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The First Data Release of LAMOST Low Resolution Single Epoch Spectra

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abstract

LAMOST Data Release 5, covering $\sim$17,000 $deg^2$ from $-10^{\circ}$ to $80^{\circ}$ in declination, contains 9 millions co-added low resolution spectra of celestial objects, each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun 2017. In this paper, We present the spectra of individual exposures for all the objects in LAMOST Data Release 5. For each spectrum, equivalent width of 60 lines from 11 different elements are calculated with a new method combining the actual line core and fitted line wings. For stars earlier than F type, the Balmer lines are fitted with both emission and absorption profiles once two components are detected. Radial velocity of each individual exposure is measured by minimizing ${\chi}^2$ between the spectrum and its best template. Database for equivalent widths of spectral lines and radial velocities of individual spectra are available online. Radial velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same objects. We notice that the radial velocity uncertainty depends on the time lag between observations. For stars observed in the same day and with signal-to-noise ratio higher than 20, the radial velocity uncertainty is below 5km/s, and increase to 10km/s for stars observed in different nights.

fields

astro-ph.IM 1

years

2024 1

verdicts

CONDITIONAL 1

representative citing papers

Why Machine Learning Models Systematically Underestimate Extreme Values

astro-ph.IM · 2024-12-08 · conditional · novelty 5.0

Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.

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  • Why Machine Learning Models Systematically Underestimate Extreme Values astro-ph.IM · 2024-12-08 · conditional · none · ref 9 · internal anchor

    Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.