What does perturbative QCD really have to say about neutron stars

The implications of perturbative QCD (PQCD) calculations on neutron stars are carefully examined. While PQCD calculations above baryon chemical potential $\mu_B\sim2.4$ GeV demonstrate the potential of ruling out a wide range of neutron star equations of state (EOSs), these types of constraints only affect the most massive neutron stars in the vicinity of the Tolman-Oppenheimer-Volkoff (TOV) limit, resulting in bounds on neutron star EOSs that are orthogonal to those from current or future astrophysical observations, even if observations near the TOV limit are made. Assuming the most constraining scenario, PQCD considerations favor low values of the speed of sound squared $C_s$ at high $\mu_B$ relevant for heavy neutron stars, but leave predictions for the radii and tidal deformabilities almost unchanged for all the masses. Such considerations become irrelevant if the maximum speed of sound squared inside neutron stars does not exceed about $C_{s,\mathrm{max}}\sim0.5$, or if the matching to PQCD is performed above $\mu_B\simeq2.9$ GeV. Furthermore, the large uncertainties associated with the current PQCD predictions make it impossible to place any meaningful bounds on neutron star EOSs as of now. Interestingly, if PQCD predictions for pressure at around $\mu_B\simeq2.4$ GeV is refined and found to be low ($\lesssim 1.5$ GeV/fm$^3$), evidence for a soft neutron star inner core EOS would point to the presence of a strongly interacting phase dominated by non-perturbative physics beyond neutron star densities.Comment: 15 pages, 11 figure

Dark Lepton Superfluid in Proto-Neutron Stars

We find that sub-GeV neutrino portal bosons that carry lepton number can condense inside a proto-neutron star (newly born neutron star). These bosons are produced copiously and form a Bose-Einstein condensate for a range of as yet unconstrained coupling strengths to neutrinos. The condensate is a lepton number superfluid with transport properties that differ dramatically from those encountered in ordinary dense baryonic matter. We discuss how this phase could alter the evolution of proto-neutron stars and comment on the implications for neutrino signals and nucleosynthesis.Comment: 7 pages, 5 figure

Constraining the neutron-matter equation of state with gravitational waves

We show how observations of gravitational waves from binary neutron star (BNS) mergers over the next few years can be combined with insights from nuclear physics to obtain useful constraints on the equation of state (EoS) of dense matter, in particular, constraining the neutron-matter EoS to within 20% between one and two times the nuclear saturation density $n_0\approx 0.16\ {\text{fm}^{-3}}$. Using Fisher information methods, we combine observational constraints from simulated BNS merger events drawn from various population models with independent measurements of the neutron star radii expected from x-ray astronomy (the Neutron Star Interior Composition Explorer (NICER) observations in particular) to directly constrain nuclear physics parameters. To parameterize the nuclear EoS, we use a different approach, expanding from pure nuclear matter rather than from symmetric nuclear matter to make use of recent quantum Monte Carlo (QMC) calculations. This method eschews the need to invoke the so-called parabolic approximation to extrapolate from symmetric nuclear matter, allowing us to directly constrain the neutron-matter EoS. Using a principal component analysis, we identify the combination of parameters most tightly constrained by observational data. We discuss sensitivity to various effects such as different component masses through population-model sensitivity, phase transitions in the core EoS, and large deviations from the central parameter values.Comment: 13 pages, 9 figures + supplement 11 page

New statistical method identifes cytokines that distinguish stool microbiomes

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website

Parallel PWMs Based Fully Digital Transmitter with Wide Carrier Frequency Range

The carrier-frequency (CF) and intermediate-frequency (IF) pulse-width modulators (PWMs) based on delay lines are proposed, where baseband signals are conveyed by both positions and pulse widths or densities of the carrier clock. By combining IF-PWM and precorrected CF-PWM, a fully digital transmitter with unit-delay autocalibration is implemented in 180 nm CMOS for high reconfiguration. The proposed architecture achieves wide CF range of 2 M–1 GHz, high power efficiency of 70%, and low error vector magnitude (EVM) of 3%, with spectrum purity of 20 dB optimized in comparison to the existing designs

New statistical method identifies cytokines that distinguish stool microbiomes.

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website