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Analyses of extended arcs in strong gravitational lensing images to date have constrained the properties of dark matter by measuring the parameters of one or two individual subhaloes. However, since such analyses are reliant on likelihood-based methods like Markov-chain Monte Carlo or nested sampling, they require various compromises to the realism of lensing models for the sake of computational tractability, such as ignoring the numerous other subhaloes and line-of-sight haloes in the system, assuming a particular form for the source model and requiring the noise to have a known likelihood function. Here, we show that a simulation-based inference method called truncated marginal neural ratio estimation (TMNRE) makes it possible to relax these requirements by training neural networks to directly compute marginal posteriors for subhalo parameters from lensing images. By performing a set of inference tasks on mock data, we verify the accuracy of TMNRE and show it can compute posteriors for subhalo parameters marginalized over populations of hundreds of substructures, as well as lens and source uncertainties. We also find that the multilayer perceptron (MLP) mixer network works far better for such tasks than the convolutional architectures explored in other lensing analyses. Furthermore, we show that since TMNRE learns a posterior function it enables direct statistical checks that would be extremely expensive with likelihood-based methods. Our results show that TMNRE is well-suited for analysing complex lensing data, and that the full subhalo and line-of-sight halo population must be included when measuring the properties of individual dark matter substructures with this technique. [ABSTRACT FROM AUTHOR]

Precision analysis of galaxy–galaxy strong gravitational lensing images provides a unique way of characterizing small-scale dark matter haloes, and could allow us to uncover the fundamental properties of dark matter's constituents. Recently, gravitational imaging techniques made it possible to detect a few heavy subhaloes. However, gravitational lenses contain numerous subhaloes and line-of-sight haloes, whose subtle imprint is extremely difficult to detect individually. Existing methods for marginalizing over this large population of subthreshold perturbers to infer population-level parameters are typically computationally expensive, or require compressing observations into hand-crafted summary statistics, such as a power spectrum of residuals. Here, we present the first analysis pipeline to combine parametric lensing models and a recently developed neural simulation-based inference technique called truncated marginal neural ratio estimation (TMNRE) to constrain the warm dark matter halo mass function cut-off scale directly from multiple lensing images. Through a proof-of-concept application to simulated data, we show that our approach enables empirically testable inference of the dark matter cut-off mass through marginalization over a large population of realistic perturbers that would be undetectable on their own, and over lens and source parameter uncertainties. To obtain our results, we combine the signal contained in a set of images with Hubble Space Telescope resolution. Our results suggest that TMNRE can be a powerful approach to put tight constraints on the mass of warm dark matter in the multi-keV regime, which will be relevant both for existing lensing data and in the large sample of lenses that will be delivered by near-future telescopes. [ABSTRACT FROM AUTHOR]

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution data sets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are (a) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (b) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (c) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimizing hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper. [ABSTRACT FROM AUTHOR]

Since upcoming telescopes will observe thousands of strong lensing systems, creating fully automated analysis pipelines for these images becomes increasingly important. In this work, we make a step towards that direction by developing the first end-to-end differentiable strong lensing pipeline. Our approach leverages and combines three important computer science developments: (i) convolutional neural networks (CNNs), (ii) efficient gradient-based sampling techniques, and (iii) deep probabilistic programming languages. The latter automatize parameter inference and enable the combination of generative deep neural networks and physics components in a single model. In the current work, we demonstrate that it is possible to combine a CNN trained on galaxy images as a source model with a fully differentiable and exact implementation of gravitational lensing physics in a single probabilistic model. This does away with hyperparameter tuning for the source model, enables the simultaneous optimization of nearly 100 source and lens parameters with gradient-based methods, and allows the use of efficient gradient-based posterior sampling techniques. These features make this automated inference pipeline potentially suitable for processing a large amount of data. By analysing mock lensing systems with different signal-to-noise ratios, we show that lensing parameters are reconstructed with per cent-level accuracy. More generally, we consider this work as one of the first steps in establishing differentiable probabilistic programming techniques in the particle astrophysics community, which have the potential to significantly accelerate and improve many complex data analysis tasks. [ABSTRACT FROM AUTHOR]

Light, asteroid-mass primordial black holes, with lifetimes in the range between hundreds to several millions times the age of the Universe, are well-motivated candidates for the cosmological dark matter. Using archival COMPTEL data, we improve over current constraints on the allowed parameter space of primordial black holes as dark matter by studying their evaporation to soft gamma rays in nearby astrophysical structures. We point out that a new generation of proposed MeV gamma-ray telescopes will offer the unique opportunity to directly detect Hawking evaporation from observations of nearby dark matter dense regions and to constrain, or discover, the primordial black hole dark matter. [ABSTRACT FROM AUTHOR]

We assessed the performance of Kalman Filtering-Smoothing and Expectation Maximization (EM-KF) in gap filling snow-depth sensor data. To this end, hourly snow-depth data from three spatially dense wireless-sensor networks were randomly removed and imputed using EM-KF. Maximum gap size was 40+ hours and differences between artificially removed and gap-filled data larger than 20 cm were removed before computing Root Mean Square Error and Bias. These differences are spurious over- and underestimations that can generally be identified through visual inspection, likely due to instability in the gap-filling process. The expected accuracy of EM-KF in this initial, controlled proof of concept is close to measurement uncertainty (1 to 2 cm for ultrasonic depth sensors). Compared to regressing missing data against nearby sensors, a frequently used strategy in the field, EM-KF tends to yield smaller errors in networks with a comparatively large number of co-located sensors (nine and eight as opposed to four in a third network). In these data-rich networks, maximum differences in daily Root Mean Square Errors between EM-KF and a regression are up to 6 to 8 cm at a daily time scale, with peaks in winter and in particular during snowfalls. EM-KF yields superior results particularly during snowfalls, likely because it exploits the temporal structure and uncertainty in the data through a state-space model. In the third network with fewer co-located sensors, differences in accuracy between EM-KF and a multilinear regression were inconsistent. This implies that the performance of EM-KF benefits from increasing the amount of available information and with increasing dependency of data across nodes. Temporally and spatially dense snow-depth data are being increasingly collected in operational contexts: EM-KF may support supervised filling of gaps in these data – particularly during snowfall events – and thus provide continuous-time information for avalanche or water-resources forecasting in snow-dominated regions. Automatic, unsupervised gap-filling using EM-KF will necessarily need more research to identify reasons of spurious over- and underestimations. Future work should also upscale this proof of concept to operational sensor networks spanning large water basins to bring conclusions closer to real-world applications. • EM-KF was used to fill gaps in snow-depth spatially dense data. • Accuracy for spatially dense data is close to measurement uncertainty (1–2 cm). • Peaks in accuracy gain compared to regression MLR during snowfall events. • Accuracy decreases with decreasing number of co-located sensors in the network. • EM-KF benefits from increasing the amount of available information and dependency. [ABSTRACT FROM AUTHOR]