Abstract: It was predicted qualitatively in ref. [I] that skyrmion matter at low density is stable in an inhomogeneous phase where skyrmions condensate into lumps while the remaining space is mostly empty. The aim of this paper is to proof quantitatively this prediction. In order to construct an inhomogeneous medium we distort the original FCC crystal to produce a phase of planar structures made of skyrmions. We implement mathematically these planar structures by means of the 't Hooft instanton solution using the Atiyah-Manton ansatz. The results of our calculation of the average density and energy confirm the prediction suggesting that the phase diagram of the dense skyrmion matter is a lot more complex than a simple phase transition from the skyrmion FCC crystal lattice to the half-skyrmion CC one. Our results show that skyrmion matter shares common properties with standard nuclear matter developing a skin and leading to a binding energy equation which resembles the Weiszacker mass formula.

Abstract: We review standard and non-standard neutrino physics probes that are based on nuclear measurements. We pay special attention on the discussion of prospects to extract new physics at prominent rare event measurements looking for neutrino-nucleus scattering, such as the coherent elastic neutrino-nucleus scattering (CE nu NS) that may involve lepton flavor violation (LFV) in neutral-currents (NC). For the latter processes several appreciably sensitive experiments are currently pursued or have been planed to operate in the near future, like the COHERENT, CONUS, CONNIE, MINER, TEXONO, RED100, vGEN, Ricochet, NUCLEUS, etc. We provide a thorough discussion on phenomenological and theoretical studies, in particular those referring to the nuclear physics aspects in order to provide accurate predictions for the relevant experiments. Motivated by the recent discovery of CE nu NS at the COHERENT experiment and the active experimental efforts for a new measurement at reactor-based experiments, we summarize the current status of the constraints as well as the future sensitivities on nuclear and electroweak physics parameters, non-standard interactions, electromagnetic neutrino properties, sterile neutrinos and simplified scenarios with novel vector Z ' or scalar phi mediators. Indirect and direct connections of CE nu NS with astrophysics, direct Dark Matter detection and charge lepton flavor violating processes are also discussed.

Abstract: Motivated by the recent observation of coherent elastic neutrino-nucleus scattering (CE nu NS) at the COHERENT experiment, our goal is to explore its potential in probing important nuclear structure parameters. We show that the recent COHERENT data offers unique opportunities to investigate the neutron nuclear form factor. Our present calculations are based on the deformed Shell Model (DSM) method which leads to a better fit of the recent CE nu NS data, as compared to known phenomenological form factors such as the Helm-type, symmetrized Fermi and Klein-Nystrand. The attainable sensitivities and the prospects of improvement during the next phase of the COHERENT experiment are also considered and analyzed in the framework of two upgrade scenarios.

Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.

Abstract: We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for pp ->(chi) over tilde (+)(1)(chi) over tilde (-)(1), (chi) over tilde (0)(2)(chi) over tilde (0)(2) and (chi) over tilde (0)(2)(chi) over tilde (+/-)(1) as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of O(10(7)) from O(min) to O(mu s) per evaluation.