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At least 343 records · Page 19

A new method of differential structural analysis of gamma-family basic parameters

The maximum likelihood method is used for the first time to restore parameters of electron photon cascades registered on X-ray films. The method permits one to carry out a structural analysis of the gamma quanta family darkening spots independent of the gamma quanta overlapping degree, and to obtain maximum admissible accuracies in estimating the energies of the gamma quanta composing a family. The parameter estimation accuracy weakly depends on the value of the parameters themselves and exceeds by an order of the values obtained by integral methods.

Melkumian, L. G.↗

Description of Pegethrix niliensis sp. nov., a Novel Cyanobacterium from the Nile River Basin, Egypt: A Polyphasic Analysis and Comparative Study of Related Genera in the Oculatellales Order

In this paper, we examine the filamentous cyanobacterial strain NILCB16 and describe it as a new species within the genus Pegethrix. The original population was sampled from a mat growing in an irrigation canal in the Nile River, Egypt. Initially classified under Plectonema or Planktolyngbya, the strain is a potential producer of the toxins microcystin and β-N-Methylamino-L-Alanine (BMAA). Additionally, we reviewed the taxonomic relationships between the Oculatellales genera. To describe the new species, we conducted a polyphasic study, encompassing 16S rRNA gene phylogenetic analyses performed using both Maximum Likelihood and Bayesian methods, sequence identity (p-distance) analysis, 16S-23S ITS secondary structures, and morphological and habitat comparisons. The phylogenetic analysis revealed that strain NILCB16 clustered within the Pegethrix clade with strong phylogenetic support, but in a distinct position from other species in the genus. The strain shared a maximum 16S rRNA gene identity of 97.3% with P. qiandaoensis and 96.1% with the type species, P. bostrychoides. Morphologically, NILCB16 can be differentiated from other species in the genus by its lack of false branching. Our phylogenetic analyses also show that Pegethrix, Cartusia, Elainella, and Maricoleus are clustered with strong phylogenetic support. They exhibit high 16S rRNA gene identity and are morphologically indistinguishable, suggesting they could potentially be merged into a single genus in the future.

Hentschke, Guilherme Scotta (ORCID:000000034396024↗

Point Source Likelihood Framework (psl) v1.0

PSL is a software framework for localizing radiation point sources in a 3D environment using a freely moving radiation detector system. The framework assumes a single point source of radiation is in the environment and uses known detector positions and orientations along with their radiation measurements to find the most likely location of the point source. This approach to the problem enables analyses that improve upon the capability of a standard Maximum Likelihood Expectation Maximization (MLEM) approach, such as setting a source detection threshold, finding a likelihood interval for the position of the source, finding a likelihood interval for the activity (intensity) of the source, determining whether more than one point source may be present, and generating a map of Minimum Detectable Activity (MDA) for a given source type. Applications include radiological and nuclear security, consequence management, decontamination and decommissioning of nuclear facilities, and radiological emergency response.

Joshi, Tenzing↗

Precise relative magnitude measurement improves fracture characterization during hydraulic fracturing

SUMMARY Microseismic monitoring is an important technique to obtain detailed knowledge of in-situ fracture size and orientation during stimulation to maximize fluid flow throughout the rock volume and optimize production. Furthermore, considering that the frequency of earthquake magnitudes empirically follows a power law (i.e. Gutenberg–Richter), the accuracy of microseismic event magnitude distributions is potentially crucial for seismic risk management. In this study, we analyse microseismicity observed during four hydraulic fracture treatments of the legacy Cotton Valley experiment in 1997 at the Carthage gas field of East Texas, where fractures were activated at the base of the sand-shale Upper Cotton Valley formation. We perform waveform cross-correlation to detect similar event clusters, measure relative amplitude from aligned waveform pairs with a principal component analysis, then measure precise relative magnitudes. The new magnitudes significantly reduce the deviations between magnitude differences and relative amplitudes of event pairs. This subsequently reduces the magnitude differences between clusters located at different depths. Reduction in magnitude differences between clusters suggests that some attenuation-related biases could be effectively mitigated with relative magnitude measurements. The maximum likelihood method is applied to understand the magnitude frequency distributions and quantify the seismogenic index of the clusters. Statistical analyses with new magnitudes suggest that fractures that are more favourably oriented for shear failure have lower b-value and higher seismogenic index, suggesting higher potential for relatively larger earthquakes, rather than fractures subparallel to maximum horizontal principal stress orientation.

58 GEOSCIENCES↗

Precise optical pulse timing

This paper evaluates the performance of the maximum likelihood and several sub-optimum arrival time estimators for optical pulses. The performance analysis of a 'sliding window' counter followed by that of a threshold detector is achieved by deriving an expression for the first crossing density of the output of the counter. A sub-optimum arrival time estimator using a threshold rather than a maximum detector is suggested and evaluated by simulation. It appears to provide near optimum performance.

Lee, G.↗

Solar neutrino: Flux, cosmic rays and the 11 year solar cycle

It is shown that the results of maximum likelihood treatment of Monte Carlo simulation with constant production rate of 7.6 SNU and 1.Epsilon SNU are consistent with the constant production rate when the tests of hypotheses (e.g., t-test, sigma squared-test, Wilcoxon-Mann-Whitney test, run test, etc.) are applied to the two groups of data formed from sunspot minimum range and sunspot maximum range, whereas the real data pulsates with the solar activity cycle. It is shown that SN flux-change is in opposition phase to the solar activity cycle and lags behind the latter by about one year. A correlation between SN flux and the cosmic rays is suggested.

Raychaudhuri, P.↗

Upper bounds on sequential decoding performance parameters

This paper presents the best obtainable random coding and expurgated upper bounds on the probabilities of undetectable error, of t-order failure (advance to depth t into an incorrect subset), and of likelihood rise in the incorrect subset, applicable to sequential decoding when the metric bias G is arbitrary. Upper bounds on the Pareto exponent are also presented. The G-values optimizing each of the parameters of interest are determined, and are shown to lie in intervals that in general have nonzero widths. The G-optimal expurgated bound on undetectable error is shown to agree with that for maximum likelihood decoding of convolutional codes, and that on failure agrees with the block code expurgated bound. Included are curves evaluating the bounds for interesting choices of G and SNR for a binary-input quantized-output Gaussian additive noise channel.

Jelinek, F.↗

Corn and soybean Landsat MSS classification performance as a function of scene characteristics

In order to fully utilize remote sensing to inventory crop production, it is important to identify the factors that affect the accuracy of Landsat classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil, and weather variables on the accuracy of Landsat classifications of corn and soybeans. Segments sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.

Batista, G. T.↗

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Early-time γ -ray constraints on cosmic-ray acceleration in the core-collapse SN 2023ixf with the Fermi Large Area Telescope

Context. While supernova remnants (SNRs) have been considered the most relevant Galactic cosmic ray (CR) accelerators for decades, core-collapse supernovae (CCSNe) could accelerate particles during the earliest stages of their evolution and hence contribute to the CR energy budget in the Galaxy. Some SNRs have indeed been associated with TeV γ -rays, yet proton acceleration efficiency during the early stages of an SN expansion remains mostly unconstrained. Aims. The multi-wavelength observation of SN 2023ixf, a Type II supernova (SN) in the nearby galaxy M 101 (at a distance of 6.85 Mpc), opens the possibility to constrain CR acceleration within a few days after the collapse of the red super-giant stellar progenitor. With this work, we intend to provide a phenomenological, quasi-model-independent constraint on the CR acceleration efficiency during this event at photon energies above 100 MeV. Methods. We performed a maximum-likelihood analysis of γ -ray data from the Fermi Large Area Telescope up to one month after the SN explosion. We searched for high-energy, non-thermal emission from its expanding shock, and estimated the underlying hadronic CR energy reservoir assuming a power-law proton distribution consistent with standard diffusive shock acceleration. Results. We do not find significant γ -ray emission from SN 2023ixf. Nonetheless, our non-detection provides the first limit on the energy transferred to the population of hadronic CRs during the very early expansion of a CCSN. Conclusions. Under reasonable assumptions, our limits would imply a maximum efficiency on the CR acceleration of as low as 1%, which is inconsistent with the common estimate of 10% in generic SNe. However, this result is highly dependent on the assumed geometry of the circumstellar medium, and could be relaxed back to 10% by challenging spherical symmetry. Consequently, a more sophisticated, inhomogeneous characterisation of the shock and the progenitor’s environment is required before establishing whether or not Type II SNe are indeed efficient CR accelerators at early times.

79 ASTRONOMY AND ASTROPHYSICS↗

Characterizing a New Surface-Based Shortwave Cloud Retrieval Technique, Based on Transmitted Radiance for Soil and Vegetated Surface Types

This paper presents an approach using the GEneralized Nonlinear Retrieval Analysis (GENRA) tool and general inverse theory diagnostics including the maximum likelihood solution and the Shannon information content to investigate the performance of a new spectral technique for the retrieval of cloud optical properties from surface based transmittance measurements. The cumulative retrieval information over broad ranges in cloud optical thickness (tau), droplet effective radius (r(sub e)), and overhead sun angles is quantified under two conditions known to impact transmitted radiation; the variability in land surface albedo and atmospheric water vapor content. Our conclusions are: (1) the retrieved cloud properties are more sensitive to the natural variability in land surface albedo than to water vapor content; (2) the new spectral technique is more accurate (but still imprecise) than a standard approach, in particular for tau between 5 and 60 and r(sub e) less than approximately 20 nm; and (3) the retrieved cloud properties are dependent on sun angle for clouds of tau from 5 to 10 and r(sub e) less than 10 nm, with maximum sensitivity obtained for an overhead sun.

Shannon information content↗

Frequentist cosmological constraints from full-shape clustering measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.

Bayesian reasoning↗

Flexible nonstationary spatiotemporal modeling of high-frequency monitoring data

Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half-spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we use this method with fully time-domain computations to obtain bona fide maximum likelihood estimators—as opposed to using Whittle-type likelihood approximations, for example—that can still be computed conveniently. Additionally, we apply this method to very high-frequency Doppler LIDAR vertical wind velocity measurements, demonstrating that the model can expressively capture the extreme nonstationarity of dynamics above and below the atmospheric boundary layer and, more importantly, the interaction of the process dynamics across it.

54 ENVIRONMENTAL SCIENCES↗

Coupled primary production and respiration in a large river contrasts with smaller rivers and streams

Abstract Although time series in ecosystem metabolism are well characterized in small and medium rivers, patterns in the world's largest rivers are almost unknown. Large rivers present technical difficulties, including depth measurements, gas exchange (, ) estimates, and the presence of large dams, which can supersaturate gases. We estimated reach‐scale metabolism for the Hanford Reach of the Columbia River (Washington state, USA), a free‐flowing stretch with an average discharge of 3173 . We calculated from semi‐empirical models and directly estimated it from tracer measurements. We fixed at the median value from these calculations (0.5 ), and used maximum likelihood to estimate reach‐scale, open‐channel metabolism. Both gross primary production (GPP) and ecosystem respiration (ER) were high (GPP range: 0.3–30.8 g , ER range: 0.8–30.6 g ), with peak GPP and ER occurring in the late summer or early fall. GPP increased exponentially with temperature, consistent with metabolic theory, while light was seasonally saturating. Annual average GPP, estimated at 1500 g carbon , was in the top 2% of estimates for other rivers. GPP and ER were tightly coupled and 90% of GPP was immediately respired, resulting in net ecosystem production near 0. Patterns in the Hanford Reach contrast with those in small‐medium rivers, suggesting that metabolism magnitudes and patterns in large rivers may not be simply scaled from knowledge of smaller rivers.

54 ENVIRONMENTAL SCIENCES↗

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗

Measurement of the inclusive and differential Higgs boson production cross sections in the leptonic WW decay mode at $\sqrt{s} =$ 13 TeV

Measurements of the fiducial inclusive and differential production cross sections of the Higgs boson in proton-proton collisions at $ \sqrt{s} $ = 13 TeV are performed using events where the Higgs boson decays into a pair of W bosons that subsequently decay into a final state with an electron, a muon, and a pair of neutrinos. The analysis is based on data collected with the CMS detector at the LHC during 2016–2018, corresponding to an integrated luminosity of 137 fb$^{−1}$. Production cross sections are measured as a function of the transverse momentum of the Higgs boson and the associated jet multiplicity. The Higgs boson signal is extracted and simultaneously unfolded to correct for selection efficiency and resolution effects using maximum-likelihood fits to the observed distributions in data. The integrated fiducial cross section is measured to be 86.5 ± 9.5 fb, consistent with the Standard Model expectation of 82.5 ± 4.2 fb. No significant deviation from the Standard Model expectations is observed in the differential measurements.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of the top-quark mass using decays with a J/ψ meson at s=13 TeV with the ATLAS detector

The top-quark mass is measured using top-quark decays producing an isolated lepton and J/ψ meson reconstructed in its μ+μ− decay mode. The data sample was recorded with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of s=13$$ \sqrt{s}=13 $$ TeV during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. The measurement is based on the invariant mass m(ℓμ+μ−) of the system made of the isolated lepton ℓ from the W boson decay and the non-isolated μ+μ− pair from a J/ψ decay of a b-hadron, exploiting its sensitivity to the top-quark mass. An unbinned maximum-likelihood fit to the m(ℓμ+μ−) distribution is performed to extract the top-quark mass. The top-quark mass is measured to be mtop = 172.17 ± 0.80 (stat) ± 0.81 (syst) ± 1.07 (recoil) GeV, with a total uncertainty of 1.56 GeV. The third uncertainty arises from changing the dipole parton shower gluon-recoil scheme used in top-quark decays.

Aad, G↗

Search for heavy resonances decaying to a pair of Lorentz-boosted Higgs bosons in final states with leptons and a bottom quark pair at $ \sqrt{s} $ = 13 TeV

A search for new heavy resonances decaying to a pair of Higgs bosons (HH) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented. Data were collected with the CMS detector at the LHC in 2016–2018, corresponding to an integrated luminosity of 138 fb - 1 . Resonances with a mass between 0.8 and 4.5 TeV are considered using events in which one Higgs boson decays into a bottom quark pair and the other into final states with either one or two charged leptons. Specifically, the single-lepton decay channel HH → b b ¯ WW * → b b ¯ ℓ v q q ¯ ' and the dilepton decay channels HH → b b ¯ WW * → b b ¯ ℓ v ℓ v and HH → b b ¯ ττ → b b ¯ ℓ vv ℓ vv are examined, where ℓ in the final state corresponds to an electron or muon. The signal is extracted using a two-dimensional maximum likelihood fit of the H → b b ¯ jet mass and HH invariant mass distributions. No significant excess above the standard model expectation is observed in data. Model-independent exclusion limits are placed on the product of the cross section and branching fraction for narrow spin-0 and spin-2 massive bosons decaying to HH. The results are also interpreted in the context of radion and bulk graviton production in models with a warped extra spatial dimension. The results provide the most stringent limits to date for X → HH signatures with final-state leptons and at some masses provide the most sensitive limits of all X → HH searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗