Rubin-LSST

Abstract:

A bstract A search for long-lived particles, which have come to rest within the ATLAS detector, is presented. The subsequent decays of these particles can produce high-momentum jets, resulting in large out-of-time energy deposits in the ATLAS calorimeters. These de- cays are detected using data collected during periods in the LHC bunch structure when collisions are absent. The analysed dataset is composed of events from proton-proton collisions produced by the Large Hadron Collider at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV and recorded by the ATLAS experiment during 2017 and 2018. The dataset used for this search corresponds to a total live time of 579 hours. The results of this search are used to derive lower limits on the mass of gluino R -hadrons, assuming a branching fraction $$ \mathcal{B}\left(\overset{\sim }{g}\to q\overline{q}{\chi}_1^0\right) $$ B g ~ → q q ¯ χ 1 0 = 100%, with masses of up to 1 . 4 TeV excluded for gluino lifetimes of 10 − 5 to 10 3 s.

Abstract:

We present the serendipitous discovery of a galaxy group in the XMM-LSS field with MIGHTEE Early Science observations. 20 galaxies are detected in H I in this z ∼ 0.044 group, with a 3σ column density sensitivity of NHI=1.6×1020cm−2⁠. This group has not been previously identified, despite residing in a well-studied extragalactic legacy field. We present spatially resolved H I total intensity and velocity maps for each of the objects which reveal environmental influence through disturbed morphologies. The group has a dynamical mass of log10(Mdyn/M⊙)=12.32⁠, and is unusually gas-rich, with an H I-to-stellar mass ratio of log10(f∗HI)=−0.2⁠, which is 0.7 dex greater than expected. The group’s high H I content, spatial, velocity, and identified galaxy type distributions strongly suggest that it is in the early stages of its assembly. The discovery of this galaxy group is an example of the importance of mapping spatially resolved H I in a wide range of environments, including galaxy groups. This scientific goal has been dramatically enhanced by the high sensitivity, large field-of-view, and wide instantaneous bandwidth of the MeerKAT telescope.

Abstract:

A bstract A search for supersymmetry in events with four or more charged leptons (electrons, muons and τ -leptons) is presented. The analysis uses a data sample corresponding to 139 fb − 1 of proton-proton collisions delivered by the Large Hadron Collider at $$ \sqrt{s} $$ s = 13 TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying τ -leptons are designed to target several supersymmetric models, while a general five-lepton signal region targets any new physics phenomena leading to a final state with five charged leptons. Data yields are consistent with Standard Model expectations and results are used to set upper limits on contributions from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of general gauge-mediated supersymmetry, excluding higgsino masses up to 540 GeV. In R -parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of 1 . 6 TeV, 1 . 2 TeV, and 2 . 5 TeV are placed on wino, slepton and gluino masses, respectively.

Abstract:

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.