Ultraviolet Properties of the Higgs Sector in the Lee-Wick Standard Model

The Lee-Wick (LW) Standard Model (SM) offers a new solution to the hierarchy problem. We discuss, using effective potential techniques, its peculiar ultraviolet (UV) behaviour. We show how quadratic divergences in the Higgs mass Mh cancel as a result of the unusual dependence of LW fields on the Higgs background (in a manner reminiscent of Little Higgses). We then extract from the effective potential the renormalization group evolution of the Higgs quartic coupling lambda above the LW scale. After clarifying an apparent discrepancy with previous results for the LW Abelian Higgs model we focus on the LWSM. In contrast with the SM case, for any Mh, lambda grows monotonically and hits a Landau pole at a fixed trans-Planckian scale (never turning negative in the UV). Then, the perturbativity and stability bounds on Mh disappear. We identify a cutoff ~10^{16} GeV for the LWSM due to the hypercharge gauge coupling hitting a Landau pole. Finally, we also discuss briefly the possible impact of the UV properties of the LW models on their behaviour at finite temperature, in particular regarding symmetry nonrestoration.Comment: 25 pages, 3 figure

A Note on Unparticle Decays

The coupling of an unparticle operator O_U to Standard Model particles opens up the possibility of unparticle decays into standard model fields. We study this issue by analyzing the pole structure (and spectral function) of the unparticle propagator, corrected to account for one-loop polarization effects from virtual SM particles. We find that the propagator of a scalar unparticle (of scaling dimension 1 < d_U < 2) with a mass gap m_g develops an isolated pole, m_p^2-i m_p Gamma_p, with m_p^2 < m_g^2 below the unparticle continuum that extends above m_g (showing that the theory would be unstable without a mass gap). If that pole lies below the threshold for decay into two standard model particles the pole corresponds to a stable unparticle state (and its width Gamma_p is zero). For m_p^2 above threshold the width is non zero and related to the unparticle decay rate into Standard Model particles. This picture is valid for any value of d_U in the considered range.Comment: 11 pages, 4 figure

A note on the fate of the Landau-Yang theorem in non-Abelian gauge theories

Using elementary considerations of Lorentz invariance, Bose symmetry and BRST invariance, we argue why the decay of a massive color-octet vector state into a pair of on-shell massless gluons is possible in a non-Abelian SU(N) Yang-Mills theory, we constrain the form of the amplitude of the process and offer a simple understanding of these results in terms of effective-action operators.Comment: 7 pages. v2: typos corrected, one reference adde

Stabilization of the Electroweak Vacuum by a Scalar Threshold Effect

We show how a heavy scalar singlet with a large vacuum expectation value can evade the potential instability of the Standard Model electroweak vacuum. The quartic interaction between the heavy scalar singlet and the Higgs doublet leads to a positive tree-level threshold correction for the Higgs quartic coupling, which is very effective in stabilizing the potential. We provide examples, such as the see-saw, invisible axion and unitarized Higgs inflation, where the proposed mechanism is automatically implemented in well-defined ranges of Higgs masses.Comment: 18 pages, 5 figure

Tunneling Potential Actions from Canonical Transformations

A new formulation for obtaining the tunneling action for vacuum decay based on the so-called tunneling potential was developed recently. In the original derivation, the new action was obtained by requiring that its variation led to the correct equations of motion and the same value for the action as evaluated on the Euclidean bounce. We present an alternative derivation of the tunneling potential action via canonical transformations. We discuss both single-field and multi-field cases as well as the case with gravity. We also comment on the possible application of the new approach to the calculation of the functional determinant prefactor in the tunneling rate.Comment: 15 page

Mechanical disassembly of human picobirnavirus like particles indicates that cargo retention is tuned by the RNA-coat protein interaction

Here we investigate the cargo retention of individual human picobirnavirus (hPBV) virus-like particles (VLPs) which differ in the N-terminal of their capsid protein (CP): (i) hPBV CP contains the full-length CP sequence; (ii) hPBV Δ45-CP lacks the first 45 N-terminal residues; and (iii) hPBV Ht-CP is the full-length CP with a N-terminal 36-residue tag that includes a 6-His segment. Consequently, each VLP variant holds a different interaction with the ssRNA cargo. We used atomic force microscopy (AFM) to induce and monitor the mechanical disassembly of individual hPBV particles. First, while Δ45-CP particles that lack ssRNA allowed a fast tip indentation after breakage, CP and Ht-CP particles that pack heterologous ssRNA showed a slower tip penetration after being fractured. Second, mechanical fatigue experiments revealed that the increased length in 8% of the N-terminal (Ht-CP) makes the virus particles to crumble ∼10 times slower than the wild type N-terminal CP, indicating enhanced RNA cargo retention. Our results show that the three differentiated N-terminal topologies of the capsid result in distinct cargo release dynamics during mechanical disassembly experiments because of the different interaction with RNAFIS2017-89549-R, FIS2017-90701-REDT, PID2021-126608OB-I00, PID2020-113287RB-I0

Biomarker Localization From Deep Learning Regression Networks

Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression networks in four different problems: pectoralis muscle area (PMA), subcutaneous fat area (SFA), liver mass area in single slice computed tomography (CT), and Agatston score estimated from non-contrast thoracic CT images (CAC). Our results show that the proposed method improves the performance with respect to direct biomarker regression methods (correlation coefficient of 0.978, 0.998, and 0.950 for the proposed method in comparison to 0.971, 0.982, and 0.936 for the reference regression methods on PMA, SFA and CAC respectively) while achieving good localization (DICE coefficients of 0.875, 0.914 for PMA and SFA respectively, p < 0.05 for all pairs). We observe the same improvement in regression results comparing the proposed method with those obtained by quantify the outputs using an U-Net segmentation network (0.989 and 0.951 respectively). We, therefore, conclude that it is possible to obtain simultaneously good biomarker regression and localization when training biomarker regression networks using only the biomarker value.This work was supported in part by the National Institutes of Health (NHLBI) under Grant R01HL116931, Grant R21HL14042, and Grant R01HL149877, in part by the COPDGene Study through the NHLBI under Grant NCT00608764, Grant U01 HL089897, and Grant U01 HL089856, and in part by the COPD Foundation through contributions made to the Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion