Estimate Of Radius Research Articles

Ensemble Learning for Stellar Classification and Radius Estimation from Multimodal Data

Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stellar evolution. With the advent of the era of astronomical big data, multimodal data are available and theoretically effective for stellar classification and radius estimation. A problem is how to improve the performance of this task by jointly using the multimodal data. However, existing research primarily focuses on using single-modal data. To this end, this paper proposes a model, Multi-Modal SCNet, and its ensemble model Multimodal Ensemble for Stellar Classification and Regression (MESCR) for improving stellar classification and radius estimation performance by fusing two modality data. In this problem, a typical phenomenon is that the sample numbers of some types of stars are evidently more than others. This imbalance has negative effects on model performance. Therefore, this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR. Some evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%, and the radius estimation performance Mean of Absolute Error and σ are 0.084 dex and 0.149 R ⊙, respectively. Moreover, we assessed the uncertainty of model predictions, confirming good consistency within a reasonable deviation range. Finally, we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.

Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring.

To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach. A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-high -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract. Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach. Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.

Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters Using Machine Learning

Dark matter subhalos play an important role in galaxy formation and evolution. However, accurate prediction of dark matter properties remains a challenge of modern-day astronomy. In recent times, machine learning (ML) tools have shown promising results in solving numerous astrophysical problems. In this paper, we use data from the EAGLE simulations to determine the total mass and the half-mass radius of dark matter subhalos using structural properties of gas, star, black hole, and photometric features using gradient boosted decision trees (GBDT) and dense neural network. GBDT does not require data preprocessing, and results in better performance compared to the neural network. According to GBDT, the most important feature for subhalo radius and mass estimation is gas radius and black hole mass respectively. The all-features combined approach results in the highest test accuracy — Pearson’s correlation coefficient = 0.947 and 0.981, coefficient of determination = 0.898 and 0.962, normalized median absolute deviation = 0.111 and 0.114 for radius and mass respectively. We evaluate our model for masses and redshifts beyond its training range and find that GBDT demonstrates significantly better extrapolation capabilities than the neural network. We also test our model on simulations with different resolutions, and find that the discrepancies lie within 10% if the resolution is changed. This novel study incorporates the structural parameters of gas and black hole to determine the dark matter properties using a ML-based approach. The promising results of this study prove that ML tools can improve our current understanding of dark matter, and answer some of the basic cosmological questions.

Rigidity for inscribed radius estimate of asymptotically hyperbolic Einstein manifold

The inscribed radius of a compact manifold with boundary is bounded above if its Ricci curvature and mean curvature are bounded from below. The rigidity result implies that the upper bound can be achieved only in space form. In this paper, we generalize this result to asymptotically hyperbolic (AH) Einstein manifold. We get an upper bound of the relative volume of AH manifold and if we combine it with the recent work of Wang and Zhou, then the rigidity is obtained.

Flow-based Generative Emulation of Grids of Stellar Evolutionary Models

We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multidimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic data sets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age uncertainties can arise when fitting only to global asteroseismic and spectroscopic parameters without prior information on initial helium abundances and mixing length parameter values. We also conduct inference using the flow at a large scale by determining revised estimates of masses and radii for 15,388 field red giants. These estimates show improved agreement with results from existing grid-based modeling, reveal distinct population-level features in the red clump, and suggest that the masses of Kepler red giants previously determined using the corrected asteroseismic scaling relations have been overestimated by 5%–10%.

Testing the asteroseismic estimates of stellar radii with surface brightness-colour relations and Gaia DR3 parallaxes. Red giants and red clump stars

We compared stellar radii derived from asteroseismic scaling relations with those estimated using two independent surface brightness-colour relations (SBCRs) combined with Gaia DR3 parallaxes. We cross-matched asteroseismic and astrometric data for over 6,400 red giant branch (RGB) and red clump (RC) stars from the APO-K2 catalogue with the TESS Input Catalogue v8.2 to obtain precise $V$ band magnitudes and $E(B-V)$ colour excesses. We then adopted two different SBCRs from the literature to derive stellar radius estimates, denoted as $R^a$ and $R^b$, respectively. We analysed the ratio of these SBCR-derived radii to the asteroseismic radius estimates, $R,$ provided in the APO-K2 catalogue. Both SBCRs exhibited good agreement with asteroseismic radius estimates. On average, $R^a$ was overestimated by 1.2<!PCT!> with respect to $R$, while $R^b$ was underestimated by 2.5<!PCT!>. For stars larger than 20 $R_ sun $, SBCR radii are systematically lower than asteroseismic ones. The dispersion in the radius ratio was similar for the two methods (around 10<!PCT!>). The agreement with asteroseismic radii shows a strong dependence on the parallax. The dispersion is halved for stars with a parallax greater than 2.5 mas. In this subsample, $R^b$ showed perfect agreement with $R$, while $R^a$ remained slightly overestimated, by 3<!PCT!>. A trend with Fe/H was found at a level of 4<!PCT!> to 6<!PCT!> per dex. Additionally, a clear trend with asteroseismic mass is found. For stars less massive than about 0.95 $M_ sun $, SBCR radii were significantly higher than asteroseismic ones, by about 6<!PCT!>. This overestimation correlated with the presence of extended helium cores in these stars' structures relative to their envelopes. Furthermore, radius ratios showed a dichotomous behaviour at higher masses, mainly due to the presence of several RC stars with SBCR radii significantly lower with respect to asteroseismology. This behaviour originates from a different response of asteroseismic scaling relations and SBCR to alpha /Fe abundance ratios for massive stars, both in RGB and RC phases, which is reported here for the first time.

Stability radius and dichotomy radius of infinite-dimensional linear systems under unbounded perturbations via Yosida distance

In this paper, by using the concept of Yosida distance between two closed linear operators, we study the stability radius r(A) of linear systems u′(t)=Au(t), where A is the generator of an analytic semigroup, under unbounded perturbations in this class of generators. We show that r(A)=1/sups∈R‖R(is,A)‖L(X), so extending a classic result by Henrichsen and Pritchard to the infinite-dimensional case. A formula of the dichotomy radius is also established. Finally we give an estimate of the stability radius of general C0-semigroups. Two examples from a parabolic equation are given.

Adaptive Curve Passing Control in Autonomous Vehicles with Integrated Dynamics and Camera-Based Radius Estimation

Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s2 upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

Can the splashback radius be an observable boundary of galaxy clusters?

The splashback radius was proposed as a physically motivated boundary of clusters as it sets the limit between the infalling and the orbitally dominated regions. However, galaxy clusters are complex objects connected to filaments of the cosmic web from which they accrete matter that disturbs them and modifies their morphology. In this context, estimating the splashback radius and the cluster boundary becomes challenging. In this work, we use a constrained hydrodynamical simulation replicating the Virgo cluster embedded in its large-scale structure to investigate the impact of its local environment on the splashback radius estimate. We identify the splashback radius from 3D radial profiles of dark matter density, gas density, and pressure in three regions representative of different dynamical states: accretion from spherical collapse, filaments, and matter outflow. We also identify the splashback radius from 2D-projected radial profiles of observation-like quantities: mass surface density, emission measure, and Compton-y. We show that the splashback radius mainly depends on the dynamics in each region and the physical processes traced by the different probes. We find multiple values for the splashback radius ranging from 3.3 ± 0.2 to 5.5 ± 0.3 Mpc. In particular, in the regions of collapsing and outflowing materials, the splashback radii estimated from gas density and pressure radial profiles overestimate that of the dark matter density profiles, which is considered the reference value given that the splashback radius was originally defined from dark matter simulations in pioneering works. Consequently, caution is required when using the splashback radius as a boundary of clusters, particularly in the case of highly disturbed clusters like Virgo. We conclude with a discussion of the detection of the splashback radius from pressure radial profiles, which could be more related to an accretion shock, and its detection from stacked radial profiles.

Accurate and Model-independent Radius Determination of Single FGK and M Dwarfs Using Gaia DR3 Data

Measuring fundamental stellar parameters is key to fully comprehending the evolution of stars. However, current theoretical models overpredict effective temperatures, and underpredict radii, compared to observations of K and M dwarfs (radius inflation problem). In this work, we have developed a model-independent method to infer precise radii of single FGK and M dwarfs using Gaia DR3 parallaxes and photometry, and used it to study the radius inflation problem. We calibrated nine surface brightness–color relations for the three Gaia magnitudes and colors using a sample of stars with angular diameter measurements. We achieved an accuracy of 4% in our angular diameter estimations, which Gaia’s parallaxes allow us to convert to physical radii. We validated our method by comparing our radius measurements with literature samples and the Gaia DR3 catalog, which confirmed the accuracy of our method and revealed systematic offsets in the Gaia measurements. Moreover, we used a sample with measured Hα equivalent width (Hα EW), a magnetic activity indicator, to study the radius inflation problem. We demonstrated that active stars have larger radii than inactive stars, showing that radius inflation is correlated with magnetic activity. We found a correlation between the radius inflation of active stars and Hα EW for the mass bin 0.5 < M[M ⊙] ≤ 0.6, but we found no correlation for lower masses. This could be due to lack of precision in our radius estimation or a physical reason. Radius measurements with smaller uncertainties are necessary to distinguish between the two scenarios.

On the Theoretical Quantification of Radii of Atoms in Molecules.

Despite the fundamental importance of radii of atoms in molecules for numerous applications in physics and chemistry, comprehensive methods for their theoretical evaluation are still scarce. Here, we present quantum chemistry-based approaches for evaluation of radii of atoms in molecules and assess their robustness by studying the agreement of van der Waals and solvent-excluded surfaces constructed by them with reference molecular surfaces. By studying a large data set of 1235 molecules, it is shown that estimation of radii via effective and free atomic volumes can accurately take the dependence of atomic radii on the chemical environment into account.

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang-Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

ASASSN-21js: A multi-year transit of a ringed disc

Aims. The early-type star ASASSN-21js started to fade in 2021, as was detected by the All Sky Automated Survey for Supernovae, undergoing a multi-year eclipse that is still underway. We interpret this event as being due to a structured disc of material transiting in front of the star. The disc is in orbit around a substellar object with the mass and luminosity of a brown dwarf or smaller. We want to determine the expected duration and ending date of the eclipse. Methods. We modelled a tilted and inclined azimuthally symmetric ring system around an unseen companion and calculated the resulting time-varying light curve as the object transited in front of the star. We made an initial estimate of the ring parameters and used these as inputs to an MCMC algorithm to determine the geometric properties of the rings with associated uncertainties. Results. The model most consistent with the light curve to date is a two-ring system at high inclination with respect to the line of sight that has a semi-major axis of 71.6 stellar radii. With an estimate of the stellar radius, the transverse velocity is around 0.7 km s−1, which if bound to the star is an orbit with a semi-major axis of around 13 000 au, placing it in the Oort cloud of the parent star. The transit is ongoing and will finish around MJD 61526 (May 1 2027). We encourage the community to continue observing this object in order to understand its properties.

A Correction Function to Improve the Accuracy of Measuring Elastic Modulus by Instrumented Spherical Indentation

Abstract Instrumented indentation combined with the classic Oliver–Pharr method has been widely utilized to measure elastic modulus of various materials. However, the elastic modulus measured by instrumented spherical indentation (ISI) is not as accurate as that measured by instrumented sharp indentation, especially at large indentation depth. In this work, the effect of the maximum indentation depth on measurement of elastic modulus by ISI was deeply investigated through finite element simulations and experiments. It was found that errors in measured elastic moduli increase significantly due to the inaccurate estimation of contact radius and excessive increase in initial unloading stiffness as maximum indentation depth increases. A correction function was then proposed to correct the measured elastic modulus. After correction, the errors were effectively reduced to within ±5 % for most cases. This work contributes to discovery of the error source in the measurement of elastic modulus by ISI, thereby improving the measurement accuracy.

Detection of open cluster members inside and beyond tidal radius by machine learning methods based on Gaia DR3

ABSTRACT In our previous work, we introduced a method that combines two unsupervised algorithms: Density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM). We applied this method to 12 open clusters based on Gaia Early Data Release 3 (EDR3) data, demonstrating its effectiveness in identifying reliable cluster members within the tidal radius. However, for studying cluster morphology, we need a method capable of detecting members both inside and outside the tidal radius. By incorporating a supervised algorithm into our approach, we successfully identified members beyond the tidal radius. In our current work, we initially applied DBSCAN and GMM to identify reliable members of cluster stars. Subsequently, we trained the random forest algorithm using DBSCAN- and GMM-selected data. Leveraging the random forest, we can identify cluster members outside the tidal radius and observe cluster morphology across a wide field of view. Our method was then applied to 15 open clusters based on Gaia DR3, which exhibit a wide range of metallicity, distances, members, and ages. Additionally, we calculated the tidal radius for each of the 15 clusters using the King profile and detected stars both inside and outside this radius. Finally, we investigated mass segregation and luminosity distribution within the clusters. Overall, our approach significantly improved the estimation of the tidal radius and detection of mass segregation compared to the previous work. We found that in Collinder 463, low-mass stars do not segregate in comparison to high-mass and intermediate-mass stars. Additionally, we detected a peak of luminosity in the clusters, some of which were located far from the centre, beyond the tidal radius.

Rotation curves in protoplanetary disks with thermal stratification

Context. In recent years, the gas kinematics probed by molecular lines detected with ALMA has opened a new window into the of study protoplanetary disks. High spatial and spectral resolution observations have revealed the complexity of protoplanetary disk structure. Drawing accurate interpretations of these data allows us to better comprehend planet formation. Aims. We investigate the impact of thermal stratification on the azimuthal velocity of protoplanetary disks. High-resolution gas observations reveal velocity differences between CO isotopologues, which cannot be adequately explained with vertically isothermal models. The aim of this work is to determine whether a stratified model can explain this discrepancy. Methods. We analytically solved the hydrostatic equilibrium for a stratified disk and we derived the azimuthal velocity. We tested the model with SPH numerical simulations and then we used it to fit for the star mass, disk mass, and scale radius of the sources in the MAPS sample. In particular, we used 12CO and 13CO datacubes. Results. When thermal stratification is taken into account, it is possible to reconcile most of the inconsistencies between rotation curves of different isotopologues. A more accurate description of the CO rotation curves offers a deeper understanding of the disk structure. The best-fit values of star mass, disk mass, and scale radius become more realistic and more in line with previous studies. In particular, the quality of the scale radius estimate significantly increases when adopting a stratified model. In light of our results, we computed the gas-to-dust ratio and the Toomre Q parameter. Within our hypothesis, for all the sources, the gas-to-dust ratio appears higher but still close to the standard value of 100 (within a factor of 2). The Toomre Q parameter suggests that the disks are gravitationally stable (Q &gt; 1). However, the systems that show spirals presence are closer to the conditions of gravitational instability (Q ~ 5).

Electromagnetic form factors of the nucleon from Nf=2+1 lattice QCD

There is a long-standing discrepancy between different measurements of the electric and magnetic radii of the proton. Lattice QCD calculations are a well-suited tool for theoretical investigations of the structure of the nucleon from first principles. However, all previous lattice studies of the proton’s electromagnetic radii have either neglected quark-disconnected contributions or were not extrapolated to the continuum and infinite-volume limit. Here, we present results for the electromagnetic form factors of the proton and neutron computed on the (2+1)-flavor coordinated lattice simulations (CLS) ensembles including both quark-connected and -disconnected contributions. From simultaneous fits to the Q2-, pion-mass, lattice-spacing, and finite-volume dependence of the form factors, we determine the electric and magnetic radii and the magnetic moments of the proton and neutron. For the proton, we obtain as our final values ⟨rE2⟩p=(0.672±0.014(stat)±0.018(syst)) fm2, ⟨rM2⟩p=(0.658±0.012(stat)±0.008(syst)) fm2, and μMp=(2.739±0.063(stat)±0.018(syst). The magnetic moment is in good agreement with the experimental value, as is the one of the neutron. On the one hand, our result for the electric (charge) radius of the proton clearly points towards a small value, as favored by muonic hydrogen spectroscopy and the recent ep-scattering experiment by PRad. Our estimate for the magnetic radius, on the other hand, is well compatible with that inferred from the A1 ep-scattering experiment. Published by the American Physical Society 2024

Impact of α enhancement on the asteroseismic age determination of field stars

Aims. We investigated the theoretical biases affecting the asteroseismic grid-based estimates of stellar mass, radius, and age in the presence of a mismatch between the heavy element mixture of observed stars and stellar models. Methods. We performed a controlled simulation adopting a stellar effective temperature, [Fe/H], an average large frequency spacing, and a frequency of maximum oscillation power as observational constraints. Synthetic stars were sampled from grids of stellar models computed with different [α/Fe] values from 0.0 to 0.4. The mass, radius, and age of these objects were then estimated by adopting a grid of models with a fixed [α/Fe] value of 0.0. The experiment was repeated assuming different sets of observational uncertainties. In the reference scenario, we adopted an uncertainty of 1.5% in seismic parameters, 50 K in effective temperature, and 0.05 dex in [Fe/H]. A higher uncertainty in the atmospheric constraints was also adopted in order to explore the impact on the precision of the observations of the estimated stellar parameters. Results. Our Monte Carlo experiment showed that estimated parameters are biased up to 3% in mass, 1.5% in radius, and 4% in age when the reference uncertainty scenario was adopted. These values correspond to 45%, 48%, and 16% of the estimated uncertainty in the stellar parameters. These non-negligible biases in mass and radius disappear when adopting larger observational uncertainties because of the possibility of the fitting algorithm exploring a wider range of possible solutions. However, in this scenario, the age is significantly biased by −8%. Finally, we verified that the stellar mass, radius, and age can be estimated with a high accuracy by adopting a grid with the incorrect value of [α/Fe] if the metallicity [Fe/H] of the target is adjusted to match the Z in the fitting grid. In this scenario, the maximum bias in the age was reduced to 1.5%.

Bulk Properties of PSR J0030+0451 Inferred with the Compactness Measurement of NICER

In 2019, the Neutron star Interior Composition ExploreR (NICER) mission released its findings on the mass and radius of the isolated neutron star (INS) PSR J0030+0451, revealing a mass of approximately 1.4 solar masses (M ⊙) and a radius near 13 km. However, the recent reanalysis by the NICER collaboration suggests that the available data primarily yield a precise inference of the compactness for this source while the resulting mass and radius are strongly model-dependent and diverse (the 68.3% credible regions just overlap slightly for the ST+PDT and PDT-U models). By integrating this compactness data with the equation of state (EOS) refined by our latest investigations, we have deduced the mass and radius for PSR J0030+0451, delivering estimates of M=1.48−0.10+0.09M⊙ and R=12.38−0.70+0.51km for the compactness found in the ST+PDT model, alongside M=1.47−0.20+0.14M⊙ and R=12.37−0.69+0.50km for the compactness in the PDT-U model. These two groups of results are well consistent with each other and the direct X-ray data inference within the ST+PDT model seems to be favored. Additionally, we have calculated the tidal deformability, moment of inertia, and gravitational binding energy for this neutron star. Furthermore, employing these refined EOS models, we have updated mass–radius estimates for three INSs with established gravitational redshifts.