Turbulence plays a central role in shaping the structure and dynamics of the interstellar medium (ISM), governing the star formation rate (SFR) and the initial mass function (IMF). A key consequence of turbulence is the generation of density fluctuations, which regulate the amount of dense gas available for star formation. Accurate measurements of the three-dimensional (3D) turbulent density dispersion are therefore essential for understanding molecular-cloud structure and star formation. However, observations typically provide only two-dimensional (2D) column densities and are often affected by measurement/detector noise. The Brunt method estimates the 3D density dispersion from 2D column-density maps, but it does not account for finite signal-to-noise ratio (SNR). Here, we extend the method to recover the 3D turbulent density dispersion from noise-contaminated observations. Using numerical simulations spanning a range of density perturbation amplitudes and noise types, we identify a characteristic noise wavenumber, k_noise, corresponding to the intersection of the signal and noise spectra. Restricting the Brunt reconstruction to wavenumbers below k_noise yields a denoised density-dispersion estimate that closely reproduces the noise-free result. We provide a practical prescription to determine k_noise directly from the measurement SNR and image resolution. Alternatively, if the noise spectrum is known, it can be subtracted directly from the observed spectrum, eliminating the need to estimate k_noise. The proposed correction recovers the noise-free density dispersion with errors of <~5% for SNR>=3 and <~15% for SNR>=1, enabling substantially more reliable estimates of turbulent density fluctuations from noisy column-density data.
Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data, remains robust under strong class contamination, and provides calibrated uncertainty quantification. We combine simulated transient injections with a contaminated survey class and train a dual-network model using asymmetric co-teaching for classes with different label-noise levels. We evaluate performance on a benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we compare MC dropout and deep ensembles and propose a low-cost hybrid strategy that exploits the dual-network setting to improve calibration. We extend the evaluation to the light-curve domain to assess recovery of light-curve classes. The method achieves strong Real-Bogus performance on the labeled subset and remains stable under severe class contamination. It recovers transient light-curve classes with high fidelity, while single-source identification is limited by ambiguity in light-curve-derived labels. Our hybrid UQ approach achieves competitive calibration relative to more expensive ensemble baselines. Latent-space analyses indicate that uncertainty aligns with the decision boundary and reveal subclasses within the bogus population. Our results show that injection-driven, weakly supervised training can enable scalable and consistent Real-Bogus classification without human-labeled training data while providing calibrated uncertainties. The method is suited for transfer to forthcoming surveys by re-running the injection-based training pipeline.
Spectral analysis using linear mixture (LM) and radiative transfer-based (RT) intimate mixture modeling based on Hapke theory at near-infrared wavelengths are applied to estimate the abundance of surface materials on Europa. Previously, Emran (2026) compared these approaches against the laboratory spectra of H$_2$O ice and H$_2$SO$_4$$\cdot$8H$_2$O mixtures with $\sim$100 $μ$m grains. Here, the effect of particle size on spectral modeling accuracy was assessed using laboratory spectra of H$_2$O ice mixtures with small ($\sim$70 $μ$m spherical) and coarse ($\sim$1 mm irregular) grains, measured over the $\sim$1.2-2.5 $μ$m wavelength range at 100 K and 120 K (Stephan et al., 2021). Modeled abundance estimates at both temperatures show consistent trends across all mixing ratios, with only minor temperature-dependent variations. The discrepancy in abundance estimates from both LM and RT models remains within $\pm$10% across all mixtures, with the error reduced to $\pm$5% when fine grains dominate. Across all mixtures, the average difference between RT- and LM-derived abundance estimates remains within $\pm$2% for mixtures containing both small and large grains. In contrast, mixtures composed solely of smaller grains render larger deviations between the models, with RT producing more accurate estimates (Emran, 2026) -- indicating that the presence of coarse H$_2$O ice grains minimizes abundance differences between LM and RT modeling. Thus, I posit that Hapke-based RT modeling is the preferred spectral modeling approach -- regardless of grain size or compositional mixture -- for constraining Europa's surface composition. Nonetheless, LM modeling remains a reliable approach for compositional analysis of terrains containing H$_2$O ice with $\sim$mm-sized grains.
Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations. We introduce a Transformer with a physics-informed positional-encoding framework for the efficient inference of eccentric binary black holes in relativistic orbits from PTA data. Our approach embeds analytical GW phase evolution directly into the model through structured positional encodings, enabling the network to learn physically meaningful representations from raw PTA timing residuals. We then use generative models, including discrete and continuous conditional normalizing flows, to infer posterior distributions within a simulation-based inference framework. Across a range of signal-to-noise ratios, the proposed method achieves improved accuracy, sharper posteriors, and faster inference compared to physics-agnostic baselines. While presented for deterministic white-noise signals, the modular framework readily generalizes to realistic PTA analyses incorporating red noise and additional components. This work highlights the potential of physics-aware deep learning models as scalable alternatives to conventional inference pipelines for next-generation PTA datasets.
The problem of signal detection under an unknown background can be framed as one of inferring the weight of a mixture model with one misspecified component. Banerjee and Algeri (2026) show that, for this problem, the conservativeness of the inference is entirely determined by one single parameter, called the compensator. They demonstrate that, when the data are independent and identically distributed, an inferential approach based on the compensator circumvents the need to estimate the density of the misspecified component and the associated challenges. The main purpose of this manuscript is to broaden the scope of such an approach and extend it to the case in which, as is often encountered in modern experiments in physics and astronomy, the data consist of Poisson counts observed over a large number of bins.
The symbiotic recurrent nova (SyRNe) TCrB (T-Coronae Borealis) is perhaps the most famous example of the group of known four symbiotic nova systems, for which at least two previous nova eruptions are known and accurately recorded: in 1866 and 1946. B.E. Schaefer (2023) has identified the dates of two other previous eruptive events: in 1787 and 1217. Its peak magnitude V was found to be 2.50+-0.10, making it the brightest of its class. In its quiescent phase, TCrB is the brightest of all known novae, with a mean magnitude of 9.8. Careful studies, especially photometric ones, have led to different predictions for the next nova eruption, taking into account the recurrence times extrapolated from previous eruptions, which an average value about 80 years. Schaefer, in particular, has produced various forecasts, including one made in 2023 based on B and V light curves for the period: 1842-2022, which predicts the next nova eruption should occur in 2025.5+-1.3 and is therefore still valid today. Using the Schaefer's remarkable work in accurately determining the key physical parameters that drive the dynamics of the TCrB symbiotic system, we propose here a new semi-empirical method to derive the variations in the nova recurrence time, Trec, and thus obtain a forecast estimate for the next eruption for the date: 26-Feb-2027, which is currently compatible and consistent with the observed behavior and would also justify the supposed "delay" for the next event of this nova as commented by various authors.
Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models. These detectors achieve exceptional performance, but they require large task-specific training datasets and cannot be redefined without retraining. In this work, we evaluate whether small, open-weight, locally run generalist Vision-Language Models (VLMs) can detect FRBs in dynamic spectra under a zero-shot, prompt-only regime, with no fine-tuning and no labeled examples, returning structured decisions with a natural-language justification. From a controlled set of 3000 simulated L-band dynamic spectra containing FRBs, structured Radio Frequency Interference (RFI), and noise, we draw a balanced binary benchmark of 2000 samples and compare two such VLMs (Gemma 4 2B and 4B), sample by sample, against the state-of-the-art specialized detector SwinYNet. At the default threshold, Gemma 4 2B reaches an accuracy of 93.65%, with no statistically significant difference from SwinYNet (92.90%), while showing a significantly lower false-positive rate on structured RFI (6.4% vs. 25.0%) and no false positives on pure noise. SwinYNet retains a perfect probabilistic ranking on this benchmark (ROC-AUC of 1.0000 vs. 0.9482), a ceiling that the zero-shot VLM approaches from general-purpose pretraining alone. Rewriting the prompt alone reconfigures the same models for three-class FRB/RFI/noise classification on the full set of 3000 spectra, where they reach up to 86% accuracy without a single false FRB.