Particle theory

Abstract:

IceCube has performed several all-sky searches for point-like neutrino sources using track-like events, including a recent time-integrated analysis using 10 years of IceCube data. This paper accompanies the public data release of these neutrino candidates detected by IceCube between April 6, 2008 and July 8, 2018. The selection includes through-going tracks, primarily due to muon neutrino candidates, that reach the detector from all directions, as well as neutrino track events that start within the instrumented volume. An updated selection and reconstruction for data taken after April 2012 slightly improves the sensitivity of the sample. While more than 80% of the sample overlaps between the old and new versions, differing events can lead to changes relative to the previous 7 year event selection. An a posteriori estimate of the significance of the 2014-2015 TXS flare is reported with an explanation of observed discrepancies with previous results. This public data release, which includes 10 years of data and binned detector response functions for muon neutrino signal events, shows improved sensitivity in generic time-integrated point source analyses and should be preferred over previous releases.

Abstract:

Pulsar wind nebulae (PWNe) are main gamma-ray emitters in the Galactic plane. They are diffuse nebulae that emit non-thermal radiation. Pulsar winds, relativistic magnetized outflows from the central star, shocked in the ambient medium produce a multiwavelength emission from the radio through gamma rays. Although the leptonic scenario is able to explain most PWNe emission, a hadronic contribution cannot be excluded. A possible hadronic contribution to the high-energy gamma-ray emission inevitably leads to the production of neutrinos. Using 9.5 years of all-sky IceCube data, we report results from a stacking analysis to search for neutrino emission from 35 PWNe that are high-energy gamma-ray emitters. In the absence of any significant correlation, we set upper limits on the total neutrino emission from those PWNe and constraints on hadronic spectral components.

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.