A key component of the phase diagram of many iron-based superconductors and electron-doped cuprates is believed to be a quantum critical point (QCP), delineating the onset of antiferromagnetic ...spin-density wave order in a quasi-two-dimensional metal. The universality class of this QCP is believed to play a fundamental role in the description of the proximate non-Fermi liquid behavior and superconducting phase. A minimal model for this transition is the O(3) spin-fermion model. Despite many efforts, a definitive characterization of its universal properties is still lacking. Here, we numerically study the O(3) spin-fermion model and extract the scaling exponents and functional form of the static and zero-momentum dynamical spin susceptibility. We do this using a Hybrid Monte Carlo (HMC) algorithm with a novel auto-tuning procedure, which allows us to study unprecedentedly large systems of 80 × 80 sites. We find a strong violation of the Hertz-Millis form, contrary to all previous numerical results. Furthermore, the form that we do observe provides good evidence that the universal scaling is actually governed by the analytically tractable fixed point discovered near perfect "hot-spot'" nesting, even for a larger nesting window. Our predictions can be directly tested with neutron scattering. Additionally, the HMC method we introduce is generic and can be used to study other fermionic models of quantum criticality, where there is a strong need to simulate large systems.
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly large experimental ...quantum devices are built in laboratories, the question of how these machine learning techniques scale with the number of qubits is becoming crucial. We empirically study the scaling of restricted Boltzmann machines (RBMs) applied to reconstruct ground-state wave functions of the one-dimensional transverse-field Ising model from projective measurement data. We define a learning criterion via a threshold on the relative error in the energy estimator of the machine. With this criterion, we observe that the number of RBM weight parameters required for accurate representation of the ground state in the worst case – near criticality – scales quadratically with the number of qubits. By pruning small parameters of the trained model, we find that the number of weights can be significantly reduced while still retaining an accurate reconstruction. This provides evidence that overparametrization of the RBM is required to facilitate the learning process.
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A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimized (trained) to ...produce samples from a distribution approximating the desired Boltzmann distribution determined by the lattice action of the theory being studied. Training the model systematically improves autocorrelation times in the Markov chain, even in regions of parameter space where standard Markov chain Monte Carlo algorithms exhibit critical slowing down in producing decorrelated updates. Moreover, the model may be trained without existing samples from the desired distribution. The algorithm is compared with HMC and local Metropolis sampling for ϕ4 theory in two dimensions.
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We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge ...theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.
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We develop a flow-based sampling algorithm for SU ( N ) lattice gauge theories that is gauge invariant by construction. Our key contribution is constructing a class of flows on an SU ( N ) variable ...or on a U ( N ) variable by a simple alternative that respects matrix conjugation symmetry. We apply this technique to sample distributions of single SU ( N ) variables and to construct flow-based samplers for SU(2) and SU(3) lattice gauge theory in two dimensions.
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In this paper, we present the extensive characterization of large-area silicon carbide-based UV sensors candidate for outdoors spectroscopic applications of gas or liquid. The proposed SiC Schottky ...devices exhibit a dark current density of 0.12 nA/cm 2 at 15 V, a 0.12-A/W responsivity at 300 nm, optimal visible blindness, and a switching time of ~190 ns. Effects of temperature on the sensor performance, of crucial interest for outdoors applications, are also examined in the range from -20 °C to 90 °C.
We report on the structure and performance of 4H-SiC p + -n APDs fabricated in a fully planar technology. A dark current density lower than 10 nA/cm 2 at 30-V reverse bias and a breakdown voltage of ...88 V were observed. A gain as high as 10 5 was measured at 94-V reverse bias, confirming the avalanche multiplication working condition. The maximum responsivity value was measured at 270 nm, increasing from 0.06 A/W (QE = 29%) at 0-V bias to 0.10 A/W (QE of about 45%) at 30-V reverse bias.
In the direct searches for Weakly Interacting Massive Particles (WIMPs) as Dark Matter candidates, the sensitivity of the detector to the incom- ing particle direction could provide a smoking gun ...signature for an interesting event. The SCENE collaboration firstly suggested the possible directional de- pendence of a dual-phase argon Time Projection Chamber through the columnar recombination effect. The Recoil Directionality project (ReD) within the Global Argon Dark Matter Collaboration aims to characterize the light and charge re- sponse of a liquid Argon dual-phase TPC to neutron-induced nuclear recoils to probe for the hint by SCENE. In this work, the directional sensitivity of the de- tector in the energy range of interest for WIMPs (20-100 keV) is investigated with a data-driven analysis involving a Machine Learning algorithm.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
10.
A compton spectrometer to monitor the ELI-NP gamma beam energy Borgheresi, R.; Adriani, O.; Albergo, S. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
08/2019, Volume:
936
Journal Article
Peer reviewed
The ELI-NP facility (Extreme Light Infrastructure-Nuclear Physics) will deliver an intense and almost monochromatic gamma beam for frontier research in nuclear physics. Peculiar devices and ...techniques have been developed to measure and monitor the beam parameters during the commissioning and the operational phase. In this work we will present the Compton Spectrometer, designed to reconstruct the γ beam energy spectrum, by measuring the energy and the position of Compton scattered electrons. The energy and the angle of the scattered electron are measured by a High Purity Germanium detector and a double sided silicon strip detector. The associated photon is detected in coincidence with the electron by barium fluoride (BaF2) crystals for trigger purpose. In this work we report the status of the characterization carried out on the detectors composing the spectrometer.
•Compton spectrometer, designed to reconstruct the ELI-NP γ beam energy spectrum.•Energy of the scattered e− measured with a High Purity Germanium detector.•Scattered angle determined by a double sided silicon strip detector.•Scattered photon detected in coincidence by BaF2 crystals for trigger purpose.•Tests carried out on the components of the spectrometer are presented.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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