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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-accuracy emulators for observables in ΛCDM, N eff, Σ m ν, and w cosmologies

ABSTRACT We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the cosmic microwave background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein–Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, wCDM, ΛCDM + Neff, and ΛCDM + Σmν. Our CMB emulators are accurate to better than 0.5 per cent out to ℓ = 104, which is sufficient for Stage-IV data analysis, and our P(k) emulators reach the same accuracy level out to $k=50 \, \, \mathrm{Mpc}^{-1}$, which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data.

Astronomy & Astrophysics↗
DOE OSTI2022

Constraints on key O 17 ( α , γ ) Ne 21 resonances and impact on the weak s process

The efficiency of the slow neutron-capture process in massive stars is strongly influenced by neutron-capture reactions on light elements. At low metallicity, 16 O is an important neutron absorber, but the effectiveness of 16 O as a light-element neutron poison is modified by competition between subsequent 17 O(α,n) 20 Ne and 17 O(α,γ) 21 Ne reactions. The strengths of key 17 O(α,γ) 21 Ne resonances within the Gamow window for core helium burning in massive stars are not well constrained by experiment. This work presents more precise measurements of resonances in the energy range E c.m. = 612–1319 keV. We extract resonance strengths of ωγ 638 = 4.85 ± 0.79 μeV, ωγ 721 =13.1$^{+3.2}_{-2.4}$ μeV, ωγ 814 = 7.72 ± 0.55 meV, and ωγ 1318 = 136 ±13 meV, for resonances at E c.m. = 638, 721, 814, and 1318 keV, respectively. We also report an upper limit for the 612 keV resonance of ωγ < 140 neV (95% c.l.), which effectively rules out any significant contribution from this resonance to the reaction rate. From this work, a new 17 O(α,γ) 21 Ne thermonuclear reaction rate is calculated and compared to the literature. The effect of present uncertainties in the 17 O(α,γ) 21 Ne reaction rate on weak s-process yields are then explored using postprocessing calculations based on a rotating 20M ⊙ low-metallicity massive star. The resulting 17 O(α,γ) 21 Ne reaction rate is lower with respect to the preexisting literature and found to enhance weak s-process yields in rotating massive star models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Design and characterization of a phonon-mediated cryogenic particle detector with an eV-scale threshold and 100 keV-scale dynamic range

We present the design and characterization of a cryogenic phonon-sensitive Si detector exploiting the Neganov-Tromov-Luke effect to detect single-charge excitations. This device achieved 2.65(2) eV phonon energy resolution when operated without a voltage bias across the crystal and a corresponding charge resolution of 0.03 electron-hole pairs at 100 V bias. With a continuous-readout data acquisition system and an oine optimum-filter trigger, we obtain a 9.2 eV threshold with a trigger rate of the order of 20 Hz. The detector's energy scale is calibrated up to 120 keV using an energy estimator based on the pulse area. The high performance of this device allows its application to different fields where excellent energy resolution, low threshold, and large dynamic range are required, including dark matter searches, precision measurements of coherent neutrino-nucleus scattering, and ionization yield measurements.

Ren, R↗
DOE OSTI2024

Optical tuning of the diamond Fermi level measured by correlated scanning probe microscopy and quantum defect spectroscopy

Quantum technologies based on quantum point defects in crystals require control over the defect charge state. Here we tune the charge state of shallow nitrogen-vacancy and silicon-vacancy centers by locally oxidizing a hydrogenated surface with moderate optical excitation and simultaneous spectral monitoring. The loss of conductivity and change in work function due to oxidation are measured in atmosphere using conductive atomic force microscopy and Kelvin probe force microscopy (KPFM). We correlate these scanning probe measurements with optical spectroscopy of the nitrogen-vacancy and silicon-vacancy centers created via implantation 15–25 nm beneath the diamond surface and annealing. The observed charge state of the defects as a function of optical exposure demonstrates that laser oxidation provides a way to precisely tune the Fermi level over a range of at least 2.00 eV. We also observe a significantly larger oxidation rate for implanted surfaces compared to unimplanted surfaces under ambient conditions. Here, combined with knowledge of the electron affinity of a surface, these results suggest KPFM is a powerful, high-spatial-resolution technique to advance surface Fermi level engineering for charge stabilization of quantum defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2020

Design and Characterization of a Phonon-Mediated Cryogenic Particle Detector with an eV-Scale Threshold and 100 keV-Scale Dynamic Range

We present the design and characterization of a cryogenic phonon-sensitive Si detector exploiting the Neganov-Trofimov-Luke effect to detect single-charge excitations. This device achieved 2.65(2) eV phonon energy resolution when operated without a voltage bias across the crystal and a corresponding charge resolution of 0.03 electron-hole pairs at 100 V bias. With a continuous-readout data acquisition system and an offline optimum-filter trigger, we obtain a 9.2 eV threshold with a trigger rate of the order of 20 Hz. The detector's energy scale is calibrated up to 120 keV using an energy estimator based on the pulse area. The high performance of this device allows its application to different fields where excellent energy resolution, low threshold, and large dynamic range are required, including dark matter searches, precision measurements of coherent neutrino-nucleus scattering, and ionization yield measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Type-I 2HDM under the Higgs and electroweak precision measurements

We explore the extent to which future precision measurements of the Standard Model (SM) observables at the proposed Z -factories and Higgs factories may have impacts on new physics beyond the Standard Model, as illustrated by studying the Type-I Two-Higgs-doublet model (Type-I 2HDM). We include the contributions from the heavy Higgs bosons at the tree-level and at the one-loop level in a full model-parameter space. While only small tan β region is strongly constrained at tree level, the large tan β region gets constrained at loop level due to tan β enhanced tri-Higgs couplings. We perform a multiple variable χ 2 fit with non-alignment and non-degenerate masses. We find that the allowed parameter ranges could be tightly constrained by the future Higgs precision measurements, especially for small and large values of tan β . Indirect limits on the masses of heavy Higgs bosons can be obtained, which can be complementary to the direct searches of the heavy Higgs bosons at hadron colliders. We also find that the expected accuracies at the Z -pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons. The typical results are | cos( β - α ) | < 0 . 05 , | Δ m Φ | < 200 GeV, and tan β ≳ 0 . 3. The reaches from CEPC, Fcc-ee and ILC are also compared, for both Higgs and Z -pole precision measurements. Comparing to the Type-II 2HDM, the 95% C.L. allowed range of cos( β - α ) is larger, especially for large values of tan β .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Finely tunable dynamical coloration using bicontinuous micrometer-domains

Nanostructures similar to those found in the vividly blue wings of Morpho butterflies and colorful photonic crystals enable structural color through constructive interference of light waves. Different from commonly studied structure-colored materials using periodic structures to manipulate optical properties, we report a previously unrecognized approach to precisely control the structural color and light transmission via a novel photonic colloidal gel without long-range order. Nanoparticles in this gel form micrometer-sized bicontinuous domains driven by the microphase separation of binary solvents. This approach enables dynamic coloration with a precise wavelength selectivity over a broad range of wavelengths extended well beyond the visible light that is not achievable with traditional methods. The dynamic wavelength selectivity is thermally tunable, reversible, and the material fabrication is easily scalable.

42 ENGINEERING↗
DOE OSTI2022

Analysis of persistent contaminants and personal care products by dispersive liquid-liquid microextraction using hydrophobic magnetic deep eutectic solvents

Here, in this work, hydrophobic magnetic deep eutectic solvents (HMDESs) were used in the development of a simple and rapid dispersive liquid-liquid microextraction (DLLME) approach coupled to high performance liquid chromatography with UV detection (HPLC-UV) for the determination of ten organic contaminants including five polycyclic aromatic hydrocarbons, four UV filters, and a pesticide from water at trace levels. The HMDESs were prepared by mixing a hydrogen bond acceptor, metal halide salt, and hydrogen bond donor in suitable molar ratios. Two HMDESs, 2 tetraoctylammonium bromide ([N 8888 + ][Br - ]): cobalt chloride (CoCl 2 ): 4 octanoic acid (OA) and 3 trioctylphosphine oxide (TOPO): neodymium chloride (NdCl 3 ): 3 OA, offered the highest analyte extraction efficiency overall and were chosen as suitable solvents for validation of the microextraction method. Under optimized extraction conditions, the method required 30 µL of HMDES as extraction solvent, acetone (87.5 µL) as disperser solvent, a NaCl concentration of 30% (w/v), and an extraction time of 120 s at 20°C. Enrichment factors of the analytes ranged from 44.6 for 3-(4-methylbenzylindene) camphor to 66.0 for 2-ethylhexyl-4-(dimethyl)aminobenzoate. The method provided low limits of detection (LODs) ranging from 0.5 to 4.5 µg L -1 , and acceptable precision, with RSD values lower than 9.6%. Furthermore, the validated method was successfully applied for tap and lake water analysis, resulting in relative recoveries of spiked samples ranging between 94.7 and 119.2%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Elucidation of Local Ordering and Atomic-Scale Structure in Polymer-Derived SiOC

Silicon oxycarbide (SiOC) is a versatile ceramic material with tunable microstructure and compositions that can be modulated through precursor chemistry and processing conditions. Though there are several noteworthy uses of SiOC across a range of application spaces, the difficulties in elucidating the short- to medium-range order within these materials have limited the maturation of strategies to precisely control SiC x O 4–x compositions for user-tailored applications. In this contribution, we implement a range of synchrotron scattering and spectroscopy methods coupled with stochastic modeling techniques to elucidate changes in local chemistry and structure associated with the pyrolysis of a commercially available SiOC polymer precursor. Stochastic modeling approaches provide valuable insights into decoupling local Si–O and Si–C environments while confirming predominate heterogeneous phases in materials. Using pyrolysis temperatures between 250 to 800 °C results in a heterogeneous material predominately composed of SiOC and amorphous SiO 2 domains. At 1100 °C, redistribution of Si–C pairs in the SiOC network and Si–O from the SiO 2 domains create a more ordered SiOC phase with local cubic SiC-like ordering. In addition, residual carbon leads to a detectable carbon phases at 1100 °C that persist at higher temperatures. These efforts address the difficulties of obtaining atomic-scale insights into the local structure and nanoscale heterogeneities in SiOC, providing pathways toward establishing structure–property relationships for future materials development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Precise Modulation of CO 2 Sorption in Ti 8 Ce 2 –Oxo Clusters: Elucidating Lewis Acidity of the Ce Metal Sites and Structural Flexibility

Investigating the structure-property correlation in porous materials is a fundamental and consistent focus in various scientific domains, especially within sorption research. Metal oxide clusters with capping ligands, characterized by intrinsic cavities formed through specific solid-state packing, demonstrate significant potential as versatile platforms for sorption investigations due to their precisely tunable atomic structures and inherent long-range order. This study presents a series of Ti 8 Ce 2 -oxo clusters with subtle variations in coordinated linkers and explores their sorption behavior. Notably, Ti 8 Ce 2 -BA (BA denotes benzoic acid) manifests a distinctive two-step profile during CO 2 adsorption, accompanied by a hysteresis loop. This observation marks a new instance within the metal oxide cluster field. Of intrigue, the presence of unsaturated Ce(IV) sites was found to be correlated with the stepped sorption property. Moreover, the introduction of an electrophilic fluorine atom, positioned ortho or para to the benzoic acid, facilitated precise control over gate pressure and stepped sorption quantities. Advanced in-situ techniques systematically unraveled the underlying mechanism behind this unique sorption behavior. The findings elucidate that robust Lewis base-acid interactions are established between CO 2 molecules and Ce ions, consequently altering the conformation of coordinated linkers. Conversely, the F atoms primarily contribute to gate pressure variation by influencing the Lewis acidity of the Ce sites. This research advances the understanding in fabricating metal-oxo clusters with structural flexibility and provides profound insights into their host-guest interaction motifs. Furthermore, these insights hold substantial promise across diverse fields and offer valuable guidance for future adsorbent designs grounded in fundamental theories of structure-property relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Resolving femtosecond photoinduced energy flow: capture of nonadiabatic reaction pathway topography and wavepacket dynamics from photoexcitation through the conical intersection seam (Final Technical Report)

The dynamics that take place within just tens to hundreds of femtoseconds following the absorption of light by a molecule can play a critical role in how the absorbed energy is directed, allowing it to be used for a specific function or dissipated harmlessly. The form of chemical change that occurs rapidly in these molecules is called a “nonadiabatic electronic transition.” Such transitions are known to mediate energy flow in natural biological systems such as the ultraviolet photoprotection mechanism of DNA and the first step of the human vision response. Understanding how these mechanisms work precisely may help scientists achieve controlled manipulation of solar energy or optical control of a wide range of energy management functions in artificial systems. Experimental methods, however, have not yet allowed a precisely resolved and complete measurement of nonadiabatic electronic transitions. This constitutes a major obstacle to progress in the field. For progress to occur that would inform a wide body of research aiming to efficiently harness the energy of light for practical purposes, it is especially important to benchmark computational models of the molecules undergoing these rapid changes with experimental measurements, in order to learn which models are accurate. With Dept. of Energy funding, we have made strong progress towards establishing a new optical method for experimentally detecting the full nonadiabatic electronic transition. This requires having coordinated pulses of light covering the visible through the mid-infrared range of the electromagnetic spectrum that last only ten femtoseconds. We have developed a new, relatively simple approach for generating such pulses of laser light, and have incorporated them into a time-resolved spectrometer for measuring rapid changes in molecules. These tools can provide the greater precision and new types of data that are needed to benchmark computational models of molecular change and thus to make progress in the field. Our tools were tested on graphene, an excellent solid-state sample for verifying the capabilities and limitations of our instrumentation. The investment made in these tools by the Dept. of Energy Office of Science will allow new fundamental scientific understanding of energy dynamics in molecules in future studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum. II. Bayesian model calibration

Dynamic shockless compression experiments provide the ability to explore material behavior at extreme pressures but relatively low temperatures. Typically, the data from these types of experiments are interpreted through an analytic method called Lagrangian analysis. Here, in this work, alternative analysis methods are explored using modern statistical methods. Specifically, Bayesian model calibration is applied to a new set of platinum data shocklessly compressed to 570 GPa. Several platinum equation-of-state models are evaluated, including traditional parametric forms as well as a novel non-parametric model concept. The results are compared to those in Paper I obtained by inverse Lagrangian analysis. The comparisons suggest that Bayesian calibration is not only a viable framework for precise quantification of the compression path, but also reveals insights pertaining to trade-offs surrounding model form selection, sensitivities of the relevant experimental uncertainties, and assumptions and limitations within Lagrangian analysis. The non-parametric model method, in particular, is found to give precise unbiased results and is expected to be useful over a wide range of applications. The calibration results in estimates of the platinum principal isentrope over the full range of experimental pressures to a standard error of 1.6%, which extends the results from Paper I while maintaining the high precision required for the platinum pressure standard.

Brown, Justin Lee↗
DOE OSTI2022

Finding quadruply imaged quasars with machine learning – I. Methods

ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A multifrequency characterization of the extragalactic hard X-ray sky

Context: Nowadays, we know that the origin of the cosmic X-ray background (CXB) is mainly due to the integrated emission of active galactic nuclei. Therefore, in order to obtain a precise estimate of the contribution of different source classes to the CXB, it is crucial to achieve full characterization of the hard-X ray sky. Aims: We present a multifrequency analysis of all sources listed in the third release of the Palermo Swift-BAT hard X-ray catalog (3PBC) with the goal of (i) identifying and classifying the largest number of sources adopting multifrequency criteria, with particular emphasis on extragalactic populations and (ii) extracting sources belonging to the class of Seyfert galaxies to present here the release of the second version of the Turin-SyCAT. Methods: We outline a classification scheme based on radio, infrared (IR), and optical criteria that allows us to distinguish between unidentified and unclassified hard X-ray sources, as well as to classify those sources belonging to the Galactic and the extragalactic populations. Results: Our revised version of the 3PBC lists 1176 classified, 820 extragalactic, and 356 Galactic sources, as well as 199 unclassified and 218 unidentified sources. According to our analysis, the hard X-ray sky is mainly populated by Seyfert galaxies and blazars. For the blazar population, we report trends between the hard X-ray and the gamma-ray emissions based on the fact that a large fraction of them also have a counterpart detected by the Fermi satellite. These trends are all in agreement with the expectations of inverse Compton models which are widely adopted to explain the blazar broadband emission. For the Seyfert galaxies, we present the second version of the Turin-SyCAT, including a total of 633 Seyfert galaxies, with 282 new sources corresponding to an increase of ~80% with respect to the previous release. Comparing the hard X-ray and the infrared emissions of Seyfert galaxies, we confirm that there is no clear difference between the flux distribution of the infrared-to-hard X-ray flux ratio of Seyfert galaxies Type 1 and Type 2. However, there is a significant trend between the mid-IR flux and hard X-ray flux, confirming previous statistical results in the literature. Conclusions: We provide two catalog tables. The first is the revised version of the 3PBC catalog based on our multifrequency analyses. The second catalog table is a release of the second version of the Turin-SyCAT catalog. Finally, we highlight that extensive soft X-ray data are already available in the form of the Swift archive which can be used to search for potential counterparts of unidentified hard X-ray sources. All these datasets will be reduced and analyzed in a forthcoming analysis to determine the precise position of low-energy counterparts in the 0.5–10 keV energy range for 3PBC sources that can be targets of future optical spectroscopic campaigns; this is necessary to obtain their precise classification.

79 ASTRONOMY AND ASTROPHYSICS↗