AD Consolidation for operation beyond 2010

The Antiproton Decelerator (AD), which started up for physics in 2000 and today supplies low-energy antiprotons to the ATRAP, ALPHA, ASACUSA and ACE experiments is based on the ACOL machine from which it still retains most of the components. ACOL construction was completed in 1986 at a total cost of approximately 80 MCHF (excluding infrastructure) which today would correspond to a value of around 160 MCHF. During recent years, a reduction of maintenance and modernisation has been unavoidable due to budgetary restraints. In order to identify the resources needed for continued AD operation beyond 2010 with a reasonably low risk of failures and to avoid increasing maintenance and repair costs, a study has been conducted involving groups from the AB, AT and TS departments. Analysis of breakdown risks, identification of items and costs for consolidation has been done as well as a risk score classification. To be noted is the relatively modest cost of the proposed items in view of the value of the facility and in comparison to the cost of the manpower necessary for running AD. The two scenarios under consideration are (1): Continued operation until the end of 2012 with no major modifications to the AD machine and (2): Operation until the end of 2016 with the possibility to implement the proposed ELENA upgrade. In both scenarios, AEGIS can carry out the measurement that it has proposed to make. The success-oriented timeline of the A EGIS proposal, which foresees installation of the experiment in 2009 and 2010, commissioning and first data taking in 2011 and carrying-out of a first gravitational measurement with antihydrogen in the following year is compatible with a scenario of AD operation only until the end of 2012. However, running the AD until 2016 would in addition allow going beyond the initial validation of the technique and would permit a more thorough investigation of the systematic errors in order to reach the initial physics goal (a measurement of the gravitational interaction of antihydrogen to 1%) and perhaps improve on it, as well as a number of ancillary physics measurements which are interesting and publishable in their own right

Dust in the Photospheric Environment II. Effect on the Near Infrared Spectra of L and T Dwarfs

We report an attempt to interpret the spectra of L and T dwarfs with the use of the Unified Cloudy Model (UCM). For this purpose, we extend the grid of the UCMs to the cases of log g = 4.5 and 5.5. The dust column density relative to the gas column density in the observable photosphere is larger at the higher gravities, and molecular line intensity is generally smaller at the higher gravities. The overall spectral energy distributions (SEDs) are f_{J} < f_{H} < f_{K} in middle and late L dwarfs, f_{J} f_{K} in early T dwarfs (L/T transition objects), and finally f_{J} > f_{H} > f_{K} in middle and late T dwarfs, where f_{J}, f_{H}, and f_{K} are the peak fluxes at J, H, and K bands, respectively, in f_{nu} unit. This tendency is the opposite to what is expected for the temperature effect, but can be accounted for as the effect of thin dust clouds formed deep in the photosphere together with the effect of the gaseous opacities including H_2 (CIA), H_2O, CH_4, and K I. Although the UCMs are semi-empirical models based on a simple assumption that thin dust clouds form in the region of T_{cr} < T < T_{cond} (T_{cr} = 1800K is an only empirical parameter while T_{cond} about 2000K is fixed by the thermodynamical data), the major observations including the overall SEDs as well as the strengths of the major spectral features are consistently accounted for throughout L and T dwarfs. In view of the formidable complexities of the cloud formation, we hope that our UCM can be of some use as a guide for future modelings of the ultracool dwarfs as well as for interpretation of observed data of L and T dwarfs.Comment: 43 pages, 13 figures, to appear in Astrophys. J. (May 20, 2004) Some minor corrections including the address of our web site, which is now read

The CERN Antiproton Decelerator (AD) Operation, Progress and Plans for the Future

The CERN Antiproton Decelerator (AD) is a simplified source providing low energy antiprotons for experiments, replacing four machines: AC (Antiproton Collector), AA (Antiproton Accumulator), PS and LEAR (Low Energy Antiproton Ring), shut down in 1996. The former AC was modified to include deceleration, electron cooling and ejection lines into the new experimental area. The AD started physics operation in July 2000 and has since delivered cooled beams at 100 MeV/c (kinetic energy of 5.3 MeV) to 3 experiments (ASACUSA, ATHENA and ATRAP). Problems encountered during the commissioning and the physics runs will be outlined as well as progress during 2001 and possible future developments

Norwegian dairy farmer's preferences for breeding goal traits and associations with herd and farm characteristics

The aims of this study were to investigate variation and clustering in breeding goal trait preferences among Norwegian dairy farmers and to identify factors with a systematic influence on their preferences. An internet-based questionnaire was sent out to dairy farmers connected to the Norwegian co-operative breeding organization Geno (N = 8222), of which 10.8% answered (N = 888). Of the 15 suggested traits fertility had the highest overall ranking, while parasite resistance and methane emission had the lowest. Four distinct preference clusters were identified by the means of cluster analysis, of which two had a high preference for milk production. Differences in terms of farm and herd characteristics between clusters suggests a mixture of systematic and intrinsic effects on breeding goal trait priorities. This study shows that Norwegian dairy farmers’ preferences for breeding goal traits fall into four distinct clusters, both affected by herd and farm characteristics along with intrinsic value

Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities

A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.Comment: 3 pages, 6 figures, submitted to ECOC 201