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DOE OSTI2017

Flux flattening in axion monodromy inflation

String theory models of axion monodromy inflation exhibit scalar potentials which are quadratic for small values of the inflaton field and evolve to a more complicated function for large field values. Oftentimes the large field behaviour is gentler than quadratic, lowering the tensor-to-scalar ratio. This effect, known as flattening, has been observed in the string theory context through the properties of the DBI+CS D-brane action. We revisit such flattening effects in type IIB flux compactifications with mobile D7-branes, with the inflaton identified with the D7-brane position. We observe that here, with a generic choice of background fluxes, flattening effects are larger than previously observed, allowing to fit these models within current experimental bounds. In particular, we compute the cosmological observables in scenarios compatible with closed-string moduli stabilisation, finding tensor-to-scalar ratios as low as r ~ 0.04. These are models of single field inflation in which the inflaton is much lighter than the other scalars through a mild tuning of the compactification data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Connecting the Light Curves of Type IIP Supernovae to the Properties of Their Progenitors

Abstract Observations of core-collapse supernovae (CCSNe) reveal a wealth of information about the dynamics of the supernova ejecta and its composition but very little direct information about the progenitor. Constraining properties of the progenitor and the explosion requires coupling the observations with a theoretical model of the explosion. Here we begin with the CCSN simulations of Couch et al., which use a nonparametric treatment of the neutrino transport while also accounting for turbulence and convection. In this work we use the SuperNova Explosion Code to evolve the CCSN hydrodynamics to later times and compute bolometric light curves. Focusing on Type IIP SNe (SNe IIP), we then (1) directly compare the theoretical STIR explosions to observations and (2) assess how properties of the progenitor’s core can be estimated from optical photometry in the plateau phase alone. First, the distribution of plateau luminosities ( L 50 ) and ejecta velocities achieved by our simulations is similar to the observed distributions. Second, we fit our models to the light curves and velocity evolution of some well-observed SNe. Third, we recover well-known correlations, as well as the difficulty of connecting any one SN property to zero-age main-sequence mass. Finally, we show that there is a usable, linear correlation between iron core mass and L 50 such that optical photometry alone of SNe IIP can give us insights into the cores of massive stars. Illustrating this by application to a few SNe, we find iron core masses of 1.3–1.5 M ⊙ with typical errors of 0.05 M ⊙ . Data are publicly available online on Zenodo: doi: 10.5281/zenodo.6631964 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization

Semi-empirical quantum mechanical methods traditionally expand the electron density in a minimal, valence-only electron basis set. The minimal-basis approximation causes molecular polarization to be underestimated, and hence intermolecular interaction energies are also underestimated, especially for intermolecular interactions involving charged species. In this work, the third-order self-consistent charge density functional tight-binding method (DFTB3) is augmented with an auxiliary response density using the chemical-potential equalization (CPE) method and an empirical dispersion correction (D3). The parameters in the CPE and D3 models are fitted to high-level CCSD(T) reference interaction energies for a broad range of chemical species, as well as dipole moments calculated at the DFT level; the impact of including polarizabilities of molecules in the parameterization is also considered. Parameters for the elements H, C, N, O, and S are presented. The Root Mean Square Deviation (RMSD) interaction energy is improved from 6.07 kcal/mol to 1.49 kcal/mol for interactions with one charged species, whereas the RMSD is improved from 5.60 kcal/mol to 1.73 for a set of 9 salt bridges, compared to uncorrected DFTB3. For large water clusters and complexes that are dominated by dispersion interactions, the already satisfactory performance of the DFTB3-D3 model is retained; polarizabilities of neutral molecules are also notably improved. Overall, the CPE extension of DFTB3-D3 provides a more balanced description of different types of non-covalent interactions than Neglect of Diatomic Differential Overlap type of semi-empirical methods (e.g., PM6-D3H4) and PBE-D3 with modest basis sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Broadband Characterization and Circuit Model Development of Transmission-Scale Transformers

This report describes broadband measurements of transmission-scale transformers typical in the electric power grid. This work was performed as part of the EMP Resilient Grid LDRD project at Sandia National Laboratories to generate circuit models that can be used for high-altitude electromagnetic pulse (HEMP) coupling simulations and response predictions. The objective of the work was to obtain characterization data of substation yard equipment across a frequency range relevant to HEMP. Vector network analyzer measurements up to 100 MHz were performed on two power transformers at ABB-Hitachi and a single ITEC potential transformer. Custom cable breakouts were designed to interface with the transformer terminals and provide ground connections to the chassis at the base of the transformer bushings. The three-phase terminals of the power transformers were measured as a common mode impedance using a parallel resistive splitter, and the single-phase terminals of the potential transformer were measured directly. A vector fitting algorithm was used to empirically fit circuit models to the resulting two-port networks and input impedances of the measured objects. Simplified circuit representations of the input impedances were also generated to assess the degree of precision needed for high-altitude electromagnetic pulse response predictions, which were performed in Sandia's XYCE circuit simulator platform. HEMP coupling simulations using the transformer models showed significant reduction in the voltage peak and broadening in the pulse width seen at the power transformer compared to the traveling wave voltage. This indicated the importance of the load condition when defining the coupled insult in an electric power substation. Simplified circuit models showed a similar voltage at the transformer with a smoothed waveform. The presence of potential transformers in the simulation did not significantly change the simulated voltage at the power transformer. Single-port input impedance models were also developed to define load conditions when transfer characteristics were not necessary.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

An automatic performance model-based scheduling tool for coupled climate system models

The prediction of the climate system is highly depended on the efficient integration of observations and simulations of the Earth. This is regarded as a canonical example of the cyber-physical system. The climate system model, the simulation engine in this cyber-physical system, is one of most challenging applications in scientific computing. It utilizes the multi-physics simulation that couples multiple components, conducts decadal to millennium simulations, and has long been an important application on supercomputers. However, current climate system models suffer from the inefficient task scheduling methods resulting in an intolerable simulation time. Take the Community Earth System Model (CESM), the most widely used climate system model, as an example, one major reason that CESM suffers from bad performances is the huge overhead to rationally distribute processes among the coupled heterogeneous components. According to the report of NCAR, every percent improvement in CESM performance frees up to the equivalent of $250,000 in computing resources in their scientific experiments. To address such challenge, our paper firstly constructs a lightweight and accurate performance model for effectively capturing and predicting the heterogeneous time-to-solution performance of end-to-end CESM components with a given simulation configuration. Then, based on the performance model, we further propose an efficient scheduling strategy based on rectangular packing method to determine the best process layout among different components, and the process numbers assigned to each component. Our evaluations show that we can achieve 58% average run time reductions on CESM comparing to the widely used sequential process layout for a scale of 144 to 480 cores on typical CPU clusters. And we can save 4 million CPU hours when we conduct one standard scientific experiment (a 2870-year simulation), which equals to save $40,089 with a charge of $0.01 per CPU hour. Meanwhile, 26% extra performance improvements could also be gained in our methods comparing to the heuristic branch and bound algorithm with the guidance of the known curve-fitting performance model.

58 GEOSCIENCES↗
DOE OSTI2015

Materials prediction via classification learning

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. In conclusion, our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Practical guide to understanding goodness-of-fit metrics used in chemical state modeling of x-ray photoelectron spectroscopy data by synthetic line shapes using nylon as an example

Chemical state analysis of a sample surface through fitting bell-shaped curves to x-ray photoelectron spectroscopic polymer data is reviewed using nylon to introduce and discuss aspects of data analysis. Different strategies for modeling chemistry in nylon spectra are presented and in so doing, a case is made to include in published science the design logic and implementation in terms of line shapes and optimization parameter constraints between components in a peak model. Imperfections in line shape relative to the true shape for photoemission lines, when compensated for using constraints to optimization parameters, are shown to provide chemical state information about a sample that justify, for peak models constructed with these limitations, metrics for goodness-of-fit different from those expected for pulse-counted data.

Materials Science↗
DOE OSTI2019

Using the Diffusion Equation to Predict CST Resin Breakthroughs - 19051

The crystalline silicotitanate, CST, resin manufactured by UOP Honeywell is well-suited for single-use decontamination of caustic liquid wastes at Hanford and Savannah River Sites. There are many measurements of column loading and breakthrough for CST resin decontamination of Cs-137 in various Hanford and SRS waste simulants. Such measurements provide a very good basis for estimating CST resin demand and these more refined predictions make the CST column loading as efficient as possible given the very expensive CST resin. Although there are a variety of tools for predicting chromatographic resin column performance, [1-4], there is a particularly useful method that is suitable as a simple spreadsheet model [4]. This spreadsheet model is an analytical error function for the mass transfer zone (mtz) diffusion width in resin columns and is well-suited for resin breakthrough and column loading. This paper describes just such an error function spreadsheet model and how well the error function agrees with more traditional models and fits the observed small scale breakthrough curves for CST that can then better predict large scale column loading efficiency. The very high sorption of Cs onto CST tends to be slower than that of other lower sorption resins and as a result of the slow CST kinetics, there is also a marked asymmetry in many CST breakthrough curves. The CST breakthrough asymmetry is largely due to the distribution of CST particle sizes because smaller particles sorb much faster than larger particles. It is then the smaller particles that determine the leading mtz at a decontamination factor of 1,000 breakthrough and therefore the decontamination efficiency for a given loading. The larger CST particles correspondingly determine the tailing mtz width and therefore the overall loading efficiency and it is important to model both leading and tailing mtz widths to best optimize CST for a particular Cs-137 decontamination application. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Constraining possible variations of the fine structure constant in strong gravitational fields with the Kα iron line

In extensions of general relativity and in theories aiming at unifying gravity with the forces of the Standard Model, the value of the ''fundamental constants'' is often determined by the vacuum expectation value of new fields, which may thus change in different backgrounds. Variations of fundamental constants with respect to the values measured today in laboratories on Earth are expected to be more evident on cosmological timescales and/or in strong gravitational fields. In this paper, I show that the analysis of the Kα iron line observed in the X-ray spectrum of black holes can potentially be used to probe the fine structure constant α in gravitational potentials relative to Earth of Δφ ≈ 0.1. At present, systematic effects not fully under control prevent to get robust and stringent bounds on possible variations of the value of α with this technique, but the fact that current data can be fitted with models based on standard physics already rules out variations of the fine structure constant larger than some percent.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Ensemble survival trees for identifying subpopulations in personalized medicine

Recently, personalized medicine has received great attention to improve safety and effectiveness in drug development. Personalized medicine aims to provide medical treatment that is tailored to the patient's characteristics such as genomic biomarkers, disease history, etc., so that the benefit of treatment can be optimized. Subpopulations identification is to divide patients into several different subgroups where each subgroup corresponds to an optimal treatment. For two subgroups, traditionally the multivariate Cox proportional hazards model is fitted and used to calculate the risk score when outcome is survival time endpoint. Median is commonly chosen as the cutoff value to separate patients. However, using median as the cutoff value is quite subjective and sometimes may be inappropriate in situations where data are imbalanced. Here, we propose a novel tree‐based method that adopts the algorithm of relative risk trees to identify subgroup patients. After growing a relative risk tree, we apply k‐means clustering to group the terminal nodes based on the averaged covariates. We adopt an ensemble Bagging method to improve the performance of a single tree since it is well known that the performance of a single tree is quite unstable. A simulation study is conducted to compare the performance between our proposed method and the multivariate Cox model. The applications of our proposed method to two public cancer data sets are also conducted for illustration.

Chen, Yu‐Chuan↗
DOE OSTI2019

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree ring chronologies for paleo-streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries come together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. Our application to 18 streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted R 2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. The spatial network structure produced a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network aggregating information from upstream gauges and tree ring chronologies. Uncertainty was reduced by more than 50% at six gauges, between 6% and 50% at one gauge, and by less than 5% at the remaining 11 gauges when compared with the traditional principal component regression reconstruction model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Measurement–model comparison of stabilized Criegee intermediate and highly oxygenated molecule production in the CLOUD chamber

Abstract. Atmospheric oxidation is an important phenomenon which produces large quantities of low-volatility compounds such as sulfuric acid and oxidized organic compounds. Such species may be involved in the nucleation of particles and enhance their subsequent growth to reach the size of cloud condensation nuclei (CCN). In this study, we investigate α -pinene, the most abundant monoterpene globally, and its oxidation products formed through ozonolysis in the Cosmic Leaving Outdoor Droplets (CLOUD) chamber at CERN(the European Organization for Nuclear Research). By scavenging hydroxyl radicals (OH) with hydrogen (H 2 ), we were able to investigate the formation of highly oxygenated molecules (HOMs) purely driven by ozonolysis and study the oxidation of sulfur dioxide (SO 2 ) driven by stabilized Criegee intermediates (sCIs). We measured the concentrations of HOM and sulfuric acid with a chemical ionization atmospheric-pressure interface time-of-flight (CI-APi-TOF) mass spectrometer and compared the measured concentrations with simulated concentrations calculated with a kinetic model.We found molar yields in the range of 3.5–6.5 % for HOM formation and 22–32 % for the formation of stabilized Criegee intermediates by fitting our model to the measured sulfuric acid concentrations. The simulated time evolution of the ozonolysis products was in good agreement with measured concentrations except that in some of the experiments sulfuric acid formation was faster than simulated. In those experiments the simulated and measured concentrations met when the concentration reached a plateau but the plateau was reached 20–50 min later in the simulations. The results shown here are consistent with the recently published yields for HOM formation from different laboratory experiments. Together with the sCI yields, these results help us to understand atmospheric oxidation processes better and make there action parameters more comprehensive for broader use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

OBSERVED VARIABILITY OF THE SOLAR Mg II h SPECTRAL LINE

The Mg ii h and k doublet are two of the primary spectral lines observed by the Sun-pointing Interface Region Imaging Spectrograph (IRIS). These lines are tracers of the magnetic and thermal environment that spans from the photosphere to the upper chromosphere. We use a double-Gaussian model to fit the Mg ii h profile for a full-Sun mosaic data set taken on 2014 August 24. We use the ensemble of high-quality profile fits to conduct a statistical study on the variability of the line profile as it relates the magnetic structure, dynamics, and center-to-limb viewing angle. The average internetwork profile contains a deeply reversed core and is weakly asymmetric at h2. In the internetwork, we find a strong correlation between h3 wavelength and profile asymmetry as well as h1 width and h2 width. The average reversal depth of the h3 core is inversely related to the magnetic field. Plage and sunspots exhibit many profiles that do not contain a reversal. These profiles also occur infrequently in the internetwork. We see indications of magnetically aligned structures in plage and network in statistics associated with the line core, but these structures are not clear or extended in the internetwork. The center-to-limb variations are compared to predictions of semi-empirical model atmospheres. We measure a pronounced limb darkening in the line core that is not predicted by the model. The aim of this work is to provide a comprehensive measurement baseline and preliminary analysis on the observed structure and formation of the Mg ii profiles observed by IRIS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Logistic Regression in Clinical Studies

• A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. • Odds ratios are produced from a logistic regression model and have a useful interpretation. • Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. • Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. • A logistic regression model that is overparameterized (ie, too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. • Logistic regression models can be fit using most standard statistical software.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

CHANDRA IMAGING AND SPECTROSCOPY OF THE EASTERN XA REGION OF THE CYGNUS LOOP SUPERNOVA REMNANT

The XA region of the Cygnus Loop is a bright knot of X-ray emission on the eastern edge of the supernova remnant. The emission results from the interaction of the supernova blast wave with density enhancements at the edge of a precursor formed cavity. However, this interaction is complex given the irregular morphology of the cavity wall. To study the nature and origin of the X-ray emission, we use high spatial resolution images from Chandra. We extract spectra from these images to analyze the physical conditions of the plasma. Our goal is to probe the density of various regions to form a picture of the cavity wall and characterize the interaction between this supernova and the local interstellar medium. We find that a series of regions along the edge of the X-ray emission appears to trace out the location of the cavity wall. The best-fit plasma models result in two temperature component equilibrium models for each region. The low-temperature components have densities that are an order of magnitude higher than the high-temperature components. The high-density plasma may exist in the cavity wall where it equilibrates rapidly and cools efficiently. The low-density plasma is interior to the enhancement and heated further by a reverse shock from the wall. Calculations of shock velocities and timescales since shock heating are consistent with this interpretation. Furthermore, we find a bright knot of emission indicative of a discrete interaction of the blast wave with a high-density cloud in the cavity wall with a size scale {approx}0.1 pc. Aside from this, other extractions made interior to the X-ray edge are confused by line-of-sight projection of various components. Some of these regions show evidence of detecting the cavity wall but their location makes the interpretation difficult. In general, the softer plasmas are well fit at temperatures (kT){approx} 0.11 keV, with harder plasmas at temperatures of (kT){approx} 0.27 keV. All regions displayed consistent metal depletions most notably in N, O, and Ne at an average of 0.54, 0.55, and 0.36 times solar, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Solid inflation

We develop a cosmological model where primordial inflation is driven by a 'solid', defined as a system of three derivatively coupled scalar fields obeying certain symmetries and spontaneously breaking a certain subgroup of these. The symmetry breaking pattern differs drastically from that of standard inflationary models: time translations are unbroken. This prevents our model from fitting into the standard effective field theory description of adiabatic perturbations, with crucial consequences for the dynamics of cosmological perturbations. Most notably, non-gaussianities in the curvature perturbations are unusually large, with f{sub NL} ∼ 1/(εc{sub s}{sup 2}), and have a novel shape: peaked in the squeezed limit, with anisotropic dependence on how the limit is approached. Other unusual features include the absence of adiabatic fluctuation modes during inflation — which does not impair their presence and near scale-invariance after inflation — and a slightly blue tilt for the tensor modes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

CALIBRATING CONVECTIVE PROPERTIES OF SOLAR-LIKE STARS IN THE KEPLER FIELD OF VIEW

Stellar models generally use simple parameterizations to treat convection. The most widely used parameterization is the so-called mixing-length theory where the convective eddy sizes are described using a single number, {alpha}, the mixing-length parameter. This is a free parameter, and the general practice is to calibrate {alpha} using the known properties of the Sun and apply that to all stars. Using data from NASA's Kepler mission we show that using the solar-calibrated {alpha} is not always appropriate, and that in many cases it would lead to estimates of initial helium abundances that are lower than the primordial helium abundance. Kepler data allow us to calibrate {alpha} for many other stars and we show that for the sample of stars we have studied, the mixing-length parameter is generally lower than the solar value. We studied the correlation between {alpha} and stellar properties, and we find that {alpha} increases with metallicity. We therefore conclude that results obtained by fitting stellar models or by using population-synthesis models constructed with solar values of {alpha} are likely to have large systematic errors. Our results also confirm theoretical expectations that the mixing-length parameter should vary with stellar properties.

79 ASTRONOMY AND ASTROPHYSICS↗