The interactions of plants with environment and insects are bi-directional and dynamic. Consequently, a myriad of mechanisms has evolved to engage organisms in different types of interactions. These ...interactions can be mediated by allelochemicals known as volatile organic compounds (VOCs) which include volatile terpenes (VTs). The emission of VTs provides a way for plants to communicate with the environment, including neighboring plants, beneficiaries (e.g., pollinators, seed dispersers), predators, parasitoids, and herbivores, by sending enticing or deterring signals. Understanding terpenoid distribution, biogenesis, and function provides an opportunity for the design and implementation of effective and efficient environmental calamity and pest management strategies. This review provides an overview of plant-environment and plant-insect interactions in the context of terpenes and terpenoids as important chemical mediators of these abiotic and biotic interactions.
Radiation pressure can be dynamically important in star-forming environments such as ultra-luminous infrared and submillimetre galaxies. Whether and how radiation drives turbulence and bulk outflows ...in star formation sites is still unclear. The uncertainty in part reflects the limitations of direct numerical schemes that are currently used to simulate radiation transfer and radiation–gas coupling. An idealized setup in which radiation is introduced at the base of a dusty atmosphere in a gravitational field has recently become the standard test for radiation-hydrodynamics methods in the context of star formation. To a series of treatments featuring the flux-limited diffusion approximation as well as a short-characteristics tracing and M1 closure for the variable Eddington tensor approximation, we here add another treatment that is based on the implicit Monte Carlo radiation transfer scheme. Consistent with all previous treatments, the atmosphere undergoes Rayleigh–Taylor instability and readjusts to a near-Eddington-limited state. We detect late-time net acceleration in which the turbulent velocity dispersion matches that reported previously with the short-characteristics-based radiation transport closure, the most accurate of the three preceding treatments. Our technical result demonstrates the importance of accurate radiation transfer in simulations of radiative feedback.
Abstract
Strong lensing offers a precious opportunity for studying the formation and early evolution of super star clusters that are rare in our cosmic backyard. The Sunburst Arc, a lensed Cosmic ...Noon galaxy, hosts a young super star cluster with escaping Lyman continuum radiation. Analyzing archival Hubble Space Telescope images and emission line data from Very Large Telescope/MUSE and X-shooter, we construct a physical model for the cluster and its surrounding photoionized nebula. We confirm that the cluster is ≲4 Myr old, is extremely massive
M
⋆
∼ 10
7
M
⊙
, and yet has a central component as compact as several parsecs, and we find a gas-phase metallicity
Z
= (0.22 ± 0.03)
Z
⊙
. The cluster is surrounded by ≳10
5
M
⊙
of dense clouds that have been pressurized to
P
∼ 10
9
K cm
−3
by perhaps stellar radiation at within 10 pc. These should have large neutral columns
N
HI
> 10
22.8
cm
−2
to survive rapid ejection by radiation pressure. The clouds are likely dusty as they show gas-phase depletion of silicon, and may be conducive to secondary star formation if
N
HI
> 10
24
cm
−2
or if they sink farther toward the cluster center. Detecting strong N
iii
λ
λ
1750,1752, we infer heavy nitrogen enrichment
log
(
N
/
O
)
=
−
0.21
−
0.11
+
0.10
. This requires efficiently retaining ≳500
M
⊙
of nitrogen in the high-pressure clouds from massive stars heavier than 60
M
⊙
up to 4 Myr. We suggest a physical origin of the high-pressure clouds from partial or complete condensation of slow massive star ejecta, which may have an important implication for the puzzle of multiple stellar populations in globular clusters.
Star-to-star dispersion of r-process elements has been observed in a significant number of old, metal-poor globular clusters (GCs). We investigate early-time neutron-star mergers as the mechanism for ...this enrichment. Through both numerical modeling and analytical arguments, we show that neutron-star mergers cannot be induced through dynamical interactions early in the history of the cluster, even when the most liberal assumptions about neutron-star segregation are assumed. Therefore, if neutron-star mergers are the primary mechanism for r-process dispersion in GCs, they likely result from the evolution of isolated, primordial binaries in the clusters. Through population modeling of double neutron-star progenitors, we find that most enrichment candidates are fast-merging systems that undergo a phase of mass transfer involving a naked He-star donor. Only models where a significant number of double neutron-star progenitors proceed through this evolutionary phase give rise to moderate fractions of GCs with enrichment; under various assumptions for the initial properties of GCs, a neutron-star merger with the potential for enrichment will occur in ∼15%-60% (∼30%-90%) of GCs if this phase of mass transfer proceeds stably (unstably). The strong anti-correlation between the pre-supernova orbital separation and post-supernova systemic velocity due to mass loss in the supernova leads to efficient ejection of most enrichment candidates from their host clusters. Thus, most enrichment events occur shortly after the double neutron stars are born. This Requires star-forming gas that can absorb the r-process ejecta to be present in the globular cluster 30-50 Myr after the initial burst of star formation. If scenarios for redistributing gas in GCs cannot act on these timescales, the number of neutron-star merger enrichment candidates drops severely, and it is likely that another mechanism, such as r-process enrichment from collapsars, is at play.
Abstract
Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the ...explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with F
ornax
, we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9−27
M
⊙
, we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction.
Abstract
Eruptive mass loss likely produces the energetic outbursts observed from some massive stars before they become core-collapse supernovae (SNe). The resulting dense circumstellar medium may ...also cause the subsequent SNe to be observed as Type IIn events. The leading hypothesis of the cause of these outbursts is the response of the envelope of the red supergiant (RSG) progenitor to energy deposition in the months to years prior to collapse. Early theoretical studies of this phenomenon were limited to 1D, leaving the 3D convective RSG structure unaddressed. Using
FLASH
's hydrodynamic capabilities, we explore the 3D outcomes by constructing convective RSG envelope models and depositing energies less than the envelope binding energies on timescales shorter than the envelope dynamical time deep within them. We confirm the 1D prediction of an outward-moving acoustic pulse steepening into a shock, unbinding the outermost parts of the envelope. However, we find that the initial 2–4 km s
−1
convective motions seed the intrinsic convective instability associated with the high-entropy material deep in the envelope, enabling gas from deep within the envelope to escape and increasing the amount of ejected mass compared to an initially “quiescent” envelope. The 3D models reveal a rich density structure, with column densities varying by ≈10× along different lines of sight. Our work highlights that the 3D convective nature of RSG envelopes impacts our ability to reliably predict the outburst dynamics, the amount, and the spatial distribution of the ejected mass associated with deep energy deposition.
Abstract
Observations indicate that turbulent motions are present on most massive star surfaces. Starting from the observed phenomena of spectral lines with widths that are much larger than their ...thermal broadening (e.g., micro- and macroturbulence), and considering the detection of stochastic low-frequency variability (SLFV) in the Transiting Exoplanet Survey Satellite photometry, these stars clearly have large-scale turbulent motions on their surfaces. The cause of this turbulence is debated, with near-surface convection zones, core internal gravity waves, and wind variability being proposed. Our 3D gray radiation hydrodynamic (RHD) models previously characterized the convective dynamics of the surfaces, driven by near-surface convection zones, and provided reasonable matches to the observed SLFV of the most luminous massive stars. We now explore the complex emitting surfaces of these 3D RHD models, which strongly violate the 1D assumption of a plane-parallel atmosphere. By post-processing the gray RHD models with the Monte Carlo radiation transport code
Sedona
, we synthesize stellar spectra and extract information from the broadening of individual photospheric lines. The use of
Sedona
enables the calculation of the viewing angle and temporal dependence of spectral absorption line profiles. By combining uncorrelated temporal snapshots together, we compare the turbulent broadening from the 3D RHD models to the thermal broadening of the extended emitting region, showing that our synthesized spectral lines closely resemble the observed macroturbulent broadening from similarly luminous stars. More generally, the new techniques that we have developed will allow for systematic studies of the origins of turbulent velocity broadening from any future 3D simulations.
Common variable star classifiers are built with the singular goal of producing the correct class labels, leaving much of the multi-task capability of deep neural networks unexplored. We present a ...periodic light curve classifier that combines a recurrent neural network autoencoder for unsupervised feature extraction and a dual-purpose estimation network for supervised classification and novelty detection. The estimation network optimizes a Gaussian mixture model in the reduced-dimension feature space, where each Gaussian component corresponds to a variable class. An estimation network with a basic structure of a single hidden layer attains a cross-validation classification accuracy of ∼99%, which is on par with the conventional workhorses, random forest classifiers. With the addition of photometric features, the network is capable of detecting previously unseen types of variability with precision 0.90, recall 0.96, and an F1 score of 0.93. The simultaneous training of the autoencoder and estimation network is found to be mutually beneficial, resulting in faster autoencoder convergence, as well as superior classification and novelty detection performance. The estimation network also delivers adequate results even when optimized with pre-trained autoencoder features, suggesting that it can readily extend existing classifiers to provide added novelty detection capabilities.
Wireless accelerometers with various operating ranges have been used to measure tibial acceleration. Accelerometers with a low operating range output distorted signals and have been found to result ...in inaccurate measurements of peaks. A restoration algorithm using spline interpolation has been proposed to restore the distorted signal. This algorithm has been validated for axial peaks within the range of 15.0-15.9
. However, the accuracy of peaks of higher magnitude and the resultant peaks have not been reported. The purpose of the present study is to evaluate the measurement agreement of the restored peaks using a low-range accelerometer (±16
) against peaks sampled using a high-range accelerometer (±200
). The measurement agreement of both the axial and resultant peaks were examined. In total, 24 runners were equipped with 2 tri-axial accelerometers at their tibia and completed an outdoor running assessment. The accelerometer with an operating range of ±200
was used as reference. The results of this study showed an average difference of -1.40 ± 4.52
and -1.23 ± 5.48
for axial and resultant peaks. Based on our findings, the restoration algorithm could skew data and potentially lead to incorrect conclusions if used without caution.
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