6,541 research outputs found
DarkHistory: A code package for calculating modified cosmic ionization and thermal histories with dark matter and other exotic energy injections
We present a new public Python package, DarkHistory, for computing the
effects of dark matter annihilation and decay on the temperature and ionization
history of the early universe. DarkHistory simultaneously solves for the
evolution of the free electron fraction and gas temperature, and for the
cooling of annihilation/decay products and the secondary particles produced in
the process. Consequently, we can self-consistently include the effects of both
astrophysical and exotic sources of heating and ionization, and automatically
take into account backreaction, where modifications to the
ionization/temperature history in turn modify the energy-loss processes for
injected particles. We present a number of worked examples, demonstrating how
to use the code in a range of different configurations, in particular for
arbitrary dark matter masses and annihilation/decay final states. Possible
applications of DarkHistory include mapping out the effects of dark matter
annihilation/decay on the global 21cm signal and the epoch of reionization, as
well as the effects of exotic energy injections other than dark matter
annihilation/decay. The code is available at
https://github.com/hongwanliu/DarkHistory with documentation at
https://darkhistory.readthedocs.io . Data files required to run the code can be
downloaded at https://doi.org/10.7910/DVN/DUOUWA .Comment: 40 pages, 17 figure
Heavy Dark Matter Annihilation from Effective Field Theory
We formulate an effective field theory description for SU(2) triplet
fermionic dark matter by combining nonrelativistic dark matter with gauge
bosons in the soft-collinear effective theory. For a given dark matter mass,
the annihilation cross section to line photons is obtained with 5% precision by
simultaneously including Sommerfeld enhancement and the resummation of
electroweak Sudakov logarithms at next-to-next-to-leading logarithmic order.
Using these results, we present more accurate and precise predictions for the
gamma-ray line signal from annihilation, updating both existing constraints and
the reach of future experiments.Comment: 5 pages, 2 figure
Concentration of norms and eigenvalues of random matrices
We prove concentration results for operator norms of rectangular
random matrices and eigenvalues of self-adjoint random matrices. The random
matrices we consider have bounded entries which are independent, up to a
possible self-adjointness constraint. Our results are based on an isoperimetric
inequality for product spaces due to Talagrand.Comment: 15 pages; AMS-LaTeX; updated one referenc
Mini-Conference on Hamiltonian and Lagrangian Methods in Fluid and Plasma Physics
A mini-conference on Hamiltonian and Lagrangian methods in fluid and plasma
physics was held on November 14, 2002, as part of the 44th meeting of the
Division of Plasma Physics of the American Physical Society. This paper
summarizes the material presented during the talks scheduled during the
Mini-Conference, which was held to honor Allan Kaufman on the occasion of his
75th birthday.Comment: 14 pages, conference summar
From Random Matrices to Stochastic Operators
We propose that classical random matrix models are properly viewed as finite
difference schemes for stochastic differential operators. Three particular
stochastic operators commonly arise, each associated with a familiar class of
local eigenvalue behavior. The stochastic Airy operator displays soft edge
behavior, associated with the Airy kernel. The stochastic Bessel operator
displays hard edge behavior, associated with the Bessel kernel. The article
concludes with suggestions for a stochastic sine operator, which would display
bulk behavior, associated with the sine kernel.Comment: 41 pages, 5 figures. Submitted to Journal of Statistical Physics.
Changes in this revision: recomputed Monte Carlo simulations, added reference
[19], fit into margins, performed minor editin
Textpresso for Neuroscience: Searching the Full Text of Thousands of Neuroscience Research Papers
Textpresso is a text-mining system for scientific literature. Its two major features are access to the full text of research papers and the development and use of categories of biological concepts as well as categories that describe or relate objects. A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. Here we describe Textpresso for
Neuroscience, part of the core Neuroscience Information Framework
(NIF). The Textpresso site currently consists of 67,500 full text
papers and 131,300 abstracts. We show that using categories in
literature can make a pure keyword query more refined and meaningful.
We also show how semantic queries can be formulated with categories
only. We explain the build and content of the database and describe the
main features of the web pages and the advanced search options. We also
give detailed illustrations of the web service developed to provide
programmatic access to Textpresso. This web service is used by the NIF
interface to access Textpresso. The standalone website of Textpresso
for Neuroscience can be accessed at
http://www.textpresso.org/neuroscience
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