The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Gail Zasowski - One of the best experts on this subject based on the ideXlab platform.
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the cannon a Data Driven Approach to stellar label determination
The Astrophysical Journal, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of stellar parameters and abundances (“stellar labels”) for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the “known” labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with , , and as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
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the cannon a Data Driven Approach to stellar label determination
arXiv: Solar and Stellar Astrophysics, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is a sub-set of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the 'known' labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with Teff, log g and [Fe/H] as the labels, and then applying it to the spectra of 56,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
David W Hogg - One of the best experts on this subject based on the ideXlab platform.
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the cannon a Data Driven Approach to stellar label determination
The Astrophysical Journal, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of stellar parameters and abundances (“stellar labels”) for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the “known” labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with , , and as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
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the cannon a Data Driven Approach to stellar label determination
arXiv: Solar and Stellar Astrophysics, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is a sub-set of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the 'known' labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with Teff, log g and [Fe/H] as the labels, and then applying it to the spectra of 56,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
Melissa Ness - One of the best experts on this subject based on the ideXlab platform.
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the cannon a Data Driven Approach to stellar label determination
The Astrophysical Journal, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of stellar parameters and abundances (“stellar labels”) for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the “known” labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with , , and as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
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the cannon a Data Driven Approach to stellar label determination
arXiv: Solar and Stellar Astrophysics, 2015Co-Authors: Melissa Ness, David W Hogg, Hanswalter Rix, Gail ZasowskiAbstract:New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is a sub-set of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new Data-Driven Approach for determining stellar labels from spectroscopic Data. The Cannon learns from the 'known' labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with Teff, log g and [Fe/H] as the labels, and then applying it to the spectra of 56,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
S. K. Das - One of the best experts on this subject based on the ideXlab platform.
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Survey of Security Advances in Smart Grid: A Data Driven Approach
IEEE Communications Surveys & Tutorials, 2017Co-Authors: S. Tan, D. De, W. Z. Song, Junjie Yang, Jihui Yang, S. K. DasAbstract:With the integration of advanced computing and communication technologies, smart grid is considered as the next-generation power system, which promises self healing, resilience, sustainability, and efficiency to the energy critical infrastructure. The smart grid innovation brings enormous challenges and initiatives across both industry and academia, in which the security issue emerges to be a critical concern. In this paper, we present a survey of recent security advances in smart grid, by a Data Driven Approach. Compared with existing related works, our survey is centered around the security vulnerabilities and solutions within the entire lifecycle of smart grid Data, which are systematically decomposed into four sequential stages: 1) Data generation; 2) Data acquisition; 3) Data storage; and 4) Data processing. Moreover, we further review the security analytics in smart grid, which employs Data analytics to ensure smart grid security. Finally, an effort to shed light on potential future research concludes this paper.
Xiangyue Liu - One of the best experts on this subject based on the ideXlab platform.
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a Data Driven Approach to determine dipole moments of diatomic molecules
Physical Chemistry Chemical Physics, 2020Co-Authors: Xiangyue Liu, Gerard Meijer, Jesus PerezriosAbstract:We present a Data-Driven Approach for the prediction of the electric dipole moment of diatomic molecules, which is one of the most relevant molecular properties. In particular, we apply Gaussian process regression to a novel Dataset to show that dipole moments of diatomic molecules can be learned, and hence predicted, with a relative error ≲5%. The Dataset contains the dipole moment of 162 diatomic molecules, the most exhaustive and unbiased Dataset of dipole moments up to date. Our findings show that the dipole moment of diatomic molecules depends on atomic properties of the constituents atoms: electron affinity and ionization potential, as well as on (a feature related to) the first derivative of the electronic kinetic energy at the equilibrium distance.
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A Data-Driven Approach to determine dipole moments of diatomic molecules
Physical chemistry chemical physics : PCCP, 2020Co-Authors: Xiangyue Liu, Gerard Meijer, Jesús Pérez-ríosAbstract:We present a Data-Driven Approach for the prediction of the electric dipole moment of diatomic molecules, which is one of the most relevant molecular properties. In particular, we apply Gaussian process regression to a novel Dataset to show that dipole moments of diatomic molecules can be learned, and hence predicted, with a relative error
