ترغب بنشر مسار تعليمي؟ اضغط هنا

What can we learn on the structure and the dynamics of the solar core with g modes?

92   0   0.0 ( 0 )
 نشر من قبل Savita Mathur
 تاريخ النشر 2008
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

The detection of the signature of dipole gravity modes has opened the path to study the solar inner radiative zone. Indeed, g modes should be the best probes to infer the properties of the solar nuclear core that represents more than half of the total mass of the Sun. Concerning the dynamics of the solar core, we can study how future observations of individual g modes could enhance our knowledge of the rotation profile of the deep radiative zone. Applying

قيم البحث

اقرأ أيضاً

Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
As one of the key properties of the Higgs boson, the Higgs total width is sensitive to global profile of the Higgs boson couplings, and thus new physics would modify the Higgs width. We investigate the total width in various new physics models, inclu ding various scalar extension, composite Higgs models, and fraternal twin Higgs model. Typically the Higgs width is smaller than the standard model value due to mixture with other scalar if the Higgs is elementary, or curved Higgs field space for the composite Higgs. On the other hand, except the possible invisible decay mode, the enhanced Yukawa coupling in the two Higgs doublet model or the exotic fermion embeddings in the composite Higgs, could enhance the Higgs width greatly. The precision measurement of the Higgs total width at the high-luminosity LHC can be used to discriminate certain new physics models.
Clustering of the large scale structure provides complementary information to the measurements of the cosmic microwave background anisotropies through power spectrum and bispectrum of density perturbations. Extracting the bispectrum information, howe ver, is more challenging than it is from the power spectrum due to the complex models and the computational cost to measure the signal and its covariance. To overcome these problems, we adopt a proxy statistic, skew spectrum which is a cross-spectrum of the density field and its quadratic field. By applying a large smoothing filter to the density field, we show the theory fits the simulations very well. With the spectra and their full covariance estimated from $N$-body simulations as our mock Universe, we perform a global fits for the cosmological parameters. The results show that adding skew spectrum to power spectrum the $1sigma$ marginalized errors for parameters $ b_1^2A_s, n_s$ and $f_{rm NL}^{rm loc}$ are reduced by $31%, 22%, 44%$, respectively. This is the answer to the question posed in the title and indicates that the skew spectrum will be a fast and effective method to access complementary information to that enclosed in the power spectrum measurements, especially for the forthcoming generation of wide-field galaxy surveys.
We discuss the features of instabilities in binary systems, in particular, for asymmetric nuclear matter. We show its relevance for the interpretation of results obtained in experiments and in ab initio simulations of the reaction between $^{124}Sn+^{124}Sn$ at 50AMeV.}
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا