Abstract: We implement a minimal linear seesaw model (LSM) for addressing the Quasi-Dirac (QD) behaviour of heavy neutrinos, focusing on the mass regime of M-N less than or similar to M-W. Here we show that for relatively low neutrino masses, covering the few GeV range, the same-sign to opposite-sign dilepton ratio, R-ll, can be anywhere between 0 and 1, thus signaling a Quasi-Dirac regime. Particular values of R-ll are controlled by the width of the QD neutrino and its mass splitting, the latter being equal to the light-neutrino mass m(nu) in the LSM scenario. The current upper bound on m(nu 1) together with the projected sensitivities of current and future |U-N l|(2) experimental measurements, set stringent constraints on our low-scale QD mass regime. Some experimental prospects of testing the model by LHC displaced vertex searches are also discussed.

Abstract: The first search for the doubly heavy Omega(0)(bc) baryon and a search for the Xi(0)(bc) baryon are performed using collision data collected via the experiment from 2016 to 2018 at a centre-of-mass energy of, corresponding to an integrated luminosity of 5.2 fb(-1). The baryons are reconstructed via their decays to Lambda(+)(-)(c)(pi) and Xi(+)(c)pi(-). No significant excess is found for invariant masses between 6700 and 7300 MeV/c(2), in a rapidity range from 2.0 to 4.5 and a transverse momentum range from 2 to 20 MeV/c. Upper limits are set on the ratio of the Omega(0)(bc) and Xi(0)(bc) production cross-section times the branching fraction to Lambda(+)(c)pi(-)(Xi(+)(c)pi(-)) relative to that of the Lambda(0)(b)(Xi(0)(b)) baryon, for different lifetime hypotheses, at 95% confidence level. The upper limits range from 0.5x10(-4) to 2.5x10(-4) for the Omega(0)(bc) -> Lambda(+)(c)pi(-) (Xi(0)(bc) -> Lambda(+)(c)pi(-)) decay, pending on the considered mass and lifetime of the Omega(0)(bc) (Xi(0)(bc)) baryon.

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.