Spectroscopy of the optical Einstein ring 0047-2808

We present optical and near-infrared spectroscopic observations of the optical Einstein ring 0047-2808. We detect both [OIII] lines 4959, 5007 near 2.3 micron, confirming the redshift of the lensed source as z=3.595. The Ly-a line is redshifted relative to the [OIII] line by 140+-20 km/s. Similar velocity shifts have been seen in nearby starburst galaxies. The [OIII] line is very narrow, 130 km/s FWHM. If the ring is the image of the centre of a galaxy the one-dimensional stellar velocity dispersion sigma=55 km/s is considerably smaller than the value predicted by Baugh et al. (1998) for the somewhat brighter Lyman-break galaxies. The Ly-a line is significantly broader than the [OIII] line, probably due to resonant scattering. The stellar central velocity dispersion of the early-type deflector galaxy at z=0.485 is 250+-30 km/s. This value is in good agreement both with the value predicted from the radius of the Einstein ring (and a singular isothermal sphere model for the deflector), and the value estimated from the D_n-sigma relation.Comment: 7 pages, 3 figures, accepted for publication in MNRA

Redshift-Space Distortions and the Real-Space Clustering of Different Galaxy Types

We study the distortions induced by peculiar velocities on the redshift-space correlation function of galaxies of different morphological types in the Pisces-Perseus redshift survey. Redshift-space distortions affect early- and late-type galaxies in different ways. In particular, at small separations, the dominant effect comes from virialized cluster cores, where ellipticals are the dominant population. The net result is that a meaningful comparison of the clustering strength of different morphological types can be performed only in real space, i.e., after projecting out the redshift distortions on the two-point correlation function xi(r_p,pi). A power-law fit to the projected function w_p(r_p) on scales smaller than 10/h Mpc gives r_o = 8.35_{-0.76}^{+0.75} /h Mpc, \gamma = 2.05_{-0.08}^{+0.10} for the early-type population, and r_o = 5.55_{-0.45}^{+0.40} /h Mpc, \gamma = 1.73_{-0.08}^{+0.07} for spirals and irregulars. These values are derived for a sample luminosity brighter than M_{Zw} = -19.5. We detect a 25% increase of r_o with luminosity for all types combined, from M_{Zw} = -19 to -20. In the framework of a simple stable-clustering model for the mean streaming of pairs, we estimate sigma_12(1), the one-dimensional pairwise velocity dispersion between 0 and 1 /h Mpc, to be 865^{+250}_{-165} km/s for early-type galaxies and 345^{+95}_{-65} km/s for late types. This latter value should be a fair estimate of the pairwise dispersion for ``field'' galaxies; it is stable with respect to the presence or absence of clusters in the sample, and is consistent with the values found for non-cluster galaxies and IRAS galaxies at similar separations.Comment: 17 LaTeX pages including 3 tables, plus 11 PS figures. Uses AASTeX macro package (aaspp4.sty) and epsf.sty. To appear on ApJ, 489, Nov 199

Some remarks on the chemical potential of a system in an external field

The chemical potential change provides a criterion for predicting the spontaneity of any physical and chemical process. If asked to calculate the chemical potential change of a system in which several forces vary, a student might find the task quite complicate at first glance. However, the chemical potential is a state function. This property permits a precise definition of the contribution of each force to the chemical potential when all other relevant parameters are kept constant. The total chemical potential change can easily be calculated by summing up the above contributions. After a brief review of the role played by some parameters of the system, like activity (a) of the components, temperature (T), pressure (p) and surface tension (gamma), as well as of external fields, i.e. gravitational (Mgh), centrifugal (Mcp) and electric field (Fz(i) Phi), an equation for the computation of the chemical potential (mu) including all the above contributes is reported:-, where refers not only to p = p degrees = 1 bar but also to a chosen value of T, h, rho, Phi and r. Finally, applicative examples are illustrated.The chemical potential change provides a criterion for predicting the spontaneity of any physical and chemical process. If asked to calculate the chemical potential change of a system in which several forces vary, a student might find the task quite complicate at first glance. However, the chemical potential is a state function. This property permits a precise definition of the contribution of each force to the chemical potential when all other relevant parameters are kept constant. The total chemical potential change can easily be calculated by summing up the above contributions. After a brief review of the role played by some parameters of the system, like activity ( of the components, temperature (T), pressure (p) and surface tension (), as well as of external fields, i.e. gravitational (ℎ, centrifugal () and electric field (Φ), an equation for the computation of the chemical potential (µ) including all the above contributes is reported: °′ ° ° ℎ Φ 2 , where ° refers not only to p = p° =1 bar but also to a chosen value of T, h, ρ, Φ and r. Finally, applicative examples are illustrated

Southern Sky Redshift Survey: Clustering of Local Galaxies

We use the two-point correlation function to calculate the clustering properties of the recently completed SSRS2 survey. The redshift space correlation function for the magnitude-limited SSRS2 is given by xi(s)=(s/5.85 h-1 Mpc)^{-1.60} for separations between 2 < s < 11 h-1 Mpc, while our best estimate for the real space correlation function is xi(r) = (r/5.36 h-1 Mpc)^{-1.86}. Both are comparable to previous measurements using surveys of optical galaxies over much larger and independent volumes. By comparing the correlation function calculated in redshift and real space we find that the redshift distortion on intermediate scales is small. This result implies that the observed redshift-space distribution of galaxies is close to that in real space, and that beta = Omega^{0.6}/b < 1, where Omega is the cosmological density parameter and b is the linear biasing factor for optical galaxies. We also use the SSRS2 to study the dependence of xi on the internal properties of galaxies. We confirm earlier results that luminous galaxies (L>L*) are more clustered than sub-L* galaxies and that the luminosity segregation is scale-independent. We find that early types are more clustered than late types, but that in the absence of rich clusters, the relative bias between early and late types in real space, is not as strong as previously estimated. Furthermore, both morphologies present a luminosity-dependent bias, with the early types showing a slightly stronger dependence on luminosity. We also find that red galaxies are significantly more clustered than blue ones, with a mean relative bias stronger than that seen for morphology. Finally, we find that the relative bias between optical and iras galaxies in real space is b_o/b_I $\sim$ 1.4.Comment: 43 pages, uses AASTeX 4.0 macros. Includes 8 tables and 16 Postscript figures, updated reference

Impact of Orbital Parameters and Greenhouse Gas on the Climate of MIS 7 and MIS 5 Glacial Inceptions

This work explores the impact of orbital parameters and greenhouse gas concentrations on the climate of marine isotope stage (MIS) 7 glacial inception and compares it to that of MIS 5. The authors use a coupled atmosphere-ocean general circulation model to simulate the mean climate state of six time slices at 115, 122, 125, 229, 236, and 239 kyr, representative of a climate evolution from interglacial to glacial inception conditions. The simulations are designed to separate the effects of orbital parameters from those of greenhouse gas (GHG). Their results show that, in all the time slices considered, MIS 7 boreal lands mean annual climate is colder than the MIS 5 one. This difference is explained at 70% by the impact of the MIS 7 GHG. While the impact of GHG over Northern Hemisphere is homogeneous, the difference in temperature between MIS 7 and MIS 5 due to orbital parameters differs regionally and is linked with the Arctic Oscillation. The perennial snow cover is larger in all the MIS 7 experiments compared to MIS 5, as a result of MIS 7 orbital parameters, strengthened by GHG. At regional scale, Eurasia exhibits the strongest response to MIS 7 cold climate with a perennial snow area 3 times larger than in MIS 5 experiments. This suggests that MIS 7 glacial inception is more favorable over this area than over North America. Furthermore, at 239 kyr, the perennial snow covers an area equivalent to that of MIS 5 glacial inception (115 kyr). The authors suggest that MIS 7 glacial inception is more extensive than MIS 5 glacial inception over the high latitudes

Diclofenac sorption from synthetic water: Kinetic and thermodynamic analysis

This work investigated diclofenac sorption on 0.5g L-1 activated carbon in a range of temperature (288-318K) and of initial sorbate concentration (24-218mgL-1). Thermodynamic modelling was carried out with the Langmuir isotherm. For kinetic modelling we compared the so-called Diffusion-Controlled Langmuir Kinetics (DCLK) and the pseudo-second order (PSO) model. The maximum sorption capacity of the sorbent, equal to 180mgg-1, was independent of temperature. Experimental data fitted well with both kinetic models, yet the DCLK model was found to be more informative about the mechanism of the process. Kinetic parameters (α, β) increased with the temperature, with α value rising from 5×10-5 to 20×10-5 L mg-1min-0.5, and β value rising from 3×10-6 to 20×10-6 L mg-1min-1 in the temperature range investigated

Failure envelopes of pile groups under inclined and eccentric load

A novel numerical procedure for defining failure envelopes of pile groups under inclined and eccentric load is proposed. The starting point is a closed-form exact solution for interaction diagram of pile groups under combined axial-moment loading recently published in the literature. Failure envelopes in the generalised force space are then derived as an extension of this solution by means of an incremental algorithm. It is shown that the axial load at foundation level has always a beneficial effect on the lateral capacity of the pile group, even if this favourable effect is often neglected in practice. On the contrary, the amount of interaction between the horizontal and moment components of the resultant action at failure is usually very small, with the exception of piles groups with end-bearing piles. Some example applications of the proposed method are provided and a simple, yet reliable procedure for ultimate limit-state analysis of pile groups subjected to inclined and eccentric loads is suggested

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region