9,044 research outputs found

    Collider constraints and new tests of color octet vectors

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    We analyze the collider sensitivity for new colored resonances in ttˉt\bar{t}, bbˉb \bar{b}, and jjjj final states. While searches in the single production channel are model-dependent, the pair production rate is model independent and the existing (JJ)(JJ)(JJ)(JJ) and 4t4t searches impose strong constraints on the relevant branching fractions, where J=jJ = j or bb. We point out the missing, complementary searches in the mixed decay modes, ttˉ(jj)t\bar{t}(jj), ttˉ(bbˉ)t\bar{t}(b\bar{b}), and (bbˉ)(jj)(b\bar{b})(jj). We propose analysis strategies for the ttˉ(jj)t\bar{t}(jj) and ttˉ(bbˉ)t\bar{t}(b\bar{b}) decays and find their sensivity surpasses that of existing searches when the decay widths to tops and light jets are comparable. If no other decays are present, collective lower limits on the resonance mass can be set at 1.5~TeV using 37~fb−1^{-1} of 13~TeV data.Comment: 22 pages, 3 figures, 2 table

    Stellar Wakes from Dark Matter Subhalos

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    We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase space distribution of ambient halo stars, we estimate sensitivities down to subhalo masses ∼107 M⊙\sim 10^7\,M_\odot or below. The detection of such subhalos would have implications for dark-matter and cosmological models that predict modifications to the halo-mass function at low halo masses. We develop an analytic formalism for describing the perturbed stellar phase-space distributions, and we demonstrate through simulations the ability to detect subhalos using the phase-space model and a likelihood framework. Our method complements existing methods for low-mass subhalo searches, such as searches for gaps in stellar streams, in that we can localize the positions and velocities of the subhalos today.Comment: 6 + 3 pages, 1 + 2 figures, code available at: https://github.com/bsafdi/stellarWake

    TORTIFOLIA Gene kontrollieren das Streckungswachstum in Arabidopsis

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    Pilk Richard Viidalepa perekonnaloole

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    Richard Viidalepp (Widebaum before Estonianising his name, and later Viidebaum; Jan. 23, 1904 - June 3, 1986), the famous Estonian folklorist, was born in the Jalapuu farm in the village of Nurmsi in Central Estonia. The same farm was the home of Urve Buschmann, the author of the article and R. Viidalepp's niece. On the basis of the 1722 list of inhabitants in the Särgavere estate and the registers of the Järva Peetri congregation, the documented genealogy of Viidalepp's family starts with Jüri Jalapuu and his wife Els (?1730-?1761). In more recent registers their son Jüri (?1771-1843) already appears under the name Widebaum. The family was a typical Estonian family, including farmers, handicraftsmen, inventive technicians, later also intellectuals and artists. Some emigrated (the Finnish and American branches of the Viidebaums) and some were deported to Siberia. The fate of family members and descriptions of family history are illustrated by Richard Viidalepp's letters and family photographs. The last Viidalepps born in the Jalapuu farm moved to Tallinn in 1950

    Introducing the Myanmar Protest Event Dataset: Motivation, Methodology, and Research Prospects

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    This article presents the Myanmar Protest Event Dataset, a unique dataset on protest assemblies in transitional Myanmar/Burma. The data contents were derived from the most visible forms of assembly - demonstrations, protest marches and labour strikes - and collected through a protest event analysis of local news reports. The coded variables range from information on the actual moment of the protest event, such as participants, issue, duration and location, to the aftermath, including variables related to legal consequences for protesters and the success of protesters’ claims, and many others. Besides a concise description of the research design and data collection process, this article discusses methodological strengths and weaknesses of the dataset
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