About Me
I completed my in physics at in 2021 with my thesis project focussing on modelling using ().
Progressing down the most logical path I ended up doing also my in physics (with a focus on astrophysics) at .
I proceeded to graduate from with my in astrophysics exploring the unsupervised classification of RR Lyrae stars.
Since I found a new big passion in and while completing this degree, I completed half of the at in parallel.
Finally, I graduated from with a in .
Not even a week later, I headed to my current institution, for my .
Research Interests
I am interested in anything in the context of astronomy.
Especially exploitation of modern machine learning such as 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
are amazing astrophysical objects due to their extreme brightness, visibility in various wavelengths and broad range of applications.
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 since they go boom once and are then gone forever.
My objects of interest are .
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 explosion.
The high energies involved in the explosion make 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 ($z \ge 0.4$) 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 at $\ge 0.4$?
- What are the properties of at $\ge 0.4$?
Photometric Classification in Big-Data Astronomy
Modern surveys such as () 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 .
Traditional methods cannot keep up with this data-production rate anymore, which leaves the requirement for alternatives such as to handle the data-load.
The method I am focussing on is i.e., identifying only based on their .
While this method is not as precise as traditional approaches such as , it is much more efficient in quickly processing large amounts of data.
Therefore, is a key component in dealing with data-streams from modern large-scale surveys.
Especially is of interest here because the nature of limits the time one has to follow up on interesting events.
The main questions in the context of astronomy I want to answer are:
- Can we photometrically classify and their subtypes?
- Is possible for ?
- What and how does the actually learn?
Hobbies
Aside from research I am very involved in Badminton.
I have been a certified 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.
