What is he best known for?
The fields of study he is best known for:
- Astronomy
- Astrophysics
- Milky Way
M. Kubiak mainly focuses on Astrophysics, Gravitational microlensing, Astronomy, Planet and Light curve.
Astrophysics is closely attributed to Parallax in his study.
His studies in Gravitational microlensing integrate themes in fields like Primary, Mass ratio, Photometry and Proper motion.
M. Kubiak works in the field of Astronomy, namely Large Magellanic Cloud.
M. Kubiak studied Light curve and Radial velocity that intersect with Wolf–Rayet star and Spectroscopy.
His research in Stars intersects with topics in Quasar and Sky.
His most cited work include:
- An eclipsing-binary distance to the Large Magellanic Cloud accurate to two per cent (606 citations)
- Discovery of a cool planet of 5.5 Earth masses through gravitational microlensing (526 citations)
- One or more bound planets per Milky Way star from microlensing observations (475 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Astrophysics, Astronomy, Gravitational lens, Gravitational microlensing and Stars.
His works in Light curve, Bulge, Planet, Variable star and Photometry are all subjects of inquiry into Astrophysics.
His study in the fields of Baade's Window under the domain of Bulge overlaps with other disciplines such as Event.
His Gravitational lens research incorporates elements of Large Magellanic Cloud, Astrometry, Weak gravitational lensing and Small Magellanic Cloud.
He interconnects Brown dwarf, Einstein radius, Mass ratio, Parallax and Dark matter in the investigation of issues within Gravitational microlensing.
His study on Red clump, Cepheid variable and Photometry is often connected to Population as part of broader study in Stars.
He most often published in these fields:
- Astrophysics (85.83%)
- Astronomy (55.24%)
- Gravitational lens (43.74%)
What were the highlights of his more recent work (between 2012-2019)?
- Astrophysics (85.83%)
- Astronomy (55.24%)
- Gravitational microlensing (43.74%)
In recent papers he was focusing on the following fields of study:
His main research concerns Astrophysics, Astronomy, Gravitational microlensing, Planet and Stars.
His study in Light curve, Bulge, Gravitational lens, Brown dwarf and Variable star falls under the purview of Astrophysics.
His Bulge research integrates issues from Metallicity, Luminosity and Galactic Center.
His research investigates the connection between Gravitational microlensing and topics such as Low Mass that intersect with issues in Minimum mass.
His Planet study combines topics in areas such as Einstein radius and Mass ratio.
His work on Red clump, Small Magellanic Cloud, Red giant and Stellar classification as part of general Stars research is often related to Population, thus linking different fields of science.
Between 2012 and 2019, his most popular works were:
- An eclipsing-binary distance to the Large Magellanic Cloud accurate to two per cent (606 citations)
- REDDENING AND EXTINCTION TOWARD THE GALACTIC BULGE FROM OGLE-III: THE INNER MILKY WAY'S RV ∼ 2.5 EXTINCTION CURVE* (430 citations)
- Deciphering the 3D structure of the old Galactic bulge from the OGLE RR Lyrae stars (151 citations)
In his most recent research, the most cited papers focused on:
- Astronomy
- Milky Way
- Astrophysics
M. Kubiak mainly investigates Astrophysics, Gravitational microlensing, Astronomy, Planet and Light curve.
His Brown dwarf, Stars, Bulge, Gravitational lens and Red clump investigations are all subjects of Astrophysics research.
His studies deal with areas such as Low Mass, Parallax, Einstein radius and Binary star as well as Gravitational microlensing.
In the field of Astronomy, his study on Proper motion and Spectroscopy overlaps with subjects such as Wavelength, Amorphous solid and Pyroxene.
His research integrates issues of Star and Mass ratio in his study of Planet.
His Light curve study integrates concerns from other disciplines, such as Spectral energy distribution and Star.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
