scholarly journals SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

2022 ◽  
Vol 163 (2) ◽  
pp. 56
Author(s):  
Julie Imig ◽  
Jon A. Holtzman ◽  
Renbin Yan ◽  
Daniel Lazarz ◽  
Yanping Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Population Analysis ◽  
Data Driven ◽  
Element Abundance ◽  
Physical Parameters ◽  
Chemical Abundance ◽  
Stellar Spectra ◽  
Evolution Experiment ◽  
Microturbulent Velocity ◽  
Nearby Galaxies

Abstract The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Stellar Library (MaStar) is a large collection of high-quality empirical stellar spectra designed to cover all spectral types and ideal for use in the stellar population analysis of galaxies observed in the MaNGA survey. The library contains 59,266 spectra of 24,130 unique stars with spectral resolution R ∼ 1800 and covering a wavelength range of 3622–10,354 Å. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (T eff), surface gravity ( log g ), metallicity ([Fe/H]), microturbulent velocity ( log ( v micro ) ), and alpha-element abundance ([α/Fe]). These parameters are derived with a flexible data-driven algorithm that uses a neural network model. We train a neural network using the subset of 1675 MaStar targets that have also been observed in the Apache Point Observatory Galactic Evolution Experiment (APOGEE), adopting the independently-derived APOGEE Stellar Parameter and Chemical Abundance Pipeline parameters for this reference set. For the regions of parameter space not well represented by the APOGEE training set (7000 ≤ T ≤ 30,000 K), we supplement with theoretical model spectra. We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature.

scholarly journals Searching for extremely alpha-poor stars in the Galactic Halo

2013 ◽  
Vol 9 (S298) ◽  
pp. 449-449
Author(s):  
Qianfan Xing ◽  
Gang Zhao
Keyword(s):  
Galactic Halo ◽  
Element Abundance ◽  
Chemical Abundance ◽  
Stellar Spectra ◽  
Large Sample ◽  
Pilot Survey ◽  
Chemical Enrichment ◽  
Halo Stars ◽  
Abundance Patterns ◽  
Abundance Trends

AbstractA few alpha-poor stars that show severe departures (over 0.4 dex deficiency in alpha-element abundance) from the general enhanced alpha-element chemical abundance trends of the halo have been discovered in recent years, such as BD +80°245, G4-36 and CS 22966-043. These ratios suggest a different chemical enrichment history for these stars than for the majority of the halo. Similarly low-alpha abundance patterns are also seen in the Sagittarius dSph galaxy. We present a method for searching of extremely alpha-poor stars from low-resolution stellar spectra of LAMOST pilot survey and attempt to create a large sample of these particular Galactic halo stars.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Recovering the star formation history of galaxies through spectral fitting: Current challenges

2019 ◽  
Vol 15 (S359) ◽  
pp. 386-390
Author(s):  
Lucimara P. Martins
Keyword(s):  
Star Formation ◽  
Mass Function ◽  
Initial Mass Function ◽  
Star Formation History ◽  
Stellar Spectra ◽  
Spectral Fitting ◽  
Formation History ◽  
History Of ◽  
Nearby Galaxies ◽  
Extract Information

AbstractWith the exception of some nearby galaxies, we cannot resolve stars individually. To recover the galaxies star formation history (SFH), the challenge is to extract information from their integrated spectrum. A widely used tool is the full spectral fitting technique. This consists of combining simple stellar populations (SSPs) of different ages and metallicities to match the integrated spectrum. This technique works well for optical spectra, for metallicities near solar and chemical histories not much different from our Galaxy. For everything else there is room for improvement. With telescopes being able to explore further and further away, and beyond the optical, the improvement of this type of tool is crucial. SSPs use as ingredients isochrones, an initial mass function, and a library of stellar spectra. My focus are the stellar libraries, key ingredient for SSPs. Here I talk about the latest developments of stellar libraries, how they influence the SSPs and how to improve them.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals Galaxy properties as revealed by MaNGA – III. Kinematic profiles and stellar population gradients in S0s

2020 ◽  
Vol 495 (3) ◽  
pp. 2894-2908 ◽  
Author(s):  
H Domínguez Sánchez ◽  
M Bernardi ◽  
F Nikakhtar ◽  
B Margalef-Bentabol ◽  
R K Sheth
Keyword(s):  
Stellar Population ◽  
Velocity Dispersion ◽  
Signal To Noise Ratio ◽  
Elliptical Galaxies ◽  
Element Abundance ◽  
High Signal ◽  
Galactocentric Distance ◽  
Massive Galaxies ◽  
Nearby Galaxies ◽  
Mass Functions

ABSTRACT This is the third paper of a series where we study the stellar population gradients (SP; ages, metallicities, α-element abundance ratios, and stellar initial mass functions) of early-type galaxies (ETGs) at $z$ ≤ 0.08 from the Mapping Nearby Galaxies at APO Data Release 15 (MaNGA-DR15) survey. In this work, we focus on the S0 population and quantify how the SP varies across the population as well as with galactocentric distance. We do this by measuring Lick indices and comparing them to SP synthesis models. This requires spectra with high signal-to-noise ratio which we achieve by stacking in bins of luminosity (Lr) and central velocity dispersion (σ0). We find that: (1) there is a bimodality in the S0 population: S0s more massive than $3\times 10^{10}\, \mathrm{M}_\odot$ show stronger velocity dispersion and age gradients (age and σr decrease outwards) but little or no metallicity gradient, while the less massive ones present relatively flat age and velocity dispersion profiles, but a significant metallicity gradient (i.e. [M/H] decreases outwards). Above $2\times 10^{11}\, \mathrm{M}_\odot$, the number of S0s drops sharply. These two mass scales are also where global scaling relations of ETGs change slope. (2) S0s have steeper velocity dispersion profiles than fast-rotating elliptical galaxies (E-FRs) of the same luminosity and velocity dispersion. The kinematic profiles and SP gradients of E-FRs are both more similar to those of slow-rotating ellipticals (E-SRs) than to S0s, suggesting that E-FRs are not simply S0s viewed face-on. (3) At fixed σ0, more luminous S0s and E-FRs are younger, more metal rich and less α-enhanced. Evidently for these galaxies, the usual statement that ‘massive galaxies are older’ is not true if σ0 is held fixed.

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