scholarly journals Exploding SNe with jets: time-scales

2011 ◽  
Vol 7 (S279) ◽  
pp. 377-379
Author(s):  
Oded Papish ◽  
Noam Soker

AbstractWe perform hydrodynamical simulations of core collapse supernovae (CCSNe) with a cylindrically-symmetrical numerical code (FLASH) to study the inflation of bubbles and the initiation of the explosion within the frame of the jittering-jets model. We study the typical time-scale of the model and compare it to the typical time-scale of the delayed neutrino mechanism. Our analysis shows that the explosion energy of the delayed neutrino mechanism is an order of magnitude less than the required 1051 erg.

2019 ◽  
Vol 491 (1) ◽  
pp. L66-L71
Author(s):  
Len Hartsuiker ◽  
Sylvia Ploeckinger

ABSTRACT Observations of compact groups of galaxies (CGs) indicate that their abundance has not significantly changed since z = 0.2. This balance between the time-scales for formation and destruction of CGs is challenging if the typical time-scale for CG members to merge into one massive galaxy is as short as historically assumed (<0.1 Hubble times). Following the evolution of CGs over time in a cosmological simulation (EAGLE), we quantify the contributions of individual processes that in the end explain the observed abundance of CGs. We find that despite the usually applied maximum line-of-sight velocity difference of 1000 km s–1 within the group members, the majority of CGs (≈60 per cent) are elongated along the line of sight by at least a factor of 2. These CGs are mostly transient as they are only compact in projection. In more spherical systems ≈80 per cent of galaxies at z > 0.4 merge into one massive galaxy before the simulation end (z = 0) and we find that the typical time-scale for this process is 2–3 Gyr. We conclude that the combination of large fractions of interlopers and the longer median group coalescence time-scale of CGs alleviates the need for a fast formation process to explain the observed abundance of CGs for z < 0.2.


Author(s):  
Eckehard Olbrich ◽  
Jens Christian Claussen ◽  
Peter Achermann

A particular property of the sleeping brain is that it exhibits dynamics on very different time scales ranging from the typical sleep oscillations such as sleep spindles and slow waves that can be observed in electroencephalogram (EEG) segments of several seconds duration over the transitions between the different sleep stages on a time scale of minutes to the dynamical processes involved in sleep regulation with typical time constants in the range of hours. There is an increasing body of work on mathematical and computational models addressing these different dynamics, however, usually considering only processes on a single time scale. In this paper, we review and present a new analysis of the dynamics of human sleep EEG at the different time scales and relate the findings to recent modelling efforts pointing out both the achievements and remaining challenges.


2020 ◽  
Vol 501 (1) ◽  
pp. 1059-1071
Author(s):  
A Reguitti ◽  
M L Pumo ◽  
P A Mazzali ◽  
A Pastorello ◽  
G Pignata ◽  
...  

ABSTRACT In this work, we present photometric and spectroscopic data of the low-luminosity (LL) Type IIP supernova (SN) 2018hwm. The object shows a faint (Mr = −15 mag) and very long (∼130 d) plateau, followed by a 2.7 mag drop in the r band to the radioactive tail. The first spectrum shows a blue continuum with narrow Balmer lines, while during the plateau the spectra show numerous metal lines, all with strong and narrow P-Cygni profiles. The expansion velocities are low, in the 1000–1400 km s−1 range. The nebular spectrum, dominated by H α in emission, reveals weak emission from [O i] and [Ca ii] doublets. The absolute light curve and spectra at different phases are similar to those of LL SNe IIP. We estimate that 0.002 M⊙ of 56Ni mass were ejected, through hydrodynamical simulations. The best fit of the model to the observed data is found for an extremely low explosion energy of 0.055 foe, a progenitor radius of 215 R⊙, and a final progenitor mass of 9–10 M⊙. Finally, we performed a modelling of the nebular spectrum, to establish the amount of oxygen and calcium ejected. We found a low M(16O)$\approx 0.02\, \mathrm{ M}_{\odot }$, but a high M(40Ca) of 0.3 M⊙. The inferred low explosion energy, the low ejected 56Ni mass, and the progenitor parameters, along with peculiar features observed in the nebular spectrum, are consistent with both an electron-capture SN explosion of a superasymptotic giant branch star and with a low-energy, Ni-poor iron core-collapse SN from a 10–12 M⊙ red supergiant.


2018 ◽  
Vol 33 (09) ◽  
pp. 1843011 ◽  
Author(s):  
René Reifarth ◽  
Stefan Fiebiger ◽  
Kathrin Göbel ◽  
Tanja Heftrich ◽  
Tanja Kausch ◽  
...  

The decay properties of long-lived excited states (isomers) can have a significant impact on the destruction channels of isotopes under stellar conditions. In sufficiently hot environments, the population of isomers can be altered via thermal excitation or de-excitation. If the corresponding lifetimes are of the same order of magnitude as the typical time scales of the environment, the isomers have to be treated explicitly. We present a general approach to the treatment of isomers in stellar nucleosynthesis codes and discuss a few illustrative examples. The corresponding code is available online at http://exp-astro.de/isomers/ .


2020 ◽  
Vol 501 (2) ◽  
pp. 1803-1822
Author(s):  
Seunghwan Lim ◽  
Douglas Scott ◽  
Arif Babul ◽  
David J Barnes ◽  
Scott T Kay ◽  
...  

ABSTRACT As progenitors of the most massive objects, protoclusters are key to tracing the evolution and star formation history of the Universe, and are responsible for ${\gtrsim }\, 20$ per cent of the cosmic star formation at $z\, {\gt }\, 2$. Using a combination of state-of-the-art hydrodynamical simulations and empirical models, we show that current galaxy formation models do not produce enough star formation in protoclusters to match observations. We find that the star formation rates (SFRs) predicted from the models are an order of magnitude lower than what is seen in observations, despite the relatively good agreement found for their mass-accretion histories, specifically that they lie on an evolutionary path to become Coma-like clusters at $z\, {\simeq }\, 0$. Using a well-studied protocluster core at $z\, {=}\, 4.3$ as a test case, we find that star formation efficiency of protocluster galaxies is higher than predicted by the models. We show that a large part of the discrepancy can be attributed to a dependence of SFR on the numerical resolution of the simulations, with a roughly factor of 3 drop in SFR when the spatial resolution decreases by a factor of 4. We also present predictions up to $z\, {\simeq }\, 7$. Compared to lower redshifts, we find that centrals (the most massive member galaxies) are more distinct from the other galaxies, while protocluster galaxies are less distinct from field galaxies. All these results suggest that, as a rare and extreme population at high z, protoclusters can help constrain galaxy formation models tuned to match the average population at $z\, {\simeq }\, 0$.


GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Ilaria Sesia ◽  
Giovanna Signorile ◽  
Tung Thanh Thai ◽  
Pascale Defraigne ◽  
Patrizia Tavella

AbstractWe present two different approaches to broadcasting information to retrieve the GNSS-to-GNSS time offsets needed by users of multi-GNSS signals. Both approaches rely on the broadcast of a single time offset of each GNSS time versus one common time scale instead of broadcasting the time offsets between each of the constellation pairs. The first common time scale is the average of the GNSS time scales, and the second time scale is the prediction of UTC already broadcast by the different systems. We show that the average GNSS time scale allows the estimation of the GNSS-to-GNSS time offset at the user level with the very low uncertainty of a few nanoseconds when the receivers at both the provider and user levels are fully calibrated. The use of broadcast UTC prediction as a common time scale has a slightly larger uncertainty, which depends on the broadcast UTC prediction quality, which could be improved in the future. This study focuses on the evaluation of two different common time scales, not considering the impact of receiver calibration, at the user and provider levels, which can nevertheless have an important impact on GNSS-to-GNSS time offset estimation.


2021 ◽  
Vol 2 (3) ◽  
pp. 1-15
Author(s):  
Cheng Wan ◽  
Andrew W. Mchill ◽  
Elizabeth B. Klerman ◽  
Akane Sano

Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, whereas the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.


2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).


2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jianzhuo Yan ◽  
Shangbin Chen ◽  
Sinuo Deng

Abstract As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.


2009 ◽  
Vol 66 (7) ◽  
pp. 2107-2115 ◽  
Author(s):  
Cegeon J. Chan ◽  
R. Alan Plumb

Abstract In simple GCMs, the time scale associated with the persistence of one particular phase of the model’s leading mode of variability can often be unrealistically large. In a particularly extreme example, the time scale in the Polvani–Kushner model is about an order of magnitude larger than the observed atmosphere. From the fluctuation–dissipation theorem, one implication of these simple models is that responses are exaggerated, since such setups are overly sensitive to any external forcing. Although the model’s equilibrium temperature is set up to represent perpetual Southern Hemisphere winter solstice, it is found that the tropospheric eddy-driven jet has a preference for two distinct regions: the subtropics and midlatitudes. Because of this bimodality, the jet persists in one region for thousands of days before “switching” to another. As a result, the time scale associated with the intrinsic variability is unrealistic. In this paper, the authors systematically vary the model’s tropospheric equilibrium temperature profile, one configuration being identical to that of Polvani and Kushner. Modest changes to the tropospheric state to either side of the parameter space removed the bimodality in the zonal-mean zonal jet’s spatial distribution and significantly reduced the time scale associated with the model’s internal mode. Consequently, the tropospheric response to the same stratospheric forcing is significantly weaker than in the Polvani and Kushner case.


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