Lorentz factor | EPFL Graph Search

The Lorentz factor or Lorentz term is a quantity expressing how much the measurements of time, length, and other physical properties change for an object while that object is moving. The expression appears in several equations in special relativity, and it arises in derivations of the Lorentz transformations. The name originates from its earlier appearance in Lorentzian electrodynamics – named after the Dutch physicist Hendrik Lorentz. It is generally denoted γ (the Greek lowercase letter gamma). Sometimes (especially in discussion of superluminal motion) the factor is written as Γ (Greek uppercase-gamma) rather than γ. Definition The Lorentz factor γ is defined as \gamma = \frac{1}{\sqrt{1 - \frac{v^2}{c^2}}} [=\sqrt{\frac{c^2}{c^2-v^2}} = \frac{c}{\sqrt{c^2-v^2}}] = \frac{1}{\sqrt{1 - \beta^2}} = \frac{dt}{d\tau} , where: *v is the relative velocity between inertial reference frames, *c is the speed of light in vacuum, *β

Teraelectronvolt pulsed emission from the Crab Pulsar detected by MAGIC

Thomas Günther Bretz

Aims. We investigate the extension of the very high-energy spectral tail of the Crab Pulsar at energies above 400 GeV. Methods. We analyzed similar to 320 h of good-quality Crab data obtained with the MAGIC telescope from February 2007 to April 2014. Results. We report the most energetic pulsed emission ever detected from the Crab Pulsar reaching up to 1.5 TeV. The pulse profile shows two narrow peaks synchronized with those measured in the GeV energy range. The spectra of the two peaks follow two different power-law functions from 70GeV up to 1.5 TeV and connect smoothly with the spectra measured above 10GeV by the Large Area Telescope (LAT) on board the Fermi satellite. When making a joint fit of the LAT and MAGIC data above 10 GeV the photon indices of the spectra differ by 0.5 +/- 0.1. Conclusions. Using data from the MAGIC telescopes we measured the most energetic pulsed photons from a pulsar to date. Such TeV pulsed photons require a parent population of electrons with a Lorentz factor of at least 5x10(6). These results strongly suggest IC scattering off low-energy photons as the emission mechanism and a gamma-ray production region in the vicinity of the light cylinder.

Edp Sciences S A2016

Assessing toxic impacts on aquatic ecosystems in life cycle assessment (LCA)

Jérôme Payet

In the last decade, several Life Cycle Assessment (LCA) methods for assessing impact of products on living resources have been developed. Beyond the quantified assessments of impacts on living systems, it also checked the feasibility of the impact assessment on human health and ecosystems quality and helps to identify the limits of such methods. Among the different impact categories, that of toxic substances on ecosystems occupies an important place. The extent of these impacts has been stressed on many occasions and the necessity of preserving ecological areas and biodiversity has become a major issue on an international level. By focusing on aquatic ecosystems, this thesis aims at identifying constraints connected with assessment of the impact of chemical substances on ecosystems in LCA and setting up a method for assessing impacts of toxic substances on aquatic ecosystems which meets the requirements of a comparative approach like Life Cycle Assessment. The overall purpose of the thesis is to propose a comparative method for the Life Cycle Impact Assessment of toxics on aquatic ecosystems. With that aim, the dissertation is going throughout 6 major issues: The feasibility of the comparative impact assessment on ecosystems and the identification of associated constraints. The development of a statistical method for comparing impact on ecosystems; The review of the data availability for calculation of Effect Factors. The choice of the most relevant ecotoxicity measure (ECxs1, NOECs2 and LOECs3) for a comparative purpose. The development of best-estimate extrapolation factors for assessing chronic effects based on acute data. The analysis of the ecological realism of the comparative assessment method. These points are analysed throughout the 7 chapters of the thesis. Chapter 1 aims at introducing the thesis. A general presentation of Life Cycle Assessment is proposed, following by a detailed description of the Life Cycle Impact Assessment on ecosystems. This description covers the state of the art of researches and identifies the development needed. Therefore the scope of the thesis and the main points that must be addressed by this research are presented. Chapter 2 starts with a review of existing methods for Life Cycle Impact Assessment on ecosystems (LCIA), the chapter presents the parametric version of the AMI method (Assessment of the Mean Impact), which has been developed during the PhD for the assessment of impact on aquatic ecosystems. For this purpose, a framework and the main requirements for the development of this method are presented. For a comparative assessment, the Hazardous Concentration of a toxic affecting 50% of the species over their chronic EC50 (Effect Concentration affecting 50% of tested individuals), also called HC50EC50, is selected for the calculation of Effect Factors to be implemented in current LCIA methods. The Confidence Interval on the HC50EC50 is provided, enabling comparison between the impact values obtained as results of a Life Cycle Assessment study. The choice of EC50s is based on review of the main ecotoxicological databases, and analysis of the availability and reliability of test results. Moreover, bearing in mind that mostly acute data are available, while LCA deals mainly with chronic exposure, best-estimate extrapolation factors for the HC50EC50 and the associated uncertainty are provided for inorganics, nonpesticide organics, and pesticide organics. Concerning the method itself, in order to find the best methods for calculation of a toxicity indicator, several statistical estimators, parametric and non-parametric approaches are compared, identifying their properties and respective strengths for a comparative method. The analysis relates to both the reliability of the estimator and its Confidence Interval, especially in terms of statistical robustness and Effect Factor stability. Based on these findings, the AMI method is described in detail, and an example of application comparing two wheat crop scenarios differing by the pesticides used is presented. Chapter 3 presents in detail the four methods currently used for the development of Effect Factors for the Life Cycle Impact Assessment (LCIA) on Ecosystems: the parametric version of AMI (Assessment of the Mean Impact) based on HC50EC50s; the Eco-Indicator based on HC50NOECs; USES-LCA based on both HC5NOECs and the Most sensitive species, and the PNEC (Predicted No- Effect Concentration) based on the Most sensitive species. After presentation of the LCIA framework and its main divergences from Environmental Risk Assessment for chemical regulation, the four methods are detailed and applied for the calculation of Effect Factors for 83 substances, covering inorganics, non-pesticide organics, and pesticide organics. Each method is therefore analysed concerning three key points: applicability in the LCA framework, environmental relevance, and statistical reliability. Particular attention is paid to possible bias and the uncertainty, highlighting the following findings: (1) HC5NOECs are on average 50 times higher than the most sensitive species, and this difference in conservatism introduces a bias in the analyses for the method mixing HC5NOECs and most sensitive species. (2) Effect Factors based on the most sensitive species increase the relative weight of the most toxic chemicals by two orders of magnitude, depending on whether the study is based on US or European ecotoxicity databases. (3) the methods based on HC50EC50s and HC5NOECs are the only ones able to provide a Confidence Interval on the Effect Factor, but the Confidence Interval on the HC5NOECs can be more than 10 orders of magnitude greater than that of the HC50EC50s. (4) compared with the Confidence Interval on the HC50EC50s, the most sensitive species cannot be distinguished from HC50EC50s for chemicals characterised by fewer than 5 species, and the HC5NOECs cannot be distinguished from the HC50EC50s for chemicals characterised by fewer than 8 species. Chapter 4 compares two statistical estimators, aiming at calculate the average toxicity of substances on biological species. The two methods provide an estimation of the HC50EC50 and the associated Confidence Interval. On the one hand, parametric method using the geometric mean and a calculation of the confidence interval with Student is considered. On the other hand, a distribution-free method calculates the HC50EC50 based on the median response of species and the confidence interval based on bootstrap. In order to facilitate the use of the non-parametric method, a table linking the number of species tested and the size of the confidence interval is provided for samples from 5 to 500 species. The comparison is based on actual data concerning 191 substances covering inorganics, non-Pesticide organics, and Pesticide organics. The mean and width of the chronic EC50s samples for all the substances are presented. The Shapiro-Wilk test is performed for the 191 EC50s samples and the assumption of log-normality of the distribution failed in more than 20% of the cases. Two causes of this non Log-normality are identified; (1) the skewness, which is shown to be an important issue for the assessment of the average toxicity of chemicals while (2) the multi-modal distributions, which are not likely to influence considerably the final result. A detailed application of the two methods is done with the comparison of two herbicides, the Sulfosulfuron and the Prosulfuron, where the distribution-free method appears to be more powerful than the parametric for a substance-to-substance comparison. Nevertheless, the distribution-free method requires a minimum of 5 chronic EC50s, that cannot be satisfied in most cases. Chapter 5 aims at illustrating the previous chapter in using the nonparametric version of the AMI method for the comparative assessment of the impacts of metals on aquatic ecosystems. This chapter briefly describes the method, then it focuses on the comparative analysis of 9 metals sometimes tested with different salts and speciations. Two interesting results can be highlighted: (1) the toxicity of metals covers the whole range of toxicity of chemicals; (2) the confidence interval of the HC50EC50 for metals is on average twice as great for metals compared with other chemicals. This increase in the variability of ecotoxicological responses from species is likely to be due to the change in bioavailability of metals associated with a change of test conditions (e.g. pH, or Organic Matter). Chapter 6 reviews and analyses the reliability of existing aquatic toxicity databases which can be used for the calculation of Effect Factors for Life Cycle Impact Assessment (LCIA). For that purpose, the main LCIA methods are presented focusing on their data requirement. It concerns: EDIP4 (based on the PNEC); AMI5 (based on parametric HC50EC50); Eco-Indicator (based on the HC50NOEC); USES-LCA (based on the HC5NOEC). Moreover 6 ecotoxicity databases available in an electronic format are analysed: Aquire; Pesticide Ecotoxicity Database (PED); IUCLID; Acute Toxicity Database (ATD); Fathead Minnow database (FMD); and ECETOC Aquatic Toxicity Database (EAT). The analysis especially focuses on the identification of the substances and organisms, the definition of the tests conditions, and the control procedure of the database. A selection of tests is done, retaining a dataset of 128,864 tests results, acute, sub-chronic and chronic. A description of the data availability on the basis of the selected test is performed, considering the available EC50s (Effect Concentration affecting 50% of the individuals tested), LOECs and NOECs (Lowest or No Observed Effect Concentration). The number of covered substances is also analysed regarding the number of species or phyla considered. On that basis, an estimation of the maximum number of possible Effect Factors is performed. The results highlight the discrepancy between the large number of test results available (128,864), and the relatively restricted number of Effect Factors (betweeen 34 and 4959 depending on the method)) that can be calculated for a comparative purpose like LCIA. Chapter 7 provides a conclusion to the work, answering the points underlined in introduction. Then, the fey features of the AMI method are restated and the perspectives and output of the work are presented. In the last part of the thesis, the AMI HC50EC50 database is presented. It provides acute and chronic HC50EC50 data calculated with the parametric version of AMI (geometric mean of the EC50s and confidence interval based on Student) for 522 susbtances. ------------------------------ 1 ECx: concentration of susbtance that affects 50% of the individuals tested for a given effect. 2 NOEC: No Observable Effect Concentration 3 LOEC: Lowest Observable Effect Concentration 4 EDIP: Environmental Design for Industrial Product 5 AMI : Assessment of the Mean Impact

EPFL2004

Can CoGeNT and DAMA modulations be due to Dark Matter?

Duccio Pappadopulo

We explore the dark matter interpretation of the anomalies claimed by the DAMA and COGENT experiments, in conjunction with the various null direct-detection experiments. An independent analysis of the COGENT data is employed and several experimental and astrophysical uncertainties are considered. Various phenomenological models are studied, including isospin violating interactions, momentum-dependent form factors, velocity-dependent form factors, inelastic scatterings (endothermic and exothermic) and channeling. We find that the severe tension between the anomalies and the null results can be ameliorated but not eliminated, unless extreme assumptions are made.

2011

Assessing toxic impacts on aquatic ecosystems in life cycle assessment (LCA)

Jérôme Payet

EPFL2004