Information field theory

Non-linear image reconstruction and signal analysis deal with complex inverse problems. To tackle such problems in a systematic way, I present information field theory (IFT) as a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms even for non-linear and non-Gaussian signal inference problems. IFT algorithms exploit spatial correlations of the signal fields and benefit from techniques developed to investigate quantum and statistical field theories, such as Feynman diagrams, re-normalisation calculations, and thermodynamic potentials. The theory can be used in many areas, and applications in cosmology and numerics are presented.Comment: 8 pages, in-a-nutshell introduction to information field theory (see http://www.mpa-garching.mpg.de/ift), accepted for the proceedings of MaxEnt 2012, the 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineerin

Milk production from leguminous forage, roots and potatoes

The aim of the present work was to investigate the effects of replacing grain concentrates with roots and potatoes in dairy cow diets based upon large amounts of grass/alfalfa silage. The emphasis was on the possible improvement of microbial protein synthesis and nitrogen balance. Alfalfa dominated silage has a large excess of ruminally degradable protein that must be balanced with feed carbohydrates to avoid urinary nitrogen losses. The effects on ruminal fermentation pattern, intake and production were also studied. The thesis is based on two batch culture in vitro experiments and three animal experiments. The in vitro experiments compared fodder beets, barley/oats and raw, boiled or frozen potatoes as supplements to a silage diet incubated with rumen fluid from cows fed different diets. With respect to amounts fermented during 5 h incubation, supplements were ranked (P barley/oats > raw potatoes = frozen potatoes = unsupplemented silage. Substrates were numerically ranked in the same order with respect to microbial protein production, but due to larger variation they could only be divided into two groups, where fodder beets, boiled potatoes and barley/oats gave microbial yields not different from each other, but higher than for raw potatoes, frozen potatoes or unsupplemented silage. Butyrate proportion was little affected by incubation substrate but fodder beets fed to rumen fluid donor cows increased butyrate molar proportion in vitro from 10.7 to 13.0%. A change-over design experiment compared barley supplementation with fodder beet and potato supplementation of a silage diet for lactating cows. The fodder beet/potato diet lowered ad libitum silage intake by 0.9 kg DM/d and milk yield decreased correspondingly by 1.7 to 2.3 kg/d. Microbial protein production and nitrogen balance were not increased by the fodder beet supplementation, but a part of N excretion was redirected from urine to feces. Fodder beets tended to decrease the ratio lipogenic/glucogenic VFA, by increasing propionate and butyrate at the expense of acetate. In an intake experiment, most of the cows consumed the maximum allowance of fodder beets (4.6 kg DM/d) while there was a huge variation in the potato intake. A more synchronous feeding of degradable protein and readily available carbohydrates lowered the urinary nitrogen loss and increased allantoin excretion numerically but not significantly. A close correlation (R2 = 0.94) was found between total urinary N excretion and the ratio urea/creatinine in urine, which implies that spot sampling of urine may be a way to facilitate N balance measurements in lactating cows. In conclusion, a full replacement of grain by roots and potatoes can be done and the effects will be lowered urinary N losses but also a reduction in silage consumption and hence also milk production

Upsilon suppression in PbPb collisions at the LHC

The Compact Muon Solenoid (CMS) has measured the suppression of the bottomonium states Y(1S), Y(2S), and Y(3S) in PbPb collisions at sqrt(s_NN) = 2.76 TeV relative to pp collisions, scaled by the number of inelastic nucleon-nucleon collisions. CMS observed a stronger suppression for the weaker bound Y(2S) and Y(3S) states than for the ground state Y(1S). Such "sequential melting" has been predicted to be a clear signature for the creation of a quark-gluon plasma. The suppression of the Y(1S) and Y(2S) has been measured as a function of collision centrality for Y in the rapidity interval |y| < 2.4 and with transverse momentum (p_T) down to 0. Furthermore, the p_T and rapidity dependence of the Y(1S) suppression are presented.Comment: 7 pages, 3 figures. To appear in Proc. 14th Int. Conf. on B-Physics at Hadron Machines (Beauty 2013), Bologna, Italy, April 8-12, 201

Gamma rays from dark matter

A leading hypothesis for the nature of the elusive dark matter are thermally produced, weakly interacting massive particles that arise in many theories beyond the standard model of particle physics. Their self-annihilation in astrophysical regions of high density provides a potential means of indirectly detecting dark matter through the annihilation products, which nicely complements direct and collider searches. Here, I review the case of gamma rays which are particularly promising in this respect: distinct and unambiguous spectral signatures would not only allow a clear discrimination from astrophysical backgrounds but also to extract important properties of the dark matter particles; powerful observational facilities like the Fermi Gamma-ray Space Telescope or upcoming large, ground-based Cherenkov telescope arrays will be able to probe a considerable part of the underlying, e.g. supersymmetric, parameter space. I conclude with a more detailed comparison of indirect and direct dark matter searches, showing that these two approaches are, indeed, complementary.Comment: 13 pages, 4 figures, World Science proceedings style. Based on an invited talk given at the ICATPP conference on cosmic rays for particle and astroparticle physics, Como, Italy, 7-8 Oct 201

Astrophysical data analysis with information field theory

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.Comment: 4 pages, 2 figures, accepted chapter to the conference proceedings for MaxEnt 2013, to be published by AI