Multivariate analysis of the EW gauge bosons' polarisation at the LHC: CSI M. DUBAU (20' + Q/A 10')

• Mathis Dubau (LAPP)

Room: Auditorium Marcel Vivargent My thesis subject is the study of polarisation of electroweak gauge bosons. The longitudinally polarised state is highly correlated to Goldstone boson so before the electroweak symmetry breaking, thus giving us a way to test the limit of the Standard Model prediction. The challenge of this observation of simultaneously produced bosons, called Vector Boson Scattering, is the very low cross section. During my second year of my PhD I continue my work on the deep neural network (DNN) optimisation for signal vs background discrimination as well as polarisation state determination for EW-WZjj. I proved that we are able to outperform the previous machine learning based method that were present in our framework. I set up the fit with a subset of systematic and we are able to compute the significance for the observation of the joint polarisation EW-W0Z0jj as well as single polarisation state. In the context of my work in the ATLAS collaboration and being an author, I did a qualification task for the Combine Performance group $e/\gamma$. I defined working points (WP) using DNN for identification (ID) of electron in the forward region ($|\eta|$ > 2.5) for Run-4 with High-Luminosity LHC, increasing greatly the number of collisions per bunch crossing (pile-up, $\mu$). Thanks to the extension of the tracker in this area I benefit from this new variables accessible to ID the electron as well as calibrate their $p_T$ due to mismatch with previous methods. We used a decorrelation technique to have the input of the DNN $p_T$-independent making the output less $p_T$ dependent. Since this work was efficient, we ported this work to make WP as well for the current Run-3 and with more data we did an interpolation to define them as function of $\mu$. On the side of my work in the ATLAS Collaboration, I am involved on a new way of scanning hyper-parameter space in Beyond the Standard Model using DNN in the context of Active Learning. In collaboration with Ugo de Noyers and Björn Herrmann from LAPTh, we're scanning scotogenic models that they work on and already scan using Monte-Carlo Markov Chain. In this way we are comparing this new methods with the pre-established one.

Low-loss stable recycling cavities for Advanced Virgo+ gravitational waves detector: CSI W. AMAR (20' + Q/A 10')

• Ward AMAR

Room: Auditorium Marcel Vivargent In this presentation, I will show a summary of the first year's work. I will present the design of the stable recycling cavities for the future upgrades of Virgo. I will show the study of the different sources of losses in the cavities and how we compensate for them. I will show cavity losses due to astigmatism, mirrors' radius of curvature errors, and mirrors' surface imperfections. I will present a new method to calculate quantum losses in the signal recycling cavity of Virgo. At the end, I will summarize the scientific productions during the first year and I will introduce future plans.