0000000000390955
AUTHOR
Marius Köppel
showing 4 related works from this author
The Mu3e Data Acquisition
2020
The Mu3e experiment aims to find or exclude the lepton flavour violating decay $\mu^+\to e^+e^-e^+$ with a sensitivity of one in 10$^{16}$ muon decays. The first phase of the experiment is currently under construction at the Paul Scherrer Institute (PSI, Switzerland), where beams with up to 10$^8$ muons per second are available. The detector will consist of an ultra-thin pixel tracker made from High-Voltage Monolithic Active Pixel Sensors (HV-MAPS), complemented by scintillating tiles and fibres for precise timing measurements. The experiment produces about 100 Gbit/s of zero-suppressed data which are transported to a filter farm using a network of FPGAs and fast optical links. On the filte…
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
2020
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Performance of the large scale HV-CMOS pixel sensor MuPix8
2019
The Mu3e experiment is searching for the charged lepton flavour violating decay $ ��^+\rightarrow e^+ e^- e^+ $, aiming for an ultimate sensitivity of one in $10^{16}$ decays. In an environment of up to $10^9$ muon decays per second the detector needs to provide precise vertex, time and momentum information to suppress accidental and physics background. The detector consists of cylindrical layers of $50\, ��\text{m}$ thin High Voltage Monolithic Active Pixel Sensors (HV-MAPS) placed in a $1\,\text{T}$ magnetic field. The measurement of the trajectories of the decay particles allows for a precise vertex and momentum reconstruction. Additional layers of fast scintillating fibre and tile detec…
Fair Pairwise Learning to Rank
2020
Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy, it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.In this work we present a family of fair pairwise learning to rank approaches based on Neur…