593 research outputs found

    Starry Messages: Searching for Signatures of Interstellar Archaeology

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    Searching for signatures of cosmic-scale archaeological artifacts such as Dyson spheres or Kardashev civilizations is an interesting alternative to conventional SETI. Uncovering such an artifact does not require the intentional transmission of a signal on the part of the original civilization. This type of search is called interstellar archaeology or sometimes cosmic archaeology. The detection of intelligence elsewhere in the Universe with interstellar archaeology or SETI would have broad implications for science. For example, the constraints of the anthropic principle would have to be loosened if a different type of intelligence was discovered elsewhere. A variety of interstellar archaeology signatures are discussed including non-natural planetary atmospheric constituents, stellar doping with isotopes of nuclear wastes, Dyson spheres, as well as signatures of stellar and galactic-scale engineering. The concept of a Fermi bubble due to interstellar migration is introduced in the discussion of galactic signatures. These potential interstellar archaeological signatures are classified using the Kardashev scale. A modified Drake equation is used to evaluate the relative challenges of finding various sources. With few exceptions interstellar archaeological signatures are clouded and beyond current technological capabilities. However SETI for so-called cultural transmissions and planetary atmosphere signatures are within reach.Comment: 29 pages including 4 figures and 1 tabl

    Negative Particle Planar and Axial Channeling and Channeling Collimation

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    While information exists on high energy negative particle channeling there has been little study of the challenges of negative particle bending and channeling collimation. Partly this is because negative dechanneling lengths are relatively much shorter. Electrons are not particularly useful for investigating negative particle channeling effects because their material interactions are dominated by channeling radiation. Another important factor is that the current central challenge in channeling collimation is the proton-proton Large Hadron Collider (LHC) where both beams are positive. On the other hand in the future the collimation question might reemerge for electron-positron or muon colliders. Dechanneling lengths increase at higher energies so that part of the negative particle experimental challenge diminishes. In the article different approaches to determining negative dechanneling lengths are reviewed. The more complicated case for axial channeling is also discussed. Muon channeling as a tool to investigate dechanneling is also discussed. While it is now possible to study muon channeling it will probably not illuminate the study of negative dechanneling.Comment: 15 pages, 1 figure, docx fil

    IRAS-based Whole-Sky Upper Limit on Dyson Spheres

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    ABSTRACT A Dyson Sphere is a hypothetical construct of a star purposely cloaked by a thick swarm of broken-up planetary material to better utilize all of the stellar energy. A clean Dyson Sphere identification would give a significant signature for intelligence at work. A search for Dyson Spheres has been carried out using the 250,000 source database of the IRAS infrared satellite which covered 96% of the sky. The search has used the Calgary database for the IRAS Low Resolution Spectrometer (LRS) to look for fits to blackbody spectra. Searches have been conducted for both pure (fully cloaked) and partial Dyson Spheres in the blackbody temperature region 100 ≤ T ≤ 600 ºK. When other stellar signatures that resemble a Dyson Sphere are used to eliminate sources that mimic Dyson Spheres very few candidates remain and even these are ambiguous. Upper limits are presented for both pure and partial Dyson Spheres. The sensitivity of the LRS was enough to find Dyson Spheres with the luminosity of the sun out to 300 pc, a reach that encompasses a million solar-type stars

    IRAS-based whole-sky upper limit on Dyson Spheres

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    A Dyson Sphere is a hypothetical construct of a star purposely cloaked by a thick swarm of broken-up planetary material to better utilize all of the stellar energy. A clean Dyson Sphere identification would give a significant signature for intelligence at work. A search for Dyson Spheres has been carried out using the 250,000 source database of the IRAS infrared satellite which covered 96% of the sky. The search has used the Calgary database for the IRAS Low Resolution Spectrometer (LRS) to look for fits to blackbody spectra. Searches have been conducted for both pure (fully cloaked) and partial Dyson Spheres in the blackbody temperature region 100 < T < 600 K. When other stellar signatures that resemble a Dyson Sphere are used to eliminate sources that mimic Dyson Spheres very few candidates remain and even these are ambiguous. Upper limits are presented for both pure and partial Dyson Spheres. The sensitivity of the LRS was enough to find Dyson Spheres with the luminosity of the sun out to 300 pc, a reach that encompasses a million solar- type stars.Comment: 32 pages, 8 figure

    Patient and public involvement to inform priorities and practice for research using existing healthcare data for children’s and young people’s cancers

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    Background: In the United Kingdom, healthcare data is collected on all patients receiving National Health Service (NHS) care, including children and young people (CYP) with cancer. This data is used to inform service delivery, and with special permissions used for research. The use of routinely collected health data in research is an advancing field with huge potential benefit, particularly in CYP with cancer where case numbers are small and the impact across the life course can be significant. Patient and public involvement (PPI) exercise aims: Identify current barriers to trust relating to the use of healthcare data for research. Determine ways to increase public and patient confidence in the use of healthcare data in research. Define areas of research importance to CYP and their carers using healthcare data. // Methods: Young people currently aged between 16 and 25 years who had a cancer diagnosis before the age of 20 years and carers of a young person with cancer were invited to take part via social media and existing networks of service users. Data was collected during two interactive online workshops totalling 5 h and comprising of presentations from health data experts, case-studies and group discussions. With participant consent the workshops were recorded, transcribed verbatim and analysed using thematic analysis. // Results: Ten young people and six carers attended workshop one. Four young people and four carers returned for workshop two. Lack of awareness of how data is used, and negative media reporting were seen as the main causes of mistrust. Better communication and education on how data is used were felt to be important to improving public confidence. Participants want the ability to have control over their own data use. Late effects, social and education outcomes and research on rare tumours were described as key research priorities for data use. // Conclusions: In order to improve public and patient trust in our use of data for research, we need to improve communication about how data is used and the benefits that arise

    Consumer\u27s Guide to Regulatory Impact Analysis: Ten Tips for Being an Informed Policymaker

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    Regulatory impact analyses (RIAs) weigh the benefits of regulations against the burdens they impose and are invaluable tools for informing decision makers.We offer 10 tips for nonspecialist policymakers and interested stakeholders who will be reading RIAs as consumers. Core problem: Determine whether the RIA identifies the core problem (compelling public need) the regulation is intended to address. Alternatives: Look for an objective, policy-neutral evaluation of the relative merits of reasonable alternatives. Baseline: Check whether the RIA presents a reasonable “counterfactual” against which benefits and costs are measured. Increments: Evaluate whether totals and averages obscure relevant distinctions and trade-offs. Uncertainty: Recognize that all estimates involve uncertainty, and ask what effect key assumptions, data, and models have on those estimates. Transparency: Look for transparency and objectivity of analytical inputs. Benefits: Examine how projected benefits relate to stated objectives. Costs: Understand what costs are included. Distribution: Consider how benefits and costs are distributed. Symmetrical treatment: Ensure that benefits and costs are presented symmetrically
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