236,158 research outputs found
Optimal strategies for observation of active galactic nuclei variability with Imaging Atmospheric Cherenkov Telescopes
Variable emission is one of the defining characteristic of active galactic
nuclei (AGN). While providing precious information on the nature and physics of
the sources, variability is often challenging to observe with time- and
field-of-view-limited astronomical observatories such as Imaging Atmospheric
Cherenkov Telescopes (IACTs). In this work, we address two questions relevant
for the observation of sources characterized by AGN-like variability: what is
the most time-efficient way to detect such sources, and what is the
observational bias that can be introduced by the choice of the observing
strategy when conducting blind surveys of the sky. Different observing
strategies are evaluated using simulated light curves and realistic instrument
response functions of the Cherenkov Telescope Array (CTA), a future gamma-ray
observatory. We show that strategies that makes use of very small observing
windows, spread over large periods of time, allows for a faster detection of
the source, and are less influenced by the variability properties of the
sources, as compared to strategies that concentrate the observing time in a
small number of large observing windows. Although derived using CTA as an
example, our conclusions are conceptually valid for any IACTs facility, and in
general, to all observatories with small field of view and limited duty cycle.Comment: 14 pages, 11 figure
A population-based approach to background discrimination in particle physics
Background properties in experimental particle physics are typically
estimated using control samples corresponding to large numbers of events. This
can provide precise knowledge of average background distributions, but
typically does not consider the effect of fluctuations in a data set of
interest. A novel approach based on mixture model decomposition is presented as
a way to estimate the effect of fluctuations on the shapes of probability
distributions in a given data set, with a view to improving on the knowledge of
background distributions obtained from control samples. Events are treated as
heterogeneous populations comprising particles originating from different
processes, and individual particles are mapped to a process of interest on a
probabilistic basis. The proposed approach makes it possible to extract from
the data information about the effect of fluctuations that would otherwise be
lost using traditional methods based on high-statistics control samples. A
feasibility study on Monte Carlo is presented, together with a comparison with
existing techniques. Finally, the prospects for the development of tools for
intensive offline analysis of individual events at the Large Hadron Collider
are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser.
Minor changes have been made to the text with respect to the published
article with a view to improving readabilit
Bayesian data assimilation in shape registration
In this paper we apply a Bayesian framework to the problem of geodesic curve matching. Given a template curve, the geodesic equations provide a mapping from initial conditions\ud
for the conjugate momentum onto topologically equivalent shapes. Here, we aim to recover the well defined posterior distribution on the initial momentum which gives rise to observed points on the target curve; this is achieved by explicitly including a reparameterisation in the formulation. Appropriate priors are chosen for the functions which together determine this field and the positions of the observation points, the initial momentum p0 and the reparameterisation vector field v, informed by regularity results about the forward model. Having done this, we illustrate how Maximum Likelihood Estimators (MLEs) can be used to find regions of high posterior density, but also how we can apply recently developed MCMC methods on function spaces to characterise the whole of the posterior density. These illustrative examples also include scenarios where the posterior distribution is multimodal and irregular, leading us to the conclusion that knowledge of a state of global maximal posterior density does not always give us the whole picture, and full posterior sampling can give better quantification of likely states and the overall uncertainty inherent in the problem
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