Rubin-LSST

Abstract:

The production of dark matter in association with Higgs bosons is predicted in several extensions of the Standard Model. An exploration of such scenarios is presented, considering final states with missing transverse momentum and b-tagged jets consistent with a Higgs boson. The analysis uses proton-proton collision data at a centre-of-mass energy of 13 TeV recorded by the ATLAS experiment at the LHC during Run 2, amounting to an integrated luminosity of 139 fb−1. The analysis, when compared with previous searches, benefits from a larger dataset, but also has further improvements providing sensitivity to a wider spectrum of signal scenarios. These improvements include both an optimised event selection and advances in the object identification, such as the use of the likelihood-based significance of the missing transverse momentum and variable-radius track-jets. No significant deviation from Standard Model expectations is observed. Limits are set, at 95% confidence level, in two benchmark models with two Higgs doublets extended by either a heavy vector boson Z′ or a pseudoscalar singlet a and which both provide a dark matter candidate χ. In the case of the two-Higgs-doublet model with an additional vector boson Z′, the observed limits extend up to a Z′ mass of 3 TeV for a mass of 100 GeV for the dark matter candidate. The two-Higgs-doublet model with a dark matter particle mass of 10 GeV and an additional pseudoscalar a is excluded for masses of the a up to 520 GeV and 240 GeV for tan β = 1 and tan β = 10 respectively. Limits on the visible cross-sections are set and range from to 0.05 fb to 3.26 fb, depending on the missing transverse momentum and b-quark jet multiplicity requirements.

Abstract:

Vela X-1 is a runaway X-ray binary system hosting a massive donor star, whose strong stellar wind creates a bow shock as it interacts with the interstellar medium (ISM). This bow shock has previously been detected in H α and infrared, but, similar to all but one bow shock from a massive runaway star (BD+43o3654), has escaped detection in other wavebands. We report on the discovery of 1.3 GHz radio emission from the Vela X-1 bow shock with the MeerKAT telescope. The MeerKAT observations reveal how the radio emission closely traces the H α line emission, both in the bow shock and in the larger scale diffuse structures known from existing H α surveys. The Vela X-1 bow shock is the first stellar-wind-driven radio bow shock detected around an X-ray binary. In the absence of a radio spectral index measurement, we explore other avenues to constrain the radio emission mechanism. We find that thermal/free-free emission can account for the radio and H α properties, for a combination of electron temperature and density consistent with earlier estimates of ISM density and the shock enhancement. In this explanation, the presence of a local ISM overdensity is essential for the detection of radio emission. Alternatively, we consider a non-thermal/synchrotron scenario, evaluating the magnetic field and broad-band spectrum of the shock. However, we find that exceptionally high fractions (13 per cent) of the kinetic wind power would need to be injected into the relativistic electron population to explain the radio emission. Assuming lower fractions implies a hybrid scenario, dominated by free-free radio emission. Finally, we speculate about the detectability of radio bow shocks and whether it requires exceptional ISM or stellar wind properties.

Abstract:

In recent years, neural network-based classifica- tion has been used to improve data analysis at collider exper- iments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high- level information without requiring a sophisticated detector simulation. Technically, we show that continuous normaliz- ing flows are a suitable generative architecture that can be efficiently trained to sample classification results using con- ditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tag- ging jets from hadronically decaying top quarks, we demon- strate the application of flows in combination with uncer- tainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights