Unconventional superfluidity and quantum geometry of topological bosons

We investigate superfluidity of bosons in gapped topological bands and discover a new phase that has no counterparts in the previous literature. This phase is characterized by a highly unconventional modulation of the order parameter, breaking the crystallographic symmetry, and for which the condensation momentum is neither zero nor any other high-symmetry vector of the Brillouin zone. This unconventional structure impacts the spectrum of Bogoliubov excitations and, consequently, the speed of sound in the system. Even in the case of perfectly flat bands, the speed of sound and Bogoliubov excitations remain nonvanishing, provided that the underlying topology and quantum geometry are nontrivial. Furthermore, we derive detailed expressions for the superfluid weight using the Popov hydrodynamic formalism for superfluidity and provide estimates for the Berezinskii-Kosterlitz-Thouless temperature, which is significantly enhanced by the nontriviality of the underlying quantum metric. These results are applicable to generic topological bosonic bands, with or without dispersion. To illustrate our findings, we employ the Haldane model with a tunable bandwidth, including the narrow lowest-band case. Within this model, we also observe a re-entrant superfluid behavior: As the Haldane's magnetic flux is varied, the Berezinskii-Kosterlitz-Thouless transition temperature initially decreases to almost zero, only to resurface with renewed vigor.Comment: 23 pages, 10 figure

Manifold Topology Divergence: a Framework for Comparing Data Manifolds

We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifolds on which the distributions are concentrated. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it to assess the performance of deep generative models in various domains: images, 3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN, CIFAR10, FFHQ, chest X-ray images, market stock data, ShapeNet. We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance. Our algorithm scales well (essentially linearly) with the increase of the dimension of the ambient high-dimensional space. It is one of the first TDA-based practical methodologies that can be applied universally to datasets of different sizes and dimensions, including the ones on which the most recent GANs in the visual domain are trained. The proposed method is domain agnostic and does not rely on pre-trained networks

Deep learning-based i-EEG classification with convolutional neural networks for drug-target interaction prediction

Drug-target interaction (DTI) prediction has become a foundational task in drug repositioning, polypharmacology, drug discovery, as well as drug resistance and side-effect prediction. DTI identification using machine learning is gaining popularity in these research areas. Through the years, numerous deep learning methods have been proposed for DTI prediction. Nevertheless, prediction accuracy and efficiency remain key challenges. Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the development of central nervous system-active drugs. Quantitative EEG analysis demonstrates high reliability in studying the effects of drugs on the brain. Earlier preclinical pharmaco-EEG studies showed that different types of drugs can be classified according to their mechanism of action on neural activity. Here, we propose a convolutional neural network for EEG-mediated DTI prediction. This new approach can explain the mechanisms underlying complicated drug actions, as it allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs

Electrode Strip Deposition for the CMS Barrel Drift Tube System

The full production ideation, design, set up and realization of the Electrode Strip Deposition for the entire construction of the CMS Barrel Drift Tube System are described in detail

Results from the first use of low radioactivity argon in a dark matter search

Liquid argon is a bright scintillator with potent particle identification properties, making it an attractive target for direct-detection dark matter searches. The DarkSide-50 dark matter search here reports the first WIMP search results obtained using a target of low-radioactivity argon. DarkSide-50 is a dark matter detector, using two-phase liquid argon time projection chamber, located at the Laboratori Nazionali del Gran Sasso. The underground argon is shown to contain Ar-39 at a level reduced by a factor (1.4 +- 0.2) x 10^3 relative to atmospheric argon. We report a background-free null result from (2616 +- 43) kg d of data, accumulated over 70.9 live-days. When combined with our previous search using an atmospheric argon, the 90 % C.L. upper limit on the WIMP-nucleon spin-independent cross section based on zero events found in the WIMP search regions, is 2.0 x 10^-44 cm^2 (8.6 x 10^-44 cm^2, 8.0 x 10^-43 cm^2) for a WIMP mass of 100 GeV/c^2 (1 TeV/c^2 , 10 TeV/c^2).Comment: Accepted by Phys. Rev.

Borexino : geo-neutrino measurement at Gran Sasso, Italy

Geo-neutrinos, electron anti-neutrinos produced in beta-decays of naturally occurring radioactive isotopes in the Earth, are a unique direct probe of our planet's interior. After a brief introduction of the geo-neutrinos' properties and of the main aims of their study, we discuss the features of a detector which has recently provided breakthrough achievements in the field, Borexino, a massive, calorimetric liquid scintillator detector installed at the underground Gran Sasso Laboratory. With its unprecedented radiopurity levels achieved in the core of the detection medium, it is the only experiment in operation able to study in real time solar neutrino interactions in the challenging sub-MeV energy region. Its superior technical properties allowed Borexino also to provide a clean detection of terrestrial neutrinos. Therefore, the description of the characteristics of the detected geo-neutrino signal and of the corresponding geological implications are the main core of the discussion contained in this work

Light Yield in DarkSide-10: a Prototype Two-phase Liquid Argon TPC for Dark Matter Searches

As part of the DarkSide program of direct dark matter searches using liquid argon TPCs, a prototype detector with an active volume containing 10 kg of liquid argon, DarkSide-10, was built and operated underground in the Gran Sasso National Laboratory in Italy. A critically important parameter for such devices is the scintillation light yield, as photon statistics limits the rejection of electron-recoil backgrounds by pulse shape discrimination. We have measured the light yield of DarkSide-10 using the readily-identifiable full-absorption peaks from gamma ray sources combined with single-photoelectron calibrations using low-occupancy laser pulses. For gamma lines of energies in the range 122-1275 keV, we get consistent light yields averaging 8.887+-0.003(stat)+-0.444(sys) p.e./keVee. With additional purification, the light yield measured at 511 keV increased to 9.142+-0.006(stat) p.e./keVee.Comment: 10 pages, 7 figures, Accepted for publication in Astroparticle Physic