A Window On The Earliest Star Formation: Extreme Photoionization Conditions of a High-Ionization, Low-Metallicity Lensed Galaxy at z~2

We report new observations of SL2SJ021737-051329, a lens system consisting of a bright arc at z=1.84435, magnified ~17x by a massive galaxy at z=0.65. SL2SJ0217 is a low-mass (M <10^9 M*), low-metallicity (Z~1/20 Z*) galaxy, with extreme star-forming conditions that produce strong nebular UV emission lines in the absence of any apparent outflows. Here we present several notable features from rest-frame UV Keck/LRIS spectroscopy: (1) Very strong narrow emission lines are measured for CIV 1548,1550, HeII 1640, OIII] 1661,1666, SiIII] 1883,1892, and CIII] 1907,1909. (2) Double-peaked LyA emission is observed with a dominant blue peak and centered near the systemic velocity. (3) The low- and high-ionization absorption features indicate very little or no outflowing gas along the sightline to the lensed galaxy. The relative emission line strengths can be reproduced with a very high-ionization, low-metallicity starburst with binaries, with the exception of He \ii, which indicates an additional ionization source is needed. We rule out large contributions from AGN and shocks to the photoionization budget, suggesting that the emission features requiring the hardest radiation field likely result from extreme stellar populations that are beyond the capabilities of current models. Therefore, SL2S0217 serves as a template for the extreme conditions that are important for reionization and thought to be more common in the early Universe.Comment: 28 pages, 16 figures, 8 tables, re-submitted to ApJ, comments welcom

The Sandman

https://digitalcommons.library.umaine.edu/mmb-vp/5965/thumbnail.jp

Observation of out-of-phase bilayer plasmons in YBa_2Cu_3O_7-delta

The temperature dependence of the c-axis optical conductivity \sigma(\omega) of optimally and overdoped YBa_2Cu_3O_x (x=6.93 and 7) is reported in the far- (FIR) and mid-infrared (MIR) range. Below T_c we observe a transfer of spectral weight from the FIR not only to the condensate at \omega = 0, but also to a new peak in the MIR. This peak is naturally explained as a transverse out-of-phase bilayer plasmon by a model for \sigma(\omega) which takes the layered crystal structure into account. With decreasing doping the plasmon shifts to lower frequencies and can be identified with the surprising and so far not understood FIR feature reported in underdoped bilayer cuprates.Comment: 7 pages, 3 eps figures, Revtex, epsfi

First-Order Type Effects in YBa2_2Cu3_3O6+x_{6+x} at the Onset of Superconductivity

We present results of Raman scattering experiments on tetragonal ${\rm (Y_{1-y}Ca_{y})Ba_{2}Cu_{3}O_{6+x}}$ for doping levels $p(x,y)$ between 0 and 0.07 holes/CuO$_2$. Below the onset of superconductivity at $p_{\rm sc1} \approx 0.06$, we find evidence of a diagonal superstructure. At $p_{\rm sc1}$, lattice and electron dynamics change discontinuously with the charge and spin properties being renormalized at all energy scales. The results indicate that charge ordering is intimately related to the transition at $p_{\rm sc1}$ and that the maximal transition temperature to superconductivity at optimal doping $T_{c}^{\rm max}$ depends on the type of ordering at $p>p_{\rm sc1}$.Comment: 4 pages, 4 figure

Ursinus College Bulletin Vol. 11, No. 1, October 1894

A digitized copy of the October 1894 Ursinus College Bulletin.https://digitalcommons.ursinus.edu/ucbulletin/1098/thumbnail.jp

Ursinus College Bulletin Vol. 11, No. 4, January 1895

A digitized copy of the January 1895 Ursinus College Bulletin.https://digitalcommons.ursinus.edu/ucbulletin/1101/thumbnail.jp

Ursinus College Bulletin Vol. 11, No. 2, November 1894

A digitized copy of the November 1894 Ursinus College Bulletin.https://digitalcommons.ursinus.edu/ucbulletin/1099/thumbnail.jp

Linear graph convolutional networks

Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity, we show that our convolution operator is more theoretically grounded than many proposals in literature, and shows improved predictive performance