اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدPopulation synthesis at short wavelengths and spectrophotometric diagnostic tools for galaxy evolution
265
0
0.0
(
0
)
تأليف
A. Buzzoni
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Taking advantage of recent important advances in the calculation of high-resolution spectral grids of stellar atmospheres at short wavelengths, and their implementation for population synthesis models, we briefly review here some special properties of ultraviolet emission in SSPs, and discuss their potential applications for identifying and tuning up effective diagnostic tools to probe distinctive evolutionary properties of early-type galaxies and other evolved stellar systems.
قيم البحث
اقرأ أيضاً
I review the basic concepts for the spectrophotometric and chemical evolution of galaxies, contrast various approaches and discuss their respective advantages and shortcomings, both for the interpretation of nearby and high redshift galaxies. Focus i
s on recent attempts to include gas and dust into galaxy evolution models and to account for the links among stars, gas and dust. Chemically consistent models are described that try to cope with extended stellar metallicity distributions observed in local galaxies and with subsolar abundances in young galaxies.
The enrichment of Fe, relative to alpha-elements such as O and Mg, represents a potential means to determine the age of quasars and probe the galaxy formation epoch. To explore how ion{Fe}{2} emission in quasars is linked to physical conditions and
abundance, we have constructed a 830-level ion{Fe}{2} model atom and investigated through photoionization calculations how ion{Fe}{2} emission strengths depend on non-abundance factors. We have split ion{Fe}{2} emission into three major wavelength bands, ion{Fe}{2} (UV), ion{Fe}{2}(Opt1), and ion{Fe}{2}(Opt2), and explore how the ion{Fe}{2}(UV)/ion{Mg}{2}, ion{Fe}{2}(UV)/ion{Fe}{2}(Opt1) and ion{Fe}{2}(UV)/ion{Fe}{2}(Opt2) emission ratios depend upon hydrogen density and ionizing flux in broad-line regions (BLRs) of quasars. Our calculations show that: 1) similar ion{Fe}{2}(UV)/ion{Mg}{2} ratios can exist over a wide range of physical conditions; 2) the ion{Fe}{2}(UV)/ion{Fe}{2}(Opt1) and ion{Fe}{2}(UV)/ion{Fe}{2}(Opt2) ratios serve to constrain ionizing luminosity and hydrogen density; and 3) flux measurements of ion{Fe}{2} bands and knowledge of ionizing flux provide tools to derive distances to BLRs in quasars. To derive all BLR physical parameters with uncertainties, comparisons of our model with observations of a large quasar sample at low redshift ($z<1$) is desirable. The STIS and NICMOS spectrographs aboard the Hubble Space Telescope (HST) offer the best means to provide such observations.
Most ocean models in current use are built upon structured meshes. It follows that most existing tools for extracting diagnostic quantities (volume and surface integrals, for example) from ocean model output are constructed using techniques and softw
are tools which assume structured meshes. The greater complexity inherent in unstructured meshes (especially fully unstructured grids which are unstructured in the vertical as well as the horizontal direction) has left some oceanographers, accustomed to traditional methods, unclear on how to calculate diagnostics on these meshes. In this paper we show that tools for extracting diagnostic data from the new generation of unstructured ocean models can be constructed with relative ease using open source software. Higher level languages such as Python, in conjunction with packages such as NumPy, SciPy, VTK and MayaVi, provide many of the high-level primitives needed to perform 3D visualisation and evaluate diagnostic quantities, e.g. density fluxes. We demonstrate this in the particular case of calculating flux of vector fields through isosurfaces, using flow data obtained from the unstructured mesh finite element ocean code ICOM, however this tool can be applied to model output from any unstructured grid ocean code.
We have undertaken an imaging survey of 34 nearby galaxies in far-ultraviolet (FUV, ~1500A) and optical (UBVRI) passbands to characterize galaxy morphology as a function of wavelength. This sample, which includes a range of classical Hubble types fro
m elliptical to irregular with emphasis on spirals at low inclination angle, provides a valuable database for comparison with images of high-z galaxies whose FUV light is redshifted into the optical and near- infrared bands. Ultraviolet data are from the UIT Astro-2 mission. We present images and surface brightness profiles for each galaxy, and we discuss the wavelength-dependence of morphology for different Hubble types in the context of understanding high-z objects. In general, the dominance of young stars in the FUV produces the patchy appearance of a morphological type later than that inferred from optical images. Prominent rings and circumnuclear star formation regions are clearly evident in FUV images of spirals, while bulges, bars, and old, red stellar disks are faint to invisible at these short wavelengths. However, the magnitude of the change in apparent morphology ranges from dramatic in early--type spirals with prominent optical bulges to slight in late-type spirals and irregulars, in which young stars dominate both the UV and optical emission. Starburst galaxies with centrally concentrated, symmetric bursts display an apparent ``E/S0 structure in the FUV, while starbursts associated with rings or mergers produce a peculiar morphology. We briefly discuss the inadequacy of the optically-defined Hubble sequence to describe FUV galaxy images and estimate morphological k-corrections, and we suggest some directions for future research with this dataset.
We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use principal component analysis to construct a set of ba
sis functions, and neural networks to learn the basis coefficients as a function of the SPS model parameters. For photometry, we parameterize the magnitudes (for the filters of interest) as a function of SPS parameters by a neural network. The resulting emulators are able to predict spectra and photometry under both simple and complicated SPS model parameterizations to percent-level accuracy, giving a factor of $10^3$-$10^4$ speed up over direct SPS computation. They have readily-computable derivatives, making them amenable to gradient-based inference and optimization methods. The emulators are also straightforward to call from a GPU, giving an additional order-of-magnitude speed-up. Rapid SPS computations delivered by emulation offers a massive reduction in the computational resources required to infer the physical properties of galaxies from observed spectra or photometry and simulate galaxy populations under SPS models, whilst maintaining the accuracy required for a range of applications.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
