اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRevealing components of the galaxy population through nonparametric techniques
164
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.
قيم البحث
اقرأ أيضاً
We present results of a search for supernova remnants (SNRs) in archival Chandra images of M33. We have identified X-ray SNRs by comparing the list of Chandra, X-ray sources in M33 with tabulations of SNR candidates identified from (1) elevated [S II
]/Halpha ratios in the optical, and (2) radio spectral indices. Of the 98 optically known SNRs in M33, 22 have been detected at > 3-sigma level in the soft band (0.35-1.1 keV). At least four of these SNR candidates are spatially extended based on a comparison of the data to simulated images of point sources. Aside from the optically matching SNRs, we have found one soft X-ray source in M33 which exhibits no optical emission and is coincident with a known radio source. The radio spectral index of this source is consistent with particle acceleration in shocks, leading us to suggest that it is a non-radiative SNR. We have also found new optical counterparts to two soft X-ray SNRs in M33. Pending confirmation from optical spectroscopy, the identification of these two optical counterparts increases the total number of known optically emitting SNRs in M33 to 100. This brings the total number of identified SNRs with X-ray counterparts, including those exclusively detected by the XMM-Newton survey of M33, to 37 SNRs. We find that while there are a similar number of confirmed X-ray SNRs in M33 and the LMC with X-ray luminosities in excess of 1e35 ergs/s, nearly 40% of the LMC SNRs are brighter than 1e36 ergs/s, while only 13% of the M33 sample exceed this luminosity. The differences in luminosity distributions cannot be fully explained by uncertainty in spectral model parameters, and is not fully accounted for by abundance differences between the galaxies (abridged).
This review summarizes recent developments in the study of fermionic quantum criticality, focusing on new progress in numerical methodologies, especially quantum Monte Carlo methods, and insights that emerged from recently large-scale numerical simul
ations. Quantum critical phenomena in fermionic systems have attracted decades of extensive research efforts, partially lured by their exotic properties and potential technology applications and partially awaked by the profound and universal fundamental principles that govern these quantum critical systems. Due to the complex and non-perturbative nature, these systems belong to the most difficult and challenging problems in the study of modern condensed matter physics, and many important fundamental problems remain open. Recently, new developments in model design and algorithm improvements enabled unbiased large-scale numerical solutions to be achieved in the close vicinity of these quantum critical points, which paves a new pathway towards achieving controlled conclusions through combined efforts of theoretical and numerical studies, as well as possible theoretical guidance for experiments in heavy-fermion compounds, Cu-based and Fe-based superconductors, ultra-cold fermionic atomic gas, twisted graphene layers, etc., where signatures of fermionic quantum criticality exist.
We study populations of High-Mass X-ray Binaries in the Galaxy using data of the INTEGRAL observatory in a hard X-ray energy band. More than two hundreds of sources were detected with INTEGRAL near the galactic plane (|b|<5 deg), most of them have a
galactic origin and belong to high (HMXB) and low mass (LMXB) X-ray binaries. We investigated properties and spectra of a large sample of HMXBs and concluded that most of them are belong to X-ray pulsars. We also build the distribution of HMXBs for the whole Galaxy and showed that its peaks are practically coincident with spiral arm tangents. The obtained results are discussed in terms of some model estimations of the density of different components of the Galaxy.
Advances in vectorial polarisation-resolved imaging are bringing new capabilities to applications ranging from fundamental physics through to clinical diagnosis. Imaging polarimetry requires determination of the Mueller matrix (MM) at every point, pr
oviding a complete description of an objects vectorial properties. Despite forming a comprehensive representation, the MM does not usually provide easily-interpretable information about the objects internal structure. Certain simpler vectorial metrics are derived from subsets of the MM elements. These metrics permit extraction of signatures that provide direct indicators of hidden optical properties of complex systems, while featuring an intriguing asymmetry about what information can or cannot be inferred via these metrics. We harness such characteristics to reveal the spin-Hall effect of light, infer microscopic structure within laser-written photonic waveguides, and conduct rapid pathological diagnosis through analysis of healthy and cancerous tissue. This provides new insight for the broader usage of such asymmetric inferred vectorial information.
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge.
However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model in this paper. Instead of using fixed-dimensional probability distributions in its generative model, we use stochastic processes. Specifically, a gamma process is assigned to each document, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different gamma process from the global gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
