Paraskevi Nousi


Last Name

Nousi

First Name

Paraskevi

ORCID

0000-0002-3087-3174

Organisational unit

02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)

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Publications 1 - 2 of 2
  • New Gravitational Wave Discoveries Enabled by Machine Learning
    Item type: Journal Article
    Koloniari, Alexandra Eleni; Koursoumpa, Evdokia Chrysovalantou; Nousi, Paraskevi; et al. (2025)
    Machine Learning: Science and Technology
    The detection of gravitational waves has revolutionized our understanding of the universe, offering unprecedented insights into its dynamics. A major goal of gravitational wave data analysis is to speed up the detection and parameter estimation process using machine learning techniques, in light of an anticipated surge in detected events that would render traditional methods impractical. Here, we present new gravitational-wave candidate events, the first to be identified in data from a network of interferometric detectors through machine learning. We discuss several new enhancements of our ResNet-based deep learning code, AresGW, that increased its sensitivity, including a new hierarchical classification of triggers, based on different noise and frequency filters. The enhancements resulted in a significant reduction in the false alarm rate, allowing AresGW to surpass traditional pipelines in the number of detected events in its effective training range (single source masses between 7 and 50 solar masses and source chirp masses between 10 and 40 solar masses), when the new detections are included. We calculate the astrophysical significance of events detected with AresGW using a logarithmic ranking statistic and injections into O3 data. Furthermore, we present spectrograms, parameter estimation, and reconstruction in the time domain for our new candidate events and discuss the distribution of their properties. In addition, the AresGW code exhibited very good performance when tested across various two-detector setups and on observational data from the O1 and O2 observing periods. Our findings underscore the remarkable potential of AresGW as a fast and sensitive detection algorithm for gravitational-wave astronomy, paving the way for a larger number of future discoveries.
  • The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years
    Item type: Journal Article
    Roth, Lukas; Boss, Mike; Kirchgessner, Norbert; et al. (2025)
    GigaScience
    Background: Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings: We provide time-series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across 6 years. Measurement data for 8 key wheat traits are included-namely, canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions: This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Publications 1 - 2 of 2