اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAdvanced Morphological Galaxy Classification: A Comparison of Real and Simulated Galaxies
209
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Encoded within the morphological structure of galaxies are clues related to their formation and evolutionary history. Recent advances pertaining to the statistics of galaxy morphology include sophisticated measures of concentration (C), asymmetry (A), and clumpiness (S). In this study, these three parameters (CAS) have been applied to a suite of simulated galaxies and compared with observational results inferred from a sample of nearby galaxies. The simulations span a range of late-type systems, with masses between ~1e10 Msun and ~1e12 Msun, and employ star formation density thresholds between 0.1 cm^-3 and 100 cm^-3. We have found that the simulated galaxies possess comparable concentrations to their real counterparts. However, the results of the CAS analysis revealed that the simulated galaxies are generally more asymmetric, and that the range of clumpiness values extends beyond the range of those observed. Strong correlations were obtained between the three CAS parameters and colour (B-V), consistent with observed galaxies. Furthermore, the simulated galaxies possess strong links between their CAS parameters and Hubble type, mostly in-line with their real counterparts.
قيم البحث
اقرأ أيضاً
We have used GIM2D to quantify the morphological properties of 40 intermediate redshift MgII absorption-selected galaxies (0.03<Wr(2796)<2.9 Ang), imaged with WFPC-2/HST, and compared them to the halo gas properties measured form HIRES/Keck and UVES/
VLT quasar spectra. We find that as the quasar-galaxy separation, D, increases the MgII equivalent decreases with large scatter, implying that D is not the only physical parameter affecting the distribution and quantity of halo gas. Our main result shows that inclination correlates with MgII absorption properties after normalizing out the relationship (and scatter) between the absorption properties and D. We find a 4.3 sigma correlation between Wr(2796) and galaxy inclination, normalized by impact parameter, i/D. Other measures of absorption optical depth also correlate with i/D at greater than 3.2 sigma significance. Overall, this result suggests that MgII gas has a co-planer geometry, not necessarily disk-like, that is coupled to the galaxy inclination. It is plausible that the absorbing gas arises from tidal streams, satellites, filaments, etc., which tend to have somewhat co-planer distributions. This result does not support a picture in which MgII absorbers with Wr(2796)<1A are predominantly produced by star-formation driven winds. We further find that; (1) MgII host galaxies have quantitatively similar bulge and disk scale length distribution to field galaxies at similar redshifts and have a mean disk and bulge scale length of 3.8kpc and 2.5kpc, respectively; (2) Galaxy color and luminosity do not correlate strongly with absorption properties, implying a lack of a connection between host galaxy star formation rates and absorption strength; (3) Parameters such as scale lengths and bulge-to-total ratios do not significantly correlate with the absorption parameters, suggesting that the absorption is independent of galaxy size or mass.
We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have p
roduced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.
Recent integral field spectroscopic (IFS) surveys have revealed radial gradients in the optical spectral indices of post-starburst galaxies, which can be used to constrain their formation histories. We study the spectral indices of post-processed moc
k IFS datacubes of binary merger simulations, carefully matched to the properties of the MaNGA IFS survey, with a variety of black hole feedback models, progenitor galaxies, orbits and mass ratios. Based on our simulation sample, we find that only major mergers on prograde-prograde or retrograde-prograde orbits in combination with a mechanical black hole feedback model can form galaxies with weak enough ongoing star formation, and therefore absent H$alpha$ emission, to be selected by traditional PSB selection methods. We find strong fluctuations in nebular emission line strengths, even within the PSB phase, suggesting that H$alpha$ selected PSBs are only a subsample of the underlying population. The global PSB population can be more robustly identified using stellar continuum-based approaches. The difficulty in reproducing the very young PSBs in simulations potentially indicates that new sub-resolution star formation recipes are required to properly model the process of star formation quenching. In our simulations, we find that the starburst peaks at the same time at all radii, but is stronger and more prolonged in the inner regions. This results in a strong time evolution in the radial gradients of the spectral indices which can be used to estimate the age of the starburst without reliance on detailed star formation histories from spectral synthesis models.
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured for each gal
axy (sizes, colours, shape parameters & stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of $75.8%$, $69.0%$, $76.2%$ and $76.0%$ respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification (`unanimous disagreement) serves as a potential indicator of human error in classification, occurring in $sim9%$ of ellipticals, $sim9%$ of Little Blue Spheroids, $sim14%$ of early-type spirals, $sim21%$ of intermediate-type spirals and $sim4%$ of late-type spirals & irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy datasets. Adopting the CTRF algorithm, the TPRs of the 5 galaxy types are : E, $70.1%$; LBS, $75.6%$; S0-Sa, $63.6%$; Sab-Scd, $56.4%$ and Sd-Irr, $88.9%$. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS & S0-Sa) and disk-dominated (Sab-Scd & Sd-Irr), achieving an overall accuracy of $89.8%$. This translates into an accuracy of $84.9%$ for spheroid-dominated systems and $92.5%$ for disk-dominated systems.
We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modifie
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
