Higgs production at the FCC-ee in the missing energy channel

The discovery of the Higgs boson in July 2012 by the ATLAS and CMS collaborations opened new doors for the search for physics beyond the Standard Model. In particular, the presence of new particles and interactions might be deduced indirectly via precision measurements. One way to conduct these precise measurements is with a circular $e^{+}e^-$ -collider. The Future Circular Collider (FCC) design study has made a great effort over the past few years in investigating a promising example of such a collider and its discovery potential. The two most important processes for Higgs production at these colliders are Higgsstrahlung and vector boson fusion. The corresponding cross sections can be measured in the missing energy channel $(\nu\bar{\nu}(H \to b\bar{b}))$. In this work the effect of detector parameters on the precision with which $\sigma_{VBF+HZ}(\sigma_{VBF})$ x $BR(H \to b\bar{b})$ at $\sqrt{s} =$ 240 (350) GeV can be measured in this channel is studied. The ILD, a detector specifically designed for a $e^{+}e^-$-collider, is compared to several variations of the CMS detector and shows an increase in precision ranging from 20-100%. The tracker radius, the tracker efficiency and the energy resolution of the hadronic calorimeter are identified as important parameters for a precise measurement. These results can be helpful for the efficient design of a detector at a future $e^{+}e^-$-collider

Search for Dark Matter produced in association with a Higgs boson decaying to -quarks using the ATLAS detector

Many extensions of the Standard Model predict the production of Dark Matter in association with Higgs bosons. This search examines the final state of missing transverse momentum accompanied by a $b\bar{b}$ pair originating from a Higgs boson decay. For this purpose proton-proton collision data is used which is produced at 13 TeV center-of-mass energy and recorded by the ATLAS experiment at the LHC, amounting to an integrated luminosity of $139\, \mathrm{fb}^{-1}$. The increase in integrated luminosity in conjunction with several analysis optimizations result in a better sensitivity in comparison to previous iterations. No significant deviation from the Standard Model is observed and the results are interpreted in the context of the Two-Higgs-Doublet models with an additional vector or pseudoscalar mediator

Dark Sector searches with jets

The presence of a non-baryonic Dark Matter (DM) component in the Universe is inferred from the observation of its gravitational interaction. If Dark Matter interacts weakly with the Standard Model (SM) it could be produced at the LHC. The ATLAS and CMS experiments have developed a broad search program for DM candidates, including resonance searches for the mediator which would couple DM to the SM and searches with large missing transverse momentum which is produced in association with other particles (e.g. light and heavy quarks, Z and H bosons) called mono-X searches. Additionally, searches have been conducted in models where the Higgs boson provides a portal to the Dark Sector leading to either invisible Higgs boson decays or decays with long lived particle requiring special reconstruction techniques. The results of recent searches on 13 TeV pp data, their interplay and interpretation will be presented

Search for Dark Matter produced in association with a StandardModel Higgs boson decaying to b-quarks with 139 fb−1 of ppcollision data with the ATLAS detector

Many extensions of the Standard Model predict the production of Dark Matter in association with Higgs bosons.This search examines the final state of missing transverse momentumaccompanied by a bb pair coming from a Higgs boson decay. For this purpose proton-protoncollision data is used which is produced at 13 TeV centre-of-mass energy and recorded by theATLAS experiment at the LHC, amounting to an integrated luminosity of 139 fb−1.The increase in integrated luminosity in conjunction with many analysis optimizations result ina better sensitivity in comparison to previous iterations. No significant deviation from theStandard Model is observed and the results are interpreted in the context of the 2-Higgsdoublet models with an additional vector or pseudoscalar mediator

Search for Dark Matter produced in association with a Standard Model Higgs boson decaying to b-quarks with 139 fb−1 of pp collision data with the ATLAS detector

Many extensions of the Standard Model predict the production of Dark Matter in association with Higgs bosons.This search examines the final state of missing transverse momentum accompanied by a bb pair coming from a Higgs boson decay. For this purpose proton-protoncollision data is used which is produced at 13 TeV centre-of-mass energy and recorded by the ATLAS experiment at the LHC, amounting to an integrated luminosity of 139 fb$^{−1}$.The increase in integrated luminosity in conjunction with many analysis optimizations result ina better sensitivity in comparison to previous iterations. No significant deviation from the Standard Model is observed and the results are interpreted in the context of the 2-Higgs doublet models with an additional vector or pseudoscalar mediator

Dark Sector searches with jets

The presence of a non-baryonic Dark Matter (DM) component in the Universe is inferred from the observation of its gravitational interaction. If Dark Matter interacts weakly with the Standard Model (SM) it could be produced at the LHC. The ATLAS and CMS experiments have developed a broad search program for DM candidates, including resonance searches for the mediator which would couple DM to the SM and searches with large missing transverse momentum which is produced in association with other particles (e.g. light and heavy quarks, Z and H bosons) called mono-X searches. Additionally, searches have been conducted in models where the Higgs boson provides a portal to the Dark Sector leading to either invisible Higgs boson decays or decays with long lived particle requiring special reconstruction techniques. The results of recent searches on 13 TeV pp data, their interplay and interpretation will be presented

Search for Dark Matter produced in association with a Higgs boson decaying to bb-quarks using the ATLAS detector

Many extensions of the Standard Model predict the production of Dark Matter particles in association with Higgs bosons. This search examines the final state of missing transverse momentum accompanied by a $b\bar{b}$ pair originating from a Higgs boson decay. For this purpose, proton-proton collision data are used, which is produced at 13 TeV center-of-mass energy and recorded by the ATLAS experiment at the LHC, amounting to an integrated luminosity of $139\, \mathrm{fb}^{-1}$. The increase in integrated luminosity in conjunction with several analysis optimizations result in a better sensitivity in comparison to previous iterations of the analysis. No significant deviation from the Standard Model is observed and the results are interpreted in the context of the Two-Higgs-Doublet models extended with an additional vector or pseudoscalar mediator

Umami: flavour tagging algorithm development for Run-3

The identification of b-jets is a powerful tool central to many ATLAS physics analyses.Therefore, a variety of different algorithms (b-taggers) is developed and maintained.Among those, a feed-forward deep neural network algorithm (DL1) which is based mainly on jet features and an algorithm exploiting Deep Sets (DIPS) using solely track information are highlighted in this contribution.The Umami framework provides a harmonized ecosystem for producing training samples and training both DL1 and DIPS algorithms in the same framework.It improves training times due to special data loading techniques and is flexible for future developments.Additionally, within this framework, a new tagger is developed: the UMAMI tagger. It combines the architectures of DL1 and DIPS and thereby simplifies the training procedure while maintaining the intermediate DIPS output. Due to its ability to better exploit correlations between the DL1 and DIPS sub-networks, compared to the two taggers independently, a major performance gain is expected.In this contribution, the advantages of the Umami framework and first performance studies of the UMAMI tagger are discussed

Efficient Search for New Physics using Active Learning in the ATLAS Experiment with RECAST

Searches for new physics and their reinterpretations constrain the parameter space of models with exclusion limits in typically no more than 2 dimensions. However, the relevant theory parameter space often extends into higher dimensions. Limited computing resources for signal process simulations impede the coverage of the full parameter space. We present an Active Learning approach to address this limitation. Compared to the usual grid sampling, it reduces the number of parameter space points for which exclusion limits need to be determined. Consequentially, it allows to extend interpretations of searches to higher dimensional parameter spaces and therefore to raise their value, e.g. via the identification of barely excluded subspaces which motivate dedicated new searches. The procedure is demonstrated by reinterpreting a Dark Matter search performed by the ATLAS experiment, extending its interpretation from a 2 to a 4-dimensional parameter space while keeping the computational effort at a low level

Efficient search for new physics using Active Learning in the ATLAS Experiment

Searches for new physics at the LHC set exclusion limits in multi-dimensional parameter spaces of various theories. Typically, these are presented as 1- or 2-dimensional parameter scans; however, the relevant theory's parameter space is usually of a higher dimension. As a result, only a subspace is covered, which is due to the computing time requirements of simulations for the signal process. An Active Learning approach is presented to address this limitation. Compared to the usual grid scan, it reduces the number of points in parameter space for which exclusion limits need to be determined. Hence it enables richer interpretations of searches in higher-dimensional parameter spaces, which increases the value of the search. For example, this may reveal regions of parameter space that are not excluded and motivate new, dedicated searches. Our Active Learning approach is an iterative procedure. First, a Gaussian Process is fit to exclude signal cross-sections. Within the region close to the exclusion contour predicted by the Gaussian Process, Poisson disc sampling is used to sample additional points in parameter space for which the cross-section limits should be evaluated. The procedure is aided by a warm-start phase based on computationally inexpensive, approximate limit estimates. A python package, excursion, provides the Gaussian Process routine. The procedure is applied to a dark matter search performed by the ATLAS experiment, extending its interpretation from a 2 to a 4-dimensional parameter space while keeping the computational effort at a low level