PPT-Supernova bounds

Author : tatiana-dople | Published Date : 2016-10-07

on dark photons Yongchao Zhang In collaboration with Rabindra Mohapatra Shmuel Nussinov Vigdor L Teplitz amp Demos Kazanas NPB890 2014 17 arXiv14100221

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Supernova bounds: Transcript

on dark photons Yongchao Zhang In collaboration with Rabindra Mohapatra Shmuel Nussinov Vigdor L Teplitz amp Demos Kazanas NPB890 2014 17 arXiv14100221 ULB Oct . Indeed developing bounds on the per formance of procedures can give complementary insights By exhibiting fundamental limits of performance perhaps over restricted classes of estimators it is possible to guarantee that an a lgorithm we have developed Supernovae . J081659.74+511233.7 . and . J07285387+3349106. . with . the BSU . Observatory. Benjamin . Lombard and Sarah Peck . (Mentor: Professor Jamie Kern). Bridgewater State University. Abstract. (Supernova Impostors) Schuyler Van Dyk IPAC/Caltech USA Supernova Impostors • Observed outburst with ~10 49 - 50 erg, i.e., comparable to that of a SN • Many impostors spectroscopica Original source: Arnett. , W.D.; et al. (1989). "Supernova 1987A". . Annual Review of Astronomy and Astrophysics. . 27. : 629–700. .. From Wikipedia article, downloaded 9/8/14, . http. ://. en.wikipedia.org/wiki/SN_1987A. 2 - . Calculations. www.waldomaths.com. Copyright © . Waldomaths.com. 2010, all rights reserved. Two ropes, . A. and . B. , have lengths:. A = . 36m to the nearest metre . B = . 23m to the nearest metre.. Physics Opportunities with. Supernova Neutrinos. Georg Raffelt, Max-Planck-Institut für Physik, München. Sanduleak -69 202. Sanduleak . -. 69 202. Large Magellanic Cloud . Distance 50 kpc . (160.000 light years). unseen problems. David . Corne. , Alan Reynolds. My wonderful new algorithm, . Bee-inspired Orthogonal Local Linear Optimal . Covariance . K. inetics . Solver. Beats CMA-ES on 7 out of 10 test problems !!. 1. John Beacom, The Ohio State University. 2. Plan of the Talk. Introduction to Supernova . N. eutrinos. Focus on . B. urst . D. etection. . What will we know after the next burst?. What . new capabilities do we need?. approximate membership. dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. A combinatorial approach to P . vs. NP. Shachar. Lovett. Computation. Input. Memory. Program . Code. Program code is . constant. Input has . variable length (n). Run time, memory – grow with input length. with WFIRST . DRM1 and DRM2. C Baltay. June 1, 2012. Supernova Surveys . using . Slitless. Spectroscopy. Use Imager for . SNe. discovery and to get . lightcurves. Use . slitless. spectroscopy to type supernovae and get redshifts. Instrument Chemistries for each step in the decontamination process. Pre-cleaning. Manual cleaning. Automated cleaning. Case Solutions. CSR Ink and adhesive remover. SchmutzOff stainless steel, acid based re-conditioner and . Instrument Chemistries for each step in the decontamination process. Pre-cleaning. Manual cleaning. Automated cleaning. © 2010 Case Medical, Inc.. Case Solutions. CSR Ink and adhesive remover. SchmutzOff stainless steel, acid based re-conditioner and . dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. is a powerful tool to prove lower bounds, e.g. in data structures.

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