Populations of double white dwarfs in Milky Way satellites and their detectability with LISA

Context. Milky Way dwarf satellites are unique objects that encode the early structure formation and therefore represent a window into the high redshift Universe. So far, their study has been conducted using electromagnetic waves only. The future Laser Interferometer Space Antenna (LISA) has the potential to reveal Milky Way satellites through gravitational waves emitted by double white dwarf (DWD) binaries. Aims. We investigate gravitational wave signals that will be detectable by LISA as a possible tool for the identification and characterisation of the Milky Way satellites. Methods. We used the binary population synthesis technique to model the population of DWDs in dwarf satellites and we assessed the impact on the number of LISA detections when making changes to the total stellar mass, distance, star formation history, and metallicity of satellites. We calibrated predictions for the known Milky Way satellites on their observed properties. Results. We find that DWDs emitting at frequencies ≳3 mHz can be detected in Milky Way satellites at large galactocentric distances. The number of these high frequency DWDs per satellite primarily depends on its mass, distance, age, and star formation history, and only mildly depends on the other assumptions regarding their evolution such as metallicity. We find that dwarf galaxies with M⋆ >  106 M⊙ can host detectable LISA sources; the number of detections scales linearly with the satellite’s mass. We forecast that out of the known satellites, Sagittarius, Fornax, Sculptor, and the Magellanic Clouds can be detected with LISA. Conclusions. As an all-sky survey that does not suffer from contamination and dust extinction, LISA will provide observations of the Milky Way and dwarf satellites galaxies, which will be valuable for Galactic archaeology and near-field cosmology

Milky Way Satellites Shining Bright in Gravitational Waves

The population of Milky Way satellite galaxies is of great interest for cosmology, fundamental physics, and astrophysics. They represent the faint end of the galaxy luminosity function, are the most dark-matter dominated objects in the local Universe, and contain the oldest and most metal-poor stellar populations. Recent surveys have revealed around 60 satellites, but this could represent less than half of the total. Characterization of these systems remains a challenge due to their low luminosity. We consider the gravitational wave observatory LISA as a potential tool for studying these satellites through observations of their short-period double white dwarf populations. LISA will observe the entire sky without selection effects due to dust extinction, complementing optical surveys, and could potentially discover massive satellites hidden behind the disk of the galaxy.Comment: 7 pages, 2 figure

A 27 day persistence model of near-Earth solar wind conditions: a long lead-time forecast and a benchmark for dynamical models

Geomagnetic activity has long been known to exhibit approximately 27 day periodicity, resulting from solar wind structures repeating each solar rotation. Thus a very simple near-Earth solar wind forecast is 27 day persistence, wherein the near-Earth solar wind conditions today are assumed to be identical to those 27 days previously. Effective use of such a persistence model as a forecast tool, however, requires the performance and uncertainty to be fully characterized. The first half of this study determines which solar wind parameters can be reliably forecast by persistence and how the forecast skill varies with the solar cycle. The second half of the study shows how persistence can provide a useful benchmark for more sophisticated forecast schemes, namely physics-based numerical models. Point-by-point assessment methods, such as correlation and mean-square error, find persistence skill comparable to numerical models during solar minimum, despite the 27 day lead time of persistence forecasts, versus 2–5 days for numerical schemes. At solar maximum, however, the dynamic nature of the corona means 27 day persistence is no longer a good approximation and skill scores suggest persistence is out-performed by numerical models for almost all solar wind parameters. But point-by-point assessment techniques are not always a reliable indicator of usefulness as a forecast tool. An event-based assessment method, which focusses key solar wind structures, finds persistence to be the most valuable forecast throughout the solar cycle. This reiterates the fact that the means of assessing the “best” forecast model must be specifically tailored to its intended use

VACUUM ULTRAVIOLET ABSORPTION SPECTRUM OF DICYANOACETYLENE

$^{1}$C. Baker and D. W. Turner, Proc. Roy. Soc. A308, 19 (1968).""Author Institution: Department of Chemistry, Northeastern UniversityThe gas phase absorption spectrum of $C_{4} N_{2}$ has been measured photoelectrically from 2000 ${\AA}$ to 1050 ${\AA}$ with a 1-m monochromator having a 1.0 ${\AA}$ bandwidth. The region from 1650--1050 ${\AA}$ is dominated by a $ns\leftarrow \pi$ Rydberg series converging to the first ionization potential at 11.81 eV. Vibrational structure on the short-wavelength side of the Rydberg bands consists of several well-resolved peaks, with an average spacing of $2120 cm^{-1}$. This is probably due to excitation of the symmetric $C\equiv N$ stretching mode $\nu_{1}$ and is very similar to the vibrational structure in the photoelectron spectrum obtained by Baker and $Turner.^{1}$. A non-Rydberg band at 1720 ${\AA}$ has an oscillator strength of 0.03. The assignment of this band will be discussed with the aid of a simple INDO molecular orbital calculation

Improving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density

ABSTRACT Accurately forecasting snow depth is a challenge. In particular, one poorly understood component of snow-depth forecasting is determining the snow ratio. The snow ratio is the ratio of snow depth to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1<=ratio<=15:1), and light (ratio>15:1). A web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural networks is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snow depth, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snow depth, given a forecasted value of liquid equivalent. The surface wind speed cannot be used as a sole discriminator for snow ratios. At Buffalo, New York, and Sault Ste. Marie, Michigan, locations susceptible to lake-effect snowstorms, wind speeds greater than 6 m s , however, only small differences exist between lake-effect and nonlake-effect events. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Improving snowfall forecasting by accounting for the climatological variability of snow density

ABSTRACT Accurately forecasting snow depth is a challenge. In particular, one poorly understood component of snow-depth forecasting is determining the snow ratio. The snow ratio is the ratio of snow depth to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1<=ratio<=15:1), and light (ratio>15:1). A web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural networks is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snow depth, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snow depth, given a forecasted value of liquid equivalent. The surface wind speed cannot be used as a sole discriminator for snow ratios. At Buffalo, New York, and Sault Ste. Marie, Michigan, locations susceptible to lake-effect snowstorms, wind speeds greater than 6 m s , however, only small differences exist between lake-effect and nonlake-effect events. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.