Primary research areas:
Higher dimensions, geometry of spacetime
Cosmology and the evolution of galaxies
I'm a lot interested in the dynamical history of our Milky Way - especially
halo formation and the disruption of globular clusters and satellites.
Also, I have a general interest in the capabilities arising from
high-performance compputing, machine-learning algorithms
and astrostatistical inference.
This has led me to various topics, among them the development of a
general-relativistic raytracer, reverberation mapping of quasars in the SDSS
the classification of variable sources in large all-sky surveys and Milky Way
structure and dynamics.
My current work is focused on understanding the spatial Galactic halo density
and kinematics by getting
high-precision velocities for RR Lyrae stars in the Milky Way. This is based on
my preceding work that
led to a catalog of variable sources for the the Pan-STARRS survey,
among them more then 44,400 RR Lyrae stars covering 3/4 of the sky and extending to as much as 130 kpc.
Me giving a brief "science teaser" about Machine-Learning for next-(and our)generation Astronomy.
Me talking as part of the AAS Journal Author Series about further implications for RR Lyrae star observations with LSST as presented in the publication Hernitschek+2022 (N. Hernitschek, K. G. Stassun, The Impact of Observing Strategy on Reliable Classification of Standard Candle Stars: Detection of Amplitude, Period, and Phase Modulation (Blazhko Effect) of RR Lyrae Stars with LSST, ApJS 258, 2 (2022) [AJ] [arXiv:2109.13212 [astro-ph.SR]]).
statistical methodology in astronomy:
- Monte-Carlo methods, advanced MCMC algorithms (e.g. Affine Invariant MCMC Ensemble sampler), in application to classification of variable objects and reverberation mapping
- maximum likelihood parameter estimation
- machine learning, classifiers (e.g. Random Forest Classifier)
- fluid dynamics
- raytracing in higher dimensions with arbitrary metrices
- C++: advanced knowledge, e.g., object-oriented programming, performant coding, parallel coding,
using external libraries such as the GNU Scientific Library,
ATLAS package (FORTRAN code for linear algebra), cfitsio
- Python: advanced knowledge, e.g. working with large datasets, plotting, machine learning
- Mathematica: advanced knowledge
- MATLAB: good knowledge
- LaTeX: advanced knowledge
- gnuplot: advanced knowledge
- Fortran77, Fortran95, Java: advanced knowledge
- microcontroller: basic knowledge
- databases and corresponding programming languages: e.g. Oracle, PL-SQL, standard SQL
- HTML: rudimentary knowledge, as exhibited by this homepage
- software development methods: advanced knowledge, e.g. UML, test cases, high-performance computing in time-critial environments (e.g. large databases in scientific and commercial environments)
- basic knowledge in several other programming languages