About Me
I completed my bsc in physics at unigraz in 2021 with my thesis project focussing on eb modelling using phoebe (Prsa2018_PHOEBE).
Progressing down the most logical path I ended up doing also my msc in physics (with a focus on astrophysics) at unigraz.
I proceeded to graduate from unigraz with my msc in astrophysics exploring the unsupervised classification of RR Lyrae stars.
Since I found a new big passion in ai and datascience while completing this degree, I completed half of the computerscience msc at tug in parallel.
Finally, I graduated from tug with a msc in computerscience.
Not even a week later, I headed to my current institution, sut for my phd.
Research Interests
I am interested in anything bigdata in the context of astronomy.
Especially exploitation of modern machine learning such as transformer in the context of photometric classification of timeseries are key interests of mine.
That includes improving their performance, interpreting their learning behavior and analyzing the resulting datasets.
Core-Collapse Supernovae
sn are amazing astrophysical objects due to their extreme brightness, visibility in various wavelengths and broad range of applications.
sn mark one outcome of the death of a star and come in different types depending on their physical origin.
All of the types have in common that they are transient since they go boom once and are then gone forever.
My objects of interest are sncc.
These are massive stars that run out of nuclear fuel in their core, which collapses as a result due to gravity overpowering radiation pressure.
Rebound effects of the star's contracting shells result in a powerful shockwave, the sn explosion.
The high energies involved in the explosion make sncc a dominant source of chemical enrichment in the universe and excellent laboratories probing extreme physics.
To learn more about the governing physics of these objects especially at high redshift ($z \ge 0.4$) I aim to use large datasets and statistical methods.
The main questions I want to answer in this context are the follwing:
- What are the rates of sncc at redshift $\ge 0.4$?
- What are the properties of sncc at redshift $\ge 0.4$?
Photometric Classification in Big-Data Astronomy
Modern surveys such as lsst (Ivezic2019_LSSTOverview) will produce data at rate unseen in optical astronomy.
As such, they are a key to obtaining the dataset sizes necessary for precise statistical analysis of high redshift sn.
Traditional methods cannot keep up with this data-production rate anymore, which leaves the requirement for alternatives such as ai to handle the data-load.
The method I am focussing on is photometricclassification i.e., identifying sncc only based on their lc.
While this method is not as precise as traditional approaches such as spectroscopy, it is much more efficient in quickly processing large amounts of data.
Therefore, photometricclassification is a key component in dealing with data-streams from modern large-scale surveys.
Especially earlyclf is of interest here because the transient nature of sn limits the time one has to follow up on interesting events.
The main questions in the context of bigdata astronomy I want to answer are:
- Can we photometrically classify sncc and their subtypes?
- Is earlyclf possible for sncc?
- What and how does the ai actually learn?
Hobbies
Aside from research I am very involved in Badminton.
I have been a certified instructor since 2018 and involved in teaching Badminton (mostly voluntarily) even before that.
I even developed a little exercise visualization tool for my coaching sessions (BEV).
Additionally, I enjoy hiking and exploring the outdoors, and as a passionate sleight-of-hand magician you will rarely find me without a pack of playing cards.
