overview poster of ML in ATLAS and showing some topics for which students could join the group
The generation of discontinuous distributions is a difficult task for most known frameworks, such as generative autoencoders and generative adversarial networks. Generative non-invertible models are unable to accurately generate such distributions, require long training and often are subject to mode collapse. Variational autoencoders (VAEs), which are based on the idea of keeping the latent...
Tommaso Bagni1, Diego Mauro1, Gianmarco Bovone1, Marta Majkut2, Alexander Rack2, Carmine Senatore1
1Department of Quantum Matter Physics, University of Geneva, Geneva, Switzerland
2ESRF – The European Synchrotron, Grenoble, France
We presents a short summary of the variable object classification effort for the third Gaia Data Release (DR3) that will become public mid 2022. Some example figures of the previous (public) DR2 release are shown to illustrate our validation process.
The past decades have witnessed the deployment of a new generation of cosmic ray (CR) observatories with unprecedented sensitivity and complexity, pushing towards ever-higher energies. To face the challenges of the multi-TeV domain, such instruments must be accompanied by equally powerful analysis techniques, able to exploit the wealth of available information. The machine learning tool set...
Jet flavour tagging using machine learning (DIPS, Umami)
In ferroelectrics, domain walls are thin interfaces separating regions with different orientations of electric polarization, either along the same crystalline axis (180° domain walls), or as ferroelastic twins. The domain walls can present physical properties quite different from the surrounding domains, allowing them to be used as active components in future device applications.
Recent...
In recent years, data processing has held an increasingly important role in the toolkit of a scanning probe microscopist. From simple parabolic baseline correction in a topography image, to utilising correlations between images to adjust for distortions between scans, external programming platforms have allowed the extraction of information well beyond the raw data.
This information...
In 2015, the first gravitational wave signals from colliding binary black holes were detected. Subsequent detections of gravitational waves lead researchers to observe a new population of massive, stellar-origin black holes. These signals are tiny ripples of the fabric of space-time. Even though the global network of gravitational-wave detectors is one of the most sensitive instruments on the...
The Cherenkov Telescope Array (CTA) will be the world's largest and the most sensitive ground-based gamma-ray observatory in the energy range from a few tens of GeV to tens of TeV. To achieve the best performance in such a wide range of primary gamma-ray photon energies, the whole array will consist of Imaging Atmospheric Cherenkov Telescopes of three different sizes. The Large-Sized...
The measurement of pathogens in field samples with in-lab microscopy produces massive amount of images (spores $\sim 5 – 30 \mu$m and scanned surface $\sim 3 $cm$^2$) whose analysis requires some automation. A trained AI-model of instance segmentation aims to recognize spores within images containing a multitude of other particles of similar sizes and/or displaying analogous...
Electrochemical phenomena in ferroelectrics are of particular interest for catalysis and sensing applications, with recent studies highlighting the combined role of the ferroelectric polarisation, applied surface voltage and overall switching history.
Here, we present a systematic Kelvin probe microscopy study of the effect of relative humidity and polarisation switching history on the...
Imaging atmospheric cherenkov telescopes are equipped with ultra high speed and single photon sensitive camera. Any extensive air shower will leave a signal of short duration (~10-20 ns) in the camera which contains the information about the primary particle type, energy and direction.
Since many years, this signal is transformed into an image from which a spatial parametrisation (Hillas...
As part of an interdisciplinary research collaboration, the summer project aimed at addressing the question of life in the multiverse by using a variational autoencoder to relate cosmic star formation to the underlying cosmological parameters. The computational method is presented here. In future research, this will allow for the compression of $n$ cosmological parameters to a low-dimensional...
Understanding the star formation and morphological properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditional approaches to characterize these properties require a significant amount of human intervention and are computationally expensive, particularly for large samples of galaxies. Deep learning models have the potential to overcome...
Pileup modelling is an integral part in the data simulation and the downstream experimental analyses. Traditionally this is done by digitising the simulated pile up (PU) hits which are overlaid on digitised Hard Scatter (HS) hits, before reconstruction. A faster alternative is to reconstruct the PU separately and overlay them on HS tracks - i.e. overlay is done at the reconstruction level....
A significant number of tasks at ATLAS involve the generation or encoding of particle flow jets. These are jets which are represented by their constituent particles as a set or attributed point cloud. Such tasks include anomaly detection and simulation. Computation on these point clouds using standard dense networks is often difficult due to the requirement of some sort of ordering. We...
Contact: guillaume.quetant@unige.ch
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information between the input and the output of both the encoder and the decoder, we are able to rediscover the loss terms usually found in adversarial autoencoders as well as various more...
Solar flares are the most energetic events in our solar system. Interacting with Earth’s magnetic field they may cause beautiful aurora or damage our modern infrastructure, but so far they cannot be reliably predicted. Novel methods such as neural networks allow us to cluster data, find rare events, or train models to attempt the prediction of solar flares. It is crucial to pre-process data...
A general overview poster of what the Gaia ESA spacecraft will deliver and the role of Switzerland in the variability processing & analyses. It mainly shows the increasingly larger number of sources we will classify in the different Gaia public data releases.
