相关论文: Predicting Stellar Rotation Periods Using XGBoost

In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH),…

The TESS mission delivers time-series photometry for millions of stars across the sky, offering a probe into stellar astrophysics, including rotation, on a population scale. However, light curve systematics related to the satellite's…

We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…

Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging…

Stellar rotation periods are valuable both for constraining models of angular momentum loss and for under- standing how magnetic features impact inferences of exoplanet parameters. Building on our previous work in the northern hemisphere,…

We present rotation period measurements for subgiants observed by CoRoT. Interpreting the modulation of stellar light that is caused by star-spots on the time scale of the rotational period depends on knowing the fundamental stellar…

Even with the diminished precision possible with only two reaction wheels, the Kepler spacecraft can obtain mmag level, time-resolved photometry of tens of thousands of sources. The presence of such a rich, large data set could be…

For ongoing studies of the role of rotation in stellar evolution, we require large catalogs of rotation periods for testing and refining gyrochronology. While there is a wealth of data from the Kepler and K2 missions, TESS presents both an…

Accurate determination of stellar rotation periods is important for estimating stellar ages as well as for understanding stellar activity and evolution. While for about thirty thousand stars in the Kepler field rotation periods can be…

In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…

We present an efficient analytical method to predict the maximum transit timing variations of a circumbinary exoplanet, given some basic parameters of the host binary. We derive an analytical model giving limits on the potential location of…

Contamination from nearby sources often compromises stellar rotation periods derived from photometric light curves, particularly in data with large pixel scales such as TESS. This problem is compounded when both the target and contaminant…

We apply a machine learning algorithm called XGBoost to explore the periodicity of two radio sources: PKS~1921-293 (OV~236) and PKS~2200+420 (BL~Lac), both radio frequency dataset obtained from University of Michigan Radio Astronomy…

Kepler ultra-high precision photometry of long and continuous observations provides a unique dataset in which surface rotation and variability can be studied for thousands of stars. Because many of these old field stars also have…

We compare the rotation rate of neutron stars in low-mass X-ray binaries (LMXBs) with the orbital period of the binaries. We find that, while short orbital period LMXBs span a range of neutron star rotation rates, all the long period LMXBs…

Many pulsar folding algorithms are currently deployed to generate strong SNRs for the total intensity profiles. But they require large observation times to improve the SNR effectively. New approaches to de-noise the pulsar total intensity…

Advances in asteroseismology of solar-like stars, now provide a unique method to estimate the stellar inclination $i_{\star}$. This enables to evaluate the spin-orbit angle of transiting planetary systems, in a complementary fashion to the…

Kepler allows the measurement of starspot variability in a large sample of field red giants for the first time. With a new method that combines autocorrelation and wavelet decomposition, we measure 361 rotation periods from the full set of…

We measure the rotation periods of 19 stars in the {\it Kepler} transiting planetary systems, $P_{\rm rot, astero}$ from asteroseismology and $P_{\rm rot, phot}$ from photometric variation of their lightcurve. Two stars exhibit two clear…

Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if…