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Based on their cosmological origin, the stars of a galaxy can be divided into two categories: those that enter through merger events (ex situ) and those born in the main progenitor (in situ). We used the TNG50 cosmological magnetohydrodynamical simulation and its Lagrangian tracer particles to explore and quantify the origin of gas that ultimately forms the in situ stars of galaxies. We tracked back the baryonic mass contributing to the $z=0$ in situ stellar populations of galaxies, studying trends in mass from dwarfs to group-scale halos. We find that more massive halos acquire this matter earlier than lower-mass halos, reflecting an overall earlier assembly of their in situ stellar mass. Defining the Lagrangian half-mass radius R$_{\rm L, 1/2}$ of a galaxy as the distance containing half of the mass that will form its in situ stars by $z=0$, we find that R$_{\rm L, 1/2}$ is larger for more massive halos at early times, reflecting larger "in situ Lagrangian regions." However, the dependence of this radius on halo mass becomes flat at $z \simeq 3$ and then inverts toward $z=0$. In addition, R$_{\rm L, 1/2}$ increases rapidly with redshift, surpassing the virial radii of halos at $z

For studying the structure of microgel particles at the air/water interface, specular and off-specular X-ray reflectivity (OSR/XRR) allows in situ measurements without any labelling techniques. Herein we investigate the vertical and lateral structure of poly(N-isopropylacrylamide) (PNIPAM) microgels (MGs) at the air/water interface and the effect of Langmuir-Blodgett (LB) transfer onto solid substrates. The initial ex situ atomic force microscopy (AFM) scans of LB-transferred MGs at the air/solid interface reveal strong lateral 2D hexagonal ordering across a broad range of lateral surface pressures at the air/water interface before LB-transfer. Notably, for the first time, these results were confirmed by OSR, demonstrating the existence of the long-range hexagonal ordering at low and intermediate surface pressures. For in situ conditions and upon uniaxial compression at the air/water interface, the MG lattice constant decreases non-monotonically. This indicates the formation of domains at low pressures that approach each other and only compress when the surface isotherm reaches a plateau. Comparing the results of in situ and ex situ measurements, our study demonstrates a clear tran

In situ synchrotron X-ray computed tomography enables dynamic material studies. However, automated segmentation remains challenging due to complex imaging artefacts - like ring and cupping effects - and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for segmentation of in situ synchrotron data, demonstrated through a metal oxide dissolution study. Using a modified SegFormer architecture, our approach achieves segmentation performance (94.7% IoU) that matches human inter-annotator reliability (94.6% IoU). This indicates the model has reached the practical upper bound for this task, while reducing processing time by 2 orders of magnitude per 3D dataset compared to manual segmentation. The method maintains robust performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, enabling the efficient analysis of the large volumes of time-resolved tomographic data generated in typical in situ experiments across scientific disciplines.

Two types of MgB2 films were prepared by pulsed laser deposition (PLD) with in situ and ex situ annealing processes respectively. Significant differences in properties between the two types of films were found. The ex situ MgB2 film has a Tc of 38.1K, while the in situ film has a depressed Tc of 34.5K. The resistivity at 40K for the in situ film is larger than that of the ex situ film by a factor of 6. The residual resistivity ratios (RRR) are 1.1 and 2.1 for the in situ and ex situ films respectively. The Jc-H curves of the in situ film show a much weaker field dependence than those of the ex situ film, attributable to stronger flux pinning in the in situ film. The small-grain feature and high oxygen level may be critical for the significant improvement of Jc in the in situ annealed MgB2 film.

The population of hot Jupiters with adjacent planetary companions is small but growing, and inner companions appear to be a nearly ubiquitous outcome within this subset of the exoplanet census. While most hot Jupiters are believed to form via tidal migration, the presence of adjacent companions is not easily explained by this formation mechanism, requiring consideration of additional formation mechanisms such as disk migration and in situ formation. In this work, we explore the possibility of in situ formation for both hot Jupiters and their interior companions. Using numerical simulations performed with the N-body integrator REBOUND, we investigate the growth of interior companions under various assumptions about disk conditions and hot Jupiter final orbital positions. Our results show that if a sufficiently high density of planetary embryos is transported to short orbital radii, it is feasible for both hot Jupiters and their interior companions to form in situ, providing a viable explanation for a subset of observed planetary architectures.

In this short paper, we present an innovative approach to limit the required bandwidth when transferring data during in transit analysis. This approach is called hybrid because it combines existing in situ and in transit solutions. It leverages the stable ABI of Catalyst version 2 and the Catalyst-ADIOS2 implementation to seamlessly switch from in situ, in transit and hybrid analysis without modifying the numerical simulation code. The typical use case is to perform data reduction in situ then generate a visualization in transit on the reduced data. This approach makes the numerical simulation workflows very flexible depending on the size of the data, the available computing resources or the analysis type. Our experiment with this hybrid approach, reducing data before sending it, demonstrated large cost reductions for some visualization pipelines compared to in situ and in transit solutions. The implementation is available under an open source permissive license to be usable broadly in any scientific community.

This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are likely to escape confinement. Such particles carry a significant amount of energy from the burning plasma inside the tokamak to the diverter and damaging the diverter plate. This study requires in situ processing because of the fast changing nature of the particle diffusion process. However, the in situ processing approach is challenging because the amount of data to be retained for the diffusion calculations increases over time, unlike in other in situ processing cases where the amount of data to be processed is constant over time. Here we report our preliminary efforts to control the memory usage while ensuring the necessary analysis tasks are completed in a timely manner. Compared with an earlier naive attempt to directly computing the same diffusion displacements in the simulation code, this in situ version reduces the memory usage from particle information by nearly 60% and computation time by about 20%.

Tailoring nanoparticles composition and morphology is of particular interest for improving their performance for catalysis. A challenge of this approach is that the nanoparticles optimized initial structure often changes during use. Visualizing the three dimensional (3D) structural transformation in situ is therefore critical, but often prohibitively difficult experimentally. Although electron tomography provides opportunities for 3D imaging, restrictions in the tilt range of in situ holders together with electron dose considerations limit the possibilities for in situ electron tomography studies. Here, we present an in situ 3D imaging methodology using single particle reconstruction (SPR) that allows 3D reconstruction of nanoparticles with controlled electron dose and without tilting the microscope stage. This in situ SPR methodology was employed to investigate the restructuring and elemental redistribution within a population of PtNi nanoparticles at elevated temperatures. We further examined the atomic structure of PtNi and found a heat induced transition from a disordered to an ordered phase. Changes in structure and elemental distribution were linked to a loss of catalytic act

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.

Silicon-based semiconductor nanofabrication technology has achieved a remarkable level of sophistication and maturity, and color centers in silicon naturally inherit this advantage. Besides, their emissions appear in telecommunication bands, which makes them play a crucial role in the construction of quantum network. To address the challenge of weak spontaneous emission, different optical cavities are fabricated to enhance the emission rate. However, the relative location between cavity and emitter is random, which greatly reduce the success probability of enhancement. Here, we report on a fluorescence-localization technique (FLT) for precisely locating single G center in silicon and embedding it in the center of a circular Bragg grating cavity in situ, achieving 240-times improvement of the success probability. We observe a 30-fold enhancement in luminescence intensity, 2.5-fold acceleration of the emission from single G center, corresponding to a Purcell factor exceeding 11. Our findings pave the way for the large-scale integration of quantum light sources including those with spins.

The Fast Fourier Transform (FFT) is a numerical operation that transforms a function into a form comprised of its constituent frequencies and is an integral part of scientific computation and data analysis. The objective of our work is to enable use of the FFT as part of a scientific in situ processing chain to facilitate the analysis of data in the spectral regime. We describe the implementation of an FFT endpoint for the transformation of multi-dimensional data within the SENSEI infrastructure. Our results show its use on a sample problem in the context of a multi-stage in situ processing workflow.

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

We present a novel algorithm explicitly tailored to estimate motion from time series of 3D images of concrete. Such volumetric images are usually acquired by Computed Tomography and can contain for example in situ tests, or more complex procedures like self-healing. Our algorithm is specifically designed to tackle the challenge of large scale in situ investigations of concrete. That means it cannot only cope with big images, but also with discontinuous displacement fields that often occur in in situ tests of concrete. We show the superior performance of our algorithm, especially regarding plausibility and time efficient processing. Core of the algorithm is a novel multiscale representation based on morphological wavelets. We use two examples for validation: A classical in situ test on refractory concrete and and a three point bending test on normal concrete. We show that for both applications structural changes like crack initiation can be already found at low scales -- a central achievement of our algorithm.

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient detail at critical timesteps. Keeping a human in the loop is impractical, and defining statistical triggers can be difficult. This paper demonstrates the potential for using a machine-learning-based simulation surrogate as an oracle to identify expected critical regions of a large-scale simulation. These critical regions are used to drive the in situ analysis, providing greater data fidelity and analysis resolution with an equivalent I/O budget to a traditional in situ framework. We develop a distributed asynchronous in situ visualization by integrating TACC Galaxy with CB-Geo MPM for material point simulation of granular flows. We employ a PyTorch-based 3D Graph Network Simulator (GNS) trained on granular flow problems as an oracle to predict the dynamics of granular flows. Critical regions of interests are manually tagged in GNS for in situ rendering in MPM.

The assembly of colloidal particles at liquid/liquid or air/liquid interfaces is a versatile procedure to create microstructured monolayers and study their behavior under compression. When combined with soft and deformable particles such as microgels, compression is used to tune not only the interparticle distance but also the underlying microstructure of the monolayer. So far, the great majority of studies on microgel-laden interfaces is conducted ex situ after transfer to solid substrates, for example, via Langmuir-Blodgett deposition. This type of analysis relies on the stringent assumption that the microstructure is conserved during transfer and subsequent drying. In this work, we couple a Langmuir trough to a custom-built small-angle light scattering setup to monitor colloidal monolayers in situ during compression. By comparing the results with ex situ and in situ microscopy measurements, we conclude that Langmuir-Blodgett deposition can alter the structural properties of the colloidal monolayers significantly.

In-plane ordering of Cs+ layers in Helmhotlz planes was studied on Pt(111) surface in 0.1 M CsF electrolyte solutions with synchtrotron surface x-ray scattering techniques. The ordering was measured in a new transmission cell, designed for in situ and non situ measurements and high-temperature sample annealing all in the cell without sample transfer steps. At -850 mV vs. Ag/AgCl, (2x2) in-plane scattering peaks were weak under in situ condition and grew rapidly under non situ condition as the surface emersed from the electrolyte. The models for the (2x2) structures are presented and differences between in situ and non situ conditions are discussed.

Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their marker-free phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, paves the way for accelerating the transition of optical computing from laboratory demonstrations to practical, real-world applications.

Remote sensing observations meet some limitations when used to study the bulk atmospheric composition of the giant planets of our solar system. A remarkable example of the superiority of in situ probe measurements is illustrated by the exploration of Jupiter, where key measurements such as the determination of the noble gases abundances and the precise measurement of the helium mixing ratio have only been made available through in situ measurements by the Galileo probe. This paper describes the main scientific goals to be addressed by the future in situ exploration of Saturn placing the Galileo probe exploration of Jupiter in a broader context and before the future probe exploration of the more remote ice giants. In situ exploration of Saturn's atmosphere addresses two broad themes that are discussed throughout this paper: first, the formation history of our solar system and second, the processes at play in planetary atmospheres. In this context, we detail the reasons why measurements of Saturn's bulk elemental and isotopic composition would place important constraints on the volatile reservoirs in the protosolar nebula. We also show that the in situ measurement of CO (or any other