اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدChromaStarPy: A stellar atmosphere and spectrum modeling and visualization lab in python
48
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We announce ChromaStarPy, an integrated general stellar atmospheric modeling and spectrum synthesis code written entirely in python V. 3. ChromaStarPy is a direct port of the ChromaStarServer (CSServ) Java modeling code described in earlier papers in this series, and many of the associated JavaScript (JS) post-processing procedures have been ported and incorporated into CSPy so that students have access to ready-made data products. A python integrated development environment (IDE) allows a student in a more advanced course to experiment with the code and to graphically visualize intermediate and final results, ad hoc, as they are running it. CSPy allows students and researchers to compare modeled to observed spectra in the same IDE in which they are processing observational data, while having complete control over the stellar parameters affecting the synthetic spectra. We also take the opportunity to describe improvements that have been made to the related codes, ChromaStar (CS), CSServ and ChromaStarDB (CSDB) that, where relevant, have also been incorporated into CSPy. The application may be found at the home page of the OpenStars project: http://www.ap.smu.ca/~ishort/OpenStars/ .
قيم البحث
اقرأ أيضاً
The next decade will be an exciting period for solar astrophysics, as new ground- and space-based instrumentation will provide unprecedented observations of the solar atmosphere and heliosphere. The synergy between modeling effort and comprehensive a
nalysis of observations is crucial for the understanding of the physical processes behind the observed phenomena. However, the unprecedented wealth of data on one hand, and the complexity of the physical phenomena on the other, require the development of new approaches in both data analysis and numerical modeling. In this white paper, we summarize recent numerical achievements to reproduce structure, dynamics, and observed phenomena from the photosphere to the low corona and outline challenges we expect to face for the interpretation of future observations.
Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each s
tellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey (SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.
The White Dwarf Evolution Code (WDEC), written in Fortran, makes models of white dwarf stars. It is fast, versatile, and includes the latest physics. The code evolves hot (~ 100,000 K) input models down to a chosen effective temperature by relaxing t
he models to be solutions of the equations of stellar structure. The code can also be used to obtain g-mode oscillation modes for the models. WDEC has a long history going back to the late 1960s. Over the years, it has been updated and re-packaged for modern computer architectures, and has specifically been used in computationally intensive asteroseismic fitting. Generations of white dwarf astronomers and dozens of publications have made use of the WDEC, although the last true instrument paper is the original one, published in 1975. This paper discusses the history of the code, necessary to understand why it works the way it does, details the physics and features in the code today, and points the reader to where to find the code and a user guide.
This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Ba
yesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.
Multilayer X-ray mirrors consist of a coating of a large number of alternate layers of high Z and low Z materials with a typical thickness of 10-100 Angstrom, on a suitable substrate. Such coatings play an important role in enhancing the reflectivity
of X-ray mirrors by allowing reflections at angles much larger than the critical angle of X-ray reflection for the given materials. Coating with an equal thickness of each bilayer enhances the reflectivity at discrete energies, satisfying Bragg condition. However, by systematically varying the bilayer thickness in the multilayer stack, it is possible to design X-ray mirrors having enhanced reflectivity over a broad energy range. One of the most important applications of such a depth graded multilayer mirror is to realize hard X-ray telescopes for astronomical purposes. Design of such multilayer X-ray mirrors and their characterization with X-ray reflectivity measurements require appropriate software tools. We have initiated the development of hard X-ray optics for future Indian X-ray astronomical missions, and in this context, we have developed a program, DarpanX, to calculate X-ray reflectivity for single and multilayer mirrors. It can be used as a stand-alone tool for designing multilayer mirrors with required characteristics. But more importantly, it has been implemented as a local model for the popular X-ray spectral fitting program, XSPEC, and thus can be used for accurate fitting of the experimentally measured X-ray reflectivity data. DarpanX is implemented as a Python 3 module, and an API is provided to access the underlying algorithms. Here we present details of DarpanX implementation and its validation for different type multilayer structures. We also demonstrate the model fitting capability of DarpanX for experimental X-ray reflectivity measurements of single and multilayer samples.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
