Aims.We present a comprehensive analysis of the performance of noise-reduction (denoising) algorithms to determine whether they provide advantages in source detection, mitigating noise on extragalactic survey images.Methods.The methods we analyze here are representative of different algorithmic families: Perona-Malik filtering, bilateral filter, total variation denoising, structure-texture image decomposition, non-local means, wavelets, and block-matching We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of theHubble,Spitzer, andEuclidSpace Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assessed their performance as a function of resolution, background level, and image type, in addition to testing their ability to preserve the objects fluxes and shapes. Finally, we analyze, in terms of completeness and purity, the catalogs that were extracted after applying denoising algorithms on a simulatedEuclidWide Survey VIS image and on real H160 andK-band (HAWK-I) observations of the CANDELS GOODS-South field.Results.Denoising algorithms often outperform the standard approach of filtering with the point spread function (PSF) of the image. Applying structure-texture image decomposition, Perona-Malik filtering, the total variation method by Chambolle, and bilateral filtering on theEuclid-VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitude than those based on the standard approach. The same result is achieved with the structure-texture image decomposition algorithm applied on the H160 image. The relative advantage of denoising techniques with respect to PSF filtering rises with increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing.Conclusions.Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys. We identify the most promising denoising algorithms among the 20 techniques considered in this study.

Since 2020, numerous guides and tools have been available for designing, planning, and running hackathons in fields such as software engineering, ICT, and healthcare. However, a substantial gap is observed in the post-event phase of hackathons' life cycle, directly affecting the sustainability of results. This bears significant implications for hackathon events per se, as well as for ecosystem stakeholders, in medium-term and long-term perspectives. This paper addresses the issue of low valorisation of hackathon-derived solutions and assists their direct exploitation. We aim to provide a framework for hackathon organisers, managers, policymakers, and intermediary bodies maximising impact of such events. To achieve this, we investigate the relationship between event-level success and ecosystem-level impact. We draw upon the practices and data from the ENTREPRENEDU project hackathons, a Horizon Europe-funded program coordinated by the E. Amaldi Foundation. This series of hackathons, held in three different countries, helped identify key aspects for further investigation and served as the groundwork to define/frame our research question. We conducted literature review, examining papers and reports to explore how hackathon-derived knowledge is valorised within entrepreneurial and innovation ecosystems. Our research aimed to identify how the already known success factors of a well-designed event contribute to the sustainability of outcomes and assess their relationship. We link hackathon success indicators juxtaposed to relevant factors. While acknowledging the complexity and multifactorial nature of innovation process and knowledge diffusion, we believe that our study advances the potential for hackathons’ greater impact by providing a cohesive framework.