In the period from the beginning of the project to the end of the period covered by the report, we want to highlight the following work and results achieved so far:
(a) We deployed all software needed to take data in real-time using two new techniques that reduce the data volume by keeping a performance comparable to traditional techniques, and increase the amount of data that can be taken and would otherwise have been discarded. We have used this software to take data since the LHC start-up in Summer 2022. These techniques are described in [https://arxiv.org/abs/2401.06630 published in JINST]. We are currently analysing this data for signs of dark matter-related particles.
(b) We are leading the work on the calibration of the data we have recorded and contribute to the evaluation of its performance as data is taken, and a PDRA in the project co-coordinates the overall ATLAS experiment work on this kind of calibration.
(c) We have published two peer-reviewed papers within the ATLAS collaboration on searches for new particles that would be evidence of a dark matter model that has not yet been fully probed, called dark QCD [https://arxiv.org/abs/2311.03944 published in JHEP, ATLAS briefing at https://atlas.cern/Updates/Briefing/Dark-Jets] [https://arxiv.org/abs/2305.18037 published in PLB, ATLAS briefing at
https://atlas.cern/Updates/Briefing/Semi-Visible-Jets](odnośnik otworzy się w nowym oknie). No evidence for these new particles has been found, so we have set constraints on the parameters of different models that will inform future searches.
(d) We are working on understanding and harmonising benchmarks for dark matter and dark sector searches, at collider and in the context of the global dark matter search across multiple experiments. A PDRA in the project has organised and joined workshops with theory and cross-experiment experts for preliminary discussions. The peer-reviewed papers and whitepapers we have edited and contributed to can be found at [https://arxiv.org/abs/2203.12035
https://arxiv.org/abs/2206.03456(odnośnik otworzy się w nowym oknie) https://arxiv.org/abs/2209.13128
https://arxiv.org/abs/2211.07027(odnośnik otworzy się w nowym oknie) https://arxiv.org/abs/2210.01770](odnośnik otworzy się w nowym oknie). In this context, we also facilitate cross-talk and information finding through the iDMEu platform (www.idmeu.org included in proceedings from the TAUP conference at
https://arxiv.org/abs/2312.14192(odnośnik otworzy się w nowym oknie)) that we developed within this project.
(e) We have developed an Open-Source software package to compress data using Machine Learning, called Baler, that can be found on GitHub [https://github.com/baler-collaboration/baler] and [https://doi.org/10.5281/zenodo.10723669]. This is still a prototype and we have not reached the ultimate performance we aim for, but it has already shown promising results in particle physics and a number of fields.
(f) We work on Open Science tools and software sustainability. We delivered a working prototype of the Dark Matter Science Project for FAIR and reproducible data analysis from different experiments searching for dark matter at [https://eoscfuture.eu/data/dark-matter/]. In terms of software sustainability, we coordinate and take part in various activities about FAIR and sustainable data, software and machine learning tools. We have used initial studies by undergraduates students within this grant to join more systematic efforts to pursue these topics in parallel.
(e) The postdocs and students in the group are presenting their work at national meetings and international conferences, as well as organising workshops. Relevant proceedings and workshop reports can be found on [https://www.epj-conferences.org/articles/epjconf/abs/2024/05/epjconf_chep2024_02030/epjconf_chep2024_02030.html
https://arxiv.org/abs/2311.16330](odnośnik otworzy się w nowym oknie)
