Particle theory

Abstract:

The heliocentric redshifts (zhel) reported for 150 Type Ia supernovae in the Pantheon compilation are significantly discrepant from their corresponding values in the JLA compilation, with 58 having differences between 5 and 137 times the measurement uncertainty. Both catalogues include corrections to the redshifts & magnitudes of the supernovae to account for the motion of the heliocentric frame relative to the `CMB rest frame', as well as corrections for the directionally coherent bulk motion of local galaxies with respect to this frame. The latter is done employing modelling of peculiar velocities which assume the ΛCDM cosmological model but nevertheless provide evidence for residual bulk flows which are discordant with this model. Until recently these peculiar velocity corrections in the Pantheon catalogue were made at redshifts exceeding 0.2 although no data on which to base such corrections is available. We study the impact of these vexed issues on the 4.4σ discrepancy between the Hubble constant of 67.4±0.5 km/s/Mpc inferred from observations of CMB anisotropies by Planck assuming ΛCDM, and the measurement of e.g. 73.5±1.4 km/s/Mpc by the SH0ES project which extended the local distance ladder using Type Ia supernovae. Using the same methodology as the latter study we find that for supernovae whose redshifts are discrepant between Pantheon and JLA with Δzhel > 0.0025, the Pantheon redshifts favour H0 ≃ 72 km/s/Mpc, while the JLA redshifts favour H0 ≃ 68 km/s/Mpc. Thus the discrepancies between publicly available SNe Ia datasets are sufficient to undermine the claimed `Hubble tension'. We further note the systematic variation of H0 by ∼6-9 km/s/Mpc across the sky seen in multiple datasets, implying that it cannot be measured to better than ∼10% precision in a model-independent manner.

Abstract:

We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models. Both supervised and unsupervised learning are considered. We find that, for a fixed compactification manifold, relatively small neural networks are capable of distinguishing consistent line bundle models with the correct gauge group and the correct chiral asymmetry from random models without these properties. The same distinction can also be achieved in the context of unsupervised learning, using an auto-encoder. Learning non-topological properties, specifically the number of Higgs multiplets, turns out to be more difficult, but is possible using sizeable networks and feature-enhanced data sets.

Abstract:

We present a measurement of atmospheric tau neutrino appearance from oscillations with three years of data from the DeepCore sub-array of the IceCube Neutrino Observatory. This analysis uses atmospheric neutrinos from the full sky with reconstructed energies between 5.6 GeV and 56 GeV to search for a statistical excess of cascade-like neutrino events which are the signature of nutau interactions. For CC+NC (CC-only) interactions, we measure the tau neutrino normalization to be 0.73 +0.30 -0.24 (0.57 +0.36 -0.30) and exclude the absence of tau neutrino oscillations at a significance of 3.2 sigma (2.0 sigma) These results are consistent with, and of similar precision to, a confirmatory IceCube analysis also presented, as well as measurements performed by other experiments.

Abstract:

The flux of high-energy neutrinos passing through the Earth is attenuated due to their interactions with matter. The interaction rate is modulated by the neutrino interaction cross section and affects the flux arriving at the IceCube Neutrino Observatory, a cubic-kilometer neutrino detector embedded in the Antarctic ice sheet. We present a measurement of the neutrino cross section between 60 TeV and 10 PeV using the high-energy starting events (HESE) sample from IceCube with 7.5 years of data. The result is binned in neutrino energy and obtained using both Bayesian and frequentist statistics. We find it compatible with predictions from the Standard Model. Flavor information is explicitly included through updated morphology classifiers, proxies for the the three neutrino flavors. This is the first such measurement to use the three morphologies as observables and the first to account for neutrinos from tau decay.