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In this presentation we look at simulated neutrino-argon interactions in the DUNE PRISM ND-LAr as a whole and then narrow studies to qausi-elastic events. We compare measurable observable between PRISM axis positions and event generator models in the context of proton exclusive events.
This report summarizes the initial results and outcomes of a physics-informed, data-driven groundwater level (GWL) mapping capability for the Hanford Site. GWL mapping at Hanford is typically conducted annually and requires a significant amount of computational and expert resources, and it does not allow assessment of the informational value of specific monitoring wells. The proposed method produces spatially and temporally resolved fields consistent with sparse, irregularly sampled, and nonuniformly distributed well measurements. Implemented successfully, this capability will allow rapid mapping of groundwater levels and provide an opportunity to optimize monitoring activities (both location and sampling frequency) based on data information value evaluation. The approach integrates a diffusion-based generative model – trained on MODFLOW simulation data from the Plateau-to-River (P2R) model – with score-based data assimilation (SDA), allowing observation-conditioned mapping without retraining for each monitoring-network layout.
MicroBooNE is an 85-tonne active mass liquid argon time projection chamber (LArTPC) neutrino detector exposed to the Booster Neutrino Beamline (BNB) at Fermilab. One of the key physics goals is the precise measurement of neutrino interactions on argon in the 1 GeV energy regime. The study of strange and heavier meson production in neutrino interactions, in particular final states containing $K^{+}$, will help to improve the background estimates for future nucleon decay searches in experiments such as DUNE, provide valuable input for improving neutrino generator models, and will allow the development of techniques to enhance the particle identification capabilities of LArTPCs. In this work, we present the first-ever cross-section measurements of charged current muon neutrino-induced kaon production on argon at MicroBooNE.
The E1039/SpinQuest experiment at Fermi National Accelerator Laboratory uses a 120~GeV proton beam from the Main Injector incident on transversely polarized proton and deuteron targets, using $NH_3$ and $ND_3$, respectively. In addition to measuring the Sivers asymmetry in Drell--Yan $pp$ and $pd$ scattering from sea quarks, SpinQuest will study transverse-spin effects, particularly the transverse single-spin asymmetry (TSSA) in $J/\psi$ production. The angular distributions from the $J/\psi$ decay could play an important role in understanding the gluon contribution to the proton spin structure. However, before extracting these angular distributions, it is necessary to isolate signal events originating from the target from events produced by other sources and from the combinatorial background. To effectively and accurately classify the target events, it is important to ensure that the simulated events are properly tuned to the experimental physics channels. We have introduced an iterative technique to match simulated and experimental events and to classify the physics channels using deep neural networks and a generative model based on normalizing flows.
In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.
Understanding DNA-ligand binding interactions requires ligand screening, crystallization, and structure determination. In order to obtain insights into the amyloid peptide precursor (APP) gene-Thioflavin T (ThT) interaction, single crystals of two DNA sequences 5'-GCCCACCACGGC-3' (PDB 8ASK) and d(CCGGGGTACCCCGG) 2 (PDB 8ASH) were grown in the presence of ThT or its analogue 2-((4-(dimethylamino)benzylidene)amino)-3,6-dimethylbenzo[d]thiazol-3-ium iodide (XRB). Both structures were solved by molecular replacement. In the case of 8ASK, the space group was H3 with unit cell dimensions of a = b = 64.49 Å, c = 46.19 Å. Phases were obtained using a model generated by X3DNA. The novel 12-base-pair B-DNA structure did not have extra density for the ThT ligand. The 14-base-pair A-DNA structure with bound ThT analog XRB was isomorphous with previously the obtained apo-DNA structure 5WV7 (space group was P 4 1 2 1 2 with unit cell dimensions a = b = 41.76 Å, c = 88.96 Å). Binding of XRB to DNA slightly changes the DNA's buckle parameters at the CpG regions. Comparison of the two conformations of the XRB molecule: alone and bound to DNA indicates that the binding results from the freedom of rotation of the two aromatic rings.
Trans-critical CO2 vapor compression (VC) refrigeration cycles require a high compression ratio, which is associated with high expansion losses. To recover these expansion losses, a pressure exchange process between the low- and high-pressure sides of the VC cycle is proposed and examined in this study. The proposed pressure exchange system is an open type constant volume process where the high- and low-pressure flows mix inside the system. This prototype is inspired by the pressure exchangers used in reverse-osmosis (RO) desalination systems. In this system, a 2D model was generated and modeled using the computational fluid dynamics (CFD) technique. The numerical model ignored any losses due to leakage or hydraulic friction and the process is considered adiabatic. For the modeling, it was assumed that the inlet conditions for the two pressure exchanger flows are similar to the flow conditions at the evaporator and gas cooler outlets in a VC cycle. Two parameters are examined to test the validity of the system and understand their effect on the performance, including the inlet flow rate represented by the inlet velocity and the process time represented by the speed of rotation. A total of nine cases were simulated and analyzed in this study.
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal-to-noise ratio spectra of M67 stars reveals atomic diffusion processes on the order of 0.05 dex, previously only measurable with differential analysis techniques in high-resolution spectra. sick is easy to use, well-tested, and freely available online through GitHub under the MIT license.
We have obtained three epochs of Chandra ACIS-I observations (totaling ∼184 ks) of the nearby spiral galaxy NGC 300 to study the logN–logS distributions of its X-ray point-source population down to ∼2 × 10{sup −15} erg s{sup −1} cm{sup −2} in the 0.35–8 keV band (equivalent to ∼10{sup 36} erg s{sup −1}). The individual epoch logN–logS distributions are best described as the sum of a background active galactic nucleus (AGN) component, a simple power law, and a broken power law, with the shape of the logN–logS distributions sometimes varying between observations. The simple power law and AGN components produce a good fit for “persistent” sources (i.e., with fluxes that remain constant within a factor of ∼2). The differential power-law index of ∼1.2 and high fluxes suggest that the persistent sources intrinsic to NGC 300 are dominated by Roche-lobe-overflowing low-mass X-ray binaries. The variable X-ray sources are described by a broken power law, with a faint-end power-law index of ∼1.7, a bright-end index of ∼2.8–4.9, and a break flux of ∼8×10{sup −15} erg s{sup −1} cm{sup −2} (∼4 × 10{sup 36} erg s{sup −1}), suggesting that they are mostly outbursting, wind-fed high-mass X-ray binaries, although the logN–logS distribution of variable sources likely also contains low-mass X-ray binaries. We generate model logN–logS distributions for synthetic X-ray binaries and constrain the distribution of maximum X-ray fluxes attained during outburst. Our observations suggest that the majority of outbursting X-ray binaries occur at sub-Eddington luminosities, where mass transfer likely occurs through direct wind accretion at ∼1%–3% of the Eddington rate.
The κ -mechanism has been successful in explaining the origin of observed oscillations of many types of “classical” pulsating variable stars. Here we examine quantitatively if that same process is prominent enough to excite the potential global oscillations within Jupiter, whose energy flux is powered by gravitational collapse rather than nuclear fusion. Additionally, we examine whether external radiative forcing, i.e., starlight, could be a driver for global oscillations in hot Jupiters orbiting various main-sequence stars at defined orbital semimajor axes. Using planetary models generated by the Modules for Experiments in Stellar Astrophysics and nonadiabatic oscillation calculations, we confirm that Jovian oscillations cannot be driven via the κ -mechanism. However, we do show that, in hot Jupiters, oscillations can likely be excited via the suppression of radiative cooling due to external radiation given a large enough stellar flux and the absence of a significant oscillatory damping zone within the planet. This trend does not seem to be dependent on the planetary mass. In future observations, we can thus expect that such planets may be pulsating, thereby giving greater insight into the internal structure of these bodies.
We present self-consistent 3D core-collapse supernova simulations of a 40 ${M}_{\odot }$ progenitor model using the isotropic diffusion source approximation for neutrino transport and an effective general relativistic potential up to `0.9 s postbounce. We consider three different rotational speeds with initial angular velocities of ${{\rm{\Omega }}}_{0}=0$, 0.5, and 1 rad s -1 and investigate the impact of rotation on shock dynamics, black hole (BH) formation, and gravitational wave (GW) signals. The rapidly rotating model undergoes an early explosion at ~250 ms postbounce and shows signs of the low $T/| W| $ instability. We do not find BH formation in this model within ~460 ms postbounce. In contrast, we find BH formation at 776 ms postbounce and 936 ms postbounce for the nonrotating and slowly rotating models, respectively. The slowly rotating model explodes at ~650 ms postbounce, and the subsequent fallback accretion onto the proto–neutron star (PNS) results in BH formation. In addition, the standing accretion shock instability induces rotation of the PNS in the model that started with a nonrotating progenitor. Assuming conservation of specific angular momentum during BH formation, this corresponds to a BH spin parameter of a = J/M = 0.046. However, if no explosion sets in, all the angular momentum will eventually be accreted by the BH, resulting in a nonspinning BH. The successful explosion of the slowly rotating model drastically slows down the accretion onto the PNS, allowing continued cooling and contraction that results in an extremely high GW frequency (f ~ 3000 Hz) at BH formation, while the nonrotating model generates GW signals similar to our corresponding 2D simulations.
Here, we introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal datasets. Individual modalities are embedded into a shared latent space and fused through a product-of-experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture-of-experts decoder that imposes inductive biases from prior scientific knowledge and thereby imparts structured disentanglement of the latent space. This approach enables cross-modal inference and the discovery of features in high-dimensional heterogeneous datasets. Consequently, this approach provides a means to discover fingerprints in multimodal scientific datasets and to avoid traditional bottlenecks related to high-fidelity measurement and characterization of scientific datasets.
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.
Recently the Radar Meteorology Group at Colorado State University has completed major case studies of some top cases from MC3E including 25 April, 20 May and 23 May 2011. A discussion on the analysis methods as well as radar quality control methods is included. For each case, a brief overview is first provided. Then, multiple Doppler (using available X-SAPR and C-SAPR data) analyses are presented including statistics on vertical air motions, sub-divided by convective and stratiform precipitation. Mean profiles and CFAD's of vertical motion are included to facilitate comparison with ASR model simulations. Retrieved vertical motion has also been verified with vertically pointing profiler data. Finally for each case, hydrometeor types are included derived from polarimetric radar observations. The latter can be used to provide comparisons to model-generated hydrometeor fields. Instructions for accessing all the data fields are also included. The web page can be found at: http://radarmet.atmos.colostate.edu/mc3e/research/
Organic matter stored in sedimentary rocks is one of the largest stocks of carbon at Earth’s surface. The fate of this rock organic carbon (OC petro ) during weathering in soils influences the geological carbon cycle, and impacts soil radiocarbon content that is used to quantify soil carbon turnover. Here, we assess the potential contribution of OC petro to soils, using a mixing model generated by a global dataset of soil radiocarbon measurements ( 14 C). Soils developed on sedimentary rocks (rather than on igneous substrate) have a paired OC content and 14 C values consistent with OC petro input, giving rise to apparent increase in soil residence time. We call for renewed assessment of OC petro input to soils, in terms of its impact on soil radiocarbon inventories, and its potential to release carbon dioxide.
The design, development, and testing of an engineering model nominal 20-millipound thrust monopropellant hydrazine resistojet program is divided into six basic tasks. Included in these tasks are analyses, design, test, and data correlation of the electrothermal hydrazine thruster (EHT). A brief summary is provided of the analyses conducted for the EHT and the design of the engineering model thruster. Some of the results of the engineering model tests are then compared with the analytical performance models generated early in the program.
The characteristics of an interactive graphics systems to facilitate the finite element method of structural analysis are described. The finite element model analysis consists of three phases: (1) preprocessing (model generation), (2) problem solution, and (3) postprocessing (interpretation of results). The advantages of interactive graphics to finite element structural analysis are defined.
The observed boundary system of the major tectonic plates on the surface of the earth lends fresh support to the hypothesis of polar wandering. A dynamic model of the outer shell of the earth under the influence of polar shift is developed. The analysis falls into two parts: (1) deriving equations for stresses caused by polar shifting; and (2) deducing the pattern according to which the fracture of the shell can be expected. For stress analysis, the theory of plates and shells is the dominant feature of this model. In order to determine the fracture pattern, the existence of a mathematical theorem of plasticity is recalled: it says that the plastic flow begins to occur when a function in terms of the differences of the three principal stresses surpasses a certain critical value. By introducing the figures for the geophysical constants, this model generates stresses which could produce an initial break in the lithosphere.