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

Identifying dynamical systems with bifurcations from noisy partial observation

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure.Comment: 16 pages, 6 figure

Strain control of superlattice implies weak charge-lattice coupling in La0.5_{0.5}Ca0.5_{0.5}MnO3_3

We have recently argued that manganites do not possess stripes of charge order, implying that the electron-lattice coupling is weak [Phys Rev Lett \textbf{94} (2005) 097202]. Here we independently argue the same conclusion based on transmission electron microscopy measurements of a nanopatterned epitaxial film of La$_{0.5}$Ca$_{0.5}$MnO$_3$. In strain relaxed regions, the superlattice period is modified by 2-3% with respect to the parent lattice, suggesting that the two are not strongly tied.Comment: 4 pages, 4 figures It is now explained why the work provides evidence to support weak-coupling, and rule out charge orde

Host Galaxy Evolution in Radio-Loud AGN

We investigate the luminosity evolution of the host galaxies of radio-loud AGN through Hubble Space Telescope imaging of 72 BL Lac objects, including new STIS imaging of nine z > 0.6 BL Lacs. With their intrinsically low accretion rates and their strongly beamed jets, BL Lacs provide a unique opportunity to probe host galaxy evolution independent of the biases and ambiguities implicit in quasar studies. We find that the host galaxies of BL Lacs evolve strongly, consistent with passive evolution from a period of active star formation in the range 0.5 <~ z <~ 2.5, and inconsistent with either passive evolution from a high formation redshift or a non-evolving population. This evolution is broadly consistent with that observed in the hosts of other radio-loud AGN, and inconsistent with the flatter luminosity evolution of quiescent early types and radio-quiet hosts. This indicates that active star formation, and hence galaxy interactions, are associated with the formation for radio-loud AGN, and that these host galaxies preferentially accrete less material after their formation epoch than galaxies without powerful radio jets. We discuss possible explanations for the link between merger history and the incidence of a radio jet.Comment: 37 pages, 8 figures, accepted for publication in ApJ, for full PDF incl. figures see http://www.ph.unimelb.edu.au/~modowd/papers/odowdurry2005.pd

On regularization methods of EM-Kaczmarz type

We consider regularization methods of Kaczmarz type in connection with the expectation-maximization (EM) algorithm for solving ill-posed equations. For noisy data, our methods are stabilized extensions of the well established ordered-subsets expectation-maximization iteration (OS-EM). We show monotonicity properties of the methods and present a numerical experiment which indicates that the extended OS-EM methods we propose are much faster than the standard EM algorithm.Comment: 18 pages, 6 figures; On regularization methods of EM-Kaczmarz typ

Deformation of the N=Z nucleus 76Sr using beta-decay studies

A novel method of deducing the deformation of the N=Z nucleus 76Sr is presented. It is based on the comparison of the experimental Gamow-Teller strength distribution B(GT) from its beta decay with the results of QRPA calculations. This method confirms previous indications of the strong prolate deformation of this nucleus in a totally independent way. The measurement has been carried out with a large Total Absorption gamma Spectrometer, "Lucrecia", newly installed at CERN-ISOLDE.Comment: Accepted in Phys. Rev. Letter

Model error estimation using the expectation maximization algorithm and a particle flow filter

Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown in most of the applications. A misspecification of the model error covariance has a strong impact on the computation of the posterior probability density function, leading to unreliable estimations and even to a total failure of the assimilation procedure. In this work, we propose the combination of the expectation maximization (EM) algorithm with an efficient particle filter to estimate the model error covariance using a batch of observations. Based on the EM algorithm principles, the proposed method encompasses two stages: the expectation stage, in which a particle filter is used with the present updated value of the model error covariance as given to find the probability density function that maximizes the likelihood, followed by a maximization stage, in which the expectation under the probability density function found in the expectation step is maximized as a function of the elements of the model error covariance. This novel algorithm here presented combines the EM algorithm with a fixed point algorithm and does not require a particle smoother to approximate the posterior densities. We demonstrate that the new method accurately and efficiently solves the linear model problem. Furthermore, for the chaotic nonlinear Lorenz-96 model the method is stable even for observation error covariance 10 times larger than the estimated model error covariance matrix and also is successful in moderately large dimensional situations where the dimension of the estimated matrix is 40 \times 40

Continuous-variable optical quantum state tomography

This review covers latest developments in continuous-variable quantum-state tomography of optical fields and photons, placing a special accent on its practical aspects and applications in quantum information technology. Optical homodyne tomography is reviewed as a method of reconstructing the state of light in a given optical mode. A range of relevant practical topics are discussed, such as state-reconstruction algorithms (with emphasis on the maximum-likelihood technique), the technology of time-domain homodyne detection, mode matching issues, and engineering of complex quantum states of light. The paper also surveys quantum-state tomography for the transverse spatial state (spatial mode) of the field in the special case of fields containing precisely one photon.Comment: Finally, a revision! Comments to lvov(at)ucalgary.ca and raymer(at)uoregon.edu are welcom

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Intelligence as inference or forcing Occam on the world

We propose to perform the optimization task of Universal Artificial Intelligence (UAI) through learning a reference machine on which good programs are short. Further, we also acknowledge that the choice of reference machine that the UAI objective is based on is arbitrary and, therefore, we learn a suitable machine for the environment we are in. This is based on viewing Occam’s razor as an imperative instead of as a proposition about the world. Since this principle cannot be true for all reference machines, we need to find a machine that makes the principle true. We both want good policies and the environment to have short implementations on the machine. Such a machine is learnt iteratively through a procedure that generalizes the principle underlying the Expectation-Maximization algorithm