Engineering topics

Giordano, Cristina [Vienna, OAW]

Publications and source records attributed to Giordano, Cristina [Vienna, OAW].

6 records

DOE OSTI2026

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The 'ABCD method' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two smoothly varying, statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCD with distance correlation (ABCDisCo) method was developed to construct two learned variables via a neural network trained to provide strong signal-background discrimination with small values of the distance correlation (DisCo) measure between the two learned variables. However, relying solely on minimizing the DisCo can result in learned variables that may not have distributions of background events that are smoothly varying and localized at extreme values, as necessary for the validity of the background estimation. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to solve this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN Large Hadron Collider. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2026

Observation of Long-Range Collective Flow in O + O and Ne + Ne Collisions and Implications for Nuclear Structure Studies

The long-range collective flow of particles produced in oxygen-oxygen ( O + O ) and neon-neon ( Ne + Ne ) collisions is measured with the CMS detector at the CERN LHC. The data samples were collected at a center-of-mass energy per nucleon pair of 5.36 TeV, with integrated luminosities of 7 nb - 1 and 0.8 nb - 1 for O + O and Ne + Ne collisions, respectively. Two- and four-particle azimuthal correlations are measured over nearly five units of pseudorapidity. Significant elliptic ( v 2 ) and triangular ( v 3 ) flow harmonics are observed in both systems. The ratios of v n coefficients between Ne + Ne and O + O collisions suggest sensitivity to the intrinsic nuclear structure of the respective Ne and O ions. Hydrodynamic models with ab initio nuclear structure inputs qualitatively reproduce the collision-overlap dependence of both the v n values and the Ne + Ne to O + O ratios. These measurements provide new constraints when modeling the interplay of nuclear structure and collective dynamics in collisions of O 16 and Ne 20 ions, respectively.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2026

Search for a Higgs boson produced in association with a charm quark and decaying to a W boson pair in proton-proton collisions at $ \sqrt{s}=13 $ TeV

This paper presents a search for a Higgs boson produced in association with a charm quark (cH) which allows to probe the Higgs-charm Yukawa coupling strength modifier κ$_{c}$. Higgs boson decays to a pair of W bosons are considered, where one W boson decays to an electron and a neutrino, and the other W boson decays to a muon and a neutrino. The data, corresponding to an integrated luminosity of 138 fb$^{−1}$, were collected between 2016 and 2018 with the CMS detector at the LHC at a center-of-mass energy of $ \sqrt{s}=13 $ TeV. Upper limits at the 95% confidence level (CL) are set on the ratio of the measured yield to the standard model expectation for cH production. The observed (expected) upper limit is 1065 (506), corresponding to an observed (expected) constraint of |κ$_{c}$| < 211 (95). When combined with the previous search for cH in the diphoton decay channel of the Higgs boson, the limits are interpreted as observed (expected) constraints at 95% CL on the value of κ$_{c}$, |κ$_{c}$| < 47 (51).[graphic not available: see fulltext]

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2026

Machine-learning techniques for model-independent searches in dijet final states

Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiveness of these approaches in enhancing sensitivity to various simulated signal samples is studied and compared using data collected in proton–proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.

CMS↗

DOE OSTI2026

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2026

Combined effective field theory interpretation of Higgs boson, electroweak vector boson, top quark, and multijet measurements

Constraints on Wilson coefficients (WCs) corresponding to dimension-6 operators of the standard model effective field theory (SMEFT) are determined from a simultaneous fit to seven sets of CMS measurements probing Higgs boson, electroweak vector boson, top quark, and multijet production. Measurements of electroweak precision observables are also included and provide complementary constraints to those from the CMS experiment. The CMS measurements, using LHC proton-proton collision data at $\sqrt{s}=13\,\text {Te}\text {V} $, corresponding to integrated luminosities of 36.3 or 138$\,\text {fb}^{-1}$, are chosen to provide sensitivity to a broad set of operators, for which consistent SMEFT predictions can be derived. These are primarily measurements of differential cross sections which are parameterized as functions of the WCs. In measurements targeting ${\text {t}} (\bar{\textrm{t}})\text {X} $ production, SMEFT effects are modelled at the detector level. Individual constraints on 64 WCs, and constraints on 43 linear combinations of WCs, are obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗