seestar: Selection functions for spectroscopic surveys of the Milky Way

ABSTRACT Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multiobject spectrographs currently in operation and becoming available soon, we require easily generalizable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generating individual, tailored selection functions for every data release of each survey. Moreover, no methods for combining these selection functions to be used for joint catalogues have been developed. We have developed a Poisson likelihood estimation method for calculating selection functions in a Bayesian framework, which can be generalized to any multiobject spectrograph. We include a robust treatment of overlapping fields within a survey as well as selection functions for combined samples with overlapping footprints. We also provide a method for transforming the selection function that depends on the sky positions, colour, and apparent magnitude of a star to one that depends on the galactic location, metallicity, mass, and age of a star. This ‘intrinsic’ selection function is invaluable for chemodynamical models of the Milky Way. We demonstrate that our method is successful at recreating synthetic spectroscopic samples selected from a mock galaxy catalogue.

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Characterizing the Gaia radial velocity sample selection function in its native photometry

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3089 ◽

2020 ◽

Vol 500 (1) ◽

pp. 397-409

Author(s):

Jan Rybizki ◽

Hans-Walter Rix ◽

Markus Demleitner ◽

Coryn A L Bailer-Jones ◽

William J Cooper

Keyword(s):

Radial Velocity ◽

Milky Way ◽

Sample Selection ◽

Small Scale ◽

Apparent Magnitude ◽

Selection Function ◽

Source Density ◽

Dimensional Phase Space ◽

Data Release ◽

Gaia Dr2

ABSTRACT The Gaia Data Release 2 (DR2) radial velocity sample (GDR2RVS), which provides six-dimensional phase-space information on 7.2 million stars, is of great value for inferring properties of the Milky Way. Yet a quantitative and accurate modelling of this sample is hindered without knowledge and inclusion of a well-characterized selection function. Here we derive the selection function through estimates of the internal completeness, i.e. the ratio of GDR2RVS sources compared to all Gaia DR2 sources (GDR2all). We show that this selection function or ‘completeness’ depends on basic observables, in particular the apparent magnitude GRVS and colour G − GRP, but also on the surrounding source density and on sky position, where the completeness exhibits distinct small-scale structure. We identify a region of magnitude and colour that has high completeness, providing an approximate but simple way of implementing the selection function. For a more rigorous and detailed description we provide python code to query our selection function, as well as tools and adql queries that produce custom selection functions with additional quality cuts.

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Completeness of the Gaia verse II: what are the odds that a star is missing from Gaia DR2?

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa2305 ◽

2020 ◽

Vol 497 (4) ◽

pp. 4246-4261 ◽

Cited By ~ 1

Author(s):

Douglas Boubert ◽

Andrew Everall

Keyword(s):

Milky Way ◽

Magellanic Clouds ◽

Probability Of Detection ◽

Easy Access ◽

Selection Function ◽

G 21 ◽

Data Release ◽

Many Sources ◽

Gaia Dr2 ◽

Python Package

ABSTRACT The second data release of the Gaia mission contained astrometry and photometry for an incredible 1692 919 135 sources, but how many sources did Gaiamiss and where do they lie on the sky? The answer to this question will be crucial for any astronomer attempting to map the Milky Way with Gaia DR2. We infer the completeness of Gaia DR2 by exploiting the fact that it only contains sources with at least five astrometric detections. The odds that a source achieves those five detections depends on both the number of observations and the probability that an observation of that source results in a detection. We predict the number of times that each source was observed by Gaia and assume that the probability of detection is either a function of magnitude or a distribution as a function of magnitude. We fit both these models to the 1.7 billion stars of Gaia DR2, and thus are able to robustly predict the completeness of Gaia across the sky as a function of magnitude. We extend our selection function to account for crowding in dense regions of the sky, and show that this is vitally important, particularly in the Galactic bulge and the Large and Small Magellanic Clouds. We find that the magnitude limit at which Gaiais still 99 per cent complete varies over the sky from G = 18.9 to fainter than G = 21. We have created a new python package selectionfunctions (https://github.com/gaiaverse/selectionfunctions) which provides easy access to our selection functions.

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Institutional Quality and Trade Flow: Empirical Evidence from Malaysia and Other OIC Member Countries in Africa

Insight on Africa ◽

10.1177/0975087820987174 ◽

2021 ◽

pp. 097508782098717

Author(s):

Hammed Agboola Yusuf ◽

Luqman Olanrewaju Afolabi ◽

Waliu Olawale Shittu ◽

Kafilah Lola Gold ◽

Murtala Muhammad

Keyword(s):

Economic Growth ◽

Bilateral Trade ◽

Trade Flow ◽

Estimation Method ◽

Institutional Quality ◽

Likelihood Estimation ◽

Political Stability ◽

Bilateral Trade Flow ◽

World Governance Indicators ◽

The Impact

This article examines the impact of institutional quality on bilateral trade flow between Malaysia and selected 25 African Organisation of Islamic Cooperation (OIC) member countries. Four institutional qualities were selected from World Governance Indicators with other trade predictors from the period from 1985 to 2016. Using gravity model of trade and Poisson pseudo-maximum likelihood estimation method (PPML) technique, the results confirm that government effectiveness, regulatory quality and political stability have an adverse effect on bilateral trade flow among the OIC countries in Africa. On the other hand, these institutional quality variables were considered as a strength for Malaysian economic growth. Therefore, better institutional quality reforms are needed among OIC member countries in Africa in order to accelerate trade, economic growth and development in their region.

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A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

METRON ◽

10.1007/s40300-021-00204-x ◽

2021 ◽

Author(s):

Carlo Cavicchia ◽

Pasquale Sarnacchiaro

Keyword(s):

Job Satisfaction ◽

Factor Analysis ◽

Secondary School Teachers ◽

Estimation Method ◽

Likelihood Estimation ◽

Higher Order ◽

Teacher Job Satisfaction ◽

Public Secondary School ◽

Order Factor ◽

Maximum Likelihood Estimation Method

AbstractTeachers’ performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers’ accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Common Assessment Framework and Education questionnaire was administered to 163 Italian public secondary school teachers to collect data, and a second-order factor analysis was used to detect which factors impact on Teacher Job Satisfaction, and to what extent. This model-based approach guarantees to detect factors which respect important properties: unidimensionality and reliability. All the coefficients are estimated according to the maximum likelihood estimation method in order to make inference on the parameters and on the validity of the model. Moreover, a new multi-group test for higher-order factor analysis was proposed and implemented. Finally, we analyzed in detail whether the factors impacting Teacher Job Satisfaction are characterized by gender.

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SiFi

ACM Transactions on Internet of Things ◽

10.1145/3450567 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-21

Author(s):

Deke Guo ◽

Xiaoqiang Teng ◽

Yulan Guo ◽

Xiaolei Zhou ◽

Zhong Liu

Keyword(s):

Rapid Development ◽

Estimation Method ◽

Likelihood Estimation ◽

Location Based Services ◽

Shopping Mall ◽

Inference Problem ◽

Indoor Location ◽

Label Semantics ◽

Geometrical Constraints ◽

Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

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Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Sensors ◽

10.3390/s21041231 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1231

Author(s):

Yunbo Shi ◽

Juanjuan Zhang ◽

Jingjing Jiao ◽

Rui Zhao ◽

Huiliang Cao

Keyword(s):

Wavelet Packet ◽

Estimation Method ◽

Likelihood Estimation ◽

Parameter Extraction ◽

Accelerometer Sensor ◽

Oscillation Phase ◽

Mems Accelerometer ◽

Optimal Tree ◽

Adaptive Decomposition ◽

Entropy Criterion

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

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Research on the Distributed Characteristics of the Delay for Network Coding Based Real-Time Multicast in MANETs

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.530-531.768 ◽

2014 ◽

Vol 530-531 ◽

pp. 768-772

Author(s):

Guo Ping Tan ◽

Lin Feng Tan ◽

Lei Cao ◽

Mei Yan Ju

Keyword(s):

Maximum Likelihood ◽

Probability Distribution ◽

Network Coding ◽

Real Time ◽

Lognormal Distribution ◽

Ad Hoc ◽

Estimation Method ◽

Likelihood Estimation ◽

Actual Distribution ◽

Delay Distribution

For the study of the applications of partial network coding based real-time multicast protocol (PNCRM) in Mobile Ad hoc networks, the researches should be developed in the probability distribution of delay. In this paper, NS2 is used to obtain the delay of data packets through simulations. Because the delay does not obey the strict normal distribution, the maximum likelihood estimate method based on the lognormal distribution is used to process the data. Using MATLAB to obtain the actual distribution of the natural logarithm of delay, then drawing the delay distribution with the maximum likelihood estimation method based on the lognormal distribution, the conclusion that the distributions obtained by the above mentioned methods are basically consistent can be obtained. So the delay distribution of PNCRM meets the lognormal distribution and the characteristic of delay probability distribution can be estimated.

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Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Entropy ◽

10.3390/e23060674 ◽

2021 ◽

Vol 23 (6) ◽

pp. 674

Author(s):

Kushani De De Silva ◽

Carlo Cafaro ◽

Adom Giffin

Keyword(s):

State Of The Art ◽

Estimation Method ◽

Synthetic Data ◽

Noisy Data ◽

Bayesian Framework ◽

Physical Systems ◽

Gradient Profile ◽

Ill Posed ◽

Profile Estimation ◽

Different Levels

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.

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VVV DR1: The first data release of the Milky Way bulge and southern plane from the near-infrared ESO public survey VISTA variables in the Vía Láctea

Astronomy and Astrophysics ◽

10.1051/0004-6361/201118407 ◽

2012 ◽

Vol 537 ◽

pp. A107 ◽

Cited By ~ 232

Author(s):

R. K. Saito ◽

M. Hempel ◽

D. Minniti ◽

P. W. Lucas ◽

M. Rejkuba ◽

...

Keyword(s):

Near Infrared ◽

Milky Way ◽

Public Survey ◽

Data Release

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Local disc model in view of Gaia DR1 and RAVE data

Astronomy and Astrophysics ◽

10.1051/0004-6361/201833228 ◽

2018 ◽

Vol 620 ◽

pp. A71 ◽

Cited By ~ 1

Author(s):

K. Sysoliatina ◽

A. Just ◽

I. Koutsouridou ◽

E. K. Grebel ◽

G. Kordopatis ◽

...

Keyword(s):

Main Sequence ◽

Galactic Plane ◽

Formation Rate ◽

Velocity Distribution Function ◽

Initial Mass Function ◽

Selection Function ◽

Thin Disc ◽

Disc Model ◽

Starting Point ◽

Data Release

Aims. We test the performance of the semi-analytic self-consistent Just-Jahreiß disc model (JJ model) with the astrometric data from the Tycho-Gaia Astrometric Solution (TGAS) sub-catalogue of the first Gaia data release (Gaia DR1), as well as the radial velocities from the fifth data release of the Radial Velocity Experiment survey (RAVE DR5). Methods. We used a sample of 19 746 thin-disc stars from the TGAS×RAVE cross-match selected in a local solar cylinder of 300 pc radius and 1 kpc height below the Galactic plane. Based on the JJ model, we simulated this sample via the forward modelling technique. First, we converted the predicted vertical density laws of the thin-disc populations into a mock sample. For this we used the Modules and Experiments in Stellar Astrophysics (MESA) Isochrones and Stellar Tracks (MIST), a star formation rate (SFR) that decreased after a peak at 10 Gyr ago, and a three-slope broken power-law initial mass function (IMF). Then the obtained mock populations were reddened with a 3D dust map and were subjected to the selection criteria corresponding to the RAVE and TGAS observational limitations as well as to additional cuts applied to the data sample. We calculated the quantities of interest separately at different heights above the Galactic plane, taking the distance error effects in horizontal and vertical directions into account separately. Results. The simulated vertical number density profile agrees well with the data. An underestimation of the stellar numbers begins at ∼800 pc from the Galactic plane, which is expected as the possible influence of populations from |z| > 1 kpc is ignored during the modelling. The lower main sequence (LMS) is found to be thinner and under-populated by 3.6% relative to the observations. The corresponding deficits for the upper main sequence (UMS) and red giant branch (RGB) are 6% and 34.7%, respectively. However, the intrinsic uncertainty related to the choice of stellar isochrones is ∼10% in the total stellar number. The vertical velocity distribution function f(|W|) simulated for the whole cylinder agrees to within 1σ with the data. This marginal agreement arises because the dynamically cold populations at heights < 200 pc from the Galactic plane are underestimated. We also find that the model gives a fully realistic representation of the vertical gradient in stellar populations when studying the Hess diagrams for different horizontal slices. We also checked and confirm the consistency of our results with the newly available second Gaia data release (DR2). Conclusions. Based on these results and considering the uncertainties in the data selection as well as the sensitivity of the simulations to the sample selection function, we conclude that the fiducial JJ model confidently reproduces the vertical trends in the thin-disc stellar population properties. Thus, it can serve as a starting point for the future extension of the JJ model to other Galactocentric distances.

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