Search

ISOLDE’s Offline 2 laboratory has been upgraded to facilitate development for the production and study of molecular ion beams. New gas injection systems have been implemented for both molecular formation in the ion source and in the radio-frequency quadrupole ion trap used for beam preparation. MagneToF detectors and time-resolved single ion counting data acquisition have been implemented for low intensity beams and studies of laser-atom or laser-molecule interactions. We present a study of the formation and ionization of BaF+ using the upgraded facility. •ISOLDE’s Offline 2 mass separator facility has been upgraded for studies of molecular ion beams.•High-power laser light interacts with molecules to ionize and dissociate.•Barium and barium fluoride ions are ionized using laser light at 355 nm.

The employment of a suitable radioactive decay spectroscopy setup is essential for the operation of radioactive ion beam (RIB) facilities. CERN-ISOLDE recently developed a new fast tape station (FTS) setup to replace its more than 40 years old predecessor. The new FTS is equipped with several β-detectors, a HPGe-detector, provisions for an α-detector, and constitutes a significant improvement in terms of transport times and detector noise characteristics. Since 2021 it is routinely used as the primary asset for yield determination and quality control of produced RIBs at ISOLDE. We address in this work its technical features and introduce the utilized control system.

A few beam orbit-corrector circuits of the Large Hadron Collider (LHC) show an unusual behavior during operation. These circuits can be ramped up to their nominal current, however their superconducting magnets quench when they are ramped down using the same ramp-rate. Furthermore, after the quench the measured circuit current increases above its initial value for a few tens of milliseconds. This transient is analyzed with the STEAM-LEDET program, which allows multiphysics transient simulations including electro-magnetic coupling between the turns, 3-dimensional thermal diffusion in the coil windings, ohmic loss, and inter-filament coupling loss. Simulation results show that this atypical magnet behavior can be fully explained by an internal turn-to-turn short. In fact, due to the strong magnetic coupling of the shorted turn to the rest of the coil, whenever the magnet transport current is changed, a current-change with opposite polarity is induced in the shorted turn. During circuit ramp-down, the current through the shorted turn reaches values significantly higher than in the rest of the coil. The simultaneous increase of the superconductor current density due to the induced current and of its temperature due to local ohmic loss in the short can lead to a quench. Subsequently, the current in the shorted turn rapidly reduces, and its energy transfers by inductive coupling to the rest of the coil, causing a temporary increase of its current. With the limited amount of available diagnostic signals, the simulation tools and the related analysis prove crucial in characterizing the short-circuit key features and help assessing the consequences of such a transient on the magnet operation. A few beam orbit-corrector circuits of the Large Hadron Collider (LHC) show an unusual behavior during operation. These circuits can be ramped up to their nominal current, however their superconducting magnets quench when they are ramped down using the same ramp-rate. Furthermore, after the quench the measured circuit current increases above its initial value for a few tens of milliseconds. This transient is analyzed with the STEAM-LEDET program, which allows multiphysics transient simulations including electro-magnetic coupling between the turns, 3-dimensional thermal diffusion in the coil windings, ohmic loss, and inter-filament coupling loss. Simulation results show that this atypical magnet behavior can be fully explained by an internal turn-to-turn short. In fact, due to the strong magnetic coupling of the shorted turn to the rest of the coil, whenever the magnet transport current is changed, a current-change with opposite polarity is induced in the shorted turn. During circuit ramp-down, the current through the shorted turn reaches values significantly higher than in the rest of the coil. The simultaneous increase of the superconductor current density due to the induced current and of its temperature due to local ohmic loss in the short can lead to a quench. Subsequently, the current in the shorted turn rapidly reduces, and its energy transfers by inductive coupling to the rest of the coil, causing a temporary increase of its current. With the limited amount of available diagnostic signals, the simulation tools and the related analysis prove crucial in characterizing the short-circuit key features and help assessing the consequences of such a transient on the magnet operation.

Impact ionization in silicon devices has been extensively studied and several models for a quantitative description of the impact ionization coefficients have been proposed. We evaluate those models against gain measurements on low-gain avalanche diodes (LGADs) and derive new parameterizations for the impact ionization coefficients optimized to describe a large set of experimental data. We present pulsed infrared (IR)-laser-based gain measurements on five different types of $50 \boldsymbol {\mu }\text{m}$ -thick LGADs from two different producers centro nacional de microelectrónica (CNM) and Hamamatsu Photonics (HPK) performed in a temperature range from $- 15\,\,{^{\circ} }\text{C}$ to $40 {^{\circ} }\text{C}$ . Detailed technology computer-aided design (TCAD) device models are conceived based on secondary ion mass spectrometry (SIMS) doping profile measurements and tuning of the device models to measure ${C}$ – ${V}$ characteristics. Electric field profiles are extracted from the TCAD simulations and used as input to an optimization procedure (least squares fit) of the impact ionization model parameters to the experimental data. It is demonstrated that the new parameterizations give a good agreement between all measured data and TCAD simulations which is not achieved with the existing models. Finally, we provide an error analysis and compare the obtained values for the electron and hole impact ionization coefficients against existing models. Impact ionization in silicon devices has been extensively studied and several models for a quantitative description of the impact ionization coefficients have been proposed. We evaluate those models against gain measurements on Low Gain Avalanche diodes (LGADs) and derive new parameterizations for the impact ionization coefficients optimized to describe a large set of experimental data. We present pulsed IR-laser based gain measurements on 5 different types of $50\mu m$-thick LGADs from two different producers (CNM and HPK) performed in a temperature range from $-15^oC$ to $40^oC$. Detailed TCAD device models are conceived based on SIMS doping profiles measurements and tuning of the device models to measured C-V characteristics. Electric field profiles are extracted from the TCAD simulations and used as input to an optimization procedure (least squares fit) of the impact ionization model parameters to the experimental data. It is demonstrated that the new parameterizations give a good agreement between all measured data and TCAD simulations which is not achieved with the existing models. Finally, we provide an error analysis and compare the obtained values for the electron and hole impact ionization coefficients against existing models.

Two new CMS PYTHIA8 tunes, CP5-CR1 and CP5-CR2, obtained respectively for the quantum chromodynamics-inspired and gluon-move color reconnection (CR) models in PYTHIA8, are introduced. In addition, the uncertainty on the top quark mass due to CR effects, calculated with the new tunes, is presented.

Optical atomic clocks are the most accurate measurement devices ever constructed and have found many applications in fundamental science and technology. The use of highly charged ions (HCI) as a new class of references for highest accuracy clocks and precision tests of fundamental physics has long been motivated by their extreme atomic properties and reduced sensitivity to perturbations from external electric and magnetic fields compared to singly charged ions or neutral atoms. Here we present the first realisation of this new class of clocks, based on an optical magnetic-dipole transition in Ar$^{13+}$. Its comprehensively evaluated systematic frequency uncertainty of $2.2\times10^{-17}$ is comparable to that of many optical clocks in operation. From clock comparisons we improve by eight and nine orders of magnitude upon the uncertainties for the absolute transition frequency and isotope shift ($^{40}$Ar vs. $^{36}$Ar), respectively. These measurements allow us to probe the largely unexplored quantum electrodynamic nuclear recoil, presented as part of improved calculations of the isotope shift which reduce the uncertainty of previous theory by a factor of three. This work establishes forbidden optical transitions in HCI as references for cutting-edge optical clocks and future high-sensitivity searches for physics beyond the standard model., Comment: Main: 20 pages, 3 figures. Supplement: 19 pages, 2 figures

CERN’s Large Hadron Collider cryomagnets embed a 1.9 K UHV chamber lined with a 5–20 K beam-screen (BS) that intercepts the synchrotron radiation and electron cloud (EC). The low–energy EC irradiates the BS and desorbs gas, creating a dynamic vacuum effect. A novel setup controllably reproduces this by irradiating an unbaked as-received BS copper sample at 15 K with 0–1.4 keV electrons, representing a slice of the EC spectrum. This collector-based setup is qualified using a HOPG reference for secondary electron yield (SEY) and $^{15}$N$_2$ as a tracer in low-energy electron stimulated desorption (ESD) measurements. Measurement at 15 K revealed sub-10 eV ESD thresholds and a maximum around 300 eV of 0.18 H2/e$^-$ and 0.13 CO/e$^-$. Irradiation with 300 eV and 1 keV electrons at ∼ 8.10$^{-4}$ C.mm$^{-2}$ conditioned ESD and SEY alike. Similar dose at 17 eV only caused minor SEY reduction and no ESD decline. The as-received H2 and CO2 yields at 300 eV decreased 5-150x between 15 and 265 K, respectively.

It is well known that the electromagnetic interactions between high-energy particles and solid matter undergo substantial modifications when the latter is crystalline and its lattice axes are almost parallel to the incident beam direction. In particular, a strong enhancement to both bremsstrahlung (by electrons and positrons) and pair production (by photons) occurs in high-$Z$, high-density oriented crystals, owing to the coherent interactions of multi-$\mathrm{GeV}$ particles with the crystalline lattice axes. The interest in the experimental investigation of this enhancement has grown stronger in recent years. The focus is twofold: to fully characterise these coherent effects in order to widen the knowledge of the interactions between particles and matter, and to exploit them in the development of next-generation tools for high-energy physics. The work presented in this thesis delves into several experimental studies regarding the interactions of high-energy electrons and photons with the strong lattice field of oriented tungsten (W) and lead tungstate (PWO) crystals, which serve as the cornerstone of the design of three applications of paramount interest: an optimised positron source for the future lepton collider (e.g., the FCC-$ee$), a high-efficiency photon converter for the neutral kaon beamline of the HIKE experiment at the CERN SPS and an ultra-compact, high-performance homogeneous electromagnetic calorimeter for forward-geometry accelerator-based experiments and space-borne $\gamma$-ray telescopes.

Flavour tagging is a crucial tool in particle physics since it allows to identify the flavour of heavy hadrons inside hadronic jets. This is especially important for challenging analyses like the search for the $t\bar{t}H(\to b\bar{b})$ signal. This search suffers from a large irreducible $t\bar{t}+b\bar{b}$ background which contains the same particles in the final state as the signal. In the background process, a radiated gluon can split into a $b\bar{b}$ pair, which can lead to a single jet that contains two $b$-hadrons ($bb$-jet). A successful identification of $bb$-jets could allow for a better rejection of this background. However, this is not possible with common flavour-tagging algorithms. In order to enable $bb$-jet identification, an extended flavour-tagging is presented in this thesis. The design of this algorithm is built on the deep-learning-based flavour-tagging algorithm DL1d, which combines the output of multiple low-level algorithms and is used in the ATLAS experiment in Run III. One of those low-level algorithms is the DIPS algorithm, which is based on the machine-learning concept of Deep Sets. The DL1d tagger receives an additional output category for $bb$-jets, which is done in two stages. First, the DIPS algorithm itself is extended ($bb$-DIPS), which exploits the information of the tracks that are associated to the jet. Afterwards, an extended version of DL1d ($bb$-DL1d) is implemented, which combines the output of $bb$-DIPS with the output of two other low-level algorithms. Furthermore, optimisation studies regarding the definition of the tagger discriminant are performed. The final $bb$-DL1d tagger is able to successfully identify $bb$-jets while still performing well in single-$b$-tagging. At a 70% single-$b$-jet identification efficiency, $bb$-DL1d achieves a $bb$-jet rejection of $8.7\pm0.3$. Moreover, the same algorithm outperforms the ATLAS DL1r tagger used in Run II, by reaching a 27% larger $c$-jet rejection while performing equally well in light-flavour-jet rejection at a 70% inclusive $b$-jet efficiency. Finally, first steps regarding a cut in a two-dimensional discriminant plane are presented, providing an outlook for future applications of $bb$-DL1d.

In this thesis, I present the dijet invariant mass analysis in proton–proton and proton–lead collisions at the center-of-mass energy per nucleon of 5.02 TeV. The measurement of the data was conducted with the ALICE detector, which is one of the four experiments along the CERN Large Hadron Collider accelerator, which is the largest particle collider in the world. The dijets were reconstructed from charged tracks in the ALICE central de- tectors in minimum bias proton–proton (pp) and proton–lead (p–Pb) collisions. I reconstructed the jets using anti-kT algorithm with radius parameter R = 0.4 implemented by the FastJet [1] package and subtracted the underlying event background with the 4-momentum background subtraction method. A notable new analysis technique I developed for this analysis is the correction of the back- ground fluctuations for the dijet invariant mass spectrum. I unfolded the mea- sured distributions for background fluctuations together with the detector re- sponse in a similar way as in earlier jet pT cross section measurements. I determined the nuclear modification factor RpA from the dijet invariant mass cross sections in proton–proton and proton–lead collisions. The final sta- tistical and systematic uncertainties remained at 10–20%. The RpA is compatible with no modifications from the cold nuclear matter effects within the uncertain- ties. This is the first measurement of the dijet invariant mass involving a heavy ion environment. I used Pythia and POWHEG+Pythia MC event generators to calculate the dijet invariant mass with nuclear parton distribution functions (nPDF), which show a slight enhancement in RpA. According to nPDFs sets of EPPS21 [2], the hard parton momentum fraction distributions of the dijet events could be attributed to the anti-shadowing domain, which is x ∼ 0.01–0.1 for glu- ons. However, the final uncertainties in the measurement are too large to make definite conclusions.

This paper presents the design and characterisation of a front-end prototype ASIC for the ATLAS High Granularity Timing Detector, which is planned for the High-Luminosity phase of the LHC. This prototype, called ALTIROC1, consists of a 5$\times$5-pad matrix and contains the analog part of the single-channel readout (preamplifier, discriminator, two TDCs and SRAM). Two preamplifier architectures (transimpedance and voltage) were implemented and tested. The ASIC was characterised both alone and as a module when connected to a 5$\times$5-pad array of LGAD sensors. In calibration measurements, the ASIC operating alone was found to satisfy the technical requirements for the project, with similar performances for both preamplifier types. In particular, the jitter was found to be 15$\pm$1~ps (35$\pm$1~ps) for an injected charge of 10~fC (4~fC). A degradation in performance was observed when the ASIC was connected to the LGAD array. This is attributed to digital couplings at the entrance of the preamplifiers. When the ASIC is connected to the LGAD array, the lowest detectable charge increased from 1.5~fC to 3.4~fC. As a consequence, the jitter increased for an injected charge of 4~fC. Despite this increase, ALTIROC1 still satisfies the maximum jitter specification (below 65~ps) for the HGTD project. This coupling issue also affects the time over threshold measurements and the time-walk correction can only be performed with transimpedance preamplifiers. Beam test measurements with a pion beam at CERN were also undertaken to evaluate the performance of the module. The best time resolution obtained using only ALTIROC TDC data was 46.3$\pm$0.7~ps for a restricted time of arrival range where the coupling issue is minimized. The residual time-walk contribution is equal to 23~ps and is the dominant electronic noise contribution to the time resolution at 15~fC., 20 pages, 15 figures

In the near future BabyIAXO will be the most powerful axion helioscope, relying on a custom-made magnet of two bores of 70 cm diameter and 10 m long, with a total available magnetic volume of more than 7 m$^3$. In this document, we propose and describe the implementation of low-frequency axion haloscope setups suitable for operation inside the BabyIAXO magnet. The RADES proposal has a potential sensitivity to the axion-photon coupling $g_{a\gamma}$ down to values corresponding to the KSVZ model, in the (currently unexplored) mass range between 1 and 2$~\mu$eV, after a total effective exposure of 440 days. This mass range is covered by the use of four differently dimensioned 5-meter-long cavities, equipped with a tuning mechanism based on inner turning plates. A setup like the one proposed would also allow an exploration of the same mass range for hidden photons coupled to photons. An additional complementary apparatus is proposed using LC circuits and exploring the low energy range ($\sim10^{-4}-10^{-1}~\mu$eV). The setup includes a cryostat and cooling system to cool down the BabyIAXO bore down to about 5 K, as well as appropriate low-noise signal amplification and detection chain.

The Leptoquark is among the undiscovered particles which are being searched for in the Large Hadron Collider. Monte. Carlo simulated events of proton-to-proton collisions corresponding to the Leptoquark are studied with the ATLAS detector. The luminosity of the produced samples corresponds to the recorded data of 140 fb$^{−1}$. Four machine learning algorithms are used (TabNet, XGBoost, MLP, and Bayesian MLP) to train models to separate events on the 2lSS+1$\tau$ channel belonging to the. pair-production mode of Leptoquark from various background processes, including ttH, ttW, ttZ, tt, VV and other minor processes. The feature importance of the top performing models is constructed and utilized to produce more efficient models with improved sensitivity. In addition, the expected upper limit of cross-section for the pair-production of Leptoquark at 95% confidence level is calculated and compared to existing results.

The task of finding the incident particles from the sensor deposits they leave on particle detectors is called event or particle reconstruction. The sensor deposits can be represented generically as a point cloud, with each point corresponding to three spatial dimensions of the sensor location, the energy deposit, and occasionally, also the time of the deposit. As particle detectors become increasingly more complex, ever-more sophisticated methods are needed to perform particle reconstruction. An example is the ongoing High Luminosity (HL) upgrade of the Large Hadron Collider (HL-LHC). The HL-HLC is the most significant milestone in experimental particle physics and aims to deliver an order of magnitude more data rate compared to the current LHC. As part of the upgrade, the endcap calorimeters of the Compact Muon Solenoid (CMS) experiment -- one of the two largest and general-purpose detectors at the LHC -- will be replaced by the radiation-hard High Granularity Calorimeter (HGCAL). The HGCAL will contain $\sim6$ million sensors to achieve the spatial resolution required for reconstructing individual particles in HL-LHC conditions. It has an irregular geometry due to its hexagonal sensors, with sizes varying across the longitudinal and transverse axes. Further, it generates sparse data as less than $10$% of the sensors register positive energy. Reconstruction in this environment, where highly irregular patterns of hits are left by the particles, is an unprecedentedly intractable and compute-intensive pattern recognition problem. This motivates the use of parallelisation-friendly deep learning approaches. More traditional deep learning methods, however, are not feasible for the HGCAL because a regular grid-like structure is assumed in those approaches. In this thesis, a reconstruction algorithm based on a dynamic graph neural network called GravNet is presented. The network is paired with a segmentation technique, Object Condensation, to first perform point-cloud segmentation on the detector hits. The property-prediction capability of the Object Condensation approach is then used for energy regression of the reconstructed particles. A range of experiments are conducted to show that this method works well in conditions expected in the HGCAL i.e., with $200$ simultaneous proton-proton collisions. Parallel algorithms based on Nvidia CUDA are also presented to address the computational challenges of the graph neural network discussed in this thesis. With the optimisations, reconstruction can be performed by this method in approximately $2$ seconds which is suitable considering the computational constraints at the LHC. The presented method is the first-ever example of deep learning based end-to-end calorimetric reconstruction in high occupancy environments. This sets the stage for the next era of particle reconstruction, which is expected to be end-to-end. While this thesis is focused on the HGCAL, the method discussed is general and can be extended not only to other calorimeters but also to other tasks such as track reconstruction.

The ALICE experiment is currently participating in the recently started Run 3 of CERN LHC, in which collision rate and thus data stream rate had been significantly increased with respect to Run 2. Several upgrades were required for ALICE prior to the start or Run 3, including implementation of a new Online-Offline (O2) system to handle the new data flow, and upgrading detectors. For instance, the AD (ALICE Diffractive) was replaced by the new Forward Diffractive Detector (FDD) and included in the Fast Interaction Trigger (FIT) project. Therefore, development of a Detector Control System (DCS) for FDD was necessary. The DCS is one of the fundamental systems of CERN experiments which allows for essential tasks such as remote and safe operation, monitoring, configuration and recording of data. The aim of this work was to collaborate in the design, development and integration of the DCS for FDD, as well as taking part of installation and calibration of the detector. Specific objectives also include communicating the DCS to its corresponding experimental equipment, implementing the FDD-DCS according to the general requirements of the FIT project and the particular ones for FDD, integrating the DCS to the ALICE central DCS observing all of its guidelines, and optimizing the configuration process of FDD through the DCS for data taking. Chapter 1 briefly defines ALICE, FIT, FDD and their main purpose. Chapter 2 describes the components of a DCS, emphasizing the software tools required for its development. Chapter 3 lists the hardware equipment to be controlled by the DCS. Chapter 4 describes the modeling of FDD-DCS as a hierarchical structure composed of finite state machines and explains the design of the various user interface panels for operators. Chapter 5 describes configuration of several DCS features essential for operation: the alarm, archiving and SAFE systems, the trending panels and the automated configuration process. A manuscript based on this work was accepted and published in the Nuclear Instruments and Methods in Physics Research A scientific journal.

With great success, deep learning as one form of machine learning is utilized for various ap- plications and shows its benefits also in the field of high-energy physics, or more specifically, for jet flavour tagging. At the same time, studies on AI safety show the susceptibility of neural networks, even if the modifications of the inputs are only marginal. Those intriguing, yet con- cerning properties are analysed in the context of b-tagging, where the chosen architecture replicates the DeepCSV algorithm, which is utilized at the CMS experiment. Investigating the response of the tagger is motivated by the later usage of the outputs in physics analyses, as the values for simulation and data are deviating. The vulnerability of the neural network will be probed by employing adversarial attacks, which involves the addition of a random Gaussian noise term or the systematic application of the Fast Gradient Sign Method. Investigations of the input shapes as well as the ROC curves show how the aforementioned adjustments influence the features and the performance. The scan of the area under the ROC curve over epoch reveals the tradeoff between model per- formance and susceptibility. A defense strategy, namely adversarial training, improves the robustness of the classifier and represents a better tradeoff, compared to the basic training. This method constitutes a promising candidate to reduce discrepancies when evaluating the model on simulated events as well as on data.

Computing demands for large scientific experiments, such as the CMS experiment at CERN, will increase dramatically in the next decades. To complement the future performance increases of software running on CPUs, explorations of coprocessor usage in data processing hold great potential and interest. We explore the novel approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-Service approach in large-scale data processing. In this setup, the main CMS Mini-AOD creation workflow is executed on CPUs, while several machine learning (ML) inference tasks are offloaded onto (remote) coprocessors, such as GPUs. With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. We also show that this approach can be easily generalized to different types of coprocessors, and even deployed on local CPUs without performance decrease. We emphasize that SONIC enables high coprocessor usage and brings the portability to run workflows on different types of coprocessors.

One of the head investigations at the LHC and ATLAS Experiment is related to the study of the Quark Gluon Plasma (QGP), formed in ultra-relativistic heavy-ion collisions. Due to its short lifetime and spa- tial limitation, the QGP properties are impossible to be measured directly, and therefore, indirect methods must be used. One method is related to the measured quantities of the collimated sprays of particles, so- called jets, generated immediately after the collision takes place. The particle shower develops in the QGP, so understanding the jet energy loss processes and the modification of the fragmentation functions is crucial to infer the properties of this state of matter. In particular, jets resulting from the bottom (b) quark fragmentation are expected to interact with the QGP differently from the other jets providing addi- tional information about the nature of the QGP so identifying them is a must. Distinguishing bottom-jets from charm- and light-jets produced in proton-proton collisions is difficult, but the huge environment of Pb+Pb collisions makes the task particularly challenging. In the ATLAS Experiment the Neural Net- works, such as the DL1 - Deep Learning 1, are the most promising tools to provide an efficient jet flavor discrimination and b-tagging.

In the past years, it has been observed at several beam test campaigns that irradiated LGAD sensors break with a typical star shaped burn mark when operated at voltages much lower than those at which they were safely operated during laboratory tests. The study presented in this paper was designed to determine the safe operating voltage that these sensors can withstand. Many irradiated sensors from various producers were tested in two test beam facilities, DESY (Hamburg) and CERN-SPS (Geneva), as part of ATLAS High Granularity Timing Detector (HGTD) beam tests. The samples were placed in the beam and kept under bias over a long period of time in order to reach a high number of particles crossing each sensor. Both beam tests lead to a similar conclusion, that these destructive events begin to occur when the average electric field in the sensor becomes larger than 12 Volts per micrometre., Published version

We present an ongoing search for new physics in proton-proton collisions at centre-of-mass energy of 13 TeV, targeting supersymmetry models with gauge-mediated supersymmetry breaking. We focus on events with a photon, an electron or a muon, and significant missing transverse momentum ($\textrm{p}_\textrm{T}^{\textrm{miss}}$), using a dataset of 137 $\text{fb}^{-1}$ collected with the CMS detector. We discuss the standard model backgrounds, event selection, and background estimation techniques in the control region to extrapolate to the signal region.

The study of Higgs boson and its interaction with other particles has been one of the main topics of particles physics in the last years. Since its discovery in 2012, many experiments have been carried out in order to understand its physical properties in detail. This thesis focuses on an interaction of a Higgs boson with a single top quark, possibly the heaviest elementary particle of the Standard Model of particle physics. It uses machine-learning algorithms to filter out all unwanted processes recorded by detector to get the highest sensitivity of our target process. For this purpose, multiple machine learning models are optimized with different optimization strategies. After the best quality model is obtained, a serie of statistical tests is performed with the TRExFitter framework. The expected median value of signal strength obtained in this thesis with inclusion of statistical uncertainties was 3.89. After the inclusion of systematic uncertainties, the resulting expected median value was 6.35.

The neutral Standard Model Higgs bo- son was discovered in 2012 at CERN, and the search for further particles of extended models continues. In particular, the search for an Axion-Like-Particle (ALP). Using machine learning technologies, this analysis addresses the separation of ALP production from unwanted background reactions. In this project, the Run-2 data from the ATLAS detector are used and the efficiency as well as the significance of the machine learning algorithm is optimized as a function of theoretical ALP mass.

The search for charged Higgs bosons, predicted by the Two Doublet Higgs Model and the Minimal Supersymmetric extension of the Standard Model, is challenging because of a large number of background processes and the unknown mass of the charged Higgs bosons. This thesis proposes to use machine learning to separate signal tbH$^+ \rightarrow$ tbW$\tau\tau$ from tth, ttW , ttZ , tt, VV and other background processes. A multi-model approach is proposed, where each model is sensitive in a certain mass range to achieve large significance in its dedicated mass section. Four different model types are optimized and the best model is selected for each mass of the charged Higgs boson analysis. Permutation feature ranking is used for each best model to determine the most important features. Based on the highest-ranking features, feature reduction is demonstrated to reduce the sensitivity only slightly. Results are expressed as expected 95% CL limits.

Deep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Using low-level detector information in end-to-end deep learning approach allows to probe the poorly explored regions for dark matter search. This note presents an implementation of the end-to-end deep learning inference framework in CMS Software framework (CMSSW) for various physics objects classifiers such as electron/photon, quark/gluon, top and tau. The inference is benchmarked on CPU and GPUs.

Since the discovery of the Higgs boson in 2012, the diphoton ($\gamma\gamma$) decay channel of the Higgs boson has been one of the most potent channels due to the contrast between the smoothly falling background and sharply peaked signal in the diphoton invariant mass. In 2018, with up to 80 fb$^{-1}$ of data at $\sqrt{s} = 13$ TeV, ATLAS observed Higgs boson production in association with a pair of top quarks ($t\bar{t}H$) with a significance of 5.8 $\sigma$ by combining measurements in the $\gamma\gamma$, $b\bar{b}$, $ZZ$, and multi-lepton Higgs decay channels. The $t\bar{t}H$ production cross-section was measured to be 670 $\pm$ 90 (stat) $^{+110}_{-100}$ (syst) fb. The diphoton channel was one of the main contributors to this result, alone providing a significance of 4.1 $\sigma$. With 140 fb$^{-1}$, a search for non-resonant Higgs pair production in the $b\bar{b}\gamma\gamma$ final state was performed. No significant signal was observed and upper limits at 95\% confidence level were set. The observed limit on the SM cross-section was 130 fb, or 4.2 times the predicted value. The observed Higgs trilinear coupling modifier was constrained to be between [-1.5, 6.7]. Both the $t\bar{t}H (H \to \gamma\gamma$ and $HH \to b\bar{b}\gamma\gamma$ analyses will benefit tremendously from the increased statistics expected from the High-Luminosity LHC (HL-LHC). To ensure the continued efficiency of the detector in the harsh HL-LHC environment, ATLAS will install a new Inner Tracker (ITk) consisting of silicon pixel sensors in its innermost layer. At SLAC National Accelerator Laboratory, a variety of electrical tests are performed for the construction of a prototype integrated pixel system, in order to provide early feedback and validation of the ITk design.

The presentation shows plots concerning the discharges observed in the GE1/1 GEM station in CMS during the first months of operation in presence of collisions of LHC beams, July and August 2022.

This contribution illustrates the short circuits generated in GE1/1 GEM detectors installed in CMS, with attention to the operation of the detectors during magnetic field ramps. The plots are updated till 1st November 2021.

ALICE (A Large Ion Collider Experiment) studies heavy-ion (mainly Pb-Pb), proton-ion and proton-proton (pp) collisions at the CERN Large Hadron Collider (LHC). In central heavy-ion collisions, a deconfined state of matter, known as Quark-Gluon Plasma (QGP), is formed. In the forward rapidity region, ALICE is equipped with a muon spectrometer, detecting quarkonia (e.g. J/$\Psi$, $\Upsilon$) and open heavy flavour via their muonic decays. Quarkonia are key probes of the QGP properties: the suppression of J/$\Psi$ has been one of the first proposed signatures for the formation of QGP in heavy-ion collisions. The identification of muons in ALICE is performed by two stations of Resistive Plate Chambers (RPCs) placed downstream of two hadron absorbers. Particles tagged as muons are tracked by a set of ten stations of multi-wire proportional chambers. The correct operation of RPCs is ensured by the choice of the proper gas mixture and currently the ALICE RPCs are operated with a mixture of \ce{C_{2}H_{2}F_{4}}, \ce{i-C_{4}H_{10}} and \ce{SF_{6}}. Starting from 2016, new European Union regulations enforced a progressive phase-out of \ce{C_{2}H_{2}F_{4}} and, although its use for scientific purposes may still be allowed, CERN has adopted a policy of reduction of its greenhouse gases consumption, to which RPC operation by the LHC experiments contributes for about 80\%. Most importantly, the ban on industrial usage of \ce{C_{2}H_{2}F_{4}} will make it difficult and costly to purchase. The work described in this thesis is devoted to the detailed study and characterization of alternative eco-friendly gas mixtures for the ALICE muon RPCs. Preliminary tests with cosmic rays, reported in literature, have indicated that mixtures where \ce{C_{2}H_{2}F_{4}} is fully replaced by a combination of \ce{CO_{2}} and \ce{C_{3}H_{2}F_{4}} (HFO-1234ze) in variable proportions are promising, hence mixtures of this type were chosen for this work. The next crucial steps are the detailed in-beam characterization of such mixtures, the study of their performance under increasing irradiation levels, and their stability over time and as a function of the integrated charge. The Gamma Irradiation Facility (GIF++) at CERN, equipped with a 12.5 TBq~\ce{^{137}Cs} source able to induce counting rates up to several~kHz/cm$^{2}$ and located along a secondary beam line of the CERN accelerator complex, is an ideal tool for these studies, allowing one to to study the RPC performances in a high background radiation environment and to accumulate the integrated charge expected in a few years of operation at the LHC in a much shorter time span. This thesis describes the methodology and results of a set of beam and aging tests carried out at the GIF++ with ALICE-like RPC prototypes, operated with several mixtures with varying proportions of \ce{CO_{2}} and \ce{C_{3}H_{2}F_{4}}. In both cases, the RPC standard gas mixture has been used to establish the baseline behavior of the RPC. The tested mixtures have been fully characterized, in terms of absorbed current, efficiency, prompt charge, cluster size and time resolution, using the muon beam provided to the GIF++ in dedicated beam time periods. Both digitized (for detailed shape and charge analysis) and discriminated signals (using the same front-end electronics as employed in ALICE) were analyzed. Throughout the test periods, the irradiation from the GIF++ source was used to simulate a high background radiation on the RPCs, to test their rate capability. The role of the new components in the RPC mixture has been analyzed and, in light of the obtained results, the most promising mixtures have been pinpointed. Outside of the allocated beam time, a (still ongoing) long-term irradiation campaign at GIF++ was started, to study the stability of the detector response (so far only in terms of absorbed current) if exposed to intense radiation for a prolonged period of time, thus obtaining the first important indications on the long-term behavior of RPC detectors operated with HFO-based mixtures.

This master thesis describes the development of an FPGA-based emulator setup for performing system and stress tests of the FELIX-based read-out chain components used in the ATLAS Inner-Tracker (ITk) upgrade. The ITk is part of the ATLAS Phase-II upgrade for operation with the High-Luminosity Large Hadron Collider (HL-LHC). The setup emulates 24 lpGBT data aggregator and 168 RD53A frontend instances on the two Xilinx UltraScale+ KCU116 and VCU128 FPGA development boards, to fully populate the 24 fibre links of the FELIX FLX-712 board. The hit data of the RD53A, as well as the general emulator configuration, is uploaded to the two boards over an ad hoc Ethernet network. A software for controlling the boards over this network and for automated testing using the FELIX software components and the YARR DAQ software has been developed. With the setup, single-frontend and multi-frontend scans were performed. The single-frontend scans showed data losses, especially in FELIX device 1. A multi-frontend scan of up to all RD53A instances could be performed, which however showed the same issues. With the setup, a tool to perform all kinds of data transfer assessment studies for the qualification of the final ITk-Pixel DAQ read-out chain has been obtained.

The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN. The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error., 15 pages, 7 figures, submitted to Machine Learning: Science and Technology

Continuous high levels of radiation in the CMS Drift Tubs can be the cause of early detector ageing. For this reason, in view of High Luminosity LHC, several measures have been taken to protect the detector and keep the background under control. The last and most effective intervention was the installation, during Long Shut down 2, of dedicated shields over the top external DT chambers, aiming at reducing the rates of thermal neutrons coming from the empty space above the detector. This note presents a comparison of rates measured before and after such intervention, along with results of monitoring detector efficiency to spot any evidence of ageing.

Developed by the RD51 collaboration, the Scalable Readout System (SRS) is widely used for the read-out of Micro-Pattern Gaseous Detectors (MPGDs). With different front-end Application-Specific Integrated Circuits (ASICs) integrated into the SRS and its capability to read out small R&D set-ups up to mid-size experiments, the SRS functions as a multi-purpose read-out system. One of the ASICs integrated into the SRS is the VMM3a, which was specifically developed for the read-out of gaseous detectors. Due to its rich feature set and high configurability, it is the ideal candidate to further enhance the multi-purpose character of the SRS and move it into its next era. In the scope of this thesis, the capabilities of the VMM3a front-end of the RD51 Scalable Readout System (VMM3a/SRS) were explored for the charge read-out of MPGDs. This includes characterisation studies and the evaluation and improvement of calibration procedures to understand and optimise the system response. There it was found that the time resolution of VMM3a/SRS is better than 2 ns and that it allows to read-out detectors with trigger rates in the MHz regime. Furthermore, it was explored how different measurement types can benefit from the capabilities of VMM3a/SRS. As such, various kinds of X-ray imaging studies were performed with a single-detector set-up. In addition, a new beam telescope with VMM3a/SRS read-out was built for the RD51 collaboration, which meant the read-out of multiple detectors at the same time. This allowed to demonstrate that VMM3a/SRS can be used as an integrated read-out system, which provides position, time and energy information at the same time. In addition to the characterisation measurements, also different applications of VMM3a/SRS were demonstrated, such as the possibility to resolve argon fluorescence interactions inside gaseous detectors and use these interactions to determine the electron drift velocity. Ultimately, the newly built beam telescope was used in the NA61/SHINE experiment at CERN, demonstrating that VMM3a/SRS can be successfully operated in particle physics experiments.

The ATLAS Pixel Detector in the Large Hadron Collider uses a system of FPGAs in the off-detector readout system. The Read Out Driver (ROD) is a major component of the system that handles processing of front end data and sending it to the next stages of filtering and storage. This thesis starts with a background on the detector and readout system and moves on to discuss efforts in support and development of ROD firmware. The first project investigated anomalous behavior in the ROD Histogrammer. This was understood and resolved after testing in the SR1 facility, which has a mockup of the detector hardware. The second project, called Smart L1A Forwarding, was restarted in 2022 with an objective to mitigate desynchronization of data in Pixel during the challenging conditions of Run-3. Initial development and tests in SR1 and the detector have shown promising results. Further modifications have been made in an attempt to resolve issues found during testing; these modifications are being tested in the detector

Acknowledgments We thank the participants of the FPF meetings and the Snowmass working groups for discussions that have contributed both directly and indirectly to this study. We gratefully acknowledge the invaluable support of the CERN Physics Beyond Colliders study group and the work of CERN technical teams related to civil engineering studies (SCE-DOD), safety discussions (HSE-OHS, HSE-RP, EP-DI-SO), integration (EN-ACE), and discussions on services (EN-CV, EN-EL, EN-AA) and simulations (SY-STI). L. A. Anchordoqui is supported by U.S. National Science Foundation (NSF) Grant PHY-2112527. A. Ariga is supported by JSPS, Japan KAKENHI Grant JP20K23373 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant 101002690). T. Ariga acknowledges support from JSPS, Japan KAKENHI Grant JP19H01909. B. Batell is supported by U.S. Department of Energy (DOE) Grant DE–SC0007914 and by PITT PACC, USA. The work of J. Bramante is supported by NSERC, Canada. F. G. Celiberto acknowledges support from the INFN/NINPHA project, USA and thanks the Università degli Studi di Pavia, Italy for the warm hospitality. G. Chachamis is supported by the Fundação para a Ciência e a Tecnologia (Portugal) under project CERN/FIS-PAR/0024/2019 and contract ‘‘Investigador auxiliar FCT - individual Call, Portugal CEECIND/03216/2017 and acknowledges funding from the European Union’s Horizon 2020 research and innovation programme (Grant 824093). P. B. Denton is supported by DOE, USA Grant DE-SC0012704. Y. Du was supported by National Science Foundation of China (NSFC) Grants 12022514 and 11875003. The work of J. L. Feng is supported in part by NSF, USA Grants PHY- 1915005 and PHY-2111427, Simons Investigator Award, USA #376204, Simons Foundation, USA Grant 623683, and Heising-Simons Foundation, USA Grants 2019-1179 and 2020-1840. The work of M. Fieg, T. B. Smith, and Y.-D. Tsai is supported in part by NSF Grant PHY-1915005. M. Fucilla, M. M. A. Mohammed, and A. Papa acknowledge support from the INFN/QFT@COLLIDERS, Italy project. The work of M. V. Garzelli was supported in part by the Bundesministerium für Bildung und Forschung (BMBF), Germany. V. P. Goncalves was partially financed by the Brazilian funding agencies CNPq, FAPERGS, and INCT-FNA (process number 464898/2014-5). M. Guzzi is supported in part by NSF, USA Grants PHY- 1820818 and PHY-2112025. J. C. Helo acknowledges support from ANID FONDECYT-Chile Grant 1201673 and from ANID Millennium Science Initiative Program ICN2019 044. K. Jodłowski is supported by the National Science Centre, Poland, Grant 2015/18/A/ST2/00748. Ahmed Ismail and R. Mammen Abraham are supported by DOE, USA Grant DE-SC0016013. Ameen Ismail is supported by NSERC, Canada (reference number 557763) and by NSF, USA Grant PHY-2014071. Y. S. Jeong is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT: Ministry of Science and ICT) (2021R1A2C1009296). The work of F. Kling is supported by DOE, USA Grant DE-AC02-76SF00515 and by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC 2121 Quantum Universe – 390833306. P. Nadolsky is supported by DOE, USA Grant DE-SC0010129. H. Otono acknowledges support from JSPS, Japan KAKENHI Grant JP20H01919. V. Pandey is supported by DOE, USA Grant DE-SC-0009824. M. H. Reno is supported in part by DOE, USA Grant DE-SC-0010113. The work of A. Ritz is supported by NSERC, Canada, Canada. The work of D. Sengupta is supported by NSF, USA Grant PHY-1915147. T. Sjöstrand is supported by Swedish Research Council Grant 2016-05996. D. Soldin acknowledges support from the NSF, USA Grant PHY-1913607. S. Trojanowski is supported by the grant ‘‘AstroCeNT: Particle Astrophysics Science and Technology Centre’’ carried out within the International Research Agendas programme of the Foundation for Polish Science financed by the European Union under the European Regional Development Fund, and by the Polish Ministry of Science and Higher Education through its scholarship for young and outstanding scientists (decision 1190/E-78/STYP/14/2019). K. Xie is supported by DOE, USA Grant DE-FG02-95ER40896, NSF, USA Grant PHY- 1820760, and in part by PITT PACC. Parts of this work have been supported by Fermi Research Alliance, LLC, USA under Contract DE-AC02-07CH11359 with the DOE., The Forward Physics Facility (FPF) is a proposal to create a cavern with the space and infrastructure to support a suite of far-forward experiments at the Large Hadron Collider during the High Luminosity era. Located along the beam collision axis and shielded from the interaction point by at least 100 m of concrete and rock, the FPF will house experiments that will detect particles outside the acceptance of the existing large LHC experiments and will observe rare and exotic processes in an extremely low-background environment. In this work, we summarize the current status of plans for the FPF, including recent progress in civil engineering in identifying promising sites for the FPF and the experiments currently envisioned to realize the FPF's physics potential. We then review the many Standard Model and new physics topics that will be advanced by the FPF, including searches for long-lived particles, probes of dark matter and dark sectors, high-statistics studies of TeV neutrinos of all three flavors, aspects of perturbative and non-perturbative QCD, and high-energy astroparticle physics., ANID FONDECYT-Chile 1201673, Fermi Research Alliance DE-AC02-07CH11359, HSE-OHS, HSE-RP, NINPHA, PITT PACC, SY-STI, National Science Foundation PHY-2112527, U.S. Department of Energy DE–SC0007914, Simons Foundation 376204, 623683, Fuel Cell Technologies Program 824093, DE-SC0012704, Horizon 2020 Framework Programme, First Peoples Fund, Heising-Simons Foundation 2019-1179, 2020-1840, Natural Sciences and Engineering Research Council of Canada, European Commission, European Research Council, Deutsche Forschungsgemeinschaft 390833306, DE-SC-0009824, DE-SC-0010113, DE-SC0010129, JP20H01919, PHY-1915147, Japan Society for the Promotion of Science JP20K23373, National Natural Science Foundation of China 11875003, 12022514, PHY-1915005, PHY-2111427, Fundação para a Ciência e a Tecnologia CERN/FIS-PAR/0024/2019, Bundesministerium für Bildung und Forschung, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Ministry of Science, ICT and Future Planning 2021R1A2C1009296, DE-AC02-76SF00515, National Research Foundation of Korea, Instituto Nazionale di Fisica Nucleare, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Narodowe Centrum Nauki 2015/18/A/ST2/00748, 557763, DE-SC0016013, PHY-2014071, Vetenskapsrådet 2016-05996, PHY-1913607, Ministerstwo Edukacji i Nauki 1190/E-78/STYP/14/2019, DE-FG02-95ER40896, PHY-1820760, Università degli Studi di Pavia, Horizon 2020 101002690, JP19H01909, European Regional Development Fund, Instituto Nacional de Ciência e Tecnologia: Física Nuclear e Aplicações 464898/2014-5, PHY-1820818, PHY-2112025, Agencia Nacional de Investigación y Desarrollo ICN2019 044

Our diverse universe consists of a handful of ’elementary’ or ’fundamental’ particles. These elementary particles can be classified as (i) matter particles, the fermions and (ii) mediator particles, the bosons. There are only four distinct ways for fundamental particles to interact, (i) strong interaction (ii) electromagnetic interaction (iii) weak interaction and (iv) gravitational interaction. At present, Quantum Chromodynamics (QCD) is the best theory that we have to understand the strong force. It describes interaction between quarks and gluons (together called partons) inside a nucleon. QCD involves two very important phenomena related to strong interaction: asymptotic freedom and confinement. At high energy density or high temperature, the coupling strength of the QCD decreases and almost vanishes. Thus at high temperature, the quarks and gluons essentially behave as interaction free. This property is referred to as asymptotic freedom. On the other hand Quarks and Gluons are never observed freely or isolated but are always found in confined colourless composite particles, the confinement is an additional property required for QCD. QCD also suggests that u and d quarks must have the same mass. This symmetry is called chiral symmetry and in observed phenomenon the chiral symmetry must be broken spontaneously. To answer such questions the ideal approach would be to directly solve QCD equations, which result from the QCD Lagrangian but it is a very challenging task. But an alternative approach is to create some physical conditions in which the QCD equations are most simple. One such system is achieved when QCD matter is subjected to high temperature via heavy-ion collision. At this high temperature, Quarks and Gluons move freely in the matter and this state is referred as Quark Gluon Plasma (QGP) state. The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL) and the Large Hadron Collider (LHC) at CERN are both designed for heavy ion collision experiments. A Large Ion Collider Experiment (ALICE) at CERN is a special set of detectors designed to study QGP. Direct observation of QGP is very difficult because of its very short lifetime ($\sim10 fm/c$). It then quickly thermalises and expands to form hadron gas. Therefore an indirect method is required to look for the formation of the QGP. QCD theory provides a large number of such observables which can be used as an evidence not only to qualify but also to quantify the QGP produced and hence also a test to QCD theory. One such probe is the study of heavy flavour hadrons which are made up of at least a charm or beauty quark. The large masses of heavy flavors have several implications: (i) Heavy flavours are produced in initial hard scattering processes. Due to their small formation time ($\Delta t\sim0.1 fm/c$) as compared to the formation time of QGP ( $\Delta t\sim0.3 fm/c$) at the LHC, they go through all the stages that occur as the hot and dense medium created in heavy-ion collisions evolves over time. As a result, heavy quarks can investigate the qualities of dense matter formed early in a collision. (ii) Heavy quark thermalization is “delayed” in comparison to light quark thermalization. Heavy quarks might “thermalize” to some extent, but not totally, on a time scale comparable to the QGP lifetime. Therefore, their spectra should be significantly modified, but still retain memory about their interaction history, and, hence, represent an ”optimal” probe. (iii)RHIC and, in particular, LHC experiments enable extremely low parton momentum fractions x, when gluon saturation effects become significant. Since they have enormous masses, heavy quarks are useful tools for studying gluon saturation because charm productions can be calculated using perturbative QCD and their yield is sensitive to the initial gluon density. As a result, measuring open charm and beauty creation allows researchers to investigate QGP features and the colour charge and mass dependency of parton in-medium energy loss. The study of open charm mesons (mesons made up of one light quark and one charm quark) is a useful tool for probing pQCD theoretical models to reproduce data. In the present work, the study of open heavy flavour (D$^+$-meson) yield in pp collisions at 13 TeV as a function of charged-particle multiplicity and transverse spherocity gives insight into the mechanism influencing their production in hadronic collisions at these energies and is a tool to test the influence of Multiple Parton Interactions (MPI). Moreover, analyzing the charm production processes using pp data could help in learning the basic differences between hard and soft processes of particle production. Along with that, they also serve as a reference for the similar measurements in p-Pb and Pb-Pb collisions. This thesis is organised as follows: Chapter 1 gives a brief introduction about the field of high energy physics. The goal of high-energy physics is to study matter that interacts strongly at extremely high energy densities. Moreover, a discussion of the Standard Model of particle physics is given, which is currently the best description about the elementary particles and interactions we have. In addition to that a general introduction to the Quantum Chromodynamics (QCD) is given. QCD is a quantum field theory of the strong force. Similar to QED, quantum chromodynamics predicts the existence of force-carrying particles called gluons, which transmit strong forces between matter particles that carry "color", a strong form of "charge". A brief presentation about Quark Gluon Plasma (QGP) is also made and its phase diagram is discussed. QGP is composed of asymptotic free quarks and gluons and proof of its discovery has been published in heavy ion collision analysis experiments. A brief introduction about the Ultrarelativistic heavy-ion collisions and stages involved in its evolution will also be discussed. Relativistic collisions of heavy ions provide a unique opportunity to study the most interesting aspects of strong interactions in the laboratory. In these collisions, the energy density is so high that a transition from hadronic state to QGP state occurs. The analysis of collision involves the study of different quantities and parameters which are related to the properties of the different particles produced in the collision. These parameters and quantities are also described in this chapter. Besides that, this chapter also discusses about the experimental signatures related to the formation of QGP medium in relativistic collisions. In this chapter 2, the ALICE Experimental setup and its main characteristics is discussed. ALICE is one of the four main detector setup at the LHC at CERN. It is designed to handle very large particle production rate occuring in most central Pb-Pb collisions. Due to its very good tracking, vertexing and PID capabilities ALICE is very well suited for the open-charm studies. The main detectors used for the present analysis are the Inner Tracking System (ITS), the Time Projection Chamber (TPC) and the Time Of Flight (TOF). All these detectors and their performances will be described in detail in this chapter. Chapter 3 introduces ALICE data taking and computing framework. In ALICE the collection and processing of data is controlled by five central online systems which are (i) Data Acquisition system (DAQ) (ii) Central Trigger Processor (CTP) (iii) the High-Level Trigger (HLT) system (iv) the Detector Control System (DCS) and (v) the Experiment Control System (ECS). During the data taking period, the configuration of different detectors is controlled by the DAQ which work in collaboration with the CTP and the HLT system to select interesting physics events, provide an efficient access to these events and finally to store this data for later analysis. The DCS allows for the monitoring of the detector hardware from a central interface whereas ECS controls all these operations. The software package required for the data analysis in this work will also be discussed. The software used for this is called "AliRoot" which is an extension of "Root" and is based on the C++ language. Further, a detailed description of the simulations and reconstruction techniques of data, primary and secondary vertex reconstruction will be discussed. Chapter 4 emphasizes the importance of heavy flavour study in understanding the QCD as well as QGP. The heavy flavour have large masses as compared to the transition temperature which is around T$\sim$ 160 MeV. Due to this, heavy flavour is produced in the hard scattering processes which occur at the very early stages of the collision. The heavy flavour, therefore, observes the production of the QGP medium and evolution of QGP medium in spacetime in a heavy-ion collision. This chapter is, therefore, dedicated to the discussion of different kinds of processes involved in the production of heavy flavour in pp, pA ans AA collisions. The different observables like nuclear modification factor ($R_{AA}$) and elliptic flow variable ($v_2$) which play a significant role for understanding the production and behaviour of heavy flavour are also discussed. It also describes different effects like Cold Nuclear Matter (CNM) effects and Hot Nuclear Matter (HNM) effects that occur due the differences in the collision systems such pp, pA and AA. A summary of the results from the study of open heavy flavours in pp, pA,and AA collisions performed in different experiments like RHIC and LHC is also presented. Chapter 5 presents the analysis strategy for the D$^+$ meson self-normalized yield as a function of charged-particle multiplicity. The data used in this work has been collected by the ALICE Experiment during the Run-II of the LHC at CERN. This data is collected with pp collisions at at $\sqrt{s}$ = 13 TeV in the year 2016, 2017 and 2018. For the analysis, two different event triggers namely Minimum Bias and High-Multiplicity triggers are used. For the Minimum Bias trigger, we have around 1600 million events and the High-Multiplicity trigger gives us around 332 million events. Before the extraction of the raw yield of D$^+$ meson, the z-vertex correction has been applied. This z-vertex correction is needed because the efficiency of the detector varies along z-axis (beam-axis or longitudinal axis) and also changes over time. The z-vertex correction removes this inefficiency. After this, the D$^+$ meson candidates are reconstructed in the central pseudorapidity ($|\eta

The ALICE Inner Tracking System (ITS) team at CERN has completed commissioning the ITS2 detector, related electronic systems, and data-taking for physics has started. With higher rates and Pb-Pb collisions, increased data rates and radiation effects are expected to challenge the system’s stability. The ALICE ITS upgrade project includes a new complex readout system that interfaces several other complex systems, and debugging the readout chain is challenging. This project consists of a requirement specification, technical analysis, and acceptance test from which a command-line utility named fastPASTA has been developed as a final product to verify readout protocols and inspect raw data to debug failures in the readout system. The project is exploratory in assessing the Rust programming language and ecosystem’s impact on developing performant and reusable software. Source code and documentation are available at the GitLab repository [1].

In these proceedings, we present several new measurements of Standard Model (SM) processes, in the Higgs sector and beyond, that push the precision frontier forward at CMS. Results are presented in the context of a framework parameterizing deviations in Higgs boson couplings, as well as in the context of SM Effective Field Theory, where new analyses targeting Higgs, top, and multi-boson processes probe an increasingly diverse set of operators. Within these frameworks, CMS is efficiently exploring a large space of new physics models. No significant deviation from SM expectations is found., Contribution to the 2023 QCD session of the 57th Rencontres de Moriond

After more than three years of maintenance, consolidation and upgrade, the Large Hadron Collider (LHC) resumed its high energy proton-proton collisions on the 5th of July 2022, with a record breaking center of mass energy of 13.6\,TeV. The year 2022 marked the beginning of a planned four year data taking period called Run 3. This first year of Run 3 is of particular interest for the experiments recording the collisions produced by the LHC. Alongside the challenge of maintaining the outstanding level of performance required for data collection, reconstruction and offline analysis, the year 2022 allowed the different experiments and detectors to get a glimpse of what is to come with detector ageing becoming ever more present and instantaneous luminosity being pushed to the machine's limits in dedicated fills. Between the 5nd of July and the 27th of November, the LHC delivered a total integrated luminosity of 41.97 fb$^{-1}$ of proton-proton collisions at a center of mass energy of 13.6\,TeV to the Compact Muon Solenoid (CMS). In this document, the performance of the CMS Silicon Strip Tracker in this time period is described. A snapshot of the detector operated in either nominal or high intensity conditions is presented, as well as trends as a function of instantaneous and integrated luminosity, showing that the detector both behaved properly and is expected to continue doing so until the end of Run 3.

An overview of a few of the most recent precision measurements performed at the CMS experiment is presented, focusing on top quark measurements. High precision measurements of top quarks are important because they allow us to better understand the properties of the top quark, crucial to test the internal consistency of the Standard Model, and to better constrain new physics. Measurements of the Lund jet plane density and of the luminosity via Z bosons are also presented.

In CMS, data quality monitoring (DQM) and data certification (DC) are crucial components in ensuring reliable data quality suitable for physics analysis. In the offline DQM procedure, the quality of recorded data, grouped in "runs", is evaluated. The current method for certification of quantities related to hadronic jets and missing transverse momentum (MET) is mostly reliant on manually monitoring reference histograms summarizing the status and performance of the detector. Given the large number of distributions that are mentioned, the process is time intensive and prone to human error when deviations from the norm are less evident. The results presented here show machine learning methods for certifying offline DQM data, focusing on hadronic jet and MET objects. Using collision data collected during 2018, we show that autoencoder techniques can accurately certify runs and detect ineffective detector regions, allowing to reduce both the time required for DC, as well as the rate of anomalies a human expert can potentially miss.

The International Space Station (ISS) orbits in the Low Earth Orbit (LEO) and is continuously occupied by astronauts. With an average altitude of $400\ \mathrm{km}$, the Station is shielded from cosmic radiation by the residual atmosphere and the geomagnetic field. However, that protection is not always complete. The solar activity can emit sporadic bursts of energetic particles, with energies between $\sim 10\ \mathrm{keV}$ and several $\mathrm{GeV}$. Solar Energetic Particle (SEP) events can last for hours or even days and can represent an actual risk for ISS occupants and equipment. The Alpha Magnetic Spectrometer (AMS) was installed on the ISS in 2011 and is expected to take data until the decommission of the Station itself, after 2030. The instrument measures CR flux continuously and can be used to detect SEPs in real-time. An algorithm for SEPs identification has been developed measuring sudden excesses in the trigger rates of AMS, using moving averages for the quiet level defintion. In the custom algorithm, the McIlwain's $L$-parameter is used to characterize the data taking areas. A real-time monitoring and alert system for SEPs near the ISS has been realized processing data from the AMS Monitoring Interface (AMI) database.

New Physics Learning Machine (NPLM) is a machine-learning based strategy to detect data departures from a Reference hypothesis (the Standard Model), with no prior bias on the source of the discrepancy responsible for it. The main idea behind the method is to approximate the log- likelihood-ratio hypothesis test parametrising the data distribution with a universal approximating function, and solving its maximum-likelihood fit as a machine-learning problem, with a customised loss function. The method returns a p-value that measures the compatibility of the data with the Reference model. A strategy to account for the uncertainties of the Reference hypothesis has been developed, opening up the way to the application of NPLM to new physics searches at the LHC experiments. Beside that, the most interesting potential applications of NPLM are validation of new Monte Carlo event generators and data quality monitoring. Using efficient large-scale implementations of kernel methods as universal approximators, the NPLM algorithm can be deployed on a GPU-based data acquisition system and be exploited to explore online the readout of an experimental setup. This would allow to identify detector malfunctions or, possibly, unexpected anomalous patterns in the data. One crucial advantage of the NPLM algorithm over most of the standard goodness-of-fit tests routinely used in many experiments is its capability of inspecting multi-dimensional problems, thus being sensitive to correlations. It also identifies the most discrepant region of the phase-space and it reconstructs the multidimensional data distribution, allowing for further inspection and interpretation of the results. The purpose of this thesis is to develop, test and deploy the NPLM strategy. After presenting its conceptual foundations and main properties, the NPLM algorithm is applied to a real new physics search and a data quality monitoring problem. For the former, we analyse the dimuon final state of proton-proton collision events collected by the CMS experiment at a center-of-mass energy of 13 TeV. For the latter, we monitor the readout of drift tubes chambers collecting cosmic muons at the INFN Legnaro national laboratory.

This note presents a measurement, in the first data collected by CMS in 2022 at 13.6 TeV, of the performances of the Level 1 Trigger (L1T) system of CMS to identify and select electrons and photons. The corresponding L1T objects are reconstructed using the information provided by the electromagnetic and hadronic calorimeters and are referred to as the "L1 EG" objects. The analyzed data set corresponds to an integrated luminosity of 10.8 fb$^{-1}$ recorded between July and September 2022.

In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured data. Many MC samples have to be generated to account for theoretical systematic uncertainties, at a significant computational cost. Therefore, the MC statistic becomes a limiting factor for most measurements and the significant computational cost of these programs a bottleneck in most physics analyses. In this note, the Deep neural network using Classification for Tuning and Reweighting (DCTR) method is evaluated on MC sample of top pair production in CMS. DCTR is a method, based on a Deep Neural Network (DNN) technique, to reweight simulations to different model parameters by using the full kinematic information in the event. This methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample. In this note, DCTR method is evaluated in two different scenarios: the discrete reweighting of the hdamp variation in Parton-level Powheg HVQ events and the continuous reweighting of a b-fragmentation function parameter in particle-level Pythia8 events. The results are presented in this note.

We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order $\sigma(f_{\rm NL}^{\rm eq})\approx 23$, $\sigma(f_{\rm NL}^{\rm orth})\approx 12$ in a joint power-spectrum and bispectrum analysis, varying both nuisance parameters and cosmology, including a conservative range of scales. Fixing cosmological parameters and quadratic bias parameter relations, the limits tighten significantly to $\sigma(f_{\rm NL}^{\rm eq})\approx 17$, $\sigma(f_{\rm NL}^{\rm orth})\approx 8$. These compare favorably with the forecasted bounds from CMB-S4: $\sigma(f_{\rm NL}^{\rm eq})\approx 21$, $\sigma(f_{\rm NL}^{\rm orth})\approx 9$, with a combined constraint of $\sigma(f_{\rm NL}^{\rm eq})\approx 14$, $\sigma(f_{\rm NL}^{\rm orth})\approx 7$; this weakens only slightly if one instead combines with data from the Simons Observatory. We additionally perform a range of Fisher analyses for the error, forecasting the dependence on nuisance parameter marginalization, scale cuts, and survey strategy. Lack of knowledge of bias and counterterm parameters is found to significantly limit the information content; this could be ameliorated by tight simulation-based priors on the nuisance parameters. The error-bars decrease significantly as the number of observed galaxies and survey depth is increased: as expected, deep dense surveys are the most constraining, though it will be difficult to reach $\sigma(f_{\rm NL})\approx 1$ with current methods. The NLPNG constraints will tighten further with improved theoretical models (incorporating higher-loop corrections), as well as the inclusion of additional higher-order statistics., Comment: 6 pages, 3 figures, submitted to Phys. Lett. B

In Run 3, track reconstruction at the CMS High-Level Trigger is based on a single iteration seeded by pixel tracks reconstructed by the Patatrack algorithm. This note presents the track reconstruction performance measured in data recorded at a center-of-mass energy of $ \sqrt{s} = $ 13.6 TeV in 2022.