Light Sneutrino Dark Matter at the LHC

In supersymmetric (SUSY) models with Dirac neutrino masses, a weak-scale trilinear A-term that is not proportional to the small neutrino Yukawa couplings can induce a sizable mixing between left and right-handed sneutrinos. The lighter sneutrino mass eigenstate can hence become the lightest SUSY particle (LSP) and a viable dark matter candidate. In particular, it can be an excellent candidate for light dark matter with mass below ~10 GeV. Such a light mixed sneutrino LSP has a dramatic effect on SUSY signatures at the LHC, as charginos decay dominantly into the light sneutrino plus a charged lepton, and neutralinos decay invisibly to a neutrino plus a sneutrino. We perform a detailed study of the LHC potential to resolve the light sneutrino dark matter scenario by means of three representative benchmark points with different gluino and squark mass hierarchies. We study in particular the determination of the LSP (sneutrino) mass from cascade decays involving charginos, using the mT2 variable. Moreover, we address measurements of additional invisible sparticles, in our case the lightest neutralino, and the question of discrimination against the MSSM.Comment: 25 pages, 16 figure

Identification des compartiments responsables de la qualité des eaux de surface d'un petit bassin versant du centre du Nouveau-Brunswick (Canada): application et analyse du modèle hydrochimique EMMA

Cette étude a été réalisée dans le cadre d'un projet multidisciplinaire sur la gestion et la protection de l'habitat des salmonidés et sur l'évaluation des perturbations que subissent les habitats de ces poissons dans les eaux courantes suite aux coupes forestières et à la construction de routes. Afin d'identifier les voies d'écoulement responsables de la qualité des eaux de surface d'un petit bassin versant forestier, une étude approfondie a été entreprise sur l'évolution de la qualité de l'eau de pluie lors de son passage à travers la phytocénose et la couverture pédologique jusqu'au ruisseau. La signature chimique des compartiments du bassin versant servira d'intrant quant à l'application et l'analyse du modèle EMMA (end-members mixing analysis).La signature chimique de l'eau du ruisseau s'explique par un graphe x-y (graphe de mélange) sur lequel la composition chimique des compartiments et celle du ruisseau sont reportées. Si trois compartiments circonscrivent la signature chimique du cours d'eau, alors on peut émettre l'hypothèse que ces compartiments se mélangent de façon conservatrice pour donner la qualité des eaux de surface du bassin versant. Plusieurs traceurs (conductivité électrique, SO42-, Cl-, NO3-, K+, Alt et Fet) naturels n'ont pas servi à l'identification des compartiments parce que le modèle ne tient pas compte de certaines conditions, tels l'activité biologique, l'état hydrique des profils, etc. Seuls le pH, Na+, Ca2+, Mg2+ et SiO2 se sont avérés des traceurs utiles. La nappe phréatique a été incluse par défaut dans le modèle puisqu'il était connu qu'elle assurait la base de l'écoulement du cours d'eau en tout temps de l'année. Les sols de la plaine d'inondation semblent également prendre part à la qualité de l'eau du ruisseau, particulièrement les horizons B podzoliques, lesquels sont saturés d'eau pendant toute la période sans gel. C'est donc dire que l'écoulement de l'eau souterraine et l'écoulement hypodermique au niveau des horizons B de la plaine d'inondation sont les voies d'écoulement qui expliquent le mieux la qualité des eaux de surface du bassin versant.Toutefois, la séparation de l'hydrogramme par l'équation du bilan massique a montré qu'un modèle à trois réservoirs (nappe phréatique, horizons B des versants sud et nord) ne peut pas donner des résultats satisfaisants quant à la simulation de la charge chimique des eaux de surface. Le modèle élimine systématiquement trop de compartiments pouvant s'avérer explicatifs de la qualité de l'eau du ruisseau. Un modèle mécaniste développé à partir des variations du niveau de la nappe phréatique, de la conductivité hydraulique et de la composition chimique des solutions de sol permettrait de reproduire plus rigoureusement l'hydrogramme du ruisseau. Le modèle EMMA demeure tout de même un bon outil pour réfuter ou confirmer une hypothèse de recherche car il met clairement en relation la composition chimique des compartiments à celle du ruisseau et enlève parfois tout doute quant à l'action d'un processus susceptible d'alimenter le cours d'eau.Intense forest harvesting is suspected as a cause of soil acidification. Inputs of acidity into the soil system may lead to high concentrations of metal ion species in the soil solution and surface waters. Some of these metal ions, e.g., Al3+, can cause toxic responses to fish and aquatic invertebrates. Timber-induced soil and surface water acidification is considered to be a short-lived phenomenon during the growing season following the cut. Vegetation loss could mean increased frequency of high Al3+ and H+concentrations in stream water rather than increased mean levels.Many types of tracers are useful for hydrograph separation. Isotopic (e.g., deuterium and tritium) and natural chemical tracers (e.g., pH, Cl-, SiO2) have been used extensively to interpret chemical data gathered in catchment studies. The ability of computer simulation models to reproduce the hydrograph and chemical species in streamwater varies. In some cases, the use of too many hydrological parameters (i.e., over-parameterization) can make the validation of reactions responsible for streamwater chemistry almost impossible. Recently, advances in hydrological modelling have been made by considering that streamwater chemistry is a mixture of groundwater and soil solutions at different depths. One model that originates from this hypothesis is EMMA (end-member mixing analysis). Chemical species that are variable with depth within a same soil profile were shown to be highly correlated with streamwater discharge. Generally, chemical species that show high concentrations in surface horizons increase in streamwater during high flow, whereas chemical species found in high concentrations in lower horizons are higher during low flow. In order to identify end-members that can potentially contribute to streamwater chemistry of a small catchment in central New Brunswick, we investigated the chemistry changes of rainwater entering the catchment, passing through vegetation and soils and reaching the stream channel. The chemical composition of the catchment's end-members will serve as input in order to run and analyse the EMMA model. Furthermore, a better knowledge of water flowpaths that dominate in the catchment could be valuable information for the Department of Fisheries and Oceans, who, in 1990, initiated a multidisciplinary project on 1) the protection and management of the salmonid habitat, and 2) the effects of forest harvesting and road construction on the freshwater habitats of these fish. Harvest operations are planned from 1996 until 1999.Streamwater chemistry is explained by a x-y graph (mixing diagram) on which the end-members and streamwater chemical composition are plotted. Because end-member chemistry is stable over time and space, mean values of tracers are plotted on the mixing diagram. Streamwater chemical compositions have all been plotted on the graph since they vary significantly with flow. If the chemical composition of three end-members enclose the streamwater chemical composition, then it can be assumed that these end-members mix conservatively to produce streamwater chemistry. If two chemical species mix non-conservatively, then the model will not accurately indicate the relative contribution of each end-member. Generally, the mixing diagram does not validate conservative mixing, but it can be used to test a mixing hypothesis. For example, if streamwater chemistry falls largely outside the end-members chemical composition, then at least one end-member is incorrectly characterized (or missing), or the end-members do not mix conservatively. The relative contribution of selected end-members are obtain from the mass balance equation: CtQt=C1Q1 + C2Q2 + C3Q3, where 1, 2 and 3 refer to the three end-members, C1,2 and 3 are the soil water concentrations of conservative elements for each end-member, and Q1,2 and 3 are the amounts of soil water for a given end-member. With this equation, the concentrations of a number of elements for each end-member (C1, C2, C3) are used simultaneously to estimate a single value for each Q1, Q2 and Q3. Since we want to quantify the contribution of each end-member to the total stream discharge, i.e., the mix of the three end- members, Qt is set to 1. Once values for Q1, Q2 and Q3 are calculated, the results are interpreted in terms of a hydrograph separation to show the contribution from each end-member to the overall stream discharge. Two soil toposequences that correspond to typical soil profiles along the northern and southern hillslopes were selected. From June to November 1995, wet deposition, throughfall, soil solutions at four depths and streamwater were collected. Samples were analyzed for pH, electrical conductivity, Na+, Ca2+, Mg2+, K+, SO42-, Cl-, NO3-, SiO2 , Alt and Fet.Many natural tracers (electrical conductivity, SO42-, Cl-, NO3-, K+, Alt and Fet) have not been used to identify end-members because the model does not always consider adequately some conditions or processes that go on in the catchment, e.g., biological activity and Eh. Because they vary considerably with depth, solution pH, Na+, Ca2+, Mg2+ and SiO2 have been shown to be useful tracers. Groundwater has been included in every diagram as one of the three end-members mixing conservatively to produce streamwater since it is certain that it contributes a large portion to the total discharge under any hydrological condition. Soils along the stream seem to contribute the rest of the streamwater chemistry, particularly B horizons which are submerged all summer by groundwater. Thus, groundwater and subsurface flow at the base of the soil profiles along the stream seem to be the principal flow mechanisms that control streamwater chemistry in the catchment. However, hydrograph separation shows that a three end-member model (i.e., groundwater, B horizons from the northern and southern hillslopes) is not enough to simulate streamwater chemistry. Saturated subsurface flow in the B horizons from both sides of the stream should contribute approximately the same amount to the total discharge since groundwater affects both end- members throughout the growing season. In that respect, groundwater level fluctuations at this depth of the soil profiles should not be considered as a cause of this discrepancy. What can be said at this point is that one end-member that is incorrectly defined in space, and that has a similar chemical composition to saturated subsurface flow coming from the southern hillslope, is the primary source (with groundwater) of stream discharge during events. It is thus better to interpret this information in terms of solution type rather than in terms of physical origin (northern or southern hillslope). In this manner, the stream water is provided by both hillslopes.In conclusion, the model eliminates systematically too many end-members that could partially explain streamwater chemistry. Results show that a more complex mixture is necessary to reproduce streamwater chemistry. A mechanistic model based on groundwater level fluctuations, hydraulic conductivity and soil solution chemistry would possibly have better success in reproducing the stream hydrograph. However, EMMA remains a useful tool to refute or confirm the possible action of a flow mechanism by correlating the chemical composition of end-members with streamwater chemistry

Estimation de la température de l'eau de rivière en utilisant les réseaux de neurones et la régression linéaire multiple

La température de l'eau en rivière est un paramètre ayant une importance majeure pour la vie aquatique. Les séries temporelles décrivant ce paramètre thermique existent, mais elles sont moins nombreuses et souvent courtes, ou comptent parfois des valeurs manquantes. Cette étude présente la modélisation de la température de l'eau en utilisant des réseaux de neurones et la régression linéaire multiple pour relier la température de l'eau à celle de l'air et le débit du ruisseau Catamaran, situé au Nouveau-Brunswick, Canada. Une recherche multidisciplinaire à long terme se déroule présentement sur ce site. Les données utilisées sont de 1991 à 2000 et comprennent la température de l'air de la journée en cours, de la veille et de l'avant-veille, le débit ainsi que le temps transformé en série trigonométrique. Les données de 1991 à 1995 ont été utilisées pour l'entraînement ou la calibration du modèle tandis que les données de 1996 à 2000 ont été utilisées pour la validation du modèle. Les coefficients de détermination obtenus pour l'entraînement sont de 94,2 % pour les réseaux de neurones et de 92,6 % pour la régression linéaire multiple, ce qui donne un écart-type des erreurs de 1,01 C pour les réseaux de neurones et de 1,05 C pour la régression linéaire multiple. Pour la validation, les coefficients de détermination sont de 92,2 % pour les réseaux de neurones et de 91,6 % pour la régression linéaire multiple, ce qui se traduit en un écart-type des erreurs de 1,10 C pour les réseaux de neurones et de 1,25 C pour la régression linéaire multiple. Durant la période d'étude (1991-2000), le biais a été calculé à +0,11 C pour le modèle de réseaux de neurones et à -0,26 °C pour le modèle de régression. Ces résultats permettent de conclure qu'il est possible de prévoir la température de l'eau de petits cours d'eau en utilisant la température de l'air et le débit, aussi bien avec les réseaux de neurones qu'avec la régression linéaire multiple. Les réseaux de neurones semblent donner un ajustement aux données légèrement meilleur que celui offert par la régression linéaire multiple, toutefois ces deux approches de modélisation démontrent une bonne performance pour la prédiction de la température de l'eau en rivière.Water temperature is a parameter of great importance for water resources. For instance, modifications of the thermal regime of a river can have a significant impact on fish habitat. Therefore, understanding and predicting water temperatures is essential in order to help prevent or forecast high temperature problems. In order to predict water temperatures, data series are necessary. Many data series exist for air temperatures, but water temperature series are relatively scarce and those available are often short or have missing values. This study presents the modelling of water temperature using neural networks and multiple linear regression to relate water temperature to air temperature and discharge in Catamaran Brook, New Brunswick, Canada.Catamaran Brook is a small stream (51 km2) where long-term multidisciplinary habitat research is being carried out. Many variables can impact water temperatures in a river, such as air temperature, solar radiation, wind speed, discharge, groundwater flow, etc. For this study, only air temperature and discharge were used. These were judged to be the most often available parameters for modelling temperatures in rivers, and to have the greatest impact on water temperature. More precisely, input variables included current air temperature (°C), air temperature of the previous day (°C), air temperature two days earlier (°C), discharge (m3 /s) and a trigonometric function of time (days). Data used for the analysis were from 1991 to 2000. Data from 1991 to 1995 were used to calibrate the model while data from 1996 to 2000 were used for validation purposes. Observed and predicted water temperatures for each model were presented for the calibration data and the validation data. The coefficient of determination, R2, was used to compare the efficiency of both models as well as the residual standard deviation and the bias. This is equivalent to basing the comparison on the standard deviation (or variance) of the residuals. Coefficients of determination for calibration were 94.2% for the neural networks and 92.6% for the multiple linear regression, which correspond to a residual standard deviation of 1.01°C for the neural networks and of 1.05°C for the multiple linear regression. For validation, coefficients of determination were 92.2% for the neural networks and 91.6% for the multiple linear regression, which correspond to a residual standard deviation of 1.10°C for the neural networks, and of 1.25°C for the multiple regression. The overall bias during the study period (1991-2000) was calculated at +0.11°C for the neural network model and at -0.26°C for the regression model. Results indicated that it was possible to predict water temperature for a small stream using air temperature, flow and time, as input variables, with neural networks and multiple linear regression. The residual series obtained by both models were very similar. Of the two models, neural networks gave slightly better results in terms of fit, but the small difference in results lets us believe that both approaches are equally good in predicting stream water temperatures

Static quantities of a neutral bilepton in the 331 model with right-handed neutrinos

A neutral vector boson can possess static electromagnetic properties provided that the associated field is no self-conjugate. This possibility is explored in the $SU_C(3) X SU_L(3) X U_N(1)$ model with right-handed neutrinos, which predicts a complex neutral gauge boson $Y^0$ in a nontrivial representation of the electroweak group. In this model the only nonvanishing form factors are the CP-even ones, which arise from both the quark and gauge sectors, and contribute to the magnetic dipole and the electric quadrupole moments of this neutral particle.Comment: 10 pages, 6 figures, submitted to Phys. Rev.

Light dark matter in the NMSSM: upper bounds on direct detection cross sections

In the Next-to-Minimal Supersymmetric Standard Model, a bino-like LSP can be as light as a few GeV and satisfy WMAP constraints on the dark matter relic density in the presence of a light CP-odd Higgs scalar. We study upper bounds on the direct detection cross sections for such a light LSP in the mass range 2-20 GeV in the NMSSM, respecting all constraints from B-physics and LEP. The OPAL constraints on e^+ e^- -> \chi^0_1 \chi^0_i (i > 1) play an important role and are discussed in some detail. The resulting upper bounds on the spin-independent and spin-dependent nucleon cross sections are ~ 10^{-42} cm^{-2} and ~ 4\times 10^{-40} cm^{-2}, respectively. Hence the upper bound on the spin-independent cross section is below the DAMA and CoGeNT regions, but could be compatible with the two events observed by CDMS-II.Comment: 17 pages, 3 figure

The quantitative soil pit method for measuring belowground carbon and nitrogen stocks

Many important questions in ecosystem science require estimates of stocks of soil C and nutrients. Quantitative soil pits provide direct measurements of total soil mass and elemental content in depth-based samples representative of large volumes, bypassing potential errors associated with independently measuring soil bulk density, rock volume, and elemental concentrations. The method also allows relatively unbiased sampling of other belowground C and nutrient stocks, including roots, coarse organic fragments, and rocks. We present a comprehensive methodology for sampling these pools with quantitative pits and assess their accuracy, precision, effort, and sampling intensity as compared to other methods. At 14 forested sites in New Hampshire, nonsoil belowground pools (which other methods may omit, double-count, or undercount) accounted for upward of 25% of total belowground C and N stocks: coarse material accounted for 4 and 1% of C and N in the O horizon; roots were 11 and 4% of C and N in the O horizon and 10 and 3% of C and N in the B horizon; and soil adhering to rocks represented 5% of total B-horizon C and N. The top 50 cm of the C horizon contained the equivalent of 17% of B-horizon carbon and N. Sampling procedures should be carefully designed to avoid treating these important pools inconsistently. Quantitative soil pits have fewer sources of systematic error than coring methods; the main disadvantage is that because they are time-consuming and create a larger zone of disturbance, fewer observations can be made than with cores

Soil weathering rates in 21 catchments of the Canadian Shield

Soil mineral weathering represents an essential source of nutrient base cation (Ca, Mg and K) for forest growth in addition to provide a buffering power against precipitation acidity for soils and surface waters. Weathering rates of base cations were obtained for 21 catchments located within the temperate and the boreal forest of the Canadian Shield with the geochemical model PROFILE. Weathering rates ranged from 0.58 to 4.46 kmol<sub>c</sub> ha<sup>−1</sup> yr<sup>−1</sup> and their spatial variation within the studied area was mostly in agreement with spatial variations in soil mineralogy. Weathering rates of Ca and Mg were significantly correlated (<i>r</i> = 0.80 and 0.64) with their respective lake concentrations. Weathering rates of K and Na did not correlate with lake concentrations of K and Na. The modeled weathering rates for each catchment were also compared with estimations of net catchment exportations. The result show that modeled weathering rates of Ca were not significantly different than the net catchment exportations while modeled weathering rates of Mg were higher by 51%. Larger differences were observed for K and Na weathering rates that were significantly different than net catchment exportations being 6.9 and 2.2 times higher than net exportations, respectively. The results for K were expected given its high reactivity with biotic compartments and suggest that most of the K produced by weathering reactions was retained within soil catchments and/or above ground biomass. This explanation does not apply to Na, however, which is a conservative element in forest ecosystems because of the insignificant needs of Na for soil microorganisms and above ground vegetations. It raises concern about the liability of the PROFILE model to provide reliable values of Na weathering rates. Overall, we concluded that the PROFILE model is powerful enough to reproduce spatial geographical gradients in weathering rates for relatively large areas as well as adequately predict absolute weathering rates values for the sum of base cations, Ca and Mg

Higgs and non-universal gaugino masses: no SUSY signal expected yet?

So far, no supersymmetric particles have been detected at the Large Hadron Collider (LHC). However, the recent Higgs results have interesting implications for the SUSY parameter space. In this paper, we study the consequences of an LHC Higgs signal for a model with non-universal gaugino masses in the context of SU(5) unification. The gaugino mass ratios associated with the higher representations produce viable spectra that are largely inaccessible to the current LHC and direct dark matter detection experiments. Thus, in light of the Higgs results, the non-observation of SUSY is no surprise.Comment: supplementary file containing plots with log priors in ancillary files. v2: added some comments on more general settings and references, accepted for publication in JHE

Loop-induced photon spectral lines from neutralino annihilation in the NMSSM

We have computed the loop-induced processes of neutralino annihilation into two photons and, for the first time, into a photon and a Z boson in the framework of the NMSSM. The photons produced from these radiative modes are monochromatic and possess a clear "smoking gun" experimental signature. This numerical analysis has been done with the help of the SloopS code, initially developed for automatic one-loop calculation in the MSSM. We have computed the rates for different benchmark points coming from SUGRA and GMSB soft SUSY breaking scenarios and compared them with the MSSM. We comment on how this signal can be enhanced, with respect to the MSSM, especially in the low mass region of the neutralino. We also discuss the possibility of this observable to constrain the NMSSM parameter space, taking into account the latest limits from the FERMI collaboration on these two modes.Comment: 18 pages, 3 figures. Minor clarifications added in the text. Typing mistakes and references corrected. Matches published versio

The generalised NMSSM at one loop: fine tuning and phenomenology

We determine the degree of fine tuning needed in a generalised version of the NMSSM that follows from an underlying Z4 or Z8 R symmetry. We find that it is significantly less than is found in the MSSM or NMSSM and extends the range of Higgs mass that have acceptable fine tuning up to Higgs masses of mh ~ 130 GeV. For universal boundary conditions analogous to the CMSSM the phenomenology is rather MSSM like with the singlet states typically rather heavy. For more general boundary conditions the singlet states can be light, leading to interesting signatures at the LHC and direct detection experiments.Comment: 20 pages, 9 figures, matches published versio