Protostellar half-life: new methodology and estimates

(Abridged) Protostellar systems evolve from prestellar cores, through the deeply embedded stage and then disk-dominated stage, before they end up on the main sequence. Knowing how much time a system spends in each stage is crucial for understanding how stars and associated planetary systems form, because a key constraint is the time available to form such systems. Equally important is understanding what the spread in these time scales is. The most commonly used method for inferring protostellar ages is to assume the lifetime of one evolutionary stage, and then scale this to the relative number of protostars in the other stages, i.e., assuming steady state. This method does not account for the underlying age distribution and apparent stochasticity of star formation, nor that relative populations are not in steady state. To overcome this, we propose a new scheme where the lifetime of each protostellar stage follows a distribution based on the formalism of sequential nuclear decay. The main assumptions are: Class 0 sources follow a straight path to Class III sources, the age distribution follows a binomial distribution, and the star-formation rate is constant. The results are that the half-life of Class 0, Class I, and Flat sources are (2.4+/-0.2)%, (4.4+/-0.3)%, and (4.3+/-0.4)% of the Class II half-life, respectively, which translates to 47+/-4, 88+/-7, and 87+/-8 kyr, respectively, for a Class II half-life of 2 Myr for protostars in the Gould Belt clouds with more than 100 protostars. The mean age of these clouds is 1.2+/-0.1 Myr, and the star formation rate is (8.3+/-0.5)x10^-4 Msun/yr. The critical parameters in arriving at these numbers are the assumed half-life of the Class II stage, and the assumption that the star-formation rate and half-lives are constant. This method presents a first step in moving from steady-state to non-steady-state solutions of protostellar populations.Comment: Accepted for publication in A&

A Systematic Search for Molecular Outflows Toward Candidate Low-Luminosity Protostars and Very Low Luminosity Objects

We present a systematic single-dish search for molecular outflows toward a sample of 9 candidate low-luminosity protostars and 30 candidate Very Low Luminosity Objects (VeLLOs; L_int < 0.1 L_sun). The sources are identified using data from the Spitzer Space Telescope catalogued by Dunham et al. toward nearby (D < 400 pc) star forming regions. Each object was observed in 12CO and 13CO J = 2-1 simultaneously using the sideband separating ALMA Band-6 prototype receiver on the Heinrich Hertz Telescope at 30 arcsecond resolution. Using 5-point grid maps we identify five new potential outflow candidates and make on-the-fly maps of the regions surrounding sources in the dense cores B59, L1148, L1228, and L1165. Of these new outflow candidates, only the map of B59 shows a candidate blue outflow lobe associated with a source in our survey. We also present larger and more sensitive maps of the previously detected L673-7 and the L1251-A IRS4 outflows and analyze their properties in comparison to other outflows from VeLLOs. The accretion luminosities derived from the outflow properties of the VeLLOs with detected CO outflows are higher than the observed internal luminosity of the protostars, indicating that these sources likely had higher accretion rates in the past. The known L1251-A IRS3 outflow is detected but not remapped. We do not detect clear, unconfused signatures of red and blue molecular wings toward the other 31 sources in the survey indicating that large-scale, distinct outflows are rare toward this sample of candidate protostars. Several potential outflows are confused with kinematic structure in the surrounding core and cloud. Interferometric imaging is needed to disentangle large-scale molecular cloud kinematics from these potentially weak protostellar outflows.Comment: 42 pages, 19 figures, Accepted for publication in the Astronomical Journa

Charge carrier induced lattice strain and stress effects on As activation in Si

We studied lattice expansion coefficient due to As using density functional theory with particular attention to separating the impact of electrons and ions. Based on As deactivation mechanism under equilibrium conditions, the effect of stress on As activation is predicted. We find that biaxial stress results in minimal impact on As activation, which is consistent with experimental observations by Sugii et al. [J. Appl. Phys. 96, 261 (2004)] and Bennett et al.[J. Vac. Sci. Tech. B 26, 391 (2008)]

Detailed pressure distribution measurements obtained on several configurations of an aspect-ratio-7 variable twist wing

Detailed pressure distribution measurements were made for 11 twist configurations of a unique, multisegmented wing model having an aspect ratio of 7 and a taper ratio of 1. These configurations encompassed span loads ranging from that of an untwisted wing to simple flapped wings both with and without upper-surface spoilers attached. For each of the wing twist configurations, electronic scanning pressure transducers were used to obtain 580 surface pressure measurements over the wing in about 0.1 sec. Integrated pressure distribution measurements compared favorably with force-balance measurements of lift on the model when the model centerbody lift was included. Complete plots and tabulations of the pressure distribution data for each wing twist configuration are provided

CO2 Ice toward Low-luminosity, Embedded Protostars: Evidence for Episodic Mass Accretion via Chemical History

We present Spitzer IRS spectroscopy of CO2 ice bending mode spectra at 15.2 micrometer toward 19 young stellar objects with luminosity lower than 1 Lsun (3 with luminosity lower than 0.1 Lsun). Ice on dust grain surfaces can encode the history of heating because pure CO2 ice forms only at elevated temperature, T > 20 K, and thus around protostars of higher luminosity. Current internal luminosities of YSOs with L < 1 Lsun do not provide the conditions needed to produce pure CO2 ice at radii where typical envelopes begin. The presence of detectable amounts of pure CO2 ice would signify a higher past luminosity. Many of the spectra require a contribution from a pure, crystalline CO2 component, traced by the presence of a characteristic band splitting in the 15.2 micrometer bending mode. About half of the sources (9 out of 19) in the low luminosity sample have evidence for pure CO2 ice, and six of these have significant double-peaked features, which are very strong evidence of pure CO2 ice. The presence of the pure CO2 ice component indicates that the dust temperature, and hence luminosity of the central star/accretion disk system, must have been higher in the past. An episodic accretion scenario, in which mixed CO-CO2 ice is converted to pure CO2 ice during each high luminosity phase, explains the presence of pure CO2 ice, the total amount of CO2 ice, and the observed residual C18O gas.Comment: Accepted for publication in ApJ, total 24 pages, 14 figure

The People\u27s Poets: Literature Born of the Texas Singer-Songwriter Movement of the Last Forty Years

The People’s Poets of Texas: Literature Born Within the Singer/Songwriter Tradition of the Last Forty Years is a creative nonfiction exploration of the poetry found within the songs of multiple generations of modern Texas singer/songwriters and a case for the consideration of their work as a genuine regional literature. Studying the roots of Texas music, the musicality of Texan manners of speech and storytelling, and re-examining the Austin, Texas music scene of the 1970s that brought a national focus to the organic, reciprocal manner in which Texas music is traditionally experienced, radically altered the ways in which the songs were written, recorded, and marketed. An examination of this phenomenon allows us to understand that, first, a proliferation of Texas singer/songwriters of unprecedented quality has emerged in recent decades and that, second, a legitimate people\u27s literature is emerging from their song-craft

Cosmic background explorer (COBE) navigation with TDRSS one-way return-link Doppler in the post-helium-venting phase

A navigation experiment was performed which establishes Ultra-Stable Oscillator (USO) frequency stabilized one way return link Doppler TDRSS tracking data as a feasible option for mission orbit determination support at the Goddard Space Center Flight Dynamics Facility. The study was conducted using both one way and two way Tracking and Data Relay Satellite System (TDRSS) tracking measurements for the Cosmic Background Explorer (COBE) spacecraft. Tracking data for a 4 week period immediately follow the depletion of the helium supply was used. The study showed that, for both definitive orbit solution and short term orbit prediction (up to 4 weeks), orbit determination results based on one way return link Doppler tracking measurements are comparable to orbit determination results based on two way range and two way Doppler tracking measurements

Applying machine learning to categorize distinct categories of network traffic

The recent rapid growth of the field of data science has made available to all fields opportunities to leverage machine learning. Computer network traffic classification has traditionally been performed using static, pre-written rules that are easily made ineffective if changes, legitimate or not, are made to the applications or protocols underlying a particular category of network traffic. This paper explores the problem of network traffic classification and analyzes the viability of having the process performed using a multitude of classical machine learning techniques against significant statistical similarities between classes of network traffic as opposed to traditional static traffic identifiers. To accomplish this, network data was captured, processed, and evaluated for 10 application labels under the categories of video conferencing, video streaming, video gaming, and web browsing as described later in Table 1. Flow-based statistical features for the dataset were derived from the network captures in accordance with the “Flow Data Feature Creation” section and were analyzed against a nearest centroid, k-nearest neighbors, Gaussian naïve Bayes, support vector machine, decision tree, random forest, and multi-layer perceptron classifier. Tools and techniques broadly available to organizations and enthusiasts were used. Observations were made on working with network data in a machine learning context, strengths and weaknesses of different models on such data, and the overall efficacy of the tested models. Ultimately, it was found that simple models freely available to anyone can achieve high accuracy, recall, and F1 scores in network traffic classification, with the best-performing model, random forest, having 89% accuracy, a macro average F1 score of .77, and a macro average recall of 76%, with the most common feature of successful classification being related to maximum packet sizes in a network flow