4 research outputs found
Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints
Online decision making under uncertainty in partially observable domains,
also known as Belief Space Planning, is a fundamental problem in robotics and
Artificial Intelligence. Due to an abundance of plausible future unravelings,
calculating an optimal course of action inflicts an enormous computational
burden on the agent. Moreover, in many scenarios, e.g., information gathering,
it is required to introduce a belief-dependent constraint. Prompted by this
demand, in this paper, we consider a recently introduced probabilistic
belief-dependent constrained POMDP. We present a technique to adaptively accept
or discard a candidate action sequence with respect to a probabilistic
belief-dependent constraint, before expanding a complete set of future
observations samples and without any loss in accuracy. Moreover, using our
proposed framework, we contribute an adaptive method to find a maximal feasible
return (e.g., information gain) in terms of Value at Risk for the candidate
action sequence with substantial acceleration. On top of that, we introduce an
adaptive simplification technique for a probabilistically constrained setting.
Such an approach provably returns an identical-quality solution while
dramatically accelerating online decision making. Our universal framework
applies to any belief-dependent constrained continuous POMDP with parametric
beliefs, as well as nonparametric beliefs represented by particles. In the
context of an information-theoretic constraint, our presented framework
stochastically quantifies if a cumulative information gain along the planning
horizon is sufficiently significant (e.g. for, information gathering, active
SLAM). We apply our method to active SLAM, a highly challenging problem of high
dimensional Belief Space Planning. Extensive realistic simulations corroborate
the superiority of our proposed ideas
Modeling of GERDA Phase II data
The GERmanium Detector Array (GERDA) experiment at the Gran Sasso underground
laboratory (LNGS) of INFN is searching for neutrinoless double-beta
() decay of Ge. The technological challenge of GERDA is
to operate in a "background-free" regime in the region of interest (ROI) after
analysis cuts for the full 100kgyr target exposure of the
experiment. A careful modeling and decomposition of the full-range energy
spectrum is essential to predict the shape and composition of events in the ROI
around for the search, to extract a precise
measurement of the half-life of the double-beta decay mode with neutrinos
() and in order to identify the location of residual
impurities. The latter will permit future experiments to build strategies in
order to further lower the background and achieve even better sensitivities. In
this article the background decomposition prior to analysis cuts is presented
for GERDA Phase II. The background model fit yields a flat spectrum in the ROI
with a background index (BI) of cts/(kgkeVyr) for the enriched BEGe data set and
cts/(kgkeVyr) for the
enriched coaxial data set. These values are similar to the one of Gerda Phase I
despite a much larger number of detectors and hence radioactive hardware
components
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Modeling of GERDA Phase II data
The GERmanium Detector Array (Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta (0νββ) decay of 76Ge. The technological challenge of Gerda is to operate in a “background-free” regime in the region of interest (ROI) after analysis cuts for the full 100 kg·yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around Qββ for the 0νββ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos (2νββ) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for Gerda Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of 16.04+0.78−0.85⋅10−3 cts/(keV·kg·yr) for the enriched BEGe data set and 14.68+0.47−0.52⋅10−3 cts/(keV·kg·yr) for the enriched coaxial data set. These values are similar to the one of Phase I despite a much larger number of detectors and hence radioactive hardware components