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Aritra Ghosh

Publications and source records attributed to Aritra Ghosh.

At least 19 recordsLinked to original sources
arXiv2026

Rubin J122659.4+090236: An Extremely Low Surface Brightness Galaxy Candidate Discovered in the Rubin LSST Early Data Preview 2

We report the serendipitous discovery of an exceptionally low surface brightness galaxy (LSBG) candidate, Rubin J122659.4+090236, in Rubin Observatory imaging of the interacting NGC 4410 system, identified in the Cosmic Treasure Chest public release. 2D S\'ersic modelling of the Rubin g, r, and i images reveals a nearly round system with a shallow profile (n ~ 0.4), an effective radius of R_e ~ 6'', and central surface brightnesses of $\mu_{0,g}=27.52\pm0.04$, $\mu_{0,r}=27.62\pm0.07$, and $\mu_{0,i}=27.04\pm0.08$ mag arcsec$^{-2}$. EAZY photo-z fitting favours an intermediate-z solution at z~0.3, while a low-redshift solution at z~0.028, consistent with the NGC 4410 system, is also permitted by a restricted EAZY fit over 0<z<0.1 without imposing a redshift prior. These alternatives imply substantially different physical interpretations, ranging from a diffuse dwarf-like system to an exceptionally extended background LSBG. This discovery demonstrates Rubin's sensitivity to extremely diffuse galaxies and highlights the potential of the LSST survey to uncover large samples of such elusive systems across wide areas, enabling systematic studies of the LSBG population and its role in galaxy evolution.

astro-ph.GA↗
arXiv2026

AGN-DB: A Unified Multi-Wavelength Database of Active Galactic Nuclei

We present the Active Galactic Nuclei Database (AGN-DB), a comprehensive, multi-wavelength catalog compiled from more than 100 publicly available AGN catalogs and samples released by the end of 2025, spanning radio to $\gamma$-ray wavelengths. The database contains approximately 8.1 million unique sources, approximately 7.8 million of which remain after flagging stellar contaminants, and approximately 6.8 million of these are classified as AGN. Source cross-matching across catalogs is performed using Lyra, a Bayesian likelihood-ratio framework that jointly considers positional uncertainties, source densities, and photometric information to compute posterior match probabilities. The resulting catalog provides astrometric coordinates, redshifts, photometry, and classifications for each unique source. All multi-catalog provenance is preserved. For every property, we store the full array of values and originating catalog identifiers, enabling multi-epoch and multi-survey analyses. In this paper, we describe the AGN-DB pipeline, including the cross-matching methodology, and present the statistical properties of the v1.0 catalog. AGN-DB is designed to enable population studies, spectral energy distribution modeling, AGN classification, and variability analyses at an unprecedented scale. Its pipeline is designed to facilitate the integration of new catalogs, allowing AGN-DB to be updated regularly, with releases planned at least annually.

astro-ph.GA↗
arXiv2026

Krylov Tomography and Finite-Uncertainty Certification of Exceptional-Point Dynamics

Exceptional points (EPs) can produce striking responses, but locating one does not reveal how much of its dynamics an excitation accesses, which responses a detector distinguishes, or whether a missing signal is absent or undetected. We introduce Krylov tomography, a preparation- and measurement-aware framework that uses time-resolved data and models with stated uncertainty limits to answer these questions, while also bounding offset-induced departures from exact-EP behavior. As an explicit illustration of the general framework, we present finite-precision simulations of a red-sideband optomechanical model, whose second-moment coherence sector contains a third-order EP, certifying preparation-sensitive access to two and three directions. Krylov tomography thus bridges EP structure and finite-precision measurements, providing a general framework for non-Hermitian systems.

quant-ph↗
arXiv2026

LEGGOS III: Mapping Star Formation and Dust in Gravitationally Lensed Galaxies with $\textit{SUMAC}$, a UMAP and Clustering Framework

Strong gravitational lensing combined with JWST's spatio-spectral resolution enables resolved studies of star-forming regions in $z\sim$ 2-4 galaxies, but identifying and characterizing such regions in lensed integral-field and multi-band data remains a manual, observer-dependent process. We present $\texttt{SUMAC}$ (Software for the Uniform Manifold Approximation of Clumps), an unsupervised learning pipeline that segments JWST imaging and spectroscopy at the "spaxel" level by combining $\texttt{UMAP}$-based manifold embedding with $\texttt{HDBSCAN}$ density clustering applied to spectral energy distributions/spectra. We demonstrate the pipeline on JWST/NIRSpec PRISM IFS observations of the lensed galaxy SGAS111020.0+645950.8 at $z = 2.481$, recovering six physically distinct stellar/nebular populations. The cluster median SEDs separate cleanly on the presence and strength of H$\beta$+[OIII], H$\alpha$+[NII], $\beta_{NUV}$ slope, Balmer break strength, and the Balmer decrement, with bluer clusters tracing unobscured star-forming regions and progressively redder clusters tracing dusty star-forming regions.

astro-ph.GA↗
arXiv2026

Triple exceptional point with unitary paths of unfolding in a three-site fermionic Swanson-like model

A fermionic three-site generalization of the popular bosonic Swanson model is studied as providing an exactly solvable five-parametric example of the quantum-mechanical unitary-evolution process leading to an ultimate loss of the observability and fall in an exceptional-point singularity (EP3). The instant of degeneracy is found to have an explicit one-parametric form. Its unitarity-compatible vicinity (i.e., the corridor of access to EP3) is also specified in closed form. The exact, numerical-error-independent solvability is found essential due to another, avoided, false energy-level crossing which is found to occur not too far from the true EP3 singularity.

quant-ph↗
arXiv2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on $\sim 4\times10^5$ Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

astro-ph.IM↗
arXiv2026

Environmental Sculpting of Galaxy Structure at Fixed Stellar Mass: A Multi-Scale Analysis Across Cosmic Time using 3 Million HSC Galaxies

The extent to which galaxy structure is shaped by environment beyond the local universe, once stellar mass is controlled, remains an open question in galaxy evolution. We address this challenge using an unprecedentedly large sample of $\sim$3 million galaxies from the Hyper Suprime-Cam Subaru Strategic Program spanning $0.3 \leq z < 0.7$ with $\log(M/M_{\odot}) \geq 8.9$. We correlate a mass-independent bulge-to-total ratio statistic with large-scale overdensity maps and cluster catalogs, propagating structural parameter posteriors through a Monte Carlo framework to robustly assess significance. We confirm with $>5\sigma$ confidence that galaxy structure depends on environment at fixed stellar mass, but this dependence is secondary to stellar mass and varies with redshift, mass, and environmental scale. At $z < 0.5$, we detect no significant structural correlation with large-scale overdensity, but cluster galaxies show statistically significant bulge enhancement compared to mass-matched field galaxies, indicating cluster-specific processes such as ram-pressure stripping and cumulative tidal interactions dominate structural transformation at these epochs. At $z \geq 0.5$, massive galaxies exhibit bulge-enhancement across both cluster- and large-scale environments, while lower-mass systems show enhancement only in cluster environments. This indicates that environmental mechanisms operate across broader spatial scales at earlier cosmic epochs, and enhanced merger rates, group preprocessing, and cosmic web stripping augment cluster-specific processes. By separating into star-forming and quiescent subsamples, we find nearly flat trends within each, demonstrating that the observed environmental effects arise from coupled morphological and star formation transformations. These results collectively reveal the multi-scale, epoch-dependent nature of environmental effects on galaxy structure.

astro-ph.GA↗
arXiv2026

Modeling the non-Markovian Brownian motion of an optomechanical resonator

We propose a representative, globally-admissible phenomenological spectral density of the bath for the non-Markovian Brownian motion of an optomechanical resonator, motivated by the near-resonance experimental observation of a non-Ohmic spectrum in [Nat. Commun. 6, 7606 (2015)]. To avoid divergences arising from a naive global extrapolation, we propose a globally-admissible, phenomenological bath spectrum that extends the experimentally-observed, near-resonance non-Ohmic behavior beyond the measurement window while ensuring finite bath-induced renormalizations and quadrature fluctuations of the resonator. The corresponding model of the structured environment produces a nonlocal mechanical response whose analytic pole structure encodes the observed linewidth. The resulting dissipation kernel exhibits a power-law-modulated exponential decay with transient negativity, signaling memory effects. In the weak-coupling regime, the optical readout based on homodyne detection enables near-resonance spectroscopy and, with a calibrated drive on the resonator, permits, in principle, the reconstruction of the full mechanical susceptibility, thereby providing access to both the dissipative and dispersive bath contributions. Our results provide a consistent route from locally-inferred spectral properties to globally-admissible open-system descriptions and establish a framework for probing structured environments in cavity optomechanics.

quant-ph↗
arXiv2026

Non-Markovian renormalization of optomechanical exceptional points

We investigate how non-Markovian mechanical dissipation affects exceptional points in linearized optomechanical systems with red-sideband drive. For a chosen non-Ohmic mechanical bath, we derive analytical conditions for the memory-renormalized exceptional point by employing a pseudomode mapping, thereby demonstrating that structured environments displace the mode coalescence away from the Markovian prediction. Crucially, we reveal that failing to account for this memory-induced shift suppresses the divergent Petermann factor by orders of magnitude, showing that accurate bath modeling is essential for the successful operation of exceptional-point-based devices whenever reservoir-induced memory is non-negligible. We finally show that non-Markovianity modifies the cavity reflection spectrum, manifesting as a shallower optomechanically-induced-transparency dip, providing therefore an experimentally-accessible signature of structured mechanical environments.

quant-ph↗
arXiv2026

Quantum jumps in open cavity optomechanics and Liouvillian versus Hamiltonian exceptional points

Exceptional points, where two or more eigenstates of a non-Hermitian system coalesce, are now of interest across many fields of physics, from the perspective of open-system dynamics, sensing, nonreciprocal transport, and topological phase transitions. In this work, we investigate exceptional points in cavity optomechanics, a platform of interest to diverse communities working on gravitational-wave detection, macroscopic quantum mechanics, quantum transduction, etc. Specifically, we clarify the role of quantum jumps in making a clear distinction between Liouvillian and Hamiltonian exceptional points in optomechanical systems. While the Liouvillian exceptional point arises from the unconditional Lindblad dynamics and is independent of the phonon-bath temperature, the Hamiltonian exceptional point emerges from the conditional no-jump evolution and acquires a thermal shift due to an enhanced conditional damping. Employing the thermofield formalism, we derive a unified spectral framework that interpolates between these regimes via an analytical hybrid-Liouvillian description. Remarkably, in the weak-quantum-jump regime, the exceptional point is perturbed only at the second order, highlighting the robustness of the Hamiltonian exceptional point under small hybrid perturbations. Our work reveals a continuous family of hybrid exceptional points, clarifies the operational and physical differences between the conditional and unconditional dissipative dynamics in optomechanical systems, and provides a probe for thermal baths.

quant-ph↗
arXiv2026

Obscured AGN at z < 1.5: X-ray to Far-Infrared SEDs and Host Galaxy Morphologies in the GOODS Fields

We present an analysis of spectral energy distributions (SEDs), galaxy light profiles, and visual morphological classifications for 194 X-ray luminous AGN (intrinsic absorption-corrected log10 LX(0.5 to 7 keV) less than 42.5, with a maximum of 45.2 ergs per second) at redshift z less than 1.5 in the GOODS fields. We generate X-ray to far-infrared SEDs normalized at 1 micron for all AGN and sort them according to their emission slopes in the ultraviolet and infrared. We visually classify their host galaxy morphologies and compute their bulge-to-total light ratios using the software Galaxy Shapes of Light (galight). Most (94 percent) GOODS AGN exhibit obscured SEDs, defined by diminished ultraviolet and/or mid-infrared emission, while only 6 percent show unobscured, quasar-like SEDs. Secular processes appear to play a large role in stimulating AGN emission, as only around one-third of galaxies are undergoing interactions. We also describe the morphological identification of a population of suspected post-merger spheroid galaxies with obscured ultraviolet and infrared SEDs, and distinguish them from the host galaxies of AGN with less obscuration in the ultraviolet or infrared.

astro-ph.GA↗
arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

astro-ph.IM↗
arXiv2026

Topological sensing of superfluid rotation using non-Hermitian optical dimers

We theoretically investigate a non-Hermitian optical dimer whose parameters are renormalized by dispersive and dissipative backaction from the coupling of the passive cavity with a ring-trapped Bose-Einstein condensate. The passive cavity is driven by a two-tone control laser, where each tone is in a coherent superposition of Laguerre-Gaussian beams carrying orbital angular momenta $\pm \ell \hbar$. This imprints an optical lattice on the ring trap, leading to Bragg-diffracted sidemode excitations. Using an exact Schur-complement reduction of the full light-matter dynamics, we derive a frequency-dependent self-energy and identify a static regime in which the atomic response produces a complex shift of the passive optical mode. This renormalized dimer supports a tunable exceptional point, enabling spectroscopic signatures in the optical transmission due to a probe field, which can in turn be utilized for estimating the winding number of the persistent current. Exploiting the associated half-integer topological charge, we propose a digital exceptional-point-based sensing scheme based on eigenmode permutation, providing a noise-resilient method to sense superfluid rotation without relying on fragile eigenvalue splittings. Importantly, the sensing proposals are intrinsically nondestructive, preserving the coherence of the atomic superfluid.

cond-mat.quant-gas↗
arXiv2025

Quasi-harmonic spectra from branched Hamiltonians

We revisit the canonical quantization to assess the spectrum of the modified Emden equation $\ddot{x} + kx\dot{x} + \omega^2 x + \frac{k^2}{9}x^3 = 0$, which is an isochronous case of the Li\'enard-Kukles equation. While its classical isochronicity and canonical quantization, leading to polynomial solutions with an exactly-equispaced spectrum have been discussed earlier, including in the recent paper [Int. J. Theor. Phys. 64, 212 (2025)], the present study focuses on the quantization of its branched Hamiltonians. For small $k$, we show numerically that the resulting energy spectrum is no longer perfectly harmonic but only approximately equispaced, exhibiting quasi-harmonic behavior characterized by deviations from uniform spacing. Our numerical results are precisely validated by analytical calculations based on perturbation theory.

quant-ph↗
arXiv2025

Twin Hamiltonians, three types of the Dyson maps, and the probabilistic interpretation problem in quasi-Hermitian quantum mechanics

In the framework of the so-called quasi-Hermitian quantum mechanics of stationary unitary systems, bound states are usually constructed as eigenstates $|\psi_n \rangle$ of a Hamiltonian operator $H$ with real spectrum which is non-Hermitian, $H \neq H^\dagger$. One of the ways of the standard probabilistic interpretation of such systems consists in a transformation of $H$ into its isospectral Hermitian ``twin" $\mathfrak{h}= \mathfrak{h}^\dagger$ via one of the so-called Dyson maps $\Omega: H \to \mathfrak{h}$. Naturally, the well known ambiguity of these $H-$dependent Dyson-map transformations implies also an ambiguity of the physical, $\Omega-$dependent probabilistic and experimental interpretation of the system in question. In the present paper, an exhaustive classification of all of the eligible $H-$dependent Dyson maps $\Omega=\Omega(H)$ is provided, implying also a systematic framework for a specification of all of the possible probabilistic interpretations of the quantum system characterized by a preselected $H$.

quant-ph↗
arXiv2025

Atomic-superfluid heat engines controlled by twisted light

We theoretically propose a quantum heat engine using a setup consisting of a ring-trapped Bose-Einstein condensate placed in a Fabry-P\'erot cavity where the optical field carries orbital angular momentum. We first show that the cavity-enhanced light-atom coupling leads to the emergence of polaritonic modes whose character can be reversibly switched between photonlike and phononlike by detuning sweeps, allowing work extraction governed by distinct reservoirs. We investigate the dependence of the engine efficiency on the orbital angular momentum. Beyond ideality, we discuss finite-time scenarios based on shortcuts to adiabaticity such that the efficiency retains its ideal-operation value, despite finite-time operation. Our analysis identifies orbital angular momentum as a control knob that can reconfigure the performance of such quantum heat engines.

quant-ph↗
arXiv2025

REFRAG: Rethinking RAG based Decoding

Large Language Models (LLMs) have demonstrated remarkable capabilities in leveraging extensive external knowledge to enhance responses in multi-turn and agentic applications, such as retrieval-augmented generation (RAG). However, processing long-context inputs introduces significant system latency and demands substantial memory for the key-value cache, resulting in reduced throughput and a fundamental trade-off between knowledge enrichment and system efficiency. While minimizing latency for long-context inputs is a primary objective for LLMs, we contend that RAG require specialized consideration. In RAG, much of the LLM context consists of concatenated passages from retrieval, with only a small subset directly relevant to the query. These passages often exhibit low semantic similarity due to diversity or deduplication during re-ranking, leading to block-diagonal attention patterns that differ from those in standard LLM generation tasks. Based on this observation, we argue that most computations over the RAG context during decoding are unnecessary and can be eliminated with minimal impact on performance. To this end, we propose REFRAG, an efficient decoding framework that compresses, senses, and expands to improve latency in RAG applications. By exploiting the sparsity structure, we demonstrate a 30.85 the time-to-first-token acceleration (3.75 improvement to previous work) without loss in perplexity. In addition, our optimization framework for large context enables REFRAG to extend the context size of LLMs by 16. We provide rigorous validation of REFRAG across diverse long-context tasks, including RAG, multi-turn conversations, and long document summarization, spanning a wide range of datasets. Experimental results confirm that REFRAG delivers substantial speedup with no loss in accuracy compared to LLaMA models and other state-of-the-art baselines across various context sizes.

cs.CL↗