Michael Bussmann

X-ray Thomson scattering (XRTS) is a common diagnostic used in the warm dense matter (WDM) regime to estimate plasma parameters like density, temperature, and charge state. Experimental analysis typically relies on a forward model to obtain estimates for these parameters, as the measured spectrum is a convolution of the dynamic structure factor (DSF) and the source-instrument function. The Chihara decomposition, where the spectrum is separated into contributions from bound and free electrons, is commonly used to estimate DSFs in the WDM regime, as it allows for the fast calculation of DSFs and therefore can easily be applied in a large-scale parameter optimization. Due to the limited availability of XRTS codes, we present “x-ray diagnostics, analysis, verification, and exploration”, a code designed to quickly estimate DSFs using the Chihara decomposition and analyze experimental spectra. The code is validated by re-analyzing an experiment with isochorically heated beryllium at the OMEGA Laser Facility. In addition, we demonstrate the applicability of the code to plan experiments and predict scattering spectra through the coupling to a ray-tracing code. Finally, the importance of accounting for the energy-dependence of spectrometer instrument functions is demonstrated by comparing ray-tracing simulations to the standard convolution for strongly compressed beryllium experiments at the National Ignition Facility, similar to previously published results.

VIPR (Versatile Inverse Problem Software Framework) is a plugin-basedsoftware framework for building reproducible machine-learning workflows forscientific inverse problems. These problems arise when researchers need toreconstruct physical properties of a sample from incomplete or indirect mea-surements, a common challenge in X-ray and neutron scattering experimentswhere only intensity measurement is available and phase infomation is lost.VIPR addresses such ill-posed reconstruction tasks where direct inversion isoften impossible. VIPR implements a modular microkernel architecture withdomain-specific plugins to produce configurable machine learning workflows,including both deterministic and probabilistic models. Workflows are de-fined via declarative YAML configurations and can be executed through acommand-line interface or a containerized web application. For a given ex-perimental dataset, VIPR produces standardized analysis artifacts, includingvisualizations and statistical summaries. Although this article demonstratesthe functionality of VIPR specifically for X-ray and neutron reflectometry,the separation of the framework’s core logic from domain extensions enablesstraightforward adaptation to other domains.

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter (WDM) physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the WDM physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations & experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments of and contemporary challenges for theoretical methods and experimental techniques needed to describe, create and diagnose WDM. A large part of this roadmap is dedicated to specific WDM systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.

Particle-in-Cell (PIC) codes are powerful tools to self-consistently simulate laser-plasma dynamics. Recent methods enable the extraction of electromagnetic fields from PIC simulations and their propagation to a virtual screen using Fourier optics. This approach allows the generation of synthetic optical images, so called shadowgrams, which reproduce experimental diagnostics where a transverse probe beam is refracted by plasma structures and imaged onto a detector. By using field data from PIC simulations, such synthetic shadowgrams capture the full laser-plasma interaction, beyond what static ray-tracing or stand-alone finite-difference time-domain solvers can represent. However, these methods were only used in post-processing until now, and require storing large 3D field datasets, which is often impractical for large-scale simulations. To overcome this limitation, we present an in-situ plugin for the PIC code PIConGPU, which performs a Fourier time integration of 2D field slices at fixed positions, avoiding full 3D spatial Fourier transforms. This enables the creation of synthetic optical images directly during runtime ("in-situ") with a significantly reduced storage footprint and reduced total simulation size. We present the implementation of the in-situ optical imaging plugin. We validate the plugin against PIConGPU's intrinsic field solver and analytic theory using a double-slit test, and demonstrate the first synthetic in-situ shadowgram generated of high-intensity laser-plasma experiments with cryogenic hydrogen-jet targets.

Magnetic reconnection drives a wide range of astrophysical phenomena, including geomagnetic storms, solar flares, and activity in blazars. However, direct measurement of key reconnection observables remains challenging due to the remote and extreme nature of these environments. While high-energy particle showers observed on Earth are often attributed to reconnection, the underlying mechanisms are not fully understood, and clear diagnostic signatures are lacking. We present a theoretical, data-driven approach for identifying reconnection radiation signatures and enabling remote diagnostics of reconnection in astrophysical settings through radiation spectra for the nonrelativistic case. Using particle-in-cell simulations of magnetic reconnection, we generate radiation spectra and establish connections between spectral features and the underlying reconnection dynamics. We develop a method to estimate the ratio of the reconnection electric field to the plasmoid magnetic field from spectral data. Analytic calculations show that other parameters can be extracted in the ultrarelativistic reconnection regime, such as the magnetic field or the current sheet width.

High-energy laser facilities such as PHELIX at GSI require excellent beam pointing stability for reproducability and relative independence for future experiments. Beam pointing stability has been traditionally achieved using simple proportional–integral–derivative (PID) control which removes the problem of slow drift, but is limited because of the time delay in knowing the diagnosis and the inertia in the mechanical system associated with mirrors. In this work, we introduce a predictive control strategy where the forecasting of beam pointing errors is performed by a patch-based multilayer perceptron (Patch-MLP) designed to capture local temporal patterns for more robust short-term jitter prediction. The subsequent conversion of these predicted errors into correction signals is handled by a PID controller. The neural network has been trained on diagnostic time-series data to predict beam pointing error. Using the hybrid predictive–feedback controller compensates for system delays. Simulations with a correction mirror placed upstream of the PHELIX pre-amplifier bridge confirm that the predictive control scheme reduces residual jitter compared to conventional PID control. In our simulations, over a 10-hour dataset the controller maintained stable performance without drift, while standard pointing metrics showed consistent improvements of the order of 10%-20%. The predictive controller operates without drift, and therefore may improve reproducibility and operational efficiency in high energy, low repetition rate laser experiment conditions.