2,779 research outputs found

    The average GeV-band Emission from Gamma-Ray Bursts

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    We analyze the emission in the 0.3-30 GeV energy range of Gamma-Ray Bursts detected with the Fermi Gamma-ray Space Telescope. We concentrate on bursts that were previously only detected with the Gamma-Ray Burst Monitor in the keV energy range. These bursts will then be compared to the bursts that were individually detected with the Large Area Telescope at higher energies. To estimate the emission of faint GRBs we use non-standard analysis methods and sum over many GRBs to find an average signal which is significantly above background level. We use a subsample of 99 GRBs listed in the Burst Catalog from the first two years of observation. Although mostly not individually detectable, the bursts not detected by the Large Area Telescope on average emit a significant flux in the energy range from 0.3 GeV to 30 GeV, but their cumulative energy fluence is only 8% of that of all GRBs. Likewise, the GeV-to-MeV flux ratio is less and the GeV-band spectra are softer. We confirm that the GeV-band emission lasts much longer than the emission found in the keV energy range. The average allsky energy flux from GRBs in the GeV band is 6.4*10^-4 erg cm^-2 yr^-1 or only 4% of the energy flux of cosmic rays above the ankle at 10^18.6 eV.Comment: Astronomy and Astrophysics, version accepted for publicatio

    Optical characteristics of Nd:YAG optics and distortions at high power

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    The intensity profile and beam caustics of a fiber coupled high power Nd:YAG laser beam through a lens system are studied. The thermal lensing effect and its influence on the beam profile and focal position are discussed. Asymmetry of the intensity profile in planes above and below the focal plane is demonstrated. Also the influence of small pollutions on the protective window is explained. Three different methods are used to measure the occurrence\ud of thermal lensing and quantify these effects

    Distribution Matching : Semi-Supervised Feature Selection for Biased Labelled Data

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    In the context of data science and machine learning, feature selection is a widely used technique that focuses on reducing the dimensionality of a dataset. It is commonly used to improve model accuracy by preventing data redundancy and over-fitting, but can also be beneficial in applications such as data compression. The majority of feature selection techniques rely on labelled data. In many real-world scenarios, however, data is only partially labelled and thus requires so-called semi-supervised techniques, which can utilise both labelled and unlabelled data. While unlabelled data is often obtainable in abundance, labelled datasets are smaller and potentially biased. This thesis presents a method called distribution matching, which offers a way to do feature selection in a semi-supervised setup. Distribution matching is a wrapper method, which trains models to select features that best affect model accuracy. It addresses the problem of biased labelled data directly by incorporating unlabelled data into a cost function which approximates expected loss on unseen data. In experiments, the method is shown to successfully minimise the expected loss transparently on a synthetic dataset. Additionally, a comparison with related methods is performed on a more complex EMNIST dataset

    NAUTILUS: boosting Bayesian importance nested sampling with deep learning

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    We introduce a novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Unlike rejection-based sampling methods such as vanilla nested sampling (NS) or Markov chain Monte Carlo (MCMC) algorithms, importance sampling techniques can use all likelihood evaluations for posterior and evidence estimation. However, for efficient importance sampling, one needs proposal distributions that closely mimic the posterior distributions. We show how to combine INS with deep learning via neural network regression to accomplish this task. We also introduce NAUTILUS, a reference open-source Python implementation of this technique for Bayesian posterior and evidence estimation. We compare NAUTILUS against popular NS and MCMC packages, including EMCEE, DYNESTY, ULTRANEST and POCOMC, on a variety of challenging synthetic problems and real-world applications in exoplanet detection, galaxy SED fitting and cosmology. In all applications, the sampling efficiency of NAUTILUS is substantially higher than that of all other samplers, often by more than an order of magnitude. Simultaneously, NAUTILUS delivers highly accurate results and needs fewer likelihood evaluations than all other samplers tested. We also show that NAUTILUS has good scaling with the dimensionality of the likelihood and is easily parallelizable to many CPUs.Comment: 15 pages, 10 figures, submitted to MNRAS; code available at https://github.com/johannesulf/nautilu

    CRISPR Gene Editing Drivers, Barriers and Prospects: A Comparative Study among Plant Scientists Globally

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    The introduction of CRISPR gene editing into food crops has potential to contribute to food security and sustainable food production globally. To date, most scientific studies have focused on consumer perception of CRISPR gene edited foods or the potential benefits and risks of the CRISPR technology and none have focused on the perceptions of plant scientists concerning CRISPR gene editing. This study aimed to explore the investments, functions, barriers, benefits for specific crops and beneficiaries of CRISPR gene editing according to plant scientists, by distributing an online survey in which 1,040 plant scientists active across six continents and in both the public and private sector participated. By asking the respondents the current (and envisioned future) percentage of the total research and development that is spend on CRISPR gene editing, we found that relative investments in CRISPR gene editing are expected to increase in the next ten years in all continents and in both the public and private sector. Moreover, plant scientists expect that fungus resistance and virus resistance are the functions most likely to be implemented using CRISPR technology. Consumer perceptions/knowledge gap and policy/legal issues were perceived as the most impeding barriers of CRISPR adoption globally, where intellectual property rights issues are a major impediment in high-income countries and high development costs in low-income countries. Maize and soybean are expected to benefit the most from CRISPR gene editing across all regions, except for Oceania. Wheat, rice and potatoes are other crops in which plant scientists see potential to benefit from the CRISPR technology. Increased yields are expected to be the biggest beneficiary of CRISPR gene editing, where public scientists also see producer profits as an important beneficiary of the technology. Importantly, plant scientists are reluctant to the idea of CRISPR gene editing being regulated in a similar way as GM crops and expect the private sector to dominate the CRISPR market. The consensus among plant scientists is that CRISPR technology can contribute significantly to the enhancement of environmental sustainability and food insecurity issues

    Constraints on Assembly Bias from Galaxy Clustering

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    We constrain the newly-introduced decorated Halo Occupation Distribution (HOD) model using SDSS DR7 measurements of projected galaxy clustering or r-band luminosity threshold samples. The decorated HOD is a model for the galaxy-halo connection that augments the HOD by allowing for the possibility of galaxy assembly bias: galaxy luminosity may be correlated with dark matter halo properties besides mass, Mvir. We demonstrate that it is not possible to rule out galaxy assembly bias using DR7 measurements of galaxy clustering alone. Moreover, galaxy samples with Mr < -20 and Mr < -20.5 favor strong central galaxy assembly bias. These samples prefer scenarios in which high-concentration are more likely to host a central galaxy relative to low-concentration halos of the same mass. We exclude zero assembly bias with high significance for these samples. Satellite galaxy assembly bias is significant for the faintest sample, Mr < -19. We find no evidence for assembly bias in the Mr < -21 sample. Assembly bias should be accounted for in galaxy clustering analyses or attempts to exploit galaxy clustering to constrain cosmology. In addition to presenting the first constraints on HOD models that accommodate assembly bias, our analysis includes several improvements over previous analyses of these data. Therefore, our inferences supersede previously-published results even in the case of a standard HOD analysis.Comment: 15 pages, 8 figures. To be submitted to MNRAS. Comments Welcome. Python scripts to perform this analysis and MCMC chains will all be made publicly availabl
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