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XRISM constraints on the velocity power spectrum in the Coma cluster
No full textThe velocity field of intracluster gas in galaxy clusters contains key information on the virialization of infalling material, the dissipation of AGN energy into the surrounding medium, and the validity of the hydrostatic hypothesis. The statistical properties of the velocity field are characterized by its fluctuation power spectrum, which is usually expected to be well described by an injection scale and a turbulent cascade. Here we propose a simulation-based inference technique to retrieve the properties of the velocity power spectrum from X-ray micro-calorimeter data by generating simulations of Gaussian random fields from a parametric power spectrum model. We forward model the measured bulk velocities and velocity dispersions by including the most relevant observational effects (projection, emissivity weighting, PSF smearing). We then train a neural network to learn the mapping between the power spectrum parameters and the generated data vectors. Considering a three-parameter model describing turbulent energy injection on large scales and a power-law cascade, we found that two XRISM/Resolve pointings are sufficient to accurately determine the turbulent Mach number and set interesting constraints on the injection scale. Applying our method to the Coma cluster data, we obtain a model that is characterized by a large injection scale that rivals the size of the cluster (ℓᵢₙⱼ =2.2_⁺².⁰ −₁.₀ Mpc). When this power spectrum model is integrated over the cluster scales (0 D,₅₀₀=0.45 ⁺⁰.¹⁸ ₋₀.₁₃, which exceeds the value derived from the velocity dispersions only. Further observations covering a wider area are required to decrease the cosmic variance and constrain the slope of the turbulent cascade.DE and MR acknowledge support from the Swiss National Science Foundation (SNSF) under grant agreement 200021_212576. Support for JAZ was provided by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. IZ acknowledges partial support from the Alfred P. Sloan Foundation through the Sloan Research Fellowship and from NASA grant 80NSSC18K1684. NT acknowledges the support by NASA under award number 80GSFC24M0006. NO acknowledges JSPS KAKENHI Grant Number JP25K07368. DRW acknowledges support from NASA grant 80NSSC23K0740.http://arxiv.org/abs/2510.2191
Habitable World Discovery and Characterization: Coronagraph Concept of Operations and Data Post-Processing
No full textThe discovery and characterization of habitable worlds was the top scientific recommendation of the Astro2020 decadal survey and is a key objective of the Habitable Worlds Observatory. Biosignature identification drives exceedingly challenging observations, which require raw contrasts of roughly 10⁻¹⁰ contrast and ultimately, 1σ photometric precision of roughly 3×10⁻¹² contrast. Despite significant advances for the Nancy Grace Roman Space Telescope's Coronagraph Instrument, technological gaps still exist in a wide range of technologies such as starlight suppression, deformable mirrors, wavefront control, low noise detectors, and high-contrast spectroscopy. Even with these new technologies matured, the Habitable Worlds Observatory must carefully obtain the observations and rely on post-processing of the data to achieve its science objectives. During the START and TAG efforts, a working group was convened to explore the Coronagraph Concept of Operations and Post Processing (COPP) in the context of the Habitable Worlds Observatory. This COPP working group evaluated coronagraphic concept of operations to enable different post processing approaches, such as reference differential imaging and angular differential imaging, polarization differential imaging, orbital differential imaging, coherent differential imaging, spectral processing, and point-spread function subtraction algorithms that incorporate ancillary telemetry and data. Future integrated modeling simulations and testbed demonstrations are needed to determine the achievable post processing gains for each approach. We report a summary of this working group's activities and findings, as well as an outlook for maturation of these techniques and infusion into the Habitable Worlds Observatory technology portfolio.This work was performed within the Concept of Operations and Post-Processing Working Group during the START and NASA TAG phase of the HWO GOMAP. The COPP focus groups were supported by a broad range of experts within the community as reported in the white papers. The research was carried out in part through the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). RJP was supported by NASA through the CRESST II cooperative agreement CA 80GSFC24M0006. HLC acknowledges this work was supported by the Programme National de Planetologie (PNP) ´ of CNRS-INSU co-funded by CNES and made use of computing facilities operated by CeSAM data center at LAM, Marseille, France.http://arxiv.org/abs/2510.0254
Laplacian Flows in Complex-valued Directed Networks: Analysis, Design, and Consensus
No full textIn the interdisciplinary field of network science, a complex-valued network, with edges assigned complex weights, provides a more nuanced representation of relationships by capturing both the magnitude and phase of interactions. Additionally, an important application of this setting arises in distribution power grids. Motivated by the richer framework, we study the necessary and sufficient conditions for achieving consensus in both strongly and weakly connected digraphs. The paper establishes that complex-valued Laplacian flows converge to consensus subject to an additional constraint termed as real dominance which relies on the phase angles of the edge weights. Our approach builds on the complex Perron-Frobenius properties to study the spectral properties of the Laplacian and its relation to graphical conditions. Finally, we propose modified flows that guarantee consensus even if the original network does not converge to consensus. Additionally, we explore diffusion in complex-valued networks as a dual process of consensus and simulate our results on synthetic and real-world networks.http://arxiv.org/abs/2509.0419
What determines the γ-ray luminosities of classical novae?
No full textClassical novae in the Milky Way have now been well-established as high-energy GeV γ-ray sources. In novae with main-sequence companions, this emission is believed to result from shocks internal to the nova ejecta, as a later fast wind collides with an earlier slow outflow. To test this model and constrain the γ-ray production mechanism, we present a systematic study of a sample of recent Galactic novae, comparing their γ-ray properties (γ-ray luminosity and duration) with their outflow velocities, peak V-band magnitudes, and the decline times of their optical light curves (t₂). We uniformly estimate distances in a luminosity-independent manner, using spectroscopic reddening estimates combined with three-dimensional Galactic dust maps. Across our sample, γ-ray luminosities (>100 MeV) vary by three orders of magnitude, spanning 10³⁴- 10³⁷erg s⁻¹. Novae with larger velocity of the fast outflow (or larger differential between the fast and slow outflow) have larger γ-ray luminosities, but are detectable for a shorter duration. The optical and γ-ray fluxes are correlated, consistent with substantial thermal emission in the optical from shock-heated gas. Across six novae with γ-ray and infrared light curves, evidence for dust formation appears soon after the end of the detected γ-ray emission. Dusty and non-dusty novae appear to have similar γ-ray luminosities, though novae that have more material processed by the shocks may be more likely to form dust. We find that the properties of the γ-ray emission in novae depend heavily on the ejecta properties, and are consistent with expectations for internal shocks.PC, AS, EA, LC, AC, AK, and KVS are grateful for support from NASA awards 80NSSC25K7334, 80NSSC23K1247, 80NSSC23K0497, and 80NSSC18K1746, They also acknowledge NSF awards AST-1751874, AST-2107070, and AST2205631, and a Cottrell fellowship of the Research Corporation. E.A. acknowledges support by NASA through the NASA Hubble Fellowship grant HST-HF2-51501.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5- 26555. JS was supported by the Packard Foundation. JLS was supported by NASA grant 80NSSC25K7068. LI was supported by grants from VILLUM FONDEN (project number 16599 and 25501). DAHB gratefully acknowledges the receipt of research grants from the National Research Foundation (NRF) of South Africa. FMW acknowledges support of the US taxpayers through NSF grant 1611443. BDM acknowledges support through NASA (grants 80NSSC22K0807, 80NSSC24K0408), and the Simons Foundation (grant 727700). The Flatiron Institute is supported by the Simons Foundation. ASAS-SN thanks the Las Cumbres Observatory and its staff for its continuing support of the ASAS-SN project. ASAS-SN is supported by the Gordon and Betty Moore Foundation through grant GBMF5490 to the Ohio State University and NSF grant AST-1515927. Development of ASASSN has been supported by NSF grant AST-0908816, the Mt. Cuba Astronomical Foundation, the Center for Cosmology and AstroParticle Physics at the Ohio State University, the Chinese Academy of Sciences South America Center for Astronomy (CASSACA), the Villum Foundation, and George Skestos. We thank Robert E. Williams for useful comments and discussion. We thank Kristen Dage and Chelsea Harris for useful comments and support during this work. We thank the AAVSO observers from around the world who contributed their magnitude measurements to the AAVSO International Database used in this work. We acknowledge all the ARAS observers for their optical spectroscopic observations which complement our data. This work is based on observations obtained at the Southern Astrophysical Research (SOAR) tele scope, which is a joint project of the Minist´erio da Ciˆencia, Tecnologia, Inova¸c˜oes e Comunica¸c˜oes (MCTIC) do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC), and Michigan State University (MSU). A part of this work is based on observations made with the Southern African Large Telescope (SALT), with the Large Science Programme on transients 2018-2-LSP-001 (PI: DAHB). This work is also partly based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere. This paper includes data gathered with the 6.5 meter Magellan Telescopes located at Las Campanas Observatory, Chile. Polish participation in SALT is funded by grant no. MNiSW DIR/WK/2016/07.http://arxiv.org/abs/2508.1590
From the Block to the Bay: Understanding the role of Scale and Power in Environmental Education in Baltimore City
No full textEnvironmental education (EE) works to address socioenvironmental crises by promoting particular values and actions. Pedagogical perspectives warn that universalizing discourses often inform the identification of problems and solutions, and can deepen environmental inequities through normative understandings of nature and place. This work investigates the relationship between power, scale, and discourse in EE in Baltimore City, Maryland, USA using a combination of semi-structured interviews, ethnographic accompaniment, and document analysis. Overall, I found that the Chesapeake Bay watershed scale is the predominant structure guiding environmental discourse in Baltimore and that the size of an organization influences the discourses they employ–particularly in the identification of problems, solutions, and responsibility. The watershed scale represents a space of engagement and the context in which EE is developed. Larger organizations frequently promoted individual responsibility for broadscale problems and prioritized deploying unpaid labor of volunteers towards prescribed solutions. Broad-scale approaches reproduce universalizing discourses by erasing cultural complexity and geographic unevenness, and preventing accountability. Universalizing discourses render people and places malleable and force them into what I call the normative mold. The normative mold is cast at the watershed scale and is reflected to varying degrees across scales. Smaller organizations resist the normative mold by prioritizing the provision of services to their communities over volunteerism; but they are often dependent upon institutional funding and forced to participate. While larger organizations have power to reproduce normative molds, they also advocate for policy and funding that allows smaller organizations to operate. This relationship complicates the scalar assessment of EE
Thermal Design and Analysis of a Next-Generation Hyper-Angular Rainbow Polarimeter (HARP) Instrument
No full textMegaHARP is a next-generation Hyper-Angular Rainbow Polarimeter (HARP) instrument. It is targeted to fly on NASA's Atmospheric Observation System (AOS) Sky satellite. Although MegaHARP leverages its design and technology from predecessor instruments, including HARP2, which is currently operating in orbit, this latest HARP instrument features key improvements. With these improvements come significant engineering challenges; the thermal system is one such challenge. This work explores the design and validation of a conceptual MegaHARP thermal system that achieves two goals: maintaining the instrument's electronics within their temperature ranges, and stabilizing its focal plane arrays (FPAs) while the instrument is acquiring images. The instrument's radiators and heaters are first ideally sized using a steady-state, orbital averaged heat load approximation. A subsequent calculation accounts for the FPA-radiator thermal path using information obtained from the HARP2 thermal model, which is correlated experimentally. Finally, MegaHARP’s thermal design is validated using a transient orbital thermal model. The model shows that the passive thermal system presented here accomplishes both design requirements
Validation of SNPP OMPS limb profiler version 2.6 ozone profile retrievals against correlative satellite and ground based measurements
No full textAbstract. The Ozone Mapping and Profiler Suite Limb Profiler (OMPS LP) was launched onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011 and began routine science operations in April 2012. The OMPS LP uses measurements of scattered solar radiation in the ultraviolet, visible and near infrared wavelengths to retrieve high vertical resolution profiles of ozone from 12 km (or cloud tops) up to 57 km. In mid-2023, version 2.6 of the OMPS LP ozone profile retrievals was released, featuring improvements in calibration, the retrieval algorithm, and data quality. We evaluate OMPS LP version 2.6 ozone retrievals using correlative data from other satellite instruments and ground based data for the period April 2012 to April 2024. Our results show agreement between OMPS LP and all correlative data sources between 20 and 50 km at all latitudes with differences of less than 10 %, with OMPS generally exhibiting a negative bias, except between 32 and 38 km in the tropics and southern mid-latitudes, where the bias is positive. In the tropics and southern mid-latitudes the differences between OMPS LP and MLS, and OMPS LP and SAGE III/ISS are less than & ±5 % between 20 and 45 km. Above 50 km, the agreement with MLS is still on the order of ~5 % or better. Larger positive biases, up to ~35 %, are seen in the upper troposphere lower stratosphere layer (~15 to 20 km) between approximately 40° South and 40° North. We find that OMPS version 2.6 ozone exhibits the same seasonal cycle as compared to all correlative measurement sources and our analysis shows that there is no significant seasonal bias in the OMPS LP. We find small drifts relative to correlative observations at all latitude bands of less than ±0.2 %/yr (±0.1 %/yr) between 25 and 50 km for the 2012-2024 period, with larger drifts above 50 km and below 20 km. These small drifts vary between correlative measurements and straddle the zero line, we therefore conclude that there is no significant systematic drift in OMPS LP version 2.6 ozone for the period 2012 to 2024. The drift results represent an improvement in the long term stability of version 2.6 ozone over that of version 2.5.This research is supported by the GESTAR II Cooperative Agreement with NASA Goddard Space Flight Centerhttps://egusphere.copernicus.org/preprints/2025/egusphere-2025-4117
Network Traffic Data Preprocessing: An Agentic AI Framework
No full textThe application of machine learning (ML) to power modern Intrusion Detection
Systems (IDS) is critically dependent on the quality of network traffic data. However,
the data preprocessing stage—which transforms complex, "dirty" raw data into a clean,
ML-ready format—remains a time-consuming, manual bottleneck that relies heavily on
domain expertise. This research addresses this gap by proposing and validating a novel,
holistic framework using agentic Generative AI (GenAI) to fully automate the end-to-
end data preprocessing pipeline.
The core of this framework is a sophisticated, multi-step "prompt-chain" that
compels a Large Language Model (LLM) to act as an expert data scientist. This agentic
process forces the AI to move beyond simple code generation; it must first perform
rigorous exploratory data analysis, construct a feature selection plan, design a
methodologically sound preprocessing strategy (preventing data leakage and handling
class imbalance), generate the code, and validate its own work through iterative
debugging and "hostile code" reviews.
This three-phase agentic framework was experimentally validated by
comparing the performance of three state-of-the-art GenAI reasoning models
(DeepSeek V3, Google Gemini 2.5 Pro, and OpenAI GPT-5) against a human-derived
manual baseline. The evaluation was conducted on three distinct network traffic
datasets of increasing complexity: the curated UNSW-NB15 (as a control), the raw
IDSIoT2024, and the highly complex, "dirty" VNFCYBERDATA. The resulting data
artifacts were evaluated using four standard ML classifiers: K-Nearest Neighbors
(KNN), Decision Tree (DT), Random Forest (RF), and Gaussian Naïve Bayes (GNB).
The results comprehensively validate the hypothesis. While the framework's
application on the already-curated UNSW-NB15 dataset led to "over-cleaning" and a
slight performance degradation, its performance on the two raw datasets was
definitively superior to the manual baseline. The GenAI framework consistently and
significantly outperformed the manual method on the IDSIoT2024 and
VNFCYBERDATA datasets. Notably, the GenAI-processed data enabled sensitive
classifiers like GNB to function, whereas the manual baseline caused them to fail
catastrophically. Furthermore, the framework proved definitively superior in preparing
the data for the critical task of minority class (rare attack) detection, where the manual
baseline failed. This research demonstrates that a prompt-chain-driven GenAI
framework is not merely a viable alternative but a more robust, comprehensive, and
powerful method than traditional manual preprocessing for raw, real-world network
traffic data
Comparison of cross-sectional to continuous measures of channel dimensions in Mid-Atlantic Piedmont streams
No full textMeasurement of stream channel dimensions provides valuable information toward understanding geomorphic processes. A recently developed geomorphon-based stream mapping algorithm enables fast, continuous generation of channel dimension estimates anywhere a high-resolution digital elevation surface is available. This study compared automated, continuous estimates of channel width and bank height to cross-sectional measurements placed manually on the same digital surface and field transects. Both types of digital measurements performed best on channels that were of sufficient size to be visible on a 1 x 1 m digital elevation model (DEM), had banks that appeared as distinct features, and could not be mistaken for valley walls. Geomorphon-based measurement required refinement for very small and very large channel reaches. For channels of moderate size (approximately 2-30 meters wide), especially those wider than about four meters, 25ᵗʰ percentile geomorphon-based bank height estimates corresponded reasonably well with cross-sectional bank height estimates, and median geomorphon-based channel width correlated with, but underestimated, cross-sectional width measurements. Additionally, the range of width or height values detected by geomorphon-based estimates tended to encapsulate mean cross-sectional estimates of each reach. The interquartile range (IQR) of geomorphon-based estimates of channel width included mean cross-sectional field top widths for 29% of reaches between two and eight meters wide. The IQR of geomorphon-based estimates of bank height included mean cross-sectional field bank heights for 22% of channels wider than two meters. Despite necessary areas for improvement, automated, digital techniques show promise for describing channel dimensions in streams of moderate size
LEVERAGING EXTERNAL DATA IN CLINICAL TRIAL DESIGN: SYNTHETIC CONTROL ARM CONSTRUCTION USING A CAUSAL INFERENCE INTEGRATED MACHINE LEARNING APPROACH
No full textThe integration of external data to construct synthetic controls represents a shift from conventional reliance on concurrent randomized controlled trials (RCTs) in evidence-based medicine. The use of real-world data (RWD) has gained momentum, driven by the rising cost and feasibility challenges of RCTs, the availability of high-quality RWD, and advances in causal inference and Bayesian modeling for estimating average treatment effects (ATE). Synthetic control methods construct control subjects from external data to approximate traditional control arms, enabling ATE estimation in RCTs with limited concurrent controls. However, challenges such as covariate heterogeneity and unmeasured confounding make valid integration of external data complex. Traditional propensity score (PS) methods, commonly used to balance covariates in observational studies, face limitations when used for integrating external data. The treatment indicator is redefined as a data source indicator. Since assignment to data sources may not depend on covariates, standard PS modeling becomes less reliable, and optimal model selection is not well defined. To address these issues, Chapter 2 proposes a achine learning approach OneClass Support Vector Machine (OCSVM), which identifies external units compatible with the current study using only current data. OCSVM avoids the need to model assignment mechanisms and better handles non-linearities and heterogeneity across datasets. PS methods, the proposed OCSVM, Bayesian approaches, and two-stage approaches were compared in the simulation study. OCSVM consistently achieved better covariate balance and improved performance relative to PS methods. Building on this foundation, Chapter 3 introduces three improvements of OCSVM: (1) a tuning procedure for the γ parameter in the radial basis function kernel, (2) a weighted OCSVM method that incorporates position and density based weights to reduce sensitivity to outliers, and (3) a custom kernel function designed to accommodate mixed-type variables. These innovations enhance the robustness, flexibility, and generalizability of OCSVM. Chapter 4 addresses a limitation of OCSVM: treating all borrowed external data within the decision boundary equally can introduce bias when covariate distributions differ between sources. To mitigate this, a hybrid method OCSVM-EB is proposed. It first applies OCSVM to trim incompatible external units, followed by entropy balancing (EB) to reweight the remaining data and align covariate distributions. EB imposes constraints to match covariate moments across current and external data. Simulation studies confirm that OCSVM-EB achieves superior covariate balance and improved estimation accuracy compared to PS-based methods