arXiv subjects

Anwesh Bhattacharya

Publications and source records attributed to Anwesh Bhattacharya.

9 recordsLinked to original sources

arXiv2026

Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework

Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in large imaging surveys because of projection effects and limited spatial resolution. Compact foreground stars and unresolved substructure can mimic dual nuclei through chance superposition, complicating automated detection. We revisit the 46,061 galaxies flagged but rejected as DAGN candidates by the GOTHIC pipeline, primarily because the two nuclei fell within the SDSS fibre aperture or exceeded its separation threshold. We train a supervised deep-learning framework based on the YOLOv11 oriented-bounding-box architecture on annotated SDSS imaging to separate genuine dual nuclei from foreground stellar contaminants and other spurious alignments. The final model attains a validation precision of 0.919, recall of 0.905, and $F_1$ of 0.912 for the dual-nuclei class, and yields 29,605 dual-nucleus candidates after removing star-dominated and blended detections. Structured visual inspection indicates that $54.5$--$62\%$ are consistent with genuine dual nuclei, implying $\sim(1.4$--$1.8)\times10^{4}$ plausible systems. Cross-calibrating the YOLO separation against the deterministic GOTHIC centroid measurement and restricting to the compact regime ($d \le 6.87''$) gives a conservative subset of $\sim 13{,}672$ candidates, reaching calibrated separations of $\sim 0.56''$. Spectroscopy of the most compact ($\le 1$~kpc) systems shows they are dominated by passive, absorption-line galaxies with no resolved double-peaked emission, so confirmation requires higher-resolution follow-up. The catalogue is a statistically refined list of candidates, not confirmed DAGN. Nonetheless, deep-learning detection substantially reduces contamination and expands the plausible DAGN census.

astro-ph.GA↗

arXiv2026

Investigating the Spectral Properties of Dual Nuclei in Galaxy Mergers from the GOTHIC survey: Supermassive Black Hole Growth, metal enrichment and Dual AGN

Dual nuclei systems are galaxy merger remnants or closely merging galaxies that have two distinct stellar cores separated by ~ 10pc to 10kpc. They are important laboratories for probing the co-evolution of stellar populations, galaxy dynamics, and central black holes during the hierarchical assembly of galaxies. In this study, we present a spectroscopic analysis of a sample of dual nuclei from the GOTHIC survey, using the penalized pixel-fitting (pPXF) code. The sample consists of star forming nuclei pairs, dual active galactic nuclei (DAGN) and mixed pairs. Using the SDSS spectra, we extracted stellar kinematics, emission line fluxes, the star formation history, metallicity of the nuclei, and derived important properties such as the supermassive black hole (SMBH) masses, accretion rates and SMBH ratios. We compared different properties of the nuclei in the dual systems, such as stellar velocity dispersion, stellar masses, black hole masses, age and metallicity. Our results show that the SMBH masses are higher for BHs in galaxy mergers compared to single nuclei for a given stellar mass, thus revealing that SMBHs grow during the galaxy merging process and not only due to the merger of SMBHs. Our study provides new observational constraints on the dynamical and evolutionary states of dual-nuclei systems, offering a deeper understanding of the role these systems play in galaxy evolution and central black hole growth.

astro-ph.GA↗

arXiv2025

Investigating the Bulge Morphology of Dual AGN Host Galaxies from the GOTHIC survey

We present a structural analysis of bulges in dual active galactic nuclei (AGN) host galaxies. Dual AGN arise in galaxy mergers where both supermassive black holes (SMBHs) are actively accreting. The AGN are typically embedded in compact bulges, which appear as luminous nuclei in optical images. Galaxy mergers can result in bulge growth, often via star formation. The bulges can be disky (pseudobulges), classical bulges, or belong to elliptical galaxies. Using SDSS DR18 gri images and GALFIT modelling, we performed 2D decomposition for 131 dual AGN bulges (comprising 61 galaxy pairs and 3 galaxy triplets) identified in the GOTHIC survey. We derived sérsic indices, luminosities, masses, and scalelengths of the bulges. Most bulges (105/131) are classical, with sérsic indices lying between $n=2$ and $n=8$. Among these, 64% are elliptical galaxies, while the remainder are classical bulges in disc galaxies. Only $\sim$20% of the sample exhibit pseudobulges. Bulge masses span $1.5\times10^9$ to $1.4\times10^{12}\,M_\odot$, with the most massive systems being ellipticals. Galaxy type matching shows that elliptical--elliptical (E--E) and elliptical--disc (E--D) mergers dominate over disc--disc (D--D) mergers. At least one galaxy in two-thirds of the dual AGN systems is elliptical and only $\sim$30% involve two disc galaxies. Although our sample is limited, our results suggest that dual AGN preferentially occur in evolved, red, quenched systems, that typically form via major mergers. They are predominantly hosted in classical bulges or elliptical galaxies rather than star-forming disc galaxies.

astro-ph.GA↗

arXiv2023

Automated Detection of Double Nuclei Galaxies using GOTHIC and the Discovery of a Large Sample of Dual AGN

We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or more closely separated nuclei. Our aim is to detect samples of dual or multiple active galactic nuclei (AGN) in galaxies. Although galaxy mergers are common, the detection of dual AGN is rare. Their detection is very important as they help us understand the formation of supermassive black hole (SMBH) binaries, SMBH growth and AGN feedback effects in multiple nuclei systems. There is thus a need for an algorithm to do a systematic survey of existing imaging data for the discovery of DNGs and dual AGN. We have tested GOTHIC on a known sample of DNGs and subsequently applied it to a sample of a million SDSS DR16 galaxies lying in the redshift range of 0 to 0.75 approximately, and have available spectroscopic data. We have detected 159 dual AGN in this sample, of which 2 are triple AGN systems. Our results show that dual AGN are not common, and triple AGN even rarer. The color (u-r) magnitude plots of the DNGs indicate that star formation is quenched as the nuclei come closer and as the AGN fraction increases. The quenching is especially prominent for dual/triple AGN galaxies that lie in the extreme end of the red sequence.

astro-ph.GA↗

arXiv2022

Fairly Constricted Multi-Objective Particle Swarm Optimization

It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm. In the single-objective setting, it leads to faster convergence and avoidance of local minima. Naturally, one would expect that the same advantages of EM carry over to the multi-objective setting. Hence, we extend the state of the art Multi-objective optimization (MOO) solver, SMPSO, by incorporating EM in it. As a consequence, we develop the mathematical formalism of constriction fairness which is at the core of extended SMPSO algorithm. The proposed solver matches the performance of SMPSO across the ZDT, DTLZ and WFG problem suites and even outperforms it in certain instances.

cs.NE↗

arXiv2022

Efficient ML Models for Practical Secure Inference

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at scale. The CrypTFlow2 framework provides a potential solution with its ability to automatically and correctly translate clear-text inference to secure inference for arbitrary models. However, the resultant secure inference from CrypTFlow2 is impractically expensive: Almost 3TB of communication is required to interpret a single X-ray on DenseNet-121. In this paper, we address this outstanding challenge of inefficiency of secure inference with three contributions. First, we show that the primary bottlenecks in secure inference are large linear layers which can be optimized with the choice of network backbone and the use of operators developed for efficient clear-text inference. This finding and emphasis deviates from many recent works which focus on optimizing non-linear activation layers when performing secure inference of smaller networks. Second, based on analysis of a bottle-necked convolution layer, we design a X-operator which is a more efficient drop-in replacement. Third, we show that the fast Winograd convolution algorithm further improves efficiency of secure inference. In combination, these three optimizations prove to be highly effective for the problem of X-ray interpretation trained on the CheXpert dataset.

cs.CR↗

arXiv2022

Encoding Involutory Invariances in Neural Networks

In certain situations, neural networks are trained upon data that obey underlying symmetries. However, the predictions do not respect the symmetries exactly unless embedded in the network structure. In this work, we introduce architectures that embed a special kind of symmetry namely, invariance with respect to involutory linear/affine transformations up to parity $p=\pm 1$. We provide rigorous theorems to show that the proposed network ensures such an invariance and present qualitative arguments for a special universal approximation theorem. An adaption of our techniques to CNN tasks for datasets with inherent horizontal/vertical reflection symmetry is demonstrated. Extensive experiments indicate that the proposed model outperforms baseline feed-forward and physics-informed neural networks while identically respecting the underlying symmetry.

cs.LG↗

arXiv2021

AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is attributed to its capability to perform good gradient approximations. We show that, the gradient of any function, differentiable or not, can be approximated by using the parameters of EMPSO. This is a novel technique to simulate GD which lies at the boundary between numerical methods and swarm intelligence. Mathematical proofs of the gradient approximation produced are also provided. AdaSwarm competes closely with several state-of-the-art (SOTA) optimizers. We also show that AdaSwarm is able to handle a variety of loss functions during backpropagation, including the maximum absolute error (MAE).

cs.NE↗

arXiv2021

A Swarm Variant for the Schrödinger Solver

This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's major advantages such as search space exploration and higher robustness to local minima compared to gradient-descent optimizers such as Adam. Neural network based solvers endowed with gradient optimization are now being used to approximate solutions to Differential Equations. Here, we demonstrate the novelty of EM-PSO in approximating gradients and leveraging the property in solving the Schrödinger equation, for the Particle-in-a-Box problem. We also provide the optimal set of hyper-parameters supported by mathematical proofs, suited for our algorithm.

cs.LG↗