When we look around us, at all the things that we can touch and see, all of this is visible matter. And yet, this makes up only a small fraction of the matter in the universe.
We know that the vast majority of matter is Dark. Dark matter does not emit nor reflect light, nor have we yet observed any known particle interacting with it. Dark matter is massive, being subject to gravity. It is through its gravitational effects on other matter in space that its existence was inferred.
What is Dark Matter made of?
This is one of the questions that physics yet has to answer. A compelling hypothesis is that Dark Matter is made of particles interacting weakly with conventional particles. In this case, Dark Matter can be produced in the collision of particles such as those produced at the Large Hadron Collider, at the CERN laboratory in Geneva.
Many previous searches for Dark Matter have been performed, with no discovery so far. However, this hypothesis has not been ruled out. A more powerful particle collider could still have a chance of discovering these elusive particles. Such a machine is now available and taking data since 2015: it is the upgraded Large Hadron Collider. It is with this machine that we are searching for subtle effects due to the presence of Dark Matter particles, using the largest detector ever built, the ATLAS experiment.
With DARKJETS and together with our colleagues at the University of Geneva, Ohio State University, the University of Heidelberg, and Oxford University, we have introduced a data taking technique that is new to the ATLAS detector. The amount of experimental data taken is generally limited by constraints in recording the selected events to storage. We have lifted this limitation by applying enhanced real-time analysis algorithms and recording only the subset of information relevant to the searches. The more data, the more chances there are to detect rare processes: the deployment of this technique will enable new and more sensitive searches for the particles that are the portal between ordinary matter and Dark Matter.
We are currently analysing data from the LHC collisions using novel techniques, to either discover or constrain the particle nature of Dark Matter. In case of discovery, the characterization of the new phenomenon in terms of Dark Matter can be confirmed using complementary experiments in space, with synergy within Sweden and internationally. In case of no discrepancy with respect to known phenomena, our work will further constrain the particle nature of Dark Matter and point to future paths in the search for answers to a mystery of our Universe.The DARKJETS project is financed by the European Research Council.
In Nature Physics and Astronomy, Insight issue on Dark Matter: March 2017 Volume 13, No 3 , I co-edited a Progress article titled Dark Matter at Colliders (open-access link coming soon).
The data taking and analysis technique called "Trigger-object Level Analysis" used for the dark matter searches in DARKJETS is described in the paper Performance of the ATLAS Trigger System in 2015 that has been submitted to the Journal of High Energy Physics in 2016.
The preliminary results of the Trigger-object Level Analysis with the 2015 dataset can be found in the ATLAS public note Search for light dijet resonances with the ATLAS detector using a Trigger-object Level Analysis in LHC pp collisions at sqrt(s) = 13 TeV .
The preliminary results of the Dijet+ISR Analysis with the 2015 and part of the 2016 dataset can be found in the ATLAS public note Search for new light resonances decaying to jet pairs and produced in association with a photon or a jet in proton-proton collisions at sqrt(s) = 13 TeV with the ATLAS detector.
Trigger Level Analysis (TLA) is also a Three Letter Acronym (TLA)
Caterina Doglioni Office A421, Fysikum 22241 Professorgatan 1, Lund
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