A Unifying Theory of Dark Energy and Dark Matter: Negative Masses and Matter Creation within a Modified Λ\LambdaCDM Framework

Dark energy and dark matter constitute 95% of the observable Universe. Yet the physical nature of these two phenomena remains a mystery. Einstein suggested a long-forgotten solution: gravitationally repulsive negative masses, which drive cosmic expansion and cannot coalesce into light-emitting structures. However, contemporary cosmological results are derived upon the reasonable assumption that the Universe only contains positive masses. By reconsidering this assumption, I have constructed a toy model which suggests that both dark phenomena can be unified into a single negative mass fluid. The model is a modified $\Lambda$CDM cosmology, and indicates that continuously-created negative masses can resemble the cosmological constant and can flatten the rotation curves of galaxies. The model leads to a cyclic universe with a time-variable Hubble parameter, potentially providing compatibility with the current tension that is emerging in cosmological measurements. In the first three-dimensional N-body simulations of negative mass matter in the scientific literature, this exotic material naturally forms haloes around galaxies that extend to several galactic radii. These haloes are not cuspy. The proposed cosmological model is therefore able to predict the observed distribution of dark matter in galaxies from first principles. The model makes several testable predictions and seems to have the potential to be consistent with observational evidence from distant supernovae, the cosmic microwave background, and galaxy clusters. These findings may imply that negative masses are a real and physical aspect of our Universe, or alternatively may imply the existence of a superseding theory that in some limit can be modelled by effective negative masses. Both cases lead to the surprising conclusion that the compelling puzzle of the dark Universe may have been due to a simple sign error.Comment: Accepted for publication in Astronomy and Astrophysics (A&A). Videos of the simulations are available online at: https://goo.gl/rZN1P

Using Climate Data to Understand the Response by Wildlife and Fisheries

Montana’s water supply varies from about 40 to 160 percent average. This is due to a large variability in the mountain snowpack, spring and summer precipitation and temperature. Nearly all of these parameters that determine the runoff will impact fish and wildlife throughout the year. Time of various climatic events in Montana, such as when snowpack starts to accumulate, when it reaches it season’s maximum, when it melts out, winter temperatures, when streams reach their annual peak flow, and when plants break dormancy (spring green-up), forage production, whether or not there is fall green-up and the time of fall green-up all have had a historical variation spanning about eight weeks. In addition, there is annual variation in climatic conditions across the state. Wildlife and fisheries managers need to take this variability into account when managing wildlife. Tools to help assess the potential variability and timing of various climatic, hydrologic and phenological parameters will be presented. Using observed climatic and hydrologic data collected over the past 100 years can be further interpreted to help understand and predict the response and effects on fish and wildlife. Relating these responses to these parameters provide better relationships than by using calendar dates

Is Fall Green-Up Significant?

Fall green-up does not occur every year.  Methods have been developed to determine whether or not fall green-up occurs at each SNOTEL and Climatological site in the Greater Yellowstone area. It is based on climatic conditions after the first killing frost (daily Tmin of 220 F or less) and growing degree days and precipitation that occur after that point.  Green-up vegetation may be available into January or February if snow covers the vegetation before another Tmin of 220 F or lower occurs. Crude protein of cured grasses is about 3 to 7 percent.  Ungulates need crude protein of about 6-8 percent in order to maintain fat reserves.  Green vegetation has a crude protein of about 10 to 13 percent.  Years with no fall green-up can make winter survival difficult for males, especially bull elk trying to recover from the rut.  Winters that are more severe can further affect survival. Predators may capitalize on animals in poorer physical condition. Methods and procedures used to determine which years fall green-up occurs will be presented and possible impacts of fall green-up will be discussed

Climatic Data for Wildlife Research and Management

There is generally a poor correlation between climatic variables at lower elevations and higher elevations. It is imperative that this relationship be understood when evaluating climatic effects on species that move from lower to higher elevation during different seasons. It is also important that valley climatic conditions are not used to define relationship of species that occupy higher elevations. Using data from NRCS SNOTEL (SNOw TELemetry) sites and NWS climatic stations can help define climatic conditions at locations occupied by concerned species.  Daily data is generally more useful than monthly or seasonal averages. There are approximately 90 SNOTEL sites across Montana that typically report daily SWE (snow water equivalent), precipitation, maximum, minimum and average temperatures yeararound and data is available in real-time. SWE can be related to travel, soil temperature, forage production and availability, migration and predator-prey relationships.  Some SNOTEL sites also report snow depth. NWS stations typically report daily precipitation, maximum and minimum temperature but data for most stations is reported monthly.  SWE can be estimated for NWS sites where daily air temperature, snow depth and precipitation are reported. Precipitation can be related to forage production, soil moisture and fall green-up. Maximum, minimum and average daily temperature can be related to forage production, phenology, the day plants break dormancy, fall green-up, critical temperatures for animals. Annual variability as well as elevational variability can be used to refine data to each area of interest.  Some examples of the relationships described above will be presented

Analyzing Climate Data for Wildlife Studies

Climate is one of the driving forces affecting wildlife and fisheries. Access to daily data from SNOTEL stations in the mountains and Climatological stations in the valleys provides data on temperature, precipitation and snow that can be interpreted to help understand response to climate variables. Dates of spring and fall green-up, growing degree-days and forage production can be derived. Snow water equivalent (SWE) can be estimated from Climatological stations. Start of snow accumulation, maximum accumulation and date of maximum SWE, and date of melt-out can be determined for each station. Critical temperatures are different for each species and accumulated effect of cold temperatures can be determined from daily minimum temperatures. Long-term trends and annual variations can help explain fluctuation in population, reproduction, predation and mortality. Relationships have been developed relating plant phenology to climatic variables for lilacs (which have been used as a surrogate for estimating growing seasons) and Whitebark Pine. SWE can be related to migration and predation Keetch Byram Drought Index (KBDI) was developed for fire spread analysis but can also be used as an index to soil moisture. Index of Winter Severity (IWS) can be calculated for various areas and species using a combination of SWE, forage production and critical temperatures. Streamflows can be related to SWE, soil moisture, spring precipitation and temperatures

Relating Climate Data to Whitebark Pine Cone Production in South-Central Montana

Whitebark pine (Pinus albicaulis) is a critical species for grizzly (Ursus arctos) and black bears (Ursus americana)in the Greater Yellowstone Area. Being able to predict the number of cones that will be produced in a year or two would help with the management of these species.  There is a strong correlation between cone production and Black Bear harvest. Climatic variables from SNOTEL stations can provide an insight into cone production. If there are not enough growing degree days to start fall cones, there will be no cones produced in year three.  Critical parameters that reduce cone production include poor soil moisture during year two and three and number of days with rain during pollination in year two. Cold spring temperatures can also reduce cone production. Within whitebark pine transects, individual trees may produce a different number of cones. These can be related to tree age and/or increased moisture from upslope areas. Cone production from ten Whitebark Pine transects in the Rock Creek-Stillwater-Boulder area of south central Montana observed by Montana Department of Fish, Wildlife and Parks has been compared to climatic data from three NRCS SNOTEL stations in the vicinity. The effects of various parameters on cone production and results of estimating the cone crop will be presented

The Influence of Snow on Ground Temperatures

Snow influences temperatures within the snowpack and soil temperatures.  Air temperatures may be well below freezing but temperatures within the snowpack and at soil surface will be near 0 0C (32 0F). When there is fall green-up and snow covers the vegetation before cold temperatures occur (less than – 5 0C or 23 0F), the native forage may stay green into January. With soil surface temperatures near freezing under snow packs that exceed about one meter, organisms can survive extremely cold winter air temperatures. Air temperatures can affect snow consistency as the seasons snowpack is being deposited which can affect foraging and animal movement. Relationships between air temperature, snow temperature and soil temperatures will be presented

Science Pipelines for the Square Kilometre Array

The Square Kilometre Array (SKA) will be both the largest radio telescope ever constructed and the largest Big Data project in the known Universe. The first phase of the project will generate on the order of 5 zettabytes of data per year. A critical task for the SKA will be its ability to process data for science, which will need to be conducted by science pipelines. Together with polarization data from the LOFAR Multifrequency Snapshot Sky Survey (MSSS), we have been developing a realistic SKA-like science pipeline that can handle the large data volumes generated by LOFAR at 150 MHz. The pipeline uses task-based parallelism to image, detect sources, and perform Faraday Tomography across the entire LOFAR sky. The project thereby provides a unique opportunity to contribute to the technological development of the SKA telescope, while simultaneously enabling cutting-edge scientific results. In this paper, we provide an update on current efforts to develop a science pipeline that can enable tight constraints on the magnetised large-scale structure of the Universe.Comment: Published in Galaxies, as part of a Special Issue on The Power of Faraday Tomograph