1,849 research outputs found
Diphotons, New Vacuum Angles, and Strong CP
The Standard Model contains a well-understood, natural, spin-0 diphoton
resonance: the . Numerous studies have pointed out that the hint of a
new diphoton resonance at 750 GeV could be a pion analog, identified with the
pseudo-Nambu-Goldstone boson of a chiral symmetry spontaneously broken by new
strong dynamics at the TeV scale. These "hypercolor" models are generically
expected to violate parity through a topological angle . We
discuss the physics of and its impact on the phenomenology of
the new sector. We also describe some of the theoretical implications of a
nonzero . In particular, can generate an threshold correction to the QCD vacuum angle near the TeV
scale, sharply constraining ultraviolet solutions to the strong CP problem.
Alternatively, finding that is small may be interpreted as
evidence in favor of UV solutions to strong CP, particularly those based on
spontaneously broken P or CP symmetries.Comment: 23 pages, 6 figures. v2: references added, fig 1 update
A Noninformative Prior on a Space of Distribution Functions
In a given problem, the Bayesian statistical paradigm requires the
specification of a prior distribution that quantifies relevant information
about the unknowns of main interest external to the data. In cases where little
such information is available, the problem under study may possess an
invariance under a transformation group that encodes a lack of information,
leading to a unique prior---this idea was explored at length by E.T. Jaynes.
Previous successful examples have included location-scale invariance under
linear transformation, multiplicative invariance of the rate at which events in
a counting process are observed, and the derivation of the Haldane prior for a
Bernoulli success probability. In this paper we show that this method can be
extended, by generalizing Jaynes, in two ways: (1) to yield families of
approximately invariant priors, and (2) to the infinite-dimensional setting,
yielding families of priors on spaces of distribution functions. Our results
can be used to describe conditions under which a particular Dirichlet Process
posterior arises from an optimal Bayesian analysis, in the sense that
invariances in the prior and likelihood lead to one and only one posterior
distribution
Diphotons from Tetraphotons in the Decay of a 125 GeV Higgs at the LHC
Recently the ATLAS and CMS experiments have presented data hinting at the
presence of a Higgs boson at GeV. The best-fit
rate averaged over the two experiments is
approximately times the Standard Model prediction. We study the
possibility that the excess relative to the Standard Model is due to
decays, where is a light pseudoscalar that decays
predominantly into . Although this process yields final
states, if the pseudoscalar has a mass of the order tens of MeV, the two
photons from each decay can be so highly collimated that they may be
identified as a single photon. Some fraction of the events then contribute to
an effective signal. We study the constraints on the
parameter space where the net rate is enhanced over
the Standard Model by this mechanism and describe some simple models that give
rise to the pseudoscalar-photon interaction. Further tests and prospects for
searches in the near future are discussed.Comment: 14 pages, 7 figures, revtex4-1; v2: references added and rearranged,
g-2 limit improved, published version; v3: typos correcte
A Tale of Two Populations: The Contribution of Merger and Secular Processes to the Evolution of Active Galactic Nuclei
Due to the co-evolution of supermassive black holes and their host galaxies,
understanding the mechanisms that trigger active galactic nuclei (AGN) are
imperative to understanding galaxy evolution and the formation of massive
galaxies. It is observationally difficult to determine the trigger of a given
AGN due to the difference between the AGN lifetime and triggering timescales.
Here, we utilize AGN population synthesis modeling to determine the importance
of different AGN triggering mechanisms. An AGN population model is computed by
combining an observationally motivated AGN triggering rate and a theoretical
AGN light curve. The free parameters of the AGN light curve are constrained by
minimizing a \chi squared test with respect to the observed AGN hard X-ray
luminosity function. The observed black hole space density, AGN number counts,
and X-ray background spectrum are also considered as observational constraints.
It is found that major mergers are not able to account for the entire AGN
population. Therefore, non-merger processes, such as secular mechanisms, must
also trigger AGN. Indeed, non-merger processes are the dominant AGN triggering
mechanism at z \lesssim 1--1.5. Furthermore, the shape and evolution of the
black hole mass function of AGN triggered by major mergers is intrinsically
different from the shape and evolution of the black hole mass function of AGN
triggered by secular processes.Comment: Accepted Ap
Power-Expected-Posterior Priors for Variable Selection in Gaussian Linear Models
In the context of the expected-posterior prior (EPP) approach to Bayesian
variable selection in linear models, we combine ideas from power-prior and
unit-information-prior methodologies to simultaneously produce a
minimally-informative prior and diminish the effect of training samples. The
result is that in practice our power-expected-posterior (PEP) methodology is
sufficiently insensitive to the size n* of the training sample, due to PEP's
unit-information construction, that one may take n* equal to the full-data
sample size n and dispense with training samples altogether. In this paper we
focus on Gaussian linear models and develop our method under two different
baseline prior choices: the independence Jeffreys (or reference) prior,
yielding the J-PEP posterior, and the Zellner g-prior, leading to Z-PEP. We
find that, under the reference baseline prior, the asymptotics of PEP Bayes
factors are equivalent to those of Schwartz's BIC criterion, ensuring
consistency of the PEP approach to model selection. We compare the performance
of our method, in simulation studies and a real example involving prediction of
air-pollutant concentrations from meteorological covariates, with that of a
variety of previously-defined variants on Bayes factors for objective variable
selection. Our prior, due to its unit-information structure, leads to a
variable-selection procedure that (1) is systematically more parsimonious than
the basic EPP with minimal training sample, while sacrificing no desirable
performance characteristics to achieve this parsimony; (2) is robust to the
size of the training sample, thus enjoying the advantages described above
arising from the avoidance of training samples altogether; and (3) identifies
maximum-a-posteriori models that achieve good out-of-sample predictive
performance
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