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DOE OSTI2022

A comparative analysis of the chemical compositions of Gaia-Enceladus/Sausage and Milky Way satellites using APOGEE

ABSTRACT We use data from the 17th data release of the Apache Point Observatory Galactic Evolution Experiment (APOGEE 2) to contrast the chemical composition of the recently discovered Gaia Enceladus/Sausage system (GE/S) to those of 10 Milky Way (MW) dwarf satellite galaxies: LMC, SMC, Boötes I, Carina, Draco, Fornax, Sagittarius, Sculptor, Sextans, and Ursa Minor. Our main focus is on the distributions of the stellar populations of those systems in the [Mg/Fe]–[Fe/H] and [Mg/Mn]–[Al/Fe] planes, which are commonly employed in the literature for chemical diagnosis and where dwarf galaxies can be distinguished from in situ populations. We show that, unlike MW satellites, a GE/S sample defined purely on the basis of orbital parameters falls almost entirely within the locus of ‘accreted’ stellar populations in chemical space, which is likely caused by an early quenching of star formation in GE/S. Due to a more protracted history of star formation, stars in the metal-rich end of the MW satellite populations are characterized by lower [Mg/Mn] than those of their GE/S counterparts. The chemical compositions of GE/S stars are consistent with a higher early star formation rate (SFR) than MW satellites of comparable and even higher mass, suggesting that star formation in the early universe was strongly influenced by other parameters in addition to mass. We find that the direction of the metallicity gradient in the [Mg/Mn]–[Al/Fe] plane of dwarf galaxies is an indicator of the early SFR of the system.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Sensitivity of the Cherenkov Telescope Array to TeV photon emission from the Large Magellanic Cloud

ABSTRACT A deep survey of the Large Magellanic Cloud at ∼0.1–100 TeV photon energies with the Cherenkov Telescope Array is planned. We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters, mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 Doradus and SN 1987A, and the constraints that can be derived on the nature of dark matter. The survey will allow for fine spectral studies of N 157B, N 132D, LMC P3, and 30 Doradus C, and half a dozen other sources should be revealed, mainly pulsar-powered objects. The remnant from SN 1987A could be detected if it produces cosmic-ray nuclei with a flat power-law spectrum at high energies, or with a steeper index 2.3–2.4 pending a flux increase by a factor of >3–4 over ∼2015–2035. Large-scale interstellar emission remains mostly out of reach of the survey if its >10 GeV spectrum has a soft photon index ∼2.7, but degree-scale 0.1–10 TeV pion-decay emission could be detected if the cosmic-ray spectrum hardens above >100 GeV. The 30 Doradus star-forming region is detectable if acceleration efficiency is on the order of 1−10 per cent of the mechanical luminosity and diffusion is suppressed by two orders of magnitude within <100 pc. Finally, the survey could probe the canonical velocity-averaged cross-section for self-annihilation of weakly interacting massive particles for cuspy Navarro–Frenk–White profiles.

Astronomy & Astrophysics↗

DOE OSTI2014

s-Step Krylov Subspace Methods as Bottom Solvers for Geometric Multigrid

Geometric multigrid solvers within adaptive mesh refinement (AMR) applications often reach a point where further coarsening of the grid becomes impractical as individual sub domain sizes approach unity. At this point the most common solution is to use a bottom solver, such as BiCGStab, to reduce the residual by a fixed factor at the coarsest level. Each iteration of BiCGStab requires multiple global reductions (MPI collectives). As the number of BiCGStab iterations required for convergence grows with problem size, and the time for each collective operation increases with machine scale, bottom solves in large-scale applications can constitute a significant fraction of the overall multigrid solve time. In this paper, we implement, evaluate, and optimize a communication-avoiding s-step formulation of BiCGStab (CABiCGStab for short) as a high-performance, distributed-memory bottom solver for geometric multigrid solvers. This is the first time s-step Krylov subspace methods have been leveraged to improve multigrid bottom solver performance. We use a synthetic benchmark for detailed analysis and integrate the best implementation into BoxLib in order to evaluate the benefit of a s-step Krylov subspace method on the multigrid solves found in the applications LMC and Nyx on up to 32,768 cores on the Cray XE6 at NERSC. Overall, we see bottom solver improvements of up to 4.2x on synthetic problems and up to 2.7x in real applications. This results in as much as a 1.5x improvement in solver performance in real applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Tunable Unidirectional Source for GUSTO's Local Oscillator at 4.74 THz

The Galactic/Extra Ultra long Duration Balloon Spectroscopic-Stratospheric Terahertz Observatory (GUSTO), is a NASA balloon-borne project and is scheduled for launch in late 2022. The balloon will carry a spectroscopic telescope that will detect three brightest emission lines from interstellar medium. GUSTO measurements will shed light on the life-cycle of the gases in the Milky Way and Large Magellanic Cloud (LMC). In this study, we will discuss the details of a quantum cascade laser used in the local oscillator for detecting the oxygen line at 4.74 THz.

42 ENGINEERING↗

DOE OSTI2016

SU-C-BRC-03: Development of a Novel Strategy for On-Demand Monte Carlo and Deterministic Dose Calculation Treatment Planning and Optimization for External Beam Photon and Particle Therapy

Purpose: Accurate and fast dose calculation is a prerequisite of precision radiation therapy in modern photon and particle therapy. While Monte Carlo (MC) dose calculation provides high dosimetric accuracy, the drastically increased computational time hinders its routine use. Deterministic dose calculation methods are fast, but problematic in the presence of tissue density inhomogeneity. We leverage the useful features of deterministic methods and MC to develop a hybrid dose calculation platform with autonomous utilization of MC and deterministic calculation depending on the local geometry, for optimal accuracy and speed. Methods: Our platform utilizes a Geant4 based “localized Monte Carlo” (LMC) method that isolates MC dose calculations only to volumes that have potential for dosimetric inaccuracy. In our approach, additional structures are created encompassing heterogeneous volumes. Deterministic methods calculate dose and energy fluence up to the volume surfaces, where the energy fluence distribution is sampled into discrete histories and transported using MC. Histories exiting the volume are converted back into energy fluence, and transported deterministically. By matching boundary conditions at both interfaces, deterministic dose calculation account for dose perturbations “downstream” of localized heterogeneities. Hybrid dose calculation was performed for water and anthropomorphic phantoms. Results: We achieved <1% agreement between deterministic and MC calculations in the water benchmark for photon and proton beams, and dose differences of 2%–15% could be observed in heterogeneous phantoms. The saving in computational time (a factor ∼4–7 compared to a full Monte Carlo dose calculation) was found to be approximately proportional to the volume of the heterogeneous region. Conclusion: Our hybrid dose calculation approach takes advantage of the computational efficiency of deterministic method and accuracy of MC, providing a practical tool for high performance dose calculation in modern RT. The approach is generalizable to all modalities where heterogeneities play a large role, notably particle therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2015

HTK Lethality Analysis CET

Sandia National Laboratories (Sandia) and the Lockheed Martin Corporation (LMC) collaborated to establish performance of the hit-to-kill lethal mechanism compared to other interceptor kill mechanisms. Multiple eulerian shock physics and couple shock physics/structural simulations were performed to evaluate the performance of two interceptor kill mechanisms – hit to kill and blast fragment – against a conventional target. These simulations showed each kill mechanism’s advantages and disadvantages. Sandia/DOE benefitted by exercising the Sandia-developed computational codes with scenarios that had not been simulated before, providing insight and experience with highly relevant and timely intercept scenarios that will be representative of future work. LMCO benefitted by the experience using the coupled code which they acquired from SNL. They also were provided data that was relevant to interceptor systems being considered and in development. The public benefitted by providing information that will be useful in choosing an interceptor system that will be used for national defense at lower cost than relying solely on an extensive testing program.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2017

Direct Down-scale Experiments of Concentration Column Designs for SHINE Process

Argonne is assisting SHINE Medical Technologies in their efforts to become a domestic Mo-99 producer. The SHINE accelerator-driven process uses a uranyl-sulfate target solution for the production of fission-product Mo-99. Argonne has developed a molybdenum recovery and purification process for this target solution. The process includes an initial Mo recovery column followed by a concentration column to reduce the product volume from 15-25 L to < 1 L prior to entry into the LEU Modified Cintichem (LMC) process for purification.1 This report discusses direct down-scale experiments of the plant-scale concentration column design, where the effects of loading velocity and temperature were investigated.

42 ENGINEERING↗

DOE OSTI2020

Carbon Calculator for Land Use and Land Management Change from Biofuels Production (CCLUB)

The Carbon Calculator for Land Use and Land Management Change from Biofuels Production (CCLUB) has been developed as an integral part of Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy use in Technologies (GREET) model to analyze greenhouse gas (GHG) emissions from land use change (LUC) and land management change (LMC) in the context of overall biofuel life-cycle analysis (LCA).

09 BIOMASS FUELS↗

DOE OSTI2015

Stockham Waste Management Contract SOW

Sandia National Laboratories, New Mexico (SNL/NM) is a multi-program Research and Development Facility, owned by the Department of Energy (DOE) and operated by Sandia Corporation, a Lockheed Martin Company (LMC). Sandia waste management operations include radioactive and mixed waste and hazardous waste (including classified and explosive). Waste management activities include managing and characterizing the waste, completing waste disposal requests, and providing guidance on sorting, packaging, & storing wastes, preparing all necessary disposal request documents, and/or recycling, maintaining accurate records, help the line organizations in assuring the compliance of all waste management activities, and preparing compliance-related documentation. Additional contaminants may include polychlorinated biphenyls (PCBs), asbestos, and beryllium. Some radioactive and hazardous waste may also be managed as classified waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

2020 U.S. Lighting Market Characterization

The Lighting Market Characterization report details how many lighting products (lamps and luminaires) were installed in the U.S. as of 2020, where they were installed, and how much energy they consumed. It also highlights key changes in U.S. lighting market characteristics between 2015 and 2020. This is the fourth edition of the LMC report; previous versions were released in 2002, 2012 and 2017. By publishing summary estimates of installed stock and energy use of various general illumination lighting products, these reports help DOE and others plan effective lighting research and development programs based on changing needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

2020 U.S. Lighting Market Characterization

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

2020 U.S. Lighting Market Characterization

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

THE CARNEGIE HUBBLE PROGRAM: THE DISTANCE AND STRUCTURE OF THE SMC AS REVEALED BY MID-INFRARED OBSERVATIONS OF CEPHEIDS

Using Spitzer observations of classical Cepheids we have measured the true average distance modulus of the Small Magellanic Cloud (SMC) to be 18.96±0.01{sub stat}±0.03{sub sys} mag (corresponding to 62±0.3 kpc), which is 0.48 ± 0.01 mag more distant than the LMC. This is in agreement with previous results from Cepheid observations, as well as with measurements from other indicators such as RR Lyrae stars and the tip of the red giant branch. Utilizing the properties of the mid-infrared Leavitt Law we measured precise distances to individual Cepheids in the SMC, and have confirmed that the galaxy is tilted and elongated such that its eastern side is up to 20 kpc closer than its western side. This is in agreement with the results from red clump stars and dynamical simulations of the Magellanic Clouds and Stream.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

The Milky Way’s hot gas kinematics: Signatures in current and future O vii absorption line observations

Detections of z ≈ 0 oxygen absorption and emission lines indicate the Milky Way hosts a hot (∼10{sup 6} K), low-density plasma extending ≳50 kpc into the Mily Way’s halo. Current X-ray telescopes cannot resolve the line profiles, but the variation of their strengths on the sky constrains the radial gas distribution. Interpreting the O vii Kα absorption line strengths has several complications, including optical depth and line of sight velocity effects. Here, we present model absorption line profiles accounting for both of these effects to show the lines can exhibit asymmetric structures and be broader than the intrinsic Doppler width. The line profiles encode the hot gas rotation curve, the net inflow or outflow of hot gas, and the hot gas angular momentum profile. We show how line of sight velocity effects impact the conversion between equivalent width and the column density, and provide modified curves of growth accounting for these effects. As an example, we analyze the LMC sight line pulsar dispersion measure and O vii equivalent width to show the average gas metallicity is ≳0.6Z{sub ⊙} and b ≳ 100 km s{sup −1}. Determining these properties offers valuable insights into the dynamical state of the Milky Way’s hot gas, and improves the line strength interpretation. We discuss future strategies to observe these effects with an instrument that has a spectral resolution of about 3000, a goal that is technically possible today.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

TESTING WIND AS AN EXPLANATION FOR THE SPIN PROBLEM IN THE CONTINUUM-FITTING METHOD

The continuum-fitting method is one of the two most advanced methods of determining the black hole spin in accreting X-ray binary systems. There are, however, still some unresolved issues with the underlying disk models. One of these issues manifests as an apparent decrease in spin for increasing source luminosity. Here, we perform a few simple tests to establish whether outflows from the disk close to the inner radius can address this problem. We employ four different parametric models to describe the wind and compare these to the apparent decrease in spin with luminosity measured in the sources LMC X-3 and GRS 1915+105. Wind models in which parameters do not explicitly depend on the accretion rate cannot reproduce the spin measurements. Models with mass accretion rate dependent outflows, however, have spectra that emulate the observed ones. The assumption of a wind thus effectively removes the artifact of spin decrease. This solution is not unique; the same conclusion can be obtained using a truncated inner disk model. To distinguish among the valid models, we will need high-resolution X-ray data and a realistic description of the Comptonization in the wind.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Evolution of molecular and atomic gas phases in the milky way

We analyze radial and azimuthal variations of the phase balance between the molecular and atomic interstellar medium (ISM) in the Milky Way (MW) using archival CO(J = 1-0) and HI 21 cm data. In particular, the azimuthal variations—between the spiral arm and interarm regions—are analyzed without any explicit definition of the spiral arm locations. We show that the molecular gas mass fraction, i.e., f{sub mol}=Σ{sub H{sub 2}}/(Σ{sub HI}+Σ{sub H{sub 2}}), varies predominantly in the radial direction: starting from ∼100% at the center, remaining ≳50% to R∼6 kpc and decreasing to ∼10%–20% at R=8.5 kpc when averaged over the whole disk thickness (from ∼100% to ≳60%, then to ∼50% in the midplane). Azimuthal, arm-interarm variations are secondary: only ∼20% in the globally molecule-dominated inner MW, but becoming larger, ∼40%–50%, in the atom-dominated outskirts. This suggests that in the inner MW the gas remains highly molecular (f{sub mol}>50%) as it moves from an interarm region into a spiral arm and back into the next interarm region. Stellar feedback does not dissociate molecules much, and the coagulation and fragmentation of molecular clouds dominate the evolution of the ISM at these radii. The trend differs in the outskirts where the gas phase is globally atomic (f{sub mol}<50%). The HI and H{sub 2} phases cycle through spiral arm passage there. These different regimes of ISM evolution are also seen in external galaxies (e.g., the LMC, M33, and M51). We explain the radial gradient of f{sub mol} using a simple flow continuity model. The effects of spiral arms on this analysis are illustrated in the Appendix.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Contact binaries as viable distance indicators: new, competitive (v)jhk{sub s} period-luminosity relations

Based on the largest catalogs currently available, comprising 6090 contact binaries (CBs) and 2167 open clusters (OCs), we determine the near-infrared (NIR) JHK {sub s} CB period–luminosity (PL) relations, for the first time achieving the low levels of intrinsic scatter that make these relations viable as competitive distance calibrators. To firmly establish our distance calibration on the basis of OC CBs, we require that (i) the CB of interest must be located inside the core radius of its host cluster, (ii) the CB’s proper motion must be located within the 2σ distribution of that of its host OC, and (iii) the CB’s age, t, must be comparable to that of its host cluster, i.e., Δlog(tyr{sup −1})<0.3. We thus select a calibration sample of 66 CBs with either OC distances or accurate space-based parallaxes. The resulting NIR PL relations, for both late-type (i.e., W Ursae Majoris-type) and—for the first time—early-type CBs, are as accurate as the well-established JHK {sub s} Cepheid PL relations, (characterized by single-band statistical uncertainties of σ < 0.10 mag). We show that CBs can be used as viable distance tracers, yielding distances with uncertainties of better than 5% for 90% of the 6090 CBs in our full sample. By combining the full JHK {sub s} photometric data set, CBs can trace distances with an accuracy, σ = 0.05 (statistical) ± 0.03 (systematic) mag. The 102 CBs in the Large Magellanic Cloud are used to determine a distance modulus to the galaxy of (m−M{sub V}){sub 0}{sup LMC}=18.41±0.20 mag.

79 ASTRONOMY AND ASTROPHYSICS↗