The initial conditions for stellar protocluster formation

Context. Galactic plane surveys of pristine molecular clouds are key for establishing a Galactic-scale view of star formation. For this reason, an unbiased sample of infrared dark clouds in the 10◦ < |l| < 65◦, |b| < 1◦ region of the Galactic plane was built using Spitzer 8 µm extinction. However, intrinsic fluctuations in the mid-infrared background can be misinterpreted as foreground clouds. Aims. The main goal of this study is to disentangle real clouds in the Spitzer Dark Cloud (SDC) catalogue from artefacts due to fluctuations in the mid-infrared background. Methods. We constructed H2 column density maps at ∼1811 resolution using the 160 µm and 250 µm data from the Herschel Galactic plane survey Hi-GAL. We also developed an automated detection scheme that confirms the existence of a SDC through its association with a peak on these Herschel column density maps. Detection simulations, along with visual inspection of a small sub-sample of SDCs, have been performed to get more insight into the limitations of our automated identification scheme. Results. Our analysis shows that 76(±19)% of the catalogued SDCs are real. This fraction drops to 55(±12)% for clouds with angular diameters larger than ∼1 arcmin. The contamination of the PF09 catalogue by large spurious sources reflects the large uncertainties associated to the construction of the 8 µm background emission, a key stage in identiying SDCs. A comparison of the Herschel confirmed SDC sample with the BGPS and ATLASGAL samples shows that SDCs probe a unique range of cloud properties, reaching down to more compact and lower column density clouds than any of these two (sub-)millimetre Galactic plane surveys. Conclusions. Even though about half of the large SDCs are spurious sources, the vast majority of the catalogued SDCs do have a Herschel counterpart. The Herschel-confirmed sample of SDCs offers a unique opportunity to study the earliest stages of both low- and high-mass star formation across the Galaxy

Low-Power Instrument Transformers and Energy Meters: Opportunities and Obstacles

Low-Power Instrument Transformers (LPITs) are becoming the preferred measurement device in the medium voltage (MV) distribution network (DN). They have several benefits compared to legacy solutions. However, the adoption of LPITs results in the need for adapting the grid and its assets to accept them. One practical example is using LPITs as the current and voltage source for energy meters (EMs), which are also used for billing purposes. The resulting measurement chain introduces several metrological challenges that must be studied and investigated. Therefore, in this work, the scenarios of LPITs and energy meters are introduced along with the latest relevant international standards. Afterwards, the opportunities and obstacles due to the implementation of the LPIT plus energy meter measurement chain are discussed. The discussion focuses on metrological requirements, accuracy evaluation, target uncertainty, and influence quantities affecting the performance of the devices

Characterizing precursors to stellar clusters with Herschel

Context. Despite their profound effect on the universe, the formation of massive stars and stellar clusters remains elusive. Recent advances in observing facilities and computing power have brought us closer to understanding this formation process. In the past decade, compelling evidence has emerged that suggests infrared dark clouds (IRDCs) may be precursors to stellar clusters. However, the usual method for identifying IRDCs is biased by the requirement that they are seen in absorption against background mid-IR emission, whereas dust continuum observations allow cold, dense pre-stellar-clusters to be identified anywhere. Aims: We aim to understand what dust temperatures and column densities characterize and distinguish IRDCs, to explore the population of dust continuum sources that are not IRDCs, and to roughly characterize the level of star formation activity in these dust continuum sources. Methods: We use Hi-GAL 70 to 500 $m data to identify dust continuum sources in the ell = 30deg and ell = 59deg Hi-GAL science demonstration phase (SDP) fields, to characterize and subtract the Galactic cirrus emission, and perform pixel-by-pixel modified blackbody fits on cirrus-subtracted Hi-GAL sources. We utilize archival Spitzer data to indicate the level of star-forming activity in each pixel, from mid-IR-dark to mid-IR-bright. Results: We present temperature and column density maps in the Hi-GAL ell = 30deg and ell = 59deg SDP fields, as well as a robust algorithm for cirrus subtraction and source identification using Hi-GAL data. We report on the fraction of Hi-GAL source pixels which are mid-IR-dark, mid-IR-neutral, or mid-IR-bright in both fields. We find significant trends in column density and temperature between mid-IR-dark and mid-IR-bright pixels; mid-IR-dark pixels are about 10 K colder and have a factor of 2 higher column density on average than mid-IR-bright pixels. We find that Hi-GAL dust continuum sources span a range of evolutionary states from pre- to star-forming, and that warmer sources are associated with more star formation tracers. Additionally, there is a trend of increasing temperature with tracer type from mid-IR-dark at the coldest, to outflow/maser sources in the middle, and finally to 8 and 24$m bright sources at the warmest. Finally, we identify five candidate IRDC-like sources on the far-side of the Galaxy. These are cold (20 K), high column density (N(H$_2$) gt 10$^22$ cm$^-2$) clouds identified with Hi-GAL which, despite bright surrounding mid-IR emission, show little to no absorption at 8 $m. These are the first inner Galaxy far-side candidate IRDCs of which the authors are aware. Herschel in an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation by NASA.The FITS files discussed in the paper would be released publicly WITH the Hi-GAL data (on the Hi-GAL website) when the Hi-GAL data is released publicly.Peer reviewe

Phase Diagram and Storage Capacity of Sequence Processing Neural Networks

We solve the dynamics of Hopfield-type neural networks which store sequences of patterns, close to saturation. The asymmetry of the interaction matrix in such models leads to violation of detailed balance, ruling out an equilibrium statistical mechanical analysis. Using generating functional methods we derive exact closed equations for dynamical order parameters, viz. the sequence overlap and correlation- and response functions, in the thermodynamic limit. We calculate the time translation invariant solutions of these equations, describing stationary limit-cycles, which leads to a phase diagram. The effective retarded self-interaction usually appearing in symmetric models is here found to vanish, which causes a significantly enlarged storage capacity of $\alpha_c\sim 0.269$, compared to \alpha_\c\sim 0.139 for Hopfield networks storing static patterns. Our results are tested against extensive computer simulations and excellent agreement is found.Comment: 17 pages Latex2e, 2 postscript figure

Innovation and Inequality from Stagnation to Growth

This study explores the evolution of income inequality in an economy featuring an endogenous transition from stagnation to growth. We incorporate heterogenous households into a Schumpeterian model of endogenous takeoff. In the pre-industrial era, the economy is in stagnation, and income inequality is determined by an unequal distribution of land ownership and remains stationary. When takeoff occurs, the economy experiences innovation and economic growth. In this industrial era, income inequality gradually rises until the economy reaches the balanced growth path. Finally, we calibrate the model for a quantitative analysis and compare the simulation results to historical data in the UK

Mapping the column density and dust temperature structure of IRDCs with Herschel

Infrared dark clouds (IRDCs) are cold and dense reservoirs of gas potentially available to form stars. Many of these clouds are likely to be pristine structures representing the initial conditions for star formation. The study presented here aims to construct and analyze accurate column density and dust temperature maps of IRDCs by using the first Herschel data from the Hi-GAL galactic plane survey. These fundamental quantities, are essential for understanding processes such as fragmentation in the early stages of the formation of stars in molecular clouds. We have developed a simple pixel-by-pixel SED fitting method, which accounts for the background emission. By fitting a grey-body function at each position, we recover the spatial variations in both the dust column density and temperature within the IRDCs. This method is applied to a sample of 22 IRDCs exhibiting a range of angular sizes and peak column densities. Our analysis shows that the dust temperature decreases significantly within IRDCs, from background temperatures of 20-30 K to minimum temperatures of 8-15 K within the clouds, showing that dense molecular clouds are not isothermal. Temperature gradients have most likely an important impact on the fragmentation of IRDCs. Local temperature minima are strongly correlated with column density peaks, which in a few cases reach NH2 = 1 x 10^{23} cm^{-2}, identifying these clouds as candidate massive prestellar cores. Applying this technique to the full Hi-GAL data set will provide important constraints on the fragmentation and thermal properties of IRDCs, and help identify hundreds of massive prestellar core candidates.Comment: Accepted for publication in A&A Herschel special issu