A Lower-Limit Flux for the Extragalactic Background Light

... A strict lower-limit flux for the evolving extragalactic background light (and in particular the cosmic infrared background) has been calculated up to redshift of 5. The computed flux is below the existing upper limits from direct observations, and in agreement with all existing limits derived from very-high energy gamma-ray observations. The corrected spectra are still in agreement with simple theoretical predictions. The derived strict lower-limit EBL flux is very close to the upper limits from gamma-ray observations. This is true for the present day EBL but also for the diffuse flux at higher redshift. If future detections of high redshift gamma-ray sources require a lower EBL flux than derived here, the physics assumptions used to derive the upper limits have to be revised. The lower-limit EBL model is not only needed for absorption features in AGN and other gamma-ray sources but is also essential when alternative particle processes are tested, which could prevent the high energy gamma-rays from being absorbed. It can also be used for a quaranteed interaction of cosmic-ray particles. The model is available online.Comment: 12 pages, 6 figures, accepted by A&

A strict lower-limit EBL: Applications on gamma-ray absorption

A strict lower limit flux for the extragalactic background light from ultraviolet to the far-infrared photon energies is presented. The spectral energy distribution is derived using an established EBL model based on galaxy formation. The model parameters are chosen to fit the lower limit data from number count observations in particular recent results by the SPITZER infrared space telescope. A lower limit EBL model is needed to calculate guaranteed absorption due to pair production in extragalactic gamma-ray sources as in TeV blazars.Comment: Comments: 4 pages, 2 figures, submitted to proceedings of "4th Heidelberg International Symposium on High Energy Gamma-Ray Astronomy 2008

Cosmic Rays VI - Starburst galaxies at multiwavelengths

We propose an explanation for the far-infrared/radio correlation of galaxies in terms of the energy balance of the interstellar medium and determine the flux from high-energy photons and neutrinos from starburst galaxies. We present a catalog of the 127 brightest starburst galaxies with redshifts of z<0.03. In order to investigate the correlation between radio- and far-infrared emission, we apply the leaky box approximation. Further, we derive photon- and neutrino spectra from proton-proton interactions in supernova remnants (SNRs). Here, we assume that a fraction of the SNR's energy is transferred to the acceleration of cosmic rays. We also investigate the possibility of detecting Gamma Ray Bursts from nearby starburst galaxies, using the catalog defined here. We show that the radio emission is only weakly dependent on the magnetic field. It turns out that the intensity of the radio signal is directly proportional to the number of supernova explosions, which scales with the far-infrared luminosity. In addition, we find that high-energy photons from proton-proton interactions in SNRs in starbursts can make up several percent of the diffuse gamma-ray background. The neutrino flux from the same sources has a maximum energy of ~1e5 GeV. Neutrinos can, on the other hand, can be observed if a Gamma Ray Burst happens in a nearby starburst. About 0.03 GRBs per year are expected to occur in the entire catalog. The true number is expected to be even higher, since we only include the brightest sources. The number of events per burst in IceCube varies between about one event and more than 1000 events. This provides good prospects for IceCube to detect a significant event, since the background for a GRB search is close to zero.Comment: 27 pages, 13 figure

A GIS-Based Planning Approach for Urban Power and Natural Gas Distribution Grids with Different Heat Pump Scenarios

Next to building insulation, heat pumps driven by electrical compressors (eHPs) or by gas engines (geHPs) can be used to reduce primary energy demand for heating. They come with different investment requirements, operating costs and emissions caused. In addition, they affect both the power and gas grids, which necessitates the assessment of both infrastructures regarding grid expansion planning. To calculate costs and CO2 emissions, 2000 electrical load profiles and 180 different heat demand profiles for single-family homes were simulated and heat pump models were applied. In a case study for a neighborhood energy model, the load profiles were assigned to buildings in an example town using public data on locations, building age and energetic refurbishment variants. In addition, the town’s gas distribution network and low voltage grid were modeled. Power and gas flows were simulated and costs for required grid extensions were calculated for 11% and 16% heat pump penetration. It was found that eHPs have the highest energy costs but will also have the lowest CO2 emissions by 2030 and 2050. For the investigated case, power grid investments of 11,800 euros/year are relatively low compared to gas grid connection costs of 70,400 euros/year. If eHPs and geHPs are combined, a slight reduction of overall costs is possible, but emissions would rise strongly compared to the all-electric case.BMWi, 03ET4020C, Verbundvorhaben: Analyse von Strukturoptionen zur Integration erneuerbarer Energien in Deutschland und Europa unter Berücksichtigung der Versorgungssicherheit, Teilvorhaben: Analyse der Verteilnetzebene (INTEEVER-AVN)BMWi, 03ET4069C, Verbundvorhaben INTEEVER-II: Analyse der Integration erneuerbarer Energien in Deutschland und Europa unter Berücksichtigung der Versorgungssicherheit und dezentraler Flexibilitäte

Open data for energy networks: introducing DAVE—a data fusion tool for automated network generation

Abstract Developing a sustainable energy system for the future requires new ways of planning and operating energy infrastructure. A large part of this involves suitable network models. Real network data is not available for research without restrictions since energy networks are part of the critical infrastructure. Using open datasets and expert rules to generate non-restricted models is one solution to this. This paper introduces open data for energy networks generated by the open-source software “DAVE”. The Python-based data fusion tool DAVE can automatically generate customized energy network models quickly and on demand. The software collects data from various databases and uses appropriate methods to fuse them. The current version of the tool can create GIS-based power networks and gas transportation networks, with output that is compatible with common network simulation software. Further developments are planned for creating thermal and gas distribution networks, as these are important for local heat power transition. Implementing a quality description for fused datasets will also be included in future development