Search

This paper presents a novel approach to constrain the $\mu$- and y- distortions in the Cosmic Microwave Background (CMB) using the COBE/FIRAS data. The analysis draws from the concept of blackbody radiation inversion (BRI), a mathematical technique typically used to determine the temperature distribution from a radiated power spectrum. We study the deviations from the ideal blackbody spectrum or the spectral distortions by incorporating first a non-zero chemical potential $\mu$ via the Bose-Einstein distribution and then the Compton parameter $y$ while keeping the monopole temperature constant. We infer the results as probability distribution functions on these distortions. Finally, we derive $\mu = (8.913 \pm 0.736) \times 10^{-5}$ and $y = (1.532 \pm 0.092) \times 10^{-5}$ at a $68\%$ confidence interval. The results are consistent with prior values and provide tighter constraints on the CMB spectral distortion and synergies of the primordial Universe., Comment: 16 pages, 4 figures, comments welcome

Understanding the evolution of galaxies cannot exclude the important role played by the central supermassive black hole and the circumgalactic medium (CGM). Simulations have strongly suggested the negative feedback of AGN Jet/wind/outflows on the ISM/CGM of a galaxy leading to the eventual decline of star formation. However, no "smoking gun" evidence exists so far where relics of feedback, observed in any band, are consistent with the time scale of a major decline in star formation, in any sample of galaxies. Relics of any AGN-driven outflows will be observed as a faint and fuzzy structure which may be difficult to characterise by automated algorithms but trained citizen scientists can possibly perform better through their intuitive vision with additional heterogeneous data available anywhere on the Internet. RAD@home, launched on 15th April 2013, is not only the first Indian Citizen Science Research (CSR) platform in astronomy but also the only CSR publishing discoveries using any Indian telescope. We briefly report 11 CSR discoveries collected over the last eleven years. While searching for such relics we have spotted cases of offset relic lobes from elliptical and spiral, episodic radio galaxies with overlapping lobes as the host galaxy is in motion, large diffuse spiral-shaped emission, cases of jet-galaxy interaction, kinks and burls on the jets, a collimated synchrotron thread etc. Such exotic sources push the boundaries of our understanding of classical Seyferts and radio galaxies with jets and the process of discovery prepares the next generation for science with the upgraded GMRT and Square Kilometre Array Observatory (SKAO)., Comment: 14 pages, 8 figures. Accepted for publication in the Springer-Nature conference proceedings for "ISRA 2023: The Relativistic Universe: From Classical to Quantum Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics". Comments and collaborations, most welcome! Please visit #RADatHomeIndia website at radathomeindia.org

The Densest $k$-Subgraph (D$k$S) problem aims to find a subgraph comprising $k$ vertices with the maximum number of edges between them. A continuous reformulation of the binary quadratic D$k$S problem is considered, which incorporates a diagonal loading term. It is shown that this non-convex, continuous relaxation is tight for a range of diagonal loading parameters, and the impact of the diagonal loading parameter on the optimization landscape is studied. On the algorithmic side, two projection-free algorithms are proposed to tackle the relaxed problem, based on Frank-Wolfe and explicit constraint parametrization, respectively. Experiments suggest that both algorithms have merits relative to the state-of-art, while the Frank-Wolfe-based algorithm stands out in terms of subgraph density, computational complexity, and ability to scale up to very large datasets.

We use the dispersion measure (DM) of localised Fast Radio Bursts (FRBs) to constrain cosmological and host galaxy parameters using simulation-based inference (SBI) for the first time. By simulating the large-scale structure of the electron density with the Generator for Large-Scale Structure (GLASS), we generate log-normal realisations of the free electron density field, accurately capturing the correlations between different FRBs. For the host galaxy contribution, we rigorously test various models, including log-normal, truncated Gaussian and Gamma distributions, while modelling the Milky Way component using pulsar data. Through these simulations, we employ the truncated sequential neural posterior estimation method to obtain the posterior. Using current observational data, we successfully recover the amplitude of the DM-redshift relation, consistent with Planck, while also fitting both the mean host contribution and its shape. Notably, we find no clear preference for a specific model of the host galaxy contribution. Although SBI may not yet be strictly necessary for FRB inference, this work lays the groundwork for the future, as the increasing volume of FRB data will demand precise modelling of both the host and large-scale structure components. Our modular simulation pipeline offers flexibility, allowing for easy integration of improved models as they become available, ensuring scalability and adaptability for upcoming analyses using FRBs. The pipeline is made publicly available under https://github.com/koustav-konar/FastNeuralBurst., Comment: Submitted to OJA, 14 pages, 10 figures, comments welcome

We develop a method to transform a collection of higher-dimensional spin systems from the thermal state with a very high temperature of a local spin-s Hamiltonian to a low-lying energy eigenstate of the same. The procedure utilizes an auxiliary system, interactions between all systems, and appropriate projective measurements of arbitrary rank performed on the auxiliary system. We refer to this process as subspace cooling. The performance of the protocol is assessed by determining the fidelity of the target state with the output one and the success probability of achieving the resulting state. For this analysis, spin-s XXZ and bilinear biquadratic models are employed as the evolving Hamiltonian. We demonstrate that in both scenarios, unit fidelity can be attained after a reasonable number of repeated measurements and a finite amount of evolution time when all the systems are aligned in an open chain, but it fails when the interactions between the spin follow the star configuration. We report that the success probability increases with the rank of the projectors in the measurement for a fixed dimension and that for each dimension, there exists a range of interaction strength and evolution period for which the fidelity gets maximized. Even when some subsystems are in contact with the thermal bath, the method proves to be resistant to decoherence., Comment: 12 pages, 10 figures

We examine the impact of long-range Dzyaloshinskii-Moriya (DM) interaction in the extended XY model on the phase diagram as well as the static and dynamical properties of quantum and classical correlation functions. It is known that in the nearest-neighbor XY model with DM interaction, the transition from the gapless chiral phase to a gapped one occurs when the strengths of the DM interaction and anisotropy coincide. We exhibit that the critical line gets modified with the range of interactions which decay according to power-law. Specifically, instead of being gapless in the presence of a strong DM interaction, a gapped region emerges which grows with the increase of the moderate fall-off rate (quasi-long range regime) in the presence of a transverse magnetic field. The gapless chiral phase can also be separated from a gapped one by the decay patterns of quantum mutual information and classical correlation with distant sites of the ground state which are independent of the fall-off rate in the gapless zone. We observe that the corresponding critical lines that depend on the fall-off rate can also be determined from the effective central charge involved in the scaling of entanglement entropy. We illustrate that in a non-equilibrium setting, the relaxation dynamics of classical correlation, the decay rate of total correlation, and the growth rate of entanglement entropy can be employed to uncover whether the evolving Hamiltonian and the Hamiltonian corresponding to the initial state are gapped or gapless., Comment: 12 pages, 7 figures

Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the constraints of traditional convolution operations that focus solely on local neighborhoods. Non-local operations typically require computing pairwise relationships between all elements in a set, leading to quadratic complexity in terms of time and memory. Due to the high computational and memory demands, scaling non-local neural networks to large-scale problems can be challenging. This article introduces a hybrid quantum-classical scalable non-local neural network, referred to as Quantum Non-Local Neural Network (QNL-Net), to enhance pattern recognition. The proposed QNL-Net relies on inherent quantum parallelism to allow the simultaneous processing of a large number of input features enabling more efficient computations in quantum-enhanced feature space and involving pairwise relationships through quantum entanglement. We benchmark our proposed QNL-Net with other quantum counterparts to binary classification with datasets MNIST and CIFAR-10. The simulation findings showcase our QNL-Net achieves cutting-edge accuracy levels in binary image classification among quantum classifiers while utilizing fewer qubits., Comment: preprint, 12 pages (including references and appendix), 5 figures

The Inert Higgs Doublet model (IDM), assisted by Peccei-Quinn (PQ) symmetry, offers a simple but natural framework of a dark sector that accommodates Weakly Interacting Massive Particle (WIMP) and axion as dark matter components. Spontaneous breaking of $U(1)_{PQ}$ symmetry, which was originally proposed as an elegant solution to the strong charge-parity (CP) problem, also ensures the stability of WIMP through a residual $\mathbb{Z}_2$ symmetry. Interestingly, additional fields necessitated by PQ symmetry further enrich the dark sector. These include a scalar field proprietor for axion DM and a vector-like quark (VLQ) that acts as a portal for the dark sector through Yukawa interactions. Moreover, this combination of the axion and WIMP components satisfies the observed DM relic density and reopens the phenomenologically exciting region of the IDM parameter space where the WIMP mass falls between 100 - 550 GeV. We investigate the model-independent pair production of VLQs exploring this region at the Large Hadron Collider (LHC), incorporating the effects of next-to-leading order (NLO) QCD corrections. After production, each VLQ decays into a top or bottom quark accompanied by an inert scalar, a consequence of the residual $\mathbb{Z}_2$ symmetry. Utilising relevant observables with a leptonic search channel and employing multivariate analysis, we demonstrate the ability of this analysis to exclude a significant portion of the parameter space with an integrated luminosity of 300 $\text{fb}^{-1}$., Comment: This version matches with the published version in JHEP

Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as "replacing the expert" in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology., Comment: Matches published in The European Physical Journal Special Topics (2024)

Grover's search algorithms, including various partial Grover searches, experience scaling problems as the number of iterations rises with increased qubits, making implementation more computationally expensive. This paper combines Partial Grover's search algorithm and Bi-directional Search to create a fast Grover's quantum search algorithm, referred to as Bi-Directional Grover Search (BDGS). We incorporated a bi-directional search tactic with a partial Grover search, starting from an initial state and a single marked state in parallel. We have shown in this article that our novel approach requires $\frac{\pi}{4\sqrt{2}}\sqrt{N}(1-\sqrt{\frac{1}{b^{r/2k}}})$ iterations over regular Grover Search and Partial Grover Search (PGS), which takes $\frac{\pi}{4}\sqrt{N}\sqrt{1-\frac{1}{b}}$ (here, $N=2^r$ elements, $b$ is the branching factor of partial search, and $k= \lceil\log_2b \rceil$). The proposed BDGS algorithm is benchmarked against the state-of-the-art Depth-First Grover's Search (DFGS) and generic Grover's Search (GS) implementations for $2$ to $20$ qubits and provides promising results. The Qiskit Python implementation of the proposed BDGS algorithm is available on Github (https://github.com/hafeezzwiz21/DFGS-BDGS)., Comment: 7 pages

Entanglement in quantum many-body systems is required for a variety of quantum information tasks, making it crucial to identify the parameter space in which the ground state is fully separable, known as the factorization surface (FS). Nonetheless, the tuning parameters indicating FS for several quantum spin models remain unknown. We employ symbolic regression (SR), a supervised learning technique, to determine a closed-form expression in the parameter regime corresponding to FS of quantum many-body Hamiltonians. We verify the effectiveness of this method by examining the analytically tractable models, namely a nearest-neighbor (NN) quantum transverse XY model with additional Kaplan-Shekhtman-Entin-Aharony interactions, for which the FS is well-known. We construct an accurate expression for the FS of the XYZ model by providing the parameter set through the SR algorithm in which the ground state is derived by matrix product state formalism. With a satisfactory level of accuracy, we estimate the FS for the long-range XY model, and the NN XY model with Dzyaloshinskii-Moriya type asymmetric interaction for which the factorization surface is not known., Comment: 15 pages, 7 figures

We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table). We validate our approach in home robot scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of success rate and data efficiency, while requiring no human supervision., Comment: Accepted to the IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS'24), Abu Dhabi, UAE, Oct 14-18, 2024

Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long-tailed distribution, a situation that is likely to yield sub-optimal performance for under-represented classes. This class imbalance problem is conventionally addressed by approaches relying on the class-wise cardinality of training examples, such as data resampling. In this paper, we demonstrate that considering solely the cardinality of classes does not cover all issues causing class imbalance. To measure class imbalance, we propose "Class Uncertainty" as the average predictive uncertainty of the training examples, and we show that this novel measure captures the differences across classes better than cardinality. We also curate SVCI-20 as a novel dataset in which the classes have equal number of training examples but they differ in terms of their hardness; thereby causing a type of class imbalance which cannot be addressed by the approaches relying on cardinality. We incorporate our "Class Uncertainty" measure into a diverse set of ten class imbalance mitigation methods to demonstrate its effectiveness on long-tailed datasets as well as on our SVCI-20. Code and datasets will be made available.

Model-independent theoretical upper bound on the thermal dark matter (DM) mass can be derived from the maximum inelastic DM cross-section featuring the whole observed DM abundance. We deploy partial-wave unitarity of the scattering matrix to derive the maximal thermally-averaged cross section for general number-changing processes $r\to 2$ (with $r\ge 2$), which may involve standard model particles or occur solely within the dark sector. The usual upper limit on the DM mass for $s$-wave annihilation is around 130 TeV (1 GeV) for $r=2$ (3), only applies in the case of a freeze-out occurring in the standard cosmological scenario. We consider the effects of two nonstandard cosmological evolutions, characterized by low-temperature reheating: $i)$ a kination-like scenario and $ii)$ an early matter-dominated scenario. In the first case, early freeze-out strengthens the unitarity bound to a few TeVs for WIMPs; while in the second case, the WIMP DM can be as heavy as $\sim 10^{10}$ GeV due to a large entropy dilution., Comment: 24 pages, 5 figures, version published in PRD

The common sense reasoning abilities and vast general knowledge of Large Language Models (LLMs) make them a natural fit for interpreting user requests in a Smart Home assistant context. LLMs, however, lack specific knowledge about the user and their home limit their potential impact. SAGE (Smart Home Agent with Grounded Execution), overcomes these and other limitations by using a scheme in which a user request triggers an LLM-controlled sequence of discrete actions. These actions can be used to retrieve information, interact with the user, or manipulate device states. SAGE controls this process through a dynamically constructed tree of LLM prompts, which help it decide which action to take next, whether an action was successful, and when to terminate the process. The SAGE action set augments an LLM's capabilities to support some of the most critical requirements for a Smart Home assistant. These include: flexible and scalable user preference management ("is my team playing tonight?"), access to any smart device's full functionality without device-specific code via API reading "turn down the screen brightness on my dryer", persistent device state monitoring ("remind me to throw out the milk when I open the fridge"), natural device references using only a photo of the room ("turn on the light on the dresser"), and more. We introduce a benchmark of 50 new and challenging smart home tasks where SAGE achieves a 75% success rate, significantly outperforming existing LLM-enabled baselines (30% success rate).

Quantum addition based on the quantum Fourier transform can be an integral part of a quantum circuit and proved to be more efficient than the existing classical ripple carry adder. Our study includes identifying the quantum resource required in a quantum adder in any arbitrary dimension and its relationship with the performance indicator in the presence of local noise acting on the circuit and when a limited number of controlled rotation operations is permitted, a procedure known as banding. We analytically prove an upper bound on the number of the controlled rotation gates required to accomplish the quantum addition up to an arbitrary defect in the fidelity between the desired and imperfect output. When the environment interacts with individual qudits, we establish a connection between quantum coherence and fidelity of the output. Interestingly, we demonstrate that when banding is employed in the presence of noise, approximate circuits of constant depth outperform circuits with a higher number of controlled rotations, establishing a complementary relationship between the approximate quantum adder and the strength of the noise. We exhibit that utilizing magnetic fields to prepare an initial state that evolves according to a one-dimensional spin chain for a specific amount of time can be a potential technique to implement quantum addition circuits in many-body systems., Comment: 17 pages, 12 figures

Millisecond pulsars (MSP) are an important subclass of rotation powered pulsars (RPP), traditionally defined as those with $P_s < 20-30$~ms and $B_s \lesssim 10^{10}$~G. We re-examine this definition by applying Gaussian mixture model (GMM) analysis to identify distinct clusters within the RPP population and find that the MSPs appear to be better demarcated by the condition $\mathbf{P_s \lesssim 16}$~ms., Comment: 3 pages, 1 figure

Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimization techniques used for training the contracted trainable weights of each model layer suffer from the correlations and entanglement structure between the model parameters on classical implementations. To address this issue, a multi-layer design of a Tensor Ring optimized variational Quantum learning classifier (Quan-TR) comprising cascading entangling gates replacing the fully connected (dense) layers of a TN is proposed, and it is referred to as Tensor Ring optimized Quantum-enhanced tensor neural Networks (TR-QNet). TR-QNet parameters are optimized through the stochastic gradient descent algorithm on qubit measurements. The proposed TR-QNet is assessed on three distinct datasets, namely Iris, MNIST, and CIFAR-10, to demonstrate the enhanced precision achieved for binary classification. On quantum simulations, the proposed TR-QNet achieves promising accuracy of $94.5\%$, $86.16\%$, and $83.54\%$ on the Iris, MNIST, and CIFAR-10 datasets, respectively. Benchmark studies have been conducted on state-of-the-art quantum and classical implementations of TN models to show the efficacy of the proposed TR-QNet. Moreover, the scalability of TR-QNet highlights its potential for exhibiting in deep learning applications on a large scale. The PyTorch implementation of TR-QNet is available on Github:https://github.com/konar1987/TR-QNet/

In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any $N$-point correlation that isn't a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any $N$-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents., Comment: Minor modifications in text and figure. Matches published version

In the search for exotic events involving displaced particles at HL-LHC, the triggering at the level-1 (L1) system will pose a significant challenge. This is particularly relevant in scenarios where low mass long-lived particles (LLPs) are coupled to a Standard Model (SM)-like 125 GeV Higgs boson and they decay into jets. The complexity arises from the low hadronic activity resulting from LLP decay, and the existing triggers' inability to efficiently select displaced events. This study introduces a novel machine learning approach to address this challenge, utilizing a lightweight autoencoder architecture designed for low latency requirements at L1. Focusing on light LLPs with decay lengths ranging from 1 to 100 cm, this approach employs "Edge convolution" on L1 reconstructed tracks. The results show notable signal acceptance at the permissible background rate, primarily originating from minimum bias and QCD di-jet events. For LLPs of mass 10, 30, and 50 GeV at decay length of 5 cm, the signal efficiencies are 33%, 70%, and 80%, respectively. At a 50 cm decay length, these efficiencies are 20%, 39%, and 45% for the same respective masses., Comment: 28 pages, 14 figures, 3 tables

Quantum algorithms have the ability to reduce runtime for executing tasks beyond the capabilities of classical algorithms. Therefore, identifying the resources responsible for quantum advantages is an interesting endeavour. We prove that nonvanishing quantum correlations, both bipartite and genuine multipartite entanglement, are required for solving nontrivial linear systems of equations in the Harrow-Hassidim-Lloyd (HHL) algorithm. Moreover, we find a nonvanishing l1-norm quantum coherence of the entire system and the register qubit which turns out to be related to the success probability of the algorithm. Quantitative analysis of the quantum resources reveals that while a significant amount of bipartite entanglement is generated in each step and required for this algorithm, multipartite entanglement content is inversely proportional to the performance indicator. In addition, we report that when imperfections chosen from Gaussian distribution are incorporated in controlled rotations, multipartite entanglement increases with the strength of the disorder, albeit error also increases while bipartite entanglement and coherence decreases, confirming the beneficial role of bipartite entanglement and coherence in this algorithm., Comment: v1: 11 pages, 8 figures, v2: close to published version

In this work, we study the $SU(2)_L$ singlet complex scalar extended KSVZ model that, in addition to providing a natural solution to the strong-CP problem by including a global Peccei-Quinn symmetry, also furnishes two components of dark matter that satisfy observer relic density without fine-tuning of model parameters. Furthermore, this model provides a rich phenomenology by introducing a vector-like quark whose presence can be sensed in collider experiments and dark matter production mechanisms. We explore the possibility of democratic Yukawa interaction of the vector-like quark with all up-type quarks and scalar dark matter candidate. We also employ next-to-leading order NLO-QCD correction for VLQ pair production to study a unique search at the LHC, generating a pair of boosted tops with sizeable missing transverse momentum. Multivariate analysis with jet substructure variables has a strong ability to explore a significant parameter space of this model at the 14 TeV LHC., Comment: 25 pages, 10 figures, 6 tables

The non-Hermitian model exhibits counter-intuitive phenomena which are not observed in the Hermitian counterparts. To probe the competition between non-Hermitian and Hermitian interacting components of the Hamiltonian, we focus on a system containing non-Hermitian XY spin chain and Hermitian Kaplan-Shekhtman-Entin-Aharony (KSEA) interactions along with the transverse magnetic field. We show that the non-Hermitian model can be an effective Hamiltonian of a Hermitian XX spin-1/2 with KSEA interaction and a local magnetic field that interacts with local and non-local reservoirs. The analytical expression of the energy spectrum divides the system parameters into two regimes -- in one region, the strength of Hermitian KSEA interactions dominates over the imaginary non-Hermiticity parameter while in the other, the opposite is true. In the former situation, we demonstrate that the nearest-neighbor entanglement and its derivative can identify quantum critical lines with the variation of the magnetic field. In this domain, we determine a surface where the entanglement vanishes, similar to the factorization surface, known in the Hermitian case. On the other hand, when non-Hermiticity parameters dominate, we report the exceptional and critical points where the energy gap vanishes and illustrate that bipartite entanglement is capable of detecting these transitions as well. Going beyond this scenario, when the ground state evolves after a sudden quench with the transverse magnetic field, both rate function and the fluctuation of bipartite entanglement quantified via its second moment can detect critical lines generated without quenching dynamics., Comment: 12 pages, 9 figures

We explore the pair production of third-generation scalar leptoquark at the Large Hadron Collider to next-to-leading order accuracy in QCD, matched to parton shower for a precise probing of the stemming model. We propose to tag two boosted top-like fatjets produced from the decay of heavy leptoquarks in association with notably large missing transverse momentum and consider them as the potential signal. Such a signal demonstrates the capability of a robust discovery prospect in the multivariate analysis with different high-level observables, including jet substructure variables. Various scalar leptoquark models predict different chirality of the top quark appearing from the decay of the leptoquark carrying same electromagnetic charge. We make use of the polarization variables sensitive to the top quark polarization in order to identify the underlying theory., Comment: 36 pages, 13 figures, 9 tables, This version matches the published version in Physical Review D

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixedvariable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using {\alpha}-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods., Comment: Accepted in International Conference on Communications (ICC) 2023

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin., Comment: Accepted in International Conference on Communications (ICC) 2023

We examine the singlet-doublet fermionic dark matter model, where the non-thermal production of the dark matter in light of a non-standard cosmology demands a significantly large interaction rate than the typical radiation-dominated Universe. Despite being a model of freeze-in light dark matter and heavy mediator, the characteristic long-lived particle searches at the collider experiment and the displaced vertex signature do not help in probing such a dark sector since this non-standard interaction mandates nearly prompt decay. We make a counterproposal to probe such signal with di-fat-jets generated from the boosted decays of massive vector bosons and Standard Model Higgs, along with the substantial missing transverse momentum to probe the dark matter at LHC. Interestingly, substructure variables associated with these fat jets have an additional handle to tackle the extensive QCD background as it encodes implicit footmarks of their origin. We adopt the multivariate analysis with the booted decision tree to constrain the measured relic density allowed parameter space of dark matter in the presence of the modified cosmological scenario. Our study shows how the non-trivial expansion affects dark matter production in the early Universe and alters the required search strategies at colliders. This probe provides the best discovery prospect at the HL-LHC for extended parameter space now opened up in the dark sector., Comment: 23 pages, 8 figures, 4 tables, journal version