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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A TYPE Ia SUPERNOVA AT REDSHIFT 1.55 IN HUBBLE SPACE TELESCOPE INFRARED OBSERVATIONS FROM CANDELS

We report the discovery of a Type Ia supernova (SN Ia) at redshift z = 1.55 with the infrared detector of the Wide Field Camera 3 (WFC3-IR) on the Hubble Space Telescope (HST). This object was discovered in CANDELS imaging data of the Hubble Ultra Deep Field and followed as part of the CANDELS+CLASH Supernova project, comprising the SN search components from those two HST multi-cycle treasury programs. This is the highest redshift SN Ia with direct spectroscopic evidence for classification. It is also the first SN Ia at z > 1 found and followed in the infrared, providing a full light curve in rest-frame optical bands. The classification and redshift are securely defined from a combination of multi-band and multi-epoch photometry of the SN, ground-based spectroscopy of the host galaxy, and WFC3-IR grism spectroscopy of both the SN and host. This object is the first of a projected sample at z > 1.5 that will be discovered by the CANDELS and CLASH programs. The full CANDELS+CLASH SN Ia sample will enable unique tests for evolutionary effects that could arise due to differences in SN Ia progenitor systems as a function of redshift. This high-z sample will also allow measurement of the SN Ia rate out to z Almost-Equal-To 2, providing a complementary constraint on SN Ia progenitor models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Observation and characterization of a cosmic muon neutrino flux from the northern hemisphere using six years of IceCube data

The IceCube Collaboration has previously discovered a high-energy astrophysical neutrino flux using neutrino events with interaction vertices contained within the instrumented volume of the IceCube detector. We present a complementary measurement using charged current muon neutrino events where the interaction vertex can be outside this volume. As a consequence of the large muon range the effective area is significantly larger but the field of view is restricted to the Northern Hemisphere. IceCube data from 2009 through 2015 have been analyzed using a likelihood approach based on the reconstructed muon energy and zenith angle. At the highest neutrino energies between 194 TeV and 7.8 PeV a significant astrophysical contribution is observed, excluding a purely atmospheric origin of these events at 5.6σ significance. The data are well described by an isotropic, unbroken power-law flux with a normalization at 100 TeV neutrino energy of (0.90{sub −0.27}{sup +0.30})×10{sup −18} GeV{sup −1} cm{sup −2} s{sup −1} sr{sup −1} and a hard spectral index of γ=2.13±0.13. The observed spectrum is harder in comparison to previous IceCube analyses with lower energy thresholds which may indicate a break in the astrophysical neutrino spectrum of unknown origin. The highest-energy event observed has a reconstructed muon energy of (4.5±1.2) PeV which implies a probability of less than 0.005% for this event to be of atmospheric origin. Analyzing the arrival directions of all events with reconstructed muon energies above 200 TeV no correlation with known γ-ray sources was found. Using the high statistics of atmospheric neutrinos we report the current best constraints on a prompt atmospheric muon neutrino flux originating from charmed meson decays which is below 1.06 in units of the flux normalization of the model in Enberg et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Data depth based clustering analysis

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.

58 GEOSCIENCES↗
DOE OSTI2022

Timing Six Energetic Rotation-powered X-Ray Pulsars, Including the Fast-spinning Young PSR J0058-7218 and Big Glitcher PSR J0537-6910

Abstract Measuring a pulsar’s rotational evolution is crucial to understanding the nature of the pulsar. Here, we provide updated timing models for the rotational evolution of six pulsars, five of which are rotation phase-connected using primarily NICER X-ray data. For the newly discovered fast energetic young pulsar, PSR J0058−7218, we increase the baseline of its timing model from 1.4 days to 8 months and not only measure more precisely its spin-down rate ν ̇ = ( − 6.2324 ± 0.0001 ) × 10 − 11 Hz s − 1 but also for the first time the second time derivative of its spin rate ν ̈ = ( 4.2 ± 0.2 ) × 10 − 21 Hz s − 2 . For the fastest and most energetic young pulsar, PSR J0537−6910 (with a 16 ms spin period), we detect four more glitches, for a total of 15 glitches over 4.5 yr of NICER monitoring, and show that its spin-down behavior continues to set this pulsar apart from all others, including a long-term braking index n = −1.234 ± 0.009 and interglitch braking indices that asymptote to ≲7 for long times after a glitch. For PSR J1101−6101, we measure a much more accurate spin-down rate that agrees with a previous value measured without phase connection. For PSR J1412+7922 (also known as Calvera), we extend the baseline of its timing model from our previous 1 yr model to 4.4 yr, and for PSR J1849−0001, we extend the baseline from 1.5 to 4.7 yr. We also present a long-term timing model of the energetic pulsar PSR J1813−1749, by fitting previous radio and X-ray spin frequencies from 2009–2019 and new ones measured here using 2018 NuSTAR and 2021 Chandra data.

Ho, Wynn C. G. (ORCID:0000000260896836)↗
DOE OSTI2021

Evaluation of Data Catalog Software for Hanford Site Environmental Datasets

Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Autonomous semantic data discovery for distributed networked systems

Systems, methods, techniques and apparatuses for managing distributed applications of networked intelligent agents are disclosed. The agents are operably to autonomously discover semantic profiles and associated data of other agents in a networked system participating in a given application. The agents need not be in direct communication with or known to all the other agents in the networked system.

Brissette, Alexander↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dinuclear Gold(I) Complexes Bearing Alkyl-Bridged Bis(N-heterocyclic carbene) Ligands as Catalysts for Carboxylative Cyclization of Propargylamine: Synthesis, Structure, and Kinetic and Mechanistic Comparison to the Mononuclear Complex [Au(IPr)Cl]

Eight new dinuclear gold(I) complexes, [Au 2 (L)X 2 ] (1–8), were synthesized using a straightforward synthetic procedure under very mild conditions. The complexes have been characterized by NMR spectroscopy, elemental analysis, and single-crystal X-ray structure analysis. Their catalytic activity was investigated in the carboxylative cyclization of propargylamine (PPA). A superior performance in comparison to [Au(IPr)Cl] (9) was obtained for complexes 1 and 2 having an eight-methylene bridge connecting two NHCs with an arene bearing an isopropyl substituent for X = Cl, Br. This prompted more detailed kinetic and mechanistic studies by FTIR comparing dinuclear complex 2 of X = Cl to complex 9. Fortuitously the FTIR studies allowed monitoring of the formation of the products carbamic acid (CA) and carbamate salt (CS), as well as a key cyclized intermediate first discovered by Ikariya. These data allow additional insight into the mechanism as well as the central role which may be played by Au(I) carbamate formation as a higher energy resting state present in the catalytic cycle. In conclusion, the crystal structures of four of the new complexes and a detailed computational study relevant to the role of carbamic acid (CA) and carbamates in the catalytic cycle are also reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Physics-constrained deep learning of nonlinear normal modes of spatiotemporal fluid flow dynamics

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.

Mechanics↗
DOE OSTI2011

WISEP J180026.60+013453.1: A NEARBY LATE-L DWARF NEAR THE GALACTIC PLANE

We report a nearby L7.5 dwarf discovered using the Preliminary Data Release of the Wide-field Infrared Survey Explorer (WISE) and the Two Micron All Sky Survey. WISEP J180026.60+013453.1 has a motion of 0.42 arcsec yr{sup -1} and an estimated distance of 8.8 {+-} 1.0 pc. With this distance, it currently ranks as the sixth closest known L dwarf, although a trigonometric parallax is needed to confirm this distance. It was previously overlooked because it lies near the Galactic plane (b = 12 Degree-Sign ). As a relatively bright and nearby late L dwarf with normal near-infrared colors, W1800+0134 will serve as a benchmark for studies of cloud-related phenomena in cool substellar atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Radial velocities of southern visual multiple stars

High-resolution spectra of visual multiple stars were taken in 2008–2009 to detect or confirm spectroscopic subsystems and to determine their orbits. Radial velocities of 93 late-type stars belonging to visual multiple systems were measured by numerical cross-correlation. We provide the individual velocities, the width, and the amplitude of the Gaussians that approximate the correlations. The new information on the multiple systems resulting from these data is discussed. We discovered double-lined binaries in HD 41742B, HD 56593C, and HD 122613AB, confirmed several other known subsystems, and constrained the existence of subsystems in some visual binaries where both components turned out to have similar velocities. The orbits of double-lined subsystems with periods of 148 and 13 days are computed for HD 104471 Aa,Ab and HD 210349 Aa,Ab, respectively. We estimate individual magnitudes and masses of the components in these triple systems and update the outer orbit of HD 104471 AB.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Double mode Cepheids in M31

Until now, double mode Cepheids (or beat Cepheids) were known only in the Galaxy, the Magellanic Clouds, and M33. Curiously, none of the more than 2000 Cepheids in M31 was claimed to show two pulsation modes. We conducted a systematic search for double mode Cepheids in the archival data of M31 and discovered four such objects. We identify one of the stars as a first and second overtone pulsator even though its secondary period is subject to strong aliasing. Two stars pulsate in the fundamental mode and the first overtone. Their fundamental periods are 9.392 days and 9.163 days. This makes them the first candidates for fundamental mode and first overtone Cepheids, of which double mode pulsations are caused by the 2:1 resonance of the fundamental mode and the second overtone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Blue straggler masses from pulsation properties. I. The case of NGC 6541

We used high spatial resolution images acquired with the Wide Field Camera 3 on board Hubble Space Telescope to probe the population of variable blue straggler stars (BSSs) in the central region of the poorly studied Galactic globular cluster NGC 6541. The time sampling of the acquired multiwavelength (F390W, F555W, and F814W) data allowed us to discover three WUMa stars and nine SX Phoenicis. Periods, mean magnitudes, and pulsation modes have been derived for the nine SX Phoenicis, and their masses have been estimated by using pulsation equations obtained from linear nonadiabatic models. We found masses in the range 1.0-1.1 M {sub ☉}, with an average value of 1.06 ± 0.09 M {sub ☉} (σ = 0.04), significantly in excess of the cluster main-sequence turn-off mass (∼0.75 M {sub ☉}). A mild trend between mass and luminosity seems also to be present. The computed pulsation masses turn out to be in very good agreement with the predictions of evolutionary tracks for single stars, indicating values in the range ∼1.0-1.2 M {sub ☉} for most of the BSS population, in agreement with what was discussed in a number of previous studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The XXL survey. V. Detection of the Sunyaev-Zel'dovich effect of the redshift 1.9 galaxy cluster XLSSU J021744.1–034536 with CARMA

We report the detection of the Sunyaev-Zel'dovich (SZ) effect of galaxy cluster XLSSU J021744.1–034536, using 30 GHz Combined Array for Research in Millimeter-wave Astronomy (CARMA) data. This cluster was discovered via its extended X-ray emission in the XMM- Newton Large Scale Structure survey, the precursor to the XXL survey. It has a photometrically determined redshift z=1.91{sub −0.21}{sup +0.19}, making it among the most distant clusters known, and nominally the most distant for which the SZ effect has been measured. The spherically integrated Comptonization is Y {sub 500} = (3.0 ± 0.4) × 10{sup –12}, a measurement that is relatively insensitive to assumptions regarding the size and redshift of the cluster, as well as the background cosmology. Using a variety of locally calibrated cluster scaling relations extrapolated to z ∼ 2, we estimate a mass M {sub 500} ∼ (1-2) × 10{sup 14} M {sub ☉} from the X-ray flux and SZ signal. The measured properties of this cluster are in good agreement with the extrapolation of an X-ray luminosity-SZ effect scaling relation calibrated from clusters discovered by the South Pole Telescope at higher masses and lower redshifts. The full XXL-CARMA sample will provide a more complete, multi-wavelength census of distant clusters in order to robustly extend the calibration of cluster scaling relations to these high redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

MEASURING TRANSIT SIGNAL RECOVERY IN THE KEPLER PIPELINE. I. INDIVIDUAL EVENTS

The Kepler mission was designed to measure the frequency of Earth-size planets in the habitable zone of Sun-like stars. A crucial component for recovering the underlying planet population from a sample of detected planets is understanding the completeness of that sample-the fraction of the planets that could have been discovered in a given data set that actually were detected. Here, we outline the information required to determine the sample completeness, and describe an experiment to address a specific aspect of that question, i.e., the issue of transit signal recovery. We investigate the extent to which the Kepler pipeline preserves individual transit signals by injecting simulated transits into the pixel-level data, processing the modified pixels through the pipeline, and comparing the measured transit signal-to-noise ratio (S/N) to that expected without perturbation by the pipeline. We inject simulated transit signals across the full focal plane for a set of observations for a duration of 89 days. On average, we find that the S/N of the injected signal is recovered at MS = 0.9973({+-} 0.0012) Multiplication-Sign BS - 0.0151({+-} 0.0049), where MS is the measured S/N and BS is the baseline, or expected, S/N. The 1{sigma} width of the distribution around this correlation is {+-}2.64%. This indicates an extremely high fidelity in reproducing the expected detection statistics for single transit events, and provides teams performing their own periodic transit searches the confidence that there is no systematic reduction in transit signal strength introduced by the pipeline. We discuss the pipeline processes that cause the measured S/N to deviate significantly from the baseline S/N for a small fraction of targets; these are primarily the handling of data adjacent to spacecraft re-pointings and the removal of harmonics prior to the measurement of the S/N. Finally, we outline the further work required to characterize the completeness of the Kepler pipeline.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Energy and scale dependence of $\hat{q}$ and the “JET puzzle”

Here, we present an attempt to probe the underlying structure of the quark-gluon plasma (QGP) at high resolution, based on the extracted jet transport coefficient $\hat{q}$. We argue that the exchanged momentum $\textit{k}$ between the hard parton and the medium varies over a range of scales, and for $\textit{k}$ ≥ 1 GeV, $\hat{q}$ can be expressed in terms of a parton distribution function (PDF). Because the mass of a QGP constituent is unknown, we define a scaling variable $x_N$ to represent the ratio of the parton momentum to the momentum of a self-contained section of the plasma which has a mass of 1 GeV. This scaling variable is used to parametrize the QGP-PDF. Calculations based on this reconstructed $\hat{q}$ are compared to data sensitive to the hardcore of jets, i.e., the single hadron suppression in terms of the nuclear modification factor $R_{ A A}$ and the azimuthal anisotropy parameter $v_2$ as a function of transverse momentum $p_T$, centrality, and energy of the collision. It is demonstrated that the scale evolution of the QGP-PDF is responsible for the reduction in the normalization of $\hat{q}$ between fits to the Relativistic Heavy-Ion Collider and the Large Hadron Collider data; a puzzle, first discovered by the JET Collaboration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗