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The Copernican principle (CP), i.e. the assumption that we are not privileged observers of the Universe, is a fundamental tenet of the standard cosmological model. A violation of this postulate implies the possibility that the apparent cosmic acceleration could be explained without the need of a cosmological constant, dark energy or covariant modifications of gravity. In this paper we present a new test of the CP relating the distance and the expansion rate, derived via Noether's theorem, which is complementary to other tests found in the literature. We also simulate fiducial data based on upcoming stage IV galaxy surveys and use them to reconstruct the Hubble rate H(z) and the angular diameter distance dA(z) in order to forecast how well our null test can constrain deviations from the cosmological constant model. We find that our new test can easily rule out several scenarios based on the Lemaître-Tolman-Bondi void model at confidence of ≳3σ at middle to high redshifts (z>0.5). [ABSTRACT FROM AUTHOR]

This paper presents a novel technique for mitigating electrode backgrounds that limit the sensitivity of searches for low-mass dark matter (DM) using xenon time projection chambers. In the Large Underground Xenon (LUX) detector, signatures of low-mass DM interactions would be very low-energy (∼keV) scatters in the active target that ionize only a few xenon atoms and seldom produce detectable scintillation signals. In this regime, extra precaution is required to reject a complex set of low-energy electron backgrounds that have long been observed in this class of detector. Noticing backgrounds from the wire grid electrodes near the top and bottom of the active target are particularly pernicious, we develop a machine learning technique based on ionization pulse shape to identify and reject these events. We demonstrate the technique can improve Poisson limits on low-mass DM interactions by a factor of 1.7-3 with improvement depending heavily on the size of ionization signals. We use the technique on events in an effective 5 tonne·day exposure from LUX's 2013 science operation to place strong limits on low-mass DM particles with masses in the range mχ∈0.15-10  GeV. This machine learning technique is expected to be useful for near-future experiments, such as LUX-ZEPLIN and XENONnT, which hope to perform low-mass DM searches with the stringent background control necessary to make a discovery. [ABSTRACT FROM AUTHOR]

The consecutive steps of cascade decay initiated by H→ττ can be useful for the measurement of Higgs couplings and in particular of the Higgs boson parity. In the previous papers we have found that multidimensional signatures of the τ±→π±π0ν and τ±→3π±ν decays can be used to distinguish between scalar and pseudoscalar Higgs state. The machine learning techniques (ML) of binary classification, offered break-through opportunities to manage such complex multidimensional signatures. The classification between two possible CP states: scalar and pseudoscalar, is now extended to the measurement of the hypothetical mixing angle of Higgs boson parity states. The functional dependence of H→ττ matrix element on the mixing angle is predicted by theory. The potential to determine preferred mixing angle of the Higgs boson events sample including τ-decays is studied using deep neural network. The problem is addressed as classification or regression with the aim to determine the per-event: (a) probability distribution (spin weight) of the mixing angle; (b) parameters of the functional form of the spin weight; (c) the most preferred mixing angle. Performance of proposed methods is evaluated and compared. [ABSTRACT FROM AUTHOR]

In this paper, we explore beyond the Standard Model top-Higgs Yukawa couplings as a function of a CP-mixing parameter ξt at the 14 TeV HL-LHC in the process pp→thj. We observe that angular variables of the decay products of the top are nontrivially sensitive to ξt. This fact is exploited in a full detector level analysis that employs machine learning techniques to optimize signal sensitivity on a suite of variables, including lepton azimuthal angle. The key result of this study is an improved projected exclusion limit on ξt even when including the realistic effects of detector smearing and a conservative estimate of systematic error. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques are rapidly finding a place among the methods of high-energy physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on physics insight into the problem, and when it is enough to rely on low-level ones, allowing ML methods to find patterns without an explicit physics model. In this paper we continue the discussion of previous publications on the CP state of the Higgs boson measurement of the H→ττ decay channel with the consecutive τ±→ρ±ν; ρ±→π±π0 and τ±→a1±ν; a1±→ρ0π±→3π± cascade decays. The discrimination of the Higgs boson CP state is studied as a binary classification problem between CP even (scalar) and CP odd (pseudoscalar) states using a deep neural network (DNN). Improvements on the classification from the constraints on directly nonmeasurable outgoing neutrinos are discussed. We find that, once added, they enhance the sensitivity sizably, even if only imperfect information is provided. In addition to DNNs we also evaluate and compare other ML methods: boosted trees, random forests, and support vector machines. [ABSTRACT FROM AUTHOR]

Deep learning algorithms, in particular neural networks, have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of deep learning approaches in gravitational wave detection, parameter estimation, and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of the deep artificial neural network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modeled the sky as a sphere and have considered cases in which the sphere is divided into 18, 50, 128, 1024, 2048, and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on its right ascension and declination values for each of these cases. To do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between 30 M⊙ and 80 M⊙ into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences, and amplitude ratios at each of the three detectors Hanford, Livingston, and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (greater than 90%) for coarse angular resolution using 18, 50, and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818, and GW170823 for 1024, 2048, and 4096 sectors and compare the result with that from BAYESTAR and parameter estimation. In addition, we report that the time taken by our model to localize one gravitational wave signal is around 0.018 s on 14 Intel Xeon CPU cores. [ABSTRACT FROM AUTHOR]

In this paper we study the use of machine learning techniques to exploit kinematic information in V H, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the standard model effective field theory (SMEFT) framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in V H respect to previous estimates. We also discuss the relation between the usual measures of performance in machine learning, such as area under the curve or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrizing systematic uncertainties. Our results show the potential of incorporating machine learning techniques to the SMEFT studies using the current datasets. [ABSTRACT FROM AUTHOR]

This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational wave transients. In this sensitivity study, we examine three machine learning algorithms (MLAs): artificial neural networks, support vector machines, and constrained subspace classifiers. The objective of this study is to compare the detection efficiencies that MLAs can achieve to the efficiency of the conventional (seedless clustering) detection algorithm discussed in an earlier paper. Comparisons are made using two distinct r-mode waveforms. For the training of the MLAs, we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that we can use the machine learning algorithms as part of an investigative stage in the pipeline that would be able to provide very fast and solid triggers for further, and more intense, investigation. [ABSTRACT FROM AUTHOR]

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid. [ABSTRACT FROM AUTHOR]