Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 ∘C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions.

Bayesian subsampling of time-domain electromagnetic data using kernel density product

The Bayesian method is a powerful tool to estimate the resistivity distribution and associate uncertainty from time-domain electromagnetic (TDEM) data. As the forward simulation of the TDEM method is computationally expensive and a large number of samples are needed to globally explore the model space, the full Bayesian inversion of TDEM data is limited to layered models. To make high-dimensional Bayesian inversion tractable, we propose a divide-and-conquer strategy to speed up the Bayesian inversion of TDEM data. First, the full datasets and model spaces are divided into disjoint batches based on the coverage of the sources so that independent and highly efficient Bayesian subsampling can be conducted. Then, the samples from each subsampling procedure are combined to get the full posterior. To obtain an asymptotically unbiased approximation to the full posterior, a kernel density product method is used to reintegrate samples from each subposterior. The model parameters and their uncertainty are estimated from the full posterior. The proposed method is tested on synthetic examples and applied to a field dataset acquired with a large fixed-loop configuration. The 2D section from the Bayesian inversion revealed several mineralized zones, one of which matches well with the information from a nearby drill hole. The field example shows the ability of Bayesian inversion to infer reliable resistivity and uncertainty.

Building FWI starting model from wells with dynamic time warping and convolutional neural networks

Full waveform inversion (FWI) needs a feasible starting model, because otherwise it might converge to a local minimum and the inversion result might suffer from detrimental artifacts. We built a feasible starting model from wells by applying dynamic time warping (DTW) localized rewarp and convolutional neural network (CNN) methods alternatively. We used the DTW localized rewarp method to extrapolate the velocities at well locations to the non-well locations in the model space. Rewarping is conducted based on the local structural coherence which is extracted from a migration image of an initial infeasible model. The extraction uses the DTW method. The purpose of velocity extrapolation is to provide sufficient training samples to train a CNN, which maps local spatial features on the migration image into the velocity quantities of each layer. We further designed an interactive workflow to reject inaccurate network predictions and to improve CNN prediction accuracy by incorporating the Monte Carlo dropout method. We demonstrated that the proposed method is robust against the kinematic incorrectness in the migration velocity model, and is capable to produce a feasible FWI starting model.

Spinning no-scale $${\mathcal {F}}$$-SU(5) in the right direction

The Fermi National Accelerator Laboratory (FNAL) recently announced confirmation of the Brookhaven National Lab (BNL) measurements of the $$g-2$$ g - 2 of the muon that uncovered a discrepancy with the theoretically calculated Standard Model value. We suggest an explanation for the combined BNL+FNAL 4.2$$\sigma $$ σ deviation within the supersymmetric grand unification theory (GUT) model No-Scale $${\mathcal {F}}$$ F -$$SU(5)$$ S U ( 5 ) supplemented with a string derived TeV-scale extra $$10+\overline{10}$$ 10 + 10 ¯ vector-like multiplet and charged vector-like singlet $$(XE,XE^c)$$ ( X E , X E c ) , dubbed flippons. We introduced these vector-like particles into No-Scale Flipped SU(5) many years ago, and as a result, the renormalization group equation (RGE) running was immediately shaped to produce a distinctive and rather beneficial two-stage gauge coupling unification process to avoid the Landau pole and lift unification to the string scale, in addition to contributing through 1-loop to the light Higgs boson mass. The flippons have long stood ready to tackle another challenge, and now do so yet again, where the charged vector-like “lepton”/singlet couples with the muon, the supersymmetric down-type Higgs $$H_d$$ H d , and a singlet S, using a chirality flip to easily accommodate the muonic $$g-2$$ g - 2 discrepancy in No-Scale $${\mathcal {F}}$$ F -$$SU(5)$$ S U ( 5 ) . Considering the phenomenological success of this string derived model over the prior 11 years that remains accommodative of all presently available LHC limits plus all other experimental constraints, including no fine-tuning, and the fact that for the first time a Starobinsky-like inflationary model consistent with all cosmological data was derived from superstring theory in No-Scale Flipped SU(5), we believe it is imperative to reconcile the BNL+FNAL developments within the model space.

Swanson hamiltonian: non-PT-symmetry phase

In this work, we study the non-hermitian Swanson hamiltonian, particularly the non-PT symmetry phase. We use the formalism of Gel’fand triplet to construct the generalized eigenfunctions and the corresponding spectrum. Depending on the region of the parameter model space, we show that the Swanson hamiltonian represents different physical systems, i.e. parabolic barrier, negative mass oscillators. We also discussed the presence of Exceptional Points of infinite order.

piCRISPR: Physically Informed Features Improve Deep Learning Models for CRISPR/Cas9 Off-Target Cleavage Prediction

CRISPR/Cas programmable nuclease systems have become ubiquitous in the field of gene editing. With progressing development, applications in in vivo therapeutic gene editing are increasingly within reach, yet limited by possible adverse side effects from unwanted edits. Recent years have thus seen continuous development of off-target prediction algorithms trained on in vitro cleavage assay data gained from immortalised cell lines. Here, we implement novel deep learning algorithms and feature encodings for off-target prediction and systematically sample the resulting model space in order to find optimal models and inform future modelling efforts. We lay emphasis on physically informed features, hence terming our approach piCRISPR, which we gain on the large, diverse crisprSQL off-target cleavage dataset. We find that our best-performing model highlights the importance of sequence context and chromatin accessibility for cleavage prediction and outperforms state-of-the-art prediction algorithms in terms of area under precision-recall curve.

Hidden-state-only speciation and extinction models provide accurate tip estimates of diversification rates

1. State-dependent speciation and extinction (SSE) models provide a framework for testing potential correlations between the evolution of an observed trait and speciation and extinction rates. Recent expansions of these models allow for the inclusion of "hidden states" that, among other things, allow for rate heterogeneity often observed among lineages sharing a particular character state. However, in reality, multiple circumstances and interacting traits related to a focal character play a role in changing diversification dynamics of a lineage over time, restricting the use of available SSE models that require trait information to be assigned at the tips. 2. Here we introduce MiSSE, an SSE approach that infers diversification rate differences from hidden states only. It can be used similarly to other trait-free methods to estimate varying speciation, extinction, but also different functions of these parameters such as net-diversification, turnover rates, and extinction fraction. Given the size of the model space, we also describe an algorithm designed for efficiently searching through a reasonably large set of models without having to be exhaustive. 3. We compare the accuracy of rates inferred at the tips of the tree by MiSSE against popular character-free methods and demonstrate that the error associated with tip estimates is generally low. Due to certain characteristics of the SSE models, this method avoids some of the recent concerns with parameter identifiability in diversification analyses and can be used alongside regular phylogenetic comparative methods in trait-related diversification hypotheses. 4. Finally, we apply MiSSE, with a renewed focus on classic comparative methods, to understand processes happening near the present, rather than deep in the past, to examine how variation in plant height has impacted turnover rates in eucalypts, a species-rich lineage of flowering plants.

Resolving the (g − 2)μ discrepancy with $$ \mathcal{F} $$–SU(5) intersecting D-branes

A discrepancy between the measured anomalous magnetic moment of the muon (g − 2)μ and computed Standard Model value now stands at a combined 4.2σ following experiments at Brookhaven National Lab (BNL) and the Fermi National Accelerator Laboratory (FNAL). A solution to the disagreement is uncovered in flipped SU(5) with additional TeV-Scale vector-like 10 + $$ \overline{\mathbf{10}} $$ 10 ¯ multiplets and charged singlet derived from local F-Theory, collectively referred to as $$ \mathcal{F} $$ F –SU(5). Here we engage general No-Scale supersymmetry (SUSY) breaking in $$ \mathcal{F} $$ F –SU(5) D-brane model building to alleviate the (g −2)μ tension between the Standard Model and observations. A robust ∆aμ(SUSY) is realized via mixing of M5 and M1X at the secondary SU(5) × U(1)X unification scale in $$ \mathcal{F} $$ F –SU(5) emanating from SU(5) breaking and U(1)X flux effects. Calculations unveil ∆aμ(SUSY) = 19.0–22.3 × 10−10 for gluino masses of M($$ \overset{\sim }{g} $$ g ~ )= 2.25–2.56 TeV and higgsino dark matter, aptly residing within the BNL+FNAL 1σ mean. This (g − 2)μ favorable region of the model space also generates the correct light Higgs boson mass and branching ratios of companion rare decay processes, and is further consistent with all LHC Run 2 constraints. Finally, we also examine the heavy SUSY Higgs boson in light of recent LHC searches for an extended Higgs sector.