CyberSec Research

Understanding the diurnal behavior of lee wave clouds on Mars provides critical insight into the planet's mesoscale atmospheric dynamics and their interaction with surface topography. Lee wave clouds exhibit distinct spatial and temporal patterns that vary over the Martian day. In this study, we investigate the diurnal distribution and frequency of lee wave cloud activity during Martian Year (MY) 36 using observations from the EXI instrument aboard the Emirates Mars Mission (EMM) "Hope" spacecraft. A total of 50 lee wave events were identified, with a pronounced peak in activity during afternoon hours between solar longitudes (Ls) 270 deg and 360 deg. Our analysis reveals a seasonal and local-time dependence for these clouds, providing a comparative framework with previous mission datasets. These findings not only enhance the current understanding of Martian weather processes but also support future efforts to model and predict terrain-induced cloud dynamics across key locations on Mars.

Poincar\'e maps play a fundamental role in nonlinear dynamics and chaos theory, offering a means to reduce the dimensionality of continuous dynamical systems by tracking the intersections of trajectories with lower-dimensional section surfaces. Traditional approaches typically rely on numerical integration and interpolation to detect these crossings, which can lead to inaccuracies and computational inefficiencies. This work presents a novel methodology for constructing Poincar\'e maps based on the Theory of Functional Connections (TFC). The constrained functionals produced by TFC yield continuous and differentiable representations of system trajectories that exactly satisfy prescribed constraints. The computation of Poincar\'e maps is formulated as either an initial value problem (IVP) or a boundary value problem (BVP). For IVPs, initial conditions are embedded into the functional, and the intersection time with a specified section surface is determined. We demonstrate linear convergence to the Taylor series, thereby enabling accurate interpolation without resorting to numerical integration or external optimization. For BVPs, periodicity conditions are encoded to identify periodic orbits in a Three-Body Problem context. Furthermore, by enforcing periodic constraints, we show how to construct first recurrence maps. The methodology is also extended to non-autonomous systems, demonstrated through applications to a Four-Body Problem. The proposed approach achieves machine-level accuracy with modest computational effort, eliminating the need for variable transformations or iterative integration schemes with adaptive step-sizing. The results illustrate that TFC offers a powerful and efficient alternative framework for constructing Poincar\'e maps, computing periodic orbits, and analyzing complex dynamical systems, particularly in astrodynamical contexts.

3 Puppis is the brightest known B[e] star. Recent work classifies this A-type object as a supergiant, yet the impact of its binarity on the circumstellar environment (CE) remains hard to characterize. To resolve its dusty region at 5-10 mas, we obtained mid-IR interferometric observations with VLTI/MATISSE over 3-12 {\mu}m. Because the (u,v) coverage supports imaging, we introduce a statistical interferometric-imaging workflow based on MiRA to generate averaged images: this systematic approach enables the selection of an optimal set of reconstructions, improving the robustness and fidelity of the recovered features. We also use SPARCO, an independent tool well suited to bright central objects embedded in fainter extended emission. Images from both tools in the L, M, and N bands agree and reveal an asymmetric, elongated feature ~17 mas (~10 au at 631 pc) southeast of the star with ~20% density contrast. A second northwest asymmetry and a skewed inner rim are detected. Simple geometric modelling, guided by the MATISSE images, constrains the morphology, location, and flux of the CE and its asymmetries. The images are consistent with earlier VLTI measurements but expose a more complex CE with large-scale clumps in the southeast and northwest parts of the disc. Hydrodynamic modelling indicates that tidal spiral-wake perturbations from the central binary, dynamically excited at Lindblad resonances in the circumbinary disc, best explain the radial extent and curvature of the elongated structures seen in all bands.

In many areas of astronomy, spectra of different objects are co-added or stacked to improve signal-to-noise and reveal population-level characteristics. As the number of exoplanets with measured transmission spectra grows, it becomes important to understand when stacking spectra from different exoplanets is appropriate and what stacked spectra represent physically. Stacking will be particularly valuable for long-period planets, where repeated observations of the same planet are time-consuming. Here, we show that stacked exoplanet transmission spectra are approximately mathematically equivalent to spectra generated from the geometric mean of each planet's abundance ratios. We test this by comparing stacked and geometric mean spectra across grids of forward models over JWST's NIRSpec/G395H wavelength range (2.8-5.2$\mu$m). For two dominant species (e.g., H$_2$O and CO$_2$), the geometric mean accurately reflects the stacked spectrum if abundance ratios are self-similar across planets. Introducing a third species (e.g., CH$_4$) makes temperature a critical factor, with stacking becoming inappropriate across the CO/CH$_4$ boundary. Surface gravity exerts only a minor influence when stacking within comparable planetary regimes. We further assess the number of stacked, distinct sub-Neptunes with high-metallicity atmospheres and low-pressure cloud decks required to rule out a flat spectrum at $>5\sigma$, as a function of both cloud deck pressure and per-planet spectral precision. These results provide guidance on when stacking is useful and on how to interpret stacked exoplanet spectra in the era of population studies of exoplanets.

We investigate the polarization patterns from the moonlit sky as observed from the European Southern Observatory at Cerro Paranal. The moonlit sky background can be significant in astronomical observations and thus be a source of contamination in polarimetric studies. Based on sky observations during full Moon with FORS2 in imaging polarimetric mode, we measure the polarization degree and intensity at different wavelengths and scattering angles from the Moon, and compare them to theoretical and phenomenological single and multiple scattering models. Single scattering Rayleigh models are able to reproduce the wavelength dependence of the polarization as long as strong depolarization factors that increase with wavelength are introduced. Intensity data, however, require the inclusion of single Mie scattering from larger aerosol particles. The best models that simultaneously fit polarization and intensity data, are a combination of both single scattering processes, Rayleigh and Mie, plus an unpolarized multiple scattering component. Both Mie and multiple scattering become more dominant at longer wavelengths. Other factors like cloud depolarization and the sunlight contribution during the twilight were also investigated. The present study underscores the importance of accounting for moonlight scattering to enhance the accuracy of polarimetric observations of astronomical targets.

Thermal emission spectra provide key insights into the atmospheric composition and especially the temperature structure of an exoplanet. With broader wavelength coverage, sensitivity and higher resolution, JWST has enabled robust constraints on these properties, including detections of photochemical products. This advances the need for retrieval frameworks capable of navigating complex parameter spaces for accurate data interpretation. In this work, we introduce the emission retrieval module of NEXOTRANS, which employs both one- and two-stream radiative transfer approximations and leverages Bayesian and machine learning techniques for retrievals. It also incorporates approximate disequilibrium chemistry models to infer photochemical species like SO2. We applied NEXOTRANS to the JWST NIRCam and MIRI emission observations of WASP-69b, covering the 2-12 microns range. The retrievals place robust constraints on the volume mixing ratios (VMR) of H2O, CO2, CO, CH4, and potential SO2. The best-fit model, i.e, free chemistry combined with non-uniform aerosol coverage, yields a log(VMR) = -3.78 (+0.15/-0.17) for H2O and -5.77 (+0.09/-0.10) for CO2 which has a sharp absorption at 4.3 micron. The second best-fit model, the hybrid equilibrium chemistry (utilizing equilibrium chemistry-grids) combined with non-uniform aerosol yields a C/O of 0.42 (+0.17/-0.13) and a metallicity of log[M/H] = 1.24 (+0.17/-0.14), corresponding to approximately 17.38 times the solar value. This hybrid chemistry retrieval also constrain SO2 with a log(VMR) = -4.85 (+0.28/-0.29), indicating possible absorption features in the 7-8 microns range. These results highlight NEXOTRANS's capability to significantly advance JWST emission spectra interpretation, offering broader insights into exoplanetary atmospheres.

Objective: ServiMon is designed to offer a scalable and intelligent pipeline for data collection and auditing to monitor distributed astronomical systems such as the ASTRI Mini-Array. The system enhances quality control, predictive maintenance, and real-time anomaly detection for telescope operations. Methods: ServiMon integrates cloud-native technologies-including Prometheus, Grafana, Cassandra, Kafka, and InfluxDB-for telemetry collection and processing. It employs machine learning algorithms, notably Isolation Forest, to detect anomalies in Cassandra performance metrics. Key indicators such as read/write latency, throughput, and memory usage are continuously monitored, stored as time-series data, and preprocessed for feature engineering. Anomalies detected by the model are logged in InfluxDB v2 and accessed via Flux for real-time monitoring and visualization. Results: AI-based anomaly detection increases system resilience by identifying performance degradation at an early stage, minimizing downtime, and optimizing telescope operations. Additionally, ServiMon supports astrostatistical analysis by correlating telemetry with observational data, thus enhancing scientific data quality. AI-generated alerts also improve real-time monitoring, enabling proactive system management. Conclusion: ServiMon's scalable framework proves effective for predictive maintenance and real-time monitoring of astronomical infrastructures. By leveraging cloud and edge computing, it is adaptable to future large-scale experiments, optimizing both performance and cost. The combination of machine learning and big data analytics makes ServiMon a robust and flexible solution for modern and next-generation observational astronomy.

The cosmic microwave background power spectra are a primary window into the early universe. However, achieving interpretable, likelihood-compatible compression and fast inference under weak model assumptions remains challenging. We propose a parameter-conditioned variational autoencoder (CVAE) that aligns a data-driven latent representation with cosmological parameters while remaining compatible with standard likelihood analyses. The model achieves high-fidelity compression of the $D_\ell^{TT}$, $D_\ell^{EE}$, and $D_\ell^{TE}$ spectra into just 5 latent dimensions, with reconstruction accuracy exceeding $99.9\%$ within Planck uncertainties. It reliably reconstructs spectra for beyond-$\Lambda$CDM scenarios, even under parameter extrapolation, and enables rapid inference, reducing the computation time from $\sim$40 hours to $\sim$2 minutes while maintaining posterior consistency. The learned latent space demonstrates a physically meaningful structure, capturing a distributed representation that mirrors known cosmological parameters and their degeneracies. Moreover, it supports highly effective unsupervised discrimination among cosmological models, achieving performance competitive with supervised approaches. Overall, this physics-informed CVAE enables anomaly detection beyond $\Lambda$CDM and points to physically meaningful directions for refinement.

Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General Circulation Models) that degrade the accuracy of the simulation. Radiative transfer calculations are an ideal candidate for machine learning emulation: fundamentally, it is a well-defined physical mapping from a static atmospheric profile to the resulting fluxes, and high-fidelity training data can be created from first principles calculations. We developed a radiative transfer emulator using an encoder-only transformer neural network architecture, trained on 1D profiles representative of solar-composition hot Jupiter atmospheres. Our emulator reproduced bolometric two-stream layer fluxes with mean test set errors of ~1% compared to the traditional method and achieved speedups of 100x. Emulating radiative transfer with machine learning opens up the possibility for faster and more accurate routines within planetary atmospheric models such as GCMs.

We analyze JWST NIRISS and NIRSpec spectroscopic observations in the Abell 2744 galaxy cluster field. From approximately 120 candidates, we identify 12 objects with at least a prominent emission lines among \Oii, \Hb, \Oiiia, \Oiiib, and \Ha that are spectroscopically confirmed by both instruments. Our key findings reveal systematic differences between the two spectrographs based on source morphology and shutter aperture placement. Compact objects show comparable or higher integrated flux in NIRSpec relative to NIRISS (within 1$\sigma$ uncertainties), while extended sources consistently display higher flux in NIRISS measurements. This pattern reflects NIRSpec's optimal coverage for compact objects while potentially undersampling extended sources. Quantitative analysis demonstrates that NIRSpec recovers at least $63\%$ of NIRISS-measured flux when the slit covers $>15\%$ of the source or when $R_e<1$kpc. For lower coverage or larger effective radii, the recovered flux varies from $24\%$ to $63\%$. When studying the \Ha/\Oiiib emission line ratio, we observe that measurements from these different spectrographs can vary by up to $\sim$0.3 dex, with significant implications for metallicity and star formation rate characterizations for individual galaxies. These results highlight the importance of considering instrumental effects when combining multi-instrument spectroscopic data and demonstrate that source morphology critically influences flux recovery between slit-based and slitless spectroscopic modes in JWST observations.

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this regime, including those that utilize raw pixel information instead of aperture photometry. However, the efficacy of image-based algorithms on high-redshift, near-infrared data remains underexplored. Here, we test the performance of Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC) on photometric redshift estimation with NIRCam images from the JWST Advanced Deep Extragalactic Survey (JADES) program. DeepDISC is designed to produce probabilistic photometric redshift estimates directly from images, after detecting and deblending sources in a scene. Using NIRCam-only images and a compiled catalog of spectroscopic redshifts, we show that DeepDISC produces reliable photo-zs and uncertainties comparable to those estimated from template fitting using HST+JWST filters; DeepDISC even outperforms template fitting (lower scatter/fewer outliers) when the input photometric filters are matched. Compared with template fitting, DeepDISC does not require measured photometry from images, and can produce a catalog of 94000 photo-zs in ~4 minutes on a single NVIDIA A40 GPU. While current spectroscopic training samples are small and incomplete in color-magnitude space, this work demonstrates the potential of DeepDISC for increasingly larger image volumes and spectroscopic samples from ongoing and future programs. We discuss the impact of the training data on applications to broader samples and produce a catalog of photo-zs for all JADES DR2 photometric sources in the GOOD-S field, with quality flags indicating caveats.

Using a custom-built scanning system, we generated maps of birefringence on reflection at $\lambda=1064$~nm from single-crystal GaAs/Al$_{0.92}$Ga$_{0.08}$As Bragg reflectors (henceforth ``AlGaAs coatings''). Ten coatings were bonded to fused silica substrates and one remained on the epitaxial growth wafer. The average phase difference on reflection between beams polarized along the fast and slow axes of the coating was found to be $\psi = 1.09 \pm 0.18$~mrad, consistent with values observed in high-finesse optical reference cavities using similar AlGaAs coatings. Scans of substrate-transferred coatings with diameters between 18 and 194 millimeters showed birefringence non-uniformity at a median level of $0.1$~mrad. A similar epitaxial multilayer that was not substrate transferred, but remained on the growth wafer, had by far the least birefringence non-uniformity of all mirrors tested at $0.02$~mrad. On the other hand, the average birefringence of the epi-on-wafer coating and substrate-transferred coatings was indistinguishable. Excluding non-uniformity found at the location of crystal and bonding defects, we conclude that the observed non-uniformity was imparted during the substrate transfer process, likely during bonding. Quantifying the impact on the scatter loss in a LIGO-like interferometer, we find that birefringence non-uniformity at the levels seen here is unlikely to have a significant impact on performance. Nonetheless, future efforts will focus on improved process control to minimize and ultimately eliminate the observed non-uniformity.

Over the past 30 years, numerous large-scale photometric astronomical surveys have been conducted, including SDSS, Pan-STARRS, Gaia,2MASS, WISE, and others. These surveys provide extensive photometric measurements that can be used to infer a wide range of physical parameters of astronomical objects. Traditionally, Bayesian approaches, such as Markov Chain Monte Carlo (MCMC) sampling have been employed for such inference tasks. However, these methods tend to be computationally intensive and often require manual tuning or expert supervision. In this work, we propose a novel machine learning model designed to perform automatic and robust inference from photometric data, offering a scalable and efficient alternative to conventional techniques.

High-sensitivity optical measurements such as those performed in interferometric gravitational wave detectors are prone to scattered light noise. To minimize it, optical components must meet tight requirements on surface roughness and bulk defects. Nonetheless, the effectiveness of these measures can be undermined by other sources of scattered light. In this article, we examine scattered light noise caused by particles deposited on surfaces, especially on the baffles inside the vacuum pipes of the Einstein Telescope's interferometer arms. First, we study light scattering by particles deposited on a surface and having diameters from about one tenth to hundred times the light wavelength: we discuss its angular distribution and dependence on particle size and refractive index, and on polarization. Then, we specialize to the case of the Einstein Telescope arms and quantify the maximum allowed density of particles on each arm baffle. We conclude with cleanliness guidelines for the assembly of the vacuum pipes, including the required cleanliness class of the installation environment.

The present-day bulk elemental composition of an exoplanet can provide insight into a planet's formation and evolutionary history. Such information is now being measured for dozens of planets with state-of-the-art facilities using Bayesian atmosphere retrievals. We collect measurements of exoplanet composition of gas giants into a Library of Exoplanet Atmospheric Composition Measurements for comparison on a population level. We develop an open-source toolkit, ExoComp, to standardize between solar abundance, metallicity, and C/O ratio definitions. We find a systematic enhancement in the metallicity of exoplanets compared to T-dwarf and stellar populations, a strict bound in C/O between 0 and 1, and statistically significant differences between measurements from direct, eclipse, and transmission spectroscopy. In particular, the transit spectroscopy population exhibits a systematically lower C/O ratio compared to planets observed with eclipse and direct spectroscopy. While such differences may be astrophysical signals, we discuss many of the challenges and subtleties of such a comparison. We characterize the mass-metallicity trend, finding a slope consistent between planets measured in transit versus eclipse, but offset in metallicity. Compared to the Solar System and constraints from interior modeling, gas giant atmospheres appear to exhibit a steeper mass-metallicity trend. We hope that the tools available in ExoComp and the data in the Library of Exoplanet Atmospheric Composition Measurements can enhance the science return of the wide-array of space- and ground-based exoplanet science being undertaken by the community.

Time-resolved atom interferometry, as employed in applications such as gravitational wave detection and searches for ultra-light dark matter, requires precise control over systematic effects. In this work, we investigate phase noise arising from shot-to-shot fluctuations in the atoms' transverse motion in the presence of the wavefront curvature of the interferometer beam, and analyse its dependence on the laser-beam geometry in long-baseline, large-momentum-transfer atom interferometers. We use a semi-classical framework to derive analytical expressions for the effective phase perturbation in position-averaged measurements and validate them using Monte Carlo simulations. Applied to 100-m and 1-km atom gradiometers representative of next-generation experiments, the model shows that configurations maximizing pulse efficiency also amplify curvature-induced phase noise, requiring micron-level control of the atom cloud's centre-of-mass position and sub-micron-per-second control of its centre-of-mass velocity to achieve sub-$10^{-5}$ rad phase stability. Alternative beam geometries can suppress this noise by up to two orders of magnitude, but at the cost of reduced pulse efficiency. To address this limitation, we propose a mitigation strategy based on position-resolved phase-shift readout, which empirically learns and corrects the wavefront-induced bias from measurable quantities such as the phase-shift gradient and final cloud position. This approach restores high-sensitivity operation in the maximum-pulse-efficiency configuration without detailed beam characterisation, providing a practical route towards next-generation, time-resolved atom interferometers operating at the $10^{-5}$ rad noise level.

We present the design and testing of a compact, low-cost stellar spectrometer developed for undergraduate and outreach applications. The instrument employs a 600 lines/mm diffraction grating, a CMOS monochrome sensor, and a 3D-printed mount integrated with reflecting telescopes. Calibration was performed using helium emission sources in the laboratory and Vega as a spectrophotometric standard, supported by a custom Python-based image-processing pipeline for wavelength calibration and spectral stacking. The spectrometer successfully recorded usable spectra of bright stars including Vega, Sirius, Procyon, Capella, and Betelgeuse, covering spectral types A through M. The results demonstrate that meaningful stellar spectroscopy can be achieved with accessible, low-cost equipment, providing a practical framework for student-led astronomical instrumentation projects.

We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFoF achieves end-to-end acceleration by performing all neighbor searches, label propagation, and group construction directly on GPUs, eliminating costly host--device transfers. We introduce two complementary neighbor-search strategies, a standard k-d tree and a novel linked-cell grid, and demonstrate that jFoF attains up to an order-of-magnitude speedup compared to optimized CPU implementations while maintaining consistent halo catalogs. Beyond performance, jFoF enables gradient propagation through discrete halo-finding operations via both frozen-assignment and topological optimization modes. Using a topological optimization approach via a REINFORCE-style estimator, our approach allows smooth optimization of halo connectivity and membership, bridging continuous simulation fields with discrete structure catalogs. These capabilities make jFoF a foundation for differentiable inference, enabling end-to-end, gradient-based optimization of structure formation models within GPU-accelerated astrophysical pipelines. We make our code publicly available at https://github.com/bhorowitz/jFOF/.