.

Reliable estimates of stellar effective temperature ($T_{\mathrm {eff}}$) are fundamental to stellar population studies and Galactic astrophysics. However, the majority of stars observed in modern large-scale photometric surveys lack spectroscopic measurements, making empirical colour--$T_{\mathrm {eff}}$ relations essential tools. In this work, we present updated empirical colour--$T_{\mathrm {eff}}$ calibrations based on Sloan Digital Sky Survey (SDSS) $ugriz$ photometry combined with 2MASS $JHK_{\mathrm s}$ data. Effective temperatures are determined on a homogeneous InfraRed Flux Method (IRFM) scale using a combined sample of 3902 GALAH and 2535 APOGEE stars with high-quality photometry and well-characterised atmospheric parameters. Using this dataset, we establish empirical relations between $T_{\mathrm {eff}}$ and colour indices constructed from SDSS and 2MASS combinations. We provide both colour--metallicity--$T_{\mathrm {eff}}$ and colour--$T_{\mathrm {eff}}$ relations for dwarfs and giants. The calibrations are derived using low-order polynomial models with iterative $3\sigma$ clipping. Their performance depends on the adopted colour index, with long-baseline colours such as $(g-K_{\mathrm s})_0$ and $(g-z)_0$ achieving internal precisions of $\sim$30--50~K. Comparisons with previous calibrations show general agreement, with differences attributable to sample selection, photometric zero-points, and functional form. The resulting relations provide a homogeneous and internally consistent framework for estimating $T_{\mathrm {eff}}$ from SDSS and 2MASS photometry alone, and are well suited for application to large photometric surveys lacking spectroscopic information.

Accurate photometric zero-points are essential for translating observed magnitudes into physical fluxes, from comparing with models to ensuring consistency across surveys. We determine the zero-points needed to place the Sloan Digital Sky Survey (SDSS) $ugriz$ system on its nominal AB definition, by exploiting the sensitivity of the Infrared Flux Method (IRFM) to broadband flux calibration. Using benchmark effective temperatures for over 6,000 FGK-type stars, we invert the method to identify the zero-point corrections required for SDSS photometry to reproduce the adopted temperature scale. The $r$ band is found to be very well standardized, while the $i$ and $z$ bands show offsets of a few hundredths of a magnitude, consistent with previous studies. We also find a small offset in the $g$ band. The largest discrepancy occurs in the $u$ band, where the derived offset depends strongly on the adopted filter transmission curves, in particular whether one uses the original definition commonly adopted in the literature or the updated measurements that account for the presence of a red leak. This effect introduces a colour-dependent zero-point offset that becomes apparent when using a sample of late-type stars. Independent comparisons with CALSPEC spectrophotometric standards and Gaia XP spectra broadly support the offsets derived from the IRFM analysis. Our results provide a revised set of SDSS zero-points anchored to the IRFM temperature scale and demonstrate that large stellar samples can be used to constrain photometric calibration. The methodology presented here offers a complementary approach to traditional spectrophotometric calibration and may prove useful for future large-scale surveys.

Ultraviolet spectroscopy with the Hubble Space Telescope (HST) provides the most direct and sensitive probe of the disk-circumgalactic medium (CGM) interface at radii of 20 kpc, where galaxies exchange gas, metals, and energy with their surroundings. Many of the key diagnostics of the multiphase circumgalactic medium -- including H I, O VI, C II-IV, Si II-IV, N V, Ne VIII, and other metal transitions -- lie in the ultraviolet and are inaccessible from the ground, making HST the only observatory capable of making the required observations. By measuring the physical (column density, density), chemical (metallicity, ionization structure), and kinematical properties of the gas at the disk-CGM interface, UV absorption-line spectroscopy reveals how galaxies acquire fresh fuel, recycle enriched material, and drive feedback into their halos. When combined with spectroscopic characterization of the host galaxy's stellar populations and the feedback they generate (outflow velocity, mass loading), we will establish a direct understanding of how stellar populations enable circulation of gas and metals through the galactic ecosystem. HST's ultraviolet (UV) spectroscopic capability provides the only comprehensive observational pathways for uncovering the physical drivers that regulate galaxy growth and evolution in the low-redshift Universe.

Accurate calibration of the gravitational-wave strain h(t) is essential for both detection and astrophysical inference. In operating detectors, slow temporal variations in the sensing response are tracked using calibration lines, but practical constraints can prevent those lines from being injected at frequencies that are favorable for precise estimation of sensing-side parameters. We present a statistical framework for preselecting evaluation frequencies under such constraints. We apply this framework to KAGRA data from the first part of the fourth LIGO-Virgo-KAGRA Observing Run, for which the nominal cavity-pole frequency was about 18 Hz, while the sensing-side calibration line used in practice was injected at 32.7 Hz. For each candidate evaluation frequency, we construct the sensing function, quantify its segment-wise statistical uncertainty from empirical percentiles of the sample distribution, and rank the candidates using a score that combines the interval widths of the amplitude and phase. When a 1% amplitude interval width and a 1 degree phase interval width are weighted equally, 244 Hz is selected in all 4096 s analysis segments throughout the analyzed period. Relative to the reference frequency of 32.7 Hz, the amplitude interval width is reduced to about one quarter over a broad frequency range, while the phase interval width remains broadly comparable. We also assess the discrepancy introduced by frequency translation separately. These results suggest that the proposed method provides a useful statistical preselection framework for evaluation frequencies under practical operational constraints.

Gaussian processes are a powerful tool for modeling continuous fields, but their naive $\mathcal{O}(N^3)$ computational cost and $\mathcal{O}(N^2)$ memory requirement often limit their practical use. Vecchia's approximation is a sparse precision matrix approximation for stationary, decaying kernels that conditions each point only on its $k$ nearest neighbors. We present GraphGP, a GPU algorithm for Vecchia's approximation that scales to nearly a billion parameters with linear time and memory requirements, handling arbitrary point distributions over a large dynamic range. Our key contributions are (1) a bit-reversed k-d tree ordering that allows efficient neighbor searches while also maximizing batch parallelism, and (2) a differentiable CUDA implementation, which is substantially faster and more memory efficient than our pure JAX baseline. GraphGP provides the building blocks for inference, including forward generation, inverse application, log-determinant, and kernel parameter derivatives.

Hot evolved stars are key objects to reconstruct the various evolutionary pathways of Sun-like stars, to probe binary interactions and the physics of supernovae. They serve as powerful observational constraints to test diffusion, mixing, and mass loss in hot stellar atmospheres. Furthermore, hot stars serve as laboratories to test and derive atomic data for highly ionised trans-iron group elements and to investigate different nucleosynthesis models. Hot evolved stars emit most of their flux in the ultraviolet (UV) and a lot of progress has been made in characterizing their UV-spectra both on the observational and on the modelling side. The unique capabilities of HST to obtain high- and medium-resolution UV-spectra played a crucial role and are needed to further advance this field also in preparation for HWO.

Low surface brightness tidal debris around galaxies, such as tails, streams, and shells, together with other interaction-driven morphological disturbances, serve as valuable indicators of past or ongoing galaxy mergers. With the growing data volume from surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), automated detection methods are essential. This paper evaluates the performance of two automated methods, a Self-Supervised Learning (SSL) model and the Concentration-Asymmetry-Smoothness (CAS) parameter method, in tracing interaction-driven disturbances and merger signatures, with visual classification used as the benchmark. Visual classification yields a high-confidence disturbance fraction of 25.1 +/- 1.5% in our sample and serves as the reference standard for assessing the completeness and precision of the automated approaches. Visual classification is affected by galaxy distance and image resolution, which limit the detectability of faint low surface brightness structures. The SSL model achieves high recall (0.86 +/- 0.04) and low contamination (0.2) by retraining only its linear classifier on a small labelled dataset, making it suitable for identifying a broad set of disturbed systems, including faint tidal debris and other interaction-driven morphological disturbances, thereby providing a more complete census of merger-related features. The CAS method, using the traditional threshold A > 0.35, shows higher precision (0.77) but lower recall (0.20), indicating a conservative approach that captures cleaner but less complete samples. Visual classification and the SSL model show a significant positive correlation between stellar mass and disturbance fraction, while the CAS method exhibits a much weaker trend.

The physical processes that shape a galaxy's spectrum are strongly degenerate in observations, obscuring which processes act independently. Leveraging the pop-cosmos generative galaxy population model, we investigate how many independent degrees of freedom the rest-frame optical SED contains. We use a $\beta$-variational autoencoder (VAE) to compress a 16-parameter stellar population synthesis (SPS) description into a disentangled latent representation interpreted through mutual information (MI). We find that five independent dimensions suffice, corresponding to stellar mass, recent star formation, dust, and two -- not the three or four assumed by standard nebular models -- degrees of freedom in the ionization state of the gas. Stellar metallicity and stellar age are not among these primary drivers; their spectral effects are distributed across the others rather than independently encoded. By tying each dimension to specific spectral features, this decomposition breaks the star-formation--dust--metallicity degeneracies that limit broadband photometry, and recovers the physical conditions of the gas more cleanly than the line-ratio diagnostics in standard use.

Synchrotron self absorption (SSA) is seen across a variety of astrophysical sources, and observation of an SSA peak in the spectrum is a powerful tool for estimating the physical conditions and the minimum energy of the emitting region. We begin with the (re)derivation of the usual SSA parameter estimates, carefully considering dependencies and assumptions, obtaining the most accurate traditional SSA minimum energy equations currently available. Traditional methods rely on the assumption that the emitting region is quasi-spherical and homogeneous. However, many observations of SSA show that the spectral index at frequencies below the peak is less than the expected $+2.5$ (non-thermal) or $+2$ (thermal). We argue that an inhomogeneous emitting region is the most likely explanation in many cases. Power law inhomogeneous cylindrical slab and broken power law inhomogeneous sphere models are used to investigate how the presence of inhomogeneity affects parameter estimates using traditional SSA methods. We find that in some cases inhomogeneity can lead to traditional SSA methods underestimating the minimum energy and the size of the emitting region by over an order of magnitude. Quantitative correction factors are found which can be applied to traditional estimates to correct for inhomogeneity, depending on the value of the observed flattened spectral index and the range in frequency over which this value is observed. Furthermore, we derive simple correction factors for non-spherical homogeneous emitting regions. Finally, we explore the effects of inhomogeneity on measurements of polarisation around the spectral peak, and on lightcurves for expanding emitting regions.

For broadband imaging surveys, filter-substrate refraction causes light at different wavelengths to follow slightly different paths through the filter substrate before reaching the detector. This effect produces two chromatic perturbations to the point spread function (PSF): a shift in the effective focal position along the optical axis (longitudinal shift), which manifests as a defocus-like perturbation, and a wavelength-dependent displacement of the image position in the focal plane (lateral shift), which manifests as image decentering. Using image simulations, we provide the first study of these two effects independently across all eight Roman imaging bands and over the full focal plane. We compute the resulting PSF and photometric errors from images with and without the effect included, and compare the magnitude of the effect to the Roman science requirements. We find that the lateral shift is the dominant contribution, producing PSF size and ellipticity residuals in most bands of order ~0.3-0.4%. These exceed the Roman science requirements for weak lensing by roughly an order of magnitude. The effect is also strongly field dependent, increasing toward the edges of the focal plane. By contrast, flux residuals remain below one third of the 1% requirement for most bands, except in R062 and W146. We find the longitudinal shift to be subdominant and negligible in most bands, including the weak lensing bands. Finally, we implement the dominant lateral-shift effect in a framework suitable for large-scale image simulations and validate that the resulting PSF size and shape changes are accurately reproduced. Overall, we find that filter-substrate refraction is a relevant chromatic effect for Roman PSF modeling, and we provide tools to model and incorporate it in large-scale image simulations.

Parity-violating physics in the Universe can generate correlations between the Cosmic Microwave Background (CMB) $E$- and $B$-modes, but detecting such signals requires extremely accurate calibration of instruments. We describe a data-driven method to calibrate the relative polarisation angle between CMB experiments using cross-correlations of observations over a common sky region. Unlike standard self-calibration approaches, this method does not assume vanishing isotropic cosmic birefringence or primordial $EB$ correlations when estimating the relative misalignment angle, and therefore preserves sensitivity to parity-violating physics. As a proof of concept, we forecast the performance of this method using the Simons Observatory (SO) Small Aperture Telescopes (SATs) as a calibrated reference. If they can be calibrated to an uncertainty of $0.08^\circ$, as anticipated from the SO wire grid calibration system, we show that the SO Large Aperture Telescope and Planck could be calibrated to uncertainties of $0.10^\circ$ and $0.17^\circ$, respectively, at $\sim 145$ GHz. This approach relies on the availability of at least one well-calibrated instrument, and provides a complementary path to improving polarisation calibration across experiments, enabling more robust searches for parity-violating physics in the CMB, such as cosmic birefringence.

The advent of Extremely Large Telescopes (ELTs) ground-based optical and infrared observatories with primary mirrors exceeding 20m marks a transformative era in observational astronomy. This article explores the dawn of this new age, focusing on two of the next generation of optical/ infrared facilities: the Giant Magellan Telescope (GMT) and the Thirty Meter Telescope (TMT)\footnote{An accompanying paper by the author is on the European Extremely Large Telescope (ELT).} We describe the key technological innovations enabling the construction of these ambitious observatories, notably, segmented mirror design, advanced adaptive optics (AO), and laser guide star systems. Collectively, these breakthroughs yield more than an order of magnitude increase in both light-gathering area and spatial resolution, delivering image sharpness that, for widefield observations, surpassing space-based telescopes. We shall also discuss the comparison of space and ground-based telescopes and highlight their individual strengths and why space telescopes are not the solution to the satellite interference to ground-based telescopes. Ground based telescopes will continue to always be leaders in astronomical observations.

The detection of astrophysical neutrino point sources remains challenging due to atmospheric backgrounds obscuring signal and statistical penalties from the look-elsewhere effect. The Tau Air-shower Mountain-Based Observatory (TAMBO) is a neutrino telescope that achieves unprecedented signal-to-background discrimination in the 1-1000 PeV energy range. Leveraging its unique deep valley geometry, TAMBO will generate an exceptionally pure neutrino sample, enabling precise investigations of neutrino sources. Preliminary sensitivity studies demonstrate TAMBO's potential to map diffuse and point-source neutrino emissions, representing a significant advancement in high-energy neutrino astronomy.

The bulk motion of E-region radar aurora provides a sparsely distributed, direct measurement of the ionospheric electric field in intermittent bursts. We present a tracking procedure for \textsc{icebear} VHF measurements of Farley-Buneman waves. Each cluster is represented as an $\alpha$-shape; frame-to-frame association is a Hungarian linear-assignment problem with a cost combining centroid distance and shape Intersection-over-Union; kinematic prediction amounts to a degenerate Kalman filter. Births, deaths, splits, and mergers are monitored; each tracked trajectory is reduced to per-segment velocities by piecewise-linear regression. We validate against \textit{in-situ} observations. During the G5 storm of 10 May 2024, on closed dayside field-lines, our method recovers a five-second cluster moving at $11{,}240\pm660$~m/s, implying an electric field strength of $\approx 560$~mV/m, a value that exceeds documented sub-auroral thermal emission speeds and the most extreme reported sub-auroral drifts. The detection is consistent with extreme E-field structures appearing as short-lived bursts, representing field variability, and we provide parameterizations of this variability for space weather modeling.

Eclipse shadow bands are transient intensity patterns that can appear on the ground near solar eclipse totality. This study presents a reusable image-processing framework for analyzing shadow-band video recordings collected with consumer-grade cameras. The framework quantifies band prominence, band orientation, band separation, band velocity and power spectral density from video recordings. Applied to two eclipse datasets, the method detected statistically significant shadow-band activity during eclipse windows that align with the scintillation theory for shadow bands. The findings also indicate that eclipse shadow bands can consist of simultaneous superimposed band modes with different orientations and velocities. This study demonstrates that consumer-grade cameras can support quantitative analysis of shadow bands and may support future observational and atmospheric studies

We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on the spectro-spatial paradigm introduced by capivara for IFS data, sagui extends this approach to imaging datasets, enabling a coherent, pixel-level treatment of spatial and spectral information across multiple bands. The method follows a two-stage strategy: a starlet-based decomposition is first used to identify and mask spatial structures across multiple scales while suppressing noise, and a spectral-similarity analysis then partitions the image into coherent pixel groups that preserve spectral consistency. In addition to compact and high-contrast structures, the framework incorporates a dedicated statistical treatment, based on a copula transform, to identify and recover faint, diffuse low-surface-brightness components. We demonstrate the method across a diverse range of galaxy morphologies, highlighting its ability to characterize complex spatial structures, including clumps, bars, interacting systems, and low-surface-brightness features. As a case study, we apply it to eleven morphologically diverse galaxies from the James Webb Space Telescope Advanced Deep Extragalactic Survey in the GOODS--South field. sagui is released under an MIT license and is available at this https URL.

This paper presents a novel nonlinear backstepping control law for continuous, low-thrust station-keeping in the Earth-Moon system. Quasi-periodic libration point orbits are targeted under a high-fidelity model of the dynamics. Almost global uniform exponential stability guarantees are attained, as shown through Lyapunov's stability theory. Saturation of the actuators is formally included in the controller design, such that these guarantees hold even in the event of saturation. The relationship between saturation threshold, control gains, and deviation is studied and an optimal procedure for gain selection is discussed. The control solution is tested numerically through a Monte Carlo analysis over representative application cases, subject to operational errors, constraints, and external perturbations. Station-keeping under actuation saturation is validated considering a conservative threshold for typical electric propulsion systems.

Bayesian inference is central to modern cosmology, yet comprehensive model comparison and tension quantification remain computationally prohibitive for many researchers. To address this, we release $\texttt{unimpeded}$, a publicly available Python library and data repository providing pre-computed nested sampling and MCMC chains. We apply this resource to conduct a systematic analysis across a grid of eight cosmological models, including $\Lambda$CDM and seven extensions, and 39 datasets, including individual probes and their pairwise combinations. Our model comparison reveals that whilst individual datasets show varied preferences for model extensions, the base $\Lambda$CDM model is most frequently preferred in combined analyses, with the general trend suggesting that evidence for new physics is diluted when probes are combined. Using five complementary statistics, we quantify tensions, finding the most significant to be between DES and Planck ($\sigma=3.57\pm0.10$) and SH0ES and Planck ($\sigma=3.27\pm0.10$) within $\Omega_k\Lambda$CDM. We characterise the $S_8$ tension as high-dimensional ($d_G=4.87\pm0.79$) and partially resolvable in certain extended models, whereas the Hubble tension is low-dimensional and persists across the model space. Caution should be exercised when combining datasets in tension. The $\texttt{unimpeded}$ data products, hosted on Zenodo, provide a powerful resource for reproducible cosmological analysis and underscore the robustness of the $\Lambda$CDM model against this comprehensive benchmark compilation.

The simulation of extensive air showers and particle cascades in general is a cornerstone of modern astroparticle physics. For more than two decades, CORSIKA, currently in version 7, has been one of the most widely used tools for this purpose. However, its architecture reflects design constraints of an earlier computing era, as well as increasingly limiting extensibility, maintainability, and adaptability to modern experimental requirements. CORSIKA 8 is a complete redesign of the original CORSIKA code, implemented in modern C++ and based on contemporary software engineering principles. It introduces a modular and extensible simulation framework with explicit handling of units, flexible geometry, and environment descriptions. In this paper, we present the design philosophy and core architecture of CORSIKA 8, describe the implementation of electromagnetic and hadronic shower physics, and validate air shower simulations against CORSIKA 7. The results demonstrate good agreement at the few-percent level for key observables, confirming the physics fidelity of CORSIKA 8. We also showcase new use cases that were beyond the capabilities of version 7, such as the simulation of cross-media showers and particle cascades in ice, including radio-signal propagation

Precise measurements of cosmic microwave background (CMB) polarization require rigorous control of instrumental systematics. For the South Pole Telescope's third-generation camera (SPT-3G), which observes in three bands centered near 95, 150, and 220 GHz, accurate beam characterization is critical for interpreting the polarized mm-wave sky. We present direct measurements of SPT-3G's polarized beam response from observations of 100 bright extragalactic point sources. Previous SPT-3G power spectrum analyses introduced a phenomenological parameter, $\beta_{\rm pol}$, to describe the polarization preserved in beam sidelobes, and found evidence for significant depolarization from the requirement of inter-frequency polarization power spectrum consistency. Our direct measurements yield $\beta_{\rm pol}=0.89\pm0.10$ at 95 GHz, $1.08\pm0.10$ at 150 GHz, and $0.90\pm0.22$ at 220 GHz, indicating minimal sidelobe depolarization. We validate these results with systematic tests of posterior sampling versus bootstrap resampling, real-space versus Fourier-space analysis, temperature-to-polarization leakage handling, covariance determination, and source selection. Compared to values inferred from previous cosmological analyses, our results differ by an effective $1.3\sigma$. This apparent discrepancy is model dependent, because the point source analysis derives much of its $\beta_{\rm pol}$ constraining power from higher multipoles than the power spectrum analysis. These measurements therefore admit three explanations for the frequency-dependent residuals observed in the power spectrum analysis: a statistical fluctuation, the need for more sophisticated polarized beam models, or systematics other than beam depolarization.

Heterodyne interferometry for precision science often comes with an optical phase modulation, for example, for intersatellite clock noise transfer for gravitational wave (GW) detectors in space, exemplified by the Laser Interferometer Space Antenna (LISA). The phase modulation potentially causes various noise couplings to the final phase extraction of heterodyne beatnotes by a phasemeter. In this paper, in the format of space-based GW detectors, we establish an analytical framework to systematically search for the coupling of various noises from the heterodyne and modulation frequency bands, which are relatively unexplored so far. In addition to the noise caused by the phase modulation, the high-frequency laser phase noise is also discussed in the same framework. The analytical result is also compared with a numerical experiment to confirm that our framework successfully captures the major noise couplings. We also demonstrate a use case of this study by taking the LISA-like parameters as an example, which enables us to derive requirements on the level of the laser and phase modulation noises in the high frequency regimes.

Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes. In this work, we develop a pipeline to create a semantic search engine from completely unlabeled image data. Our method leverages Vision-Language Models (VLMs) to generate descriptions for galaxy images, then contrastively aligns a pre-trained astronomy foundation model with these embedded descriptions to produce searchable embeddings at scale. We find that current VLMs provide descriptions that are sufficiently informative to train a semantic search model that outperforms direct image similarity search. Our model, AION-Search, achieves state-of-the-art zero-shot performance on finding rare phenomena despite training on randomly selected images with no deliberate curation for rare cases. Furthermore, we introduce a VLM-based re-ranking method that nearly doubles the recall for our most challenging targets in the top-100 results. For the first time, AION-Search enables flexible semantic search for over 100 million galaxy images, enabling discovery from previously infeasible searches, including the identification of 36 new extragalactic stellar stream candidates. More broadly, our work provides an approach for making large, unlabeled scientific image archives semantically searchable, expanding data exploration capabilities in fields from Earth observation to microscopy. The code, data, and app are publicly available at this https URL