Photometry of K2 Campaign 9 bulge data

Poleski, R.; Penny, Matthew T.; Gaudi, B. Scott; Udalski, A.; Ranc, Clément; Barentsen, Geert; Gould, Andrew

doi:10.1051/0004-6361/201834544

Cited by 16 publications

(19 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also used the subclasses (e.g., CEPH F, 1O, and Other) of each class if these exist. These seven classes of variable sources are from Kim & Bailer-Jones (2016), who originally compiled and cleaned the list of periodic variable stars from several sources, including Soszynski et al (2008), Soszyński et al (2008), Soszyński et al (2009), Soszyñski et al (2009), Poleski et al (2010), Graczyk et al (2011), andKim et al (2014). As described in these papers, light curves are visually examined and cleaned in order to remove light curves that do not show variability.…”

Section: Training Setmentioning

confidence: 99%

Deep transfer learning for the classification of variable sources

Kim

Yeo

Bailer‐Jones

et al. 2021

A&A

View full text Add to dashboard Cite

Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe the light curves of billions or more astronomical sources. This presents new challenges for identifying interesting and important types of variability. Collecting a sufficient amount of labeled data for training is difficult, especially in the early stages of a new survey. Here we develop a singleband light-curve classifier based on deep neural networks and use transfer learning to address the training data paucity problem by conveying knowledge from one data set to another. First we train a neural network on 16 variability features extracted from the light curves of OGLE and EROS-2 variables. We then optimize this model using a small set (e.g., 5%) of periodic variable light curves from the ASAS data set in order to transfer knowledge inferred from OGLE and EROS-2 to a new ASAS classifier. With this we achieve good classification results on ASAS, thereby showing that knowledge can be successfully transferred between data sets. We demonstrate similar transfer learning using Hipparcos and ASAS-SN data. We therefore find that it is not necessary to train a neural network from scratch for every new survey; rather, transfer learning can be used, even when only a small set of labeled data is available in the new survey.

show abstract

Section: Training Setmentioning

confidence: 99%

Deep transfer learning for the classification of variable sources

Kim

Yeo

Bailer‐Jones

et al. 2021

A&A

View full text Add to dashboard Cite

show abstract

“…Shahaf & Mazeh 2019), and microlensing observations (e.g. Han et al 2016;Poleski et al 2017) of main-sequence stars show that substellar objects exist in orbits of ∼ 1 AU. The mass distribution suggests that there are two distinct populations, giant planets with masses 30 M jup , and brown dwarfs with masses 60 M jup , with a gap in between, the brown dwarf desert (Marcy & Butler 2000;Grether & Lineweaver 2006;Sahlmann et al 2011;Ma & Ge 2014).…”

Section: Introductionmentioning

confidence: 99%

ZTFJ0038+2030: A Long-period Eclipsing White Dwarf and a Substellar Companion

Roestel

Kupfer

Bell

et al. 2021

ApJL

View full text Add to dashboard Cite

In a search for eclipsing white dwarfs using the Zwicky Transient Facility lightcurves, we identified a deep eclipsing white dwarf with a dark, substellar companion. The lack of an infrared excess and an orbital period of 10 hours made this a potential exoplanet candidate. We obtained high-speed photometry and radial velocity measurements to characterize the system. The white dwarf has a mass of 0.50 ± 0.02 M and a temperature of 10900 ± 200 K. The companion has a mass of 0.059 ± 0.004 M and a small radius of 0.0783 ± 0.0013 R . It is one of the smallest transiting brown dwarfs known and likely old, 8 Gyr. The ZTF discovery efficiency of substellar objects transiting white dwarfs is limited by the number of epochs and as ZTF continues to collect data we expect to find more of these systems. This will allow us to measure period and mass distributions and allows us to understand the formation channels of white dwarfs with substellar companions.

show abstract

“…As noted by Poleski et al (2014Poleski et al ( , 2017, events of this type provide a unique probe of very wide separation planets ("Uranus" and "Neptune" analogs), provided of course that they can be unambiguously interpreted as due to very wide (rather than very close) separation companions.…”

Section: Future Prospects For the Major/minor Image Hollywood Degeneracymentioning

confidence: 88%

“…The short, smooth bump experienced by KMT-2016-BLG-1107 Einstein crossing times after peak makes it similar in some respects to OGLE-2008-BLG-092 (Poleski et al 2014), MOA-2012-BLG-006 (Poleski et al 2017), and MOA-2013-BLG-605 (Sumi et al 2016). These had u perturb = 4.87, u perturb = 4.18, and u perturb = 1.91 respectively.…”

Section: Future Prospects For the Major/minor Image Hollywood Degeneracymentioning

confidence: 94%

KMT-2016-BLG-1107: A New Hollywood-planet Close/Wide Degeneracy

Hwang

Ryu

Kim

et al. 2018

View full text Add to dashboard Cite

We show that microlensing event KMT-2016-BLG-1107 displays a new type of degeneracy between wide-binary and close-binary Hollywood events in which a giant-star source envelops the planetary caustic. The planetary anomaly takes the form of a smooth, two-day "bump" far out on the falling wing of the light curve, which can be interpreted either as the source completely enveloping a minorimage caustic due to a close companion with mass ratio q = 0.036, or partially

show abstract

Photometry of K2 Campaign 9 bulge data

Cited by 16 publications

References 46 publications

Deep transfer learning for the classification of variable sources

Deep transfer learning for the classification of variable sources

ZTFJ0038+2030: A Long-period Eclipsing White Dwarf and a Substellar Companion

KMT-2016-BLG-1107: A New Hollywood-planet Close/Wide Degeneracy

Contact Info

Product

Resources

About