arXiv subjects

Sagnik Chatterjee

Publications and source records attributed to Sagnik Chatterjee.

At least 19 recordsLinked to original sources

arXiv2026

Thermal evolution of neo-neutron stars. II. Temperature-dependent crusts

The neo-neutron star phase is an intermediate stage in the evolution from a hot lepton-rich compact object, which is formed in the aftermath of a core-collapse supernova or an accretion-induced collapse of a white dwarf, to a cold deleptonized neutron star (NS). Alternatively, this phase can occur after a binary NS merger if the final compact object does not collapse immediately into a black hole. Radial temperature profiles provided by numerical simulations of proto-NS evolution suggest that the composition and mechanical structure of the star's core at the beginning of the neo-NS phase are, as a good approximation, identical to those of a cold NS. In contrast, the composition and mechanical structure of the outer layers (i.e., the crust) are expected to change as the star cools. The thermal evolution of neo-NSs with temperature-dependent (inner) envelopes was considered by Beznogov et al. (2020). Here, we investigated what happens if the equation of state (EoS) of the entire crust, including its composition, depends on temperature. We analyzed the thermal, mechanical, and chemical evolution of neo-NSs by further developing and employing neo-NSCool, our NS thermal evolution code. First, we proved that thermally dripped neutrons slightly slow down the cooling during the early thermal relaxation stage. Then, we showed that the EoSs with exotic light species in the deepest layers of the crust result in significantly slower cooling in the photon cooling era compared with the EoSs that favor massive nuclei. The crust's composition also impacts the crystallization process as well as the way in which the crust contracts while it cools. The EoSs with light nuclei cause the innermost layers to remain liquid for a longer time than the EoSs with heavy nuclei.

astro-ph.HE↗

arXiv2026

Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption

We study the problem of robustly learning Gaussian Single Index Models (SIMs) in the presence of heavy-tailed noise and a constant fraction of adversarially corrupted covariates and responses. Prior work on robust recovery has considered settings such as linear regression (Pensia et al., JASA 2024), strictly monotonic link functions (Awasthi et al., NeurIPS 2022), and phase retrieval (Buna and Rebeschini, AISTATS 2025). However, these techniques do not extend to generic asymmetric non-monotonic link functions such as \textsc{GeLU} and \textsc{Swish}, which arise naturally as scalar primitives in modern gated neural architectures. We close this gap by giving the first robust recovery algorithm with near-linear sample and time complexity for generic non-monotonic link functions, thereby establishing the first robust recovery guarantees for a broad family of nonlinear SIMs for which \textit{no guarantees were previously known}. Our central contribution is a new structural understanding of the Gaussian squared-loss landscape under adversarial contamination. Crucially, we prove that for a broad class of nonlinear non-monotonic SIMs, a dimension-independent, constant-radius convex basin exists around the ground truth and is efficiently reachable via robust spectral initialization even under adversarial contamination. Prior works fail to establish both guarantees simultaneously, thereby either breaking down under adversarial contamination or failing to handle generic non-monotonic link functions. Together, these structural insights yield a principled warm start for robust gradient descent that provably converges to a final estimation error of $O(\sigma\sqrt{\epsilon})$ in $\tilde{O}(nd)$ time with $\tilde{O}(d)$ samples, where $\epsilon$ is the contamination fraction.

cs.LG↗

arXiv2026

Trajectory Supervision for Continual Tool-Use Learning in LLMs

Most language-model training data shows final artifacts, not the process that produced them. We study a tractable version of this question in tool use: when a model learns a stream of new API domains, does keeping tool-use trajectories help compared with stripping the intermediate API trace? We fine-tune Llama 3.1 8B Instruct with QLoRA on API-Bank using four sequential domain blocks. Condition A strips previous API request/response lines from the prompt and trains the model to predict the next API call. Condition B keeps the trajectory context. In a single-seed pilot, full held-out generation evaluation shows that Condition B reaches 56.9\% final exact full-call accuracy compared with 39.2\% for Condition A. B also improves final API-name accuracy by 7.7 points. However, B uses 25.1\% more training tokens, the run uses one seed, and the task is next-call prediction rather than full dialogue success.

cs.SE↗

arXiv2026

SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification

Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction of regulatory networks, functional annotation of unannotated genes, and drug candidate development. Experimental approaches such as RT-PCR and RNA-seq provide precise evidence of operon structure, but are laborious and largely limited to well-studied model organisms, making scalable computational methods essential for genome-wide operon identification. Prior computational approaches have employed traditional classifiers such as logistic regression and decision trees, motivating our use of these as physicochemical baselines. The DGEB benchmark evaluates operonic pair classification by embedding each sequence independently with a pre-trained protein language model and computing pairwise cosine similarity. In contrast, our Siamese MLP learns a classifier over the fused embedding space, which is theoretically better motivated for binary classification, as cosine similarity can yield meaningless scores depending on the regularization of the embedding model. While protein language model embeddings substantially outperform physicochemical features in ROC-AUC, a learned Siamese MLP head does not significantly improve over unsupervised cosine similarity in Average Precision, suggesting that the geometry of the embedding space already captures the functional relationships needed for this task. Nonetheless, our Siamese MLP achieves a ROC-AUC of 0.71, competitive with state-of-the-art models on the DGEB leaderboard. These findings indicate that protein language model embeddings are a viable, scalable foundation for operonic pair classification across diverse microbial genomes, with implications for automated genome annotation, regulatory network reconstruction, and characterization of organisms lacking experimental operon annotations.

q-bio.GN↗

arXiv2026

Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs

Large Language Models (LLMs) solve many reasoning tasks via chain-of-thought (CoT) prompting, but smaller models (about 7 to 8B parameters) still struggle with multi-step reasoning under tight compute and token budgets. Existing test time reasoning methods such as self consistency (sampling multiple rationales and voting), Tree-of-Thoughts (search over intermediate thoughts), and critique revise loops improve performance, but often at high token cost and without fine-grained step-level control. This project1 aims to address that gap: can Small Language Models (SLMs) reason reliably using the same or fewer tokens? This question is both scientific and practical. Scientifically, it probes whether process supervision and simple test-time controls (such as token budgets and rejection of redundant steps) can substitute for model scale or large sampling counts. Practically, many deployments (on-device, low-latency, or cost-constrained settings) cannot afford huge models or dozens of sampled rationales per query. A method that improves SLM reasoning at fixed cost would therefore be directly useful.

cs.CL↗

arXiv2026

Exciton-Selective Phonon Coupling in a Lead Halide Perovskite

Exciton-phonon interactions govern the optical response of semiconductors, yet disentangling multiple coupling channels in lead halide perovskites remains challenging. We investigate CsPbBr3 microcrystals using photoluminescence, Raman and reflectance spectroscopy at low temperature, revealing the simultaneous presence of high-energy and Rashba excitons, each accompanied by distinct phonon replica series. High-energy exciton replicas are uniquely spaced by approximately 9 meV, whereas Rashba exciton replicas exhibit a characteristic approximately 6 meV spacing, indicating the specificity of the exciton-phonon coupling. Unsupervised machine learning applied to a large low-temperature photoluminescence dataset reveals these replica features are prevalent. With increasing temperature, replica features broaden and merge, evolving into a dominant longitudinal optical phonon coupling regime at room temperature. This work establishes direct spectroscopic evidence for concurrent, exciton-specific phonon coupling within a single material, offering new pathways to engineer light-matter interactions for optoelectronic and phonon-photon-based quantum device applications.

cond-mat.mes-hall↗

arXiv2026

The Quantum Learning Menagerie (A survey on Quantum learning for Classical concepts)

This paper surveys various results in the field of Quantum Learning theory, specifically focusing on learning quantum-encoded classical concepts in the Probably Approximately Correct (PAC) framework. The cornerstone of this work is the emphasis on query, sample, and time complexity separations between classical and quantum learning that emerge under learning with query access to different labeling oracles. This paper aims to consolidate all known results in the area under the above umbrella and underscore the limits of our understanding by leaving the reader with 23 open problems.

quant-ph↗

arXiv2026

Perfect Secret Key Generation for a class of Hypergraphical Sources

Nitinawarat and Narayan proposed a perfect secret key generation scheme for the so-called \emph{pairwise independent network (PIN) model} by exploiting the combinatorial properties of the underlying graph, namely the spanning tree packing rate. This work considers a generalization of the PIN model where the underlying graph is replaced with a hypergraph, and makes progress towards designing similar perfect secret key generation schemes by exploiting the combinatorial properties of the hypergraph. Our contributions are two-fold. We first provide a capacity achieving scheme for a complete $t$-uniform hypergraph on $m$ vertices by leveraging a packing of the complete $t$-uniform hypergraphs by what we refer to as star hypergraphs, and designing a scheme that gives $\binom{m-2}{t-2}$ bits of perfect secret key per star graph. Our second contribution is a 2-bit perfect secret key generation scheme for 3-uniform star hypergraphs whose projections are cycles. This scheme is then extended to a perfect secret key generation scheme for generic 3-uniform hypergraphs by exploiting star graph packing of 3-uniform hypergraphs and Hamiltonian packings of graphs. The scheme is then shown to be capacity achieving for certain classes of hypergraphs.

cs.IT↗

arXiv2025

Generalized cross-resonance scheme for maximally-entangling two-qutrit gates

To utilize higher-dimensional quantum systems, in this Letter, we derive a generalized cross-resonance (GCR) scheme for realizing maximally entangling two-qutrit gates on fixed-frequency transmons beyond the 0-1 subspace. Our two-qutrit gates, namely, $U_{CR}^{01}$ and $U_{CR}^{12}$, acting on the $0{\text -}1$ and $1{\text -}2$ energy transitions of transmons, respectively, directly allow for entanglement on the $1{\text -}2$ levels. Unlike the known works, our gate is parametric in nature, enabling us to construct multiple entangling gates of interest. By performing simulations in Qiskit, we demonstrate two-qutrit generalized controlled-$X$ ($U_{CX}^{01}$ and $U_{CX}^{12}$) and controlled-$H$ ($U_{CH}^{01}$ and $U_{CH}^{12}$) gates, which are instances of the proposed $U_{CR}$ gates, with reported gate fidelities of $86.14\%~(99.73\%),~84.6\%~(97.88\%),~92.35\%~(99.39\%)$, and $91.99\%~(98.99\%)$, respectively with (and without) noise. We also reveal a two-qutrit Bell state with a fidelity of $99.06 \pm 0.01\%$, with a complete Bell state preparation in a $\sim514$ ns pulse sequence, which is less than the gate time of the known scheme by cross-Kerr-based entangling gates.

quant-ph↗

arXiv2025

Distinct Signatures of the Nature of Phase Transition in Binary Neutron Star Mergers

Binary neutron-star mergers offer crucial insights into the matter properties of neutron stars. We present the possible imprints in the gravitational wave signal from the nature of phase transition from such events. Our study employs a one-parameter family of equation of states built using a polytropic approach with a control parameter $\Delta p$ surveying the features of hadron-quark phase transition, from Maxwell construction to the Gibbs construction. It allows us to explore the extent of mixed phases and analyse their direct impact on merger dynamics. Post-merger gravitational wave emissions reveal the expression of specific signatures in the spectrogram and power spectral density, serving as a distinct signature of equations of state with mixed phases. We found additional peaks in power spectral density that are exclusively generated from the post-merger remnant experiencing a phase transition. Additionally, the nature of phase transition leaves specific imprints on the spectrogram, leading to a two-folded signature from gravitational wave analysis. Furthermore, we establish the first correlation between $\Delta p$ and the threshold mass for prompt collapse. Our analysis shows that $\Delta p \lesssim 0.04$ is required if GW170817 formed a long-lived remnant or has experienced a delayed collapse into a black hole.

astro-ph.HE↗

arXiv2025

Insights Into Neutron Stars From Gravitational Redshifts and Universal Relations

The universal relations in neutron stars form an essential entity to understand their properties. The moment of inertia, dimensionless tidal deformability, mass quadrupole moment, and oscillation modes are some of the properties that have been studied previously in the context of universal relations. All of these quantities are measurable; thus, analyzing them is of utmost importance. In this article we provide new universal relations in the context of a neutron star's gravitational redshift. Using the redshift measurements of RBS 1223, RX J0720.4-3125, and RX J1856.5-3754, we provide theoretical estimates of moment of inertia, dimensionless tidal deformability, mass quadrupole moment, the mass of the star times the ratio of angular frequency over the spin angular moment, and the average of the speed of sound squared. In the case of the redshift measurement of RX J0720.4-3125, we found that the theoretical estimate using universal relations aligns closely with the Bayesian estimate. Our findings indicate that such theoretical predictions are highly reliable for observations with low uncertainty and can be used as an alternative for statistical analysis. Additionally, we report a violation of the universality of the dimensionless tidal deformability and average of the speed of sound squared with respect to the gravitational redshift. Our calculations further indicate that, under current astrophysical constraints, the maximum gravitational redshift attainable by neutron stars does not exceed $0.763$.

astro-ph.HE↗

arXiv2024

Analyzing the dense matter equation of states in the light of the compact object HESS J1731-347

The recent mass ($0.77 \pm ^{0.20}_{0.17}M_{\odot}$) and radius ($10.4\pm^{0.86}_{0.78} \text{km}$) measurement of HESS J1731-347 made it one of the most fascinating object if it is indeed a neutron star. In this work, we examine the current status of the dense matter equation of states in the context of this compact object being a neutron star. We use three sets of equation of states corresponding to the three classes - neutron stars, strange stars, and hybrid stars and perform Bayesian model selection on them. Our results show that for hadronic models, the EoS is preferred to be stiff at the intermediate densities. This makes the Brueckner-Hartree-Fock approximation and models based on effective interactions deviate from current astrophysical observations on the inclusion of HESS J1731-347. Furthermore, for the strange star family, the equation of states composed of three flavor quarks prefers relatively smaller bag parameters. Analyzing the hybrid family of equation of states consisting of a first-order phase transition revealed preferences for early first-order phase transition. Comparing all the preferred equations of state among each family, it was found that the current astrophysical constraints prefer the hybrid equation of states the most.

astro-ph.HE↗

arXiv2024

Prospect of unraveling the first-order phase transition in neutron stars with $f$ and $p_1$ modes

Quasi-normal modes of neutron stars are an exciting prospect for analyzing the internal composition of NSs and studying matter at high densities. In this work, we focus on studying the $f$- and $p$- quadrupolar oscillation modes, which couple with gravitational waves. We construct two different equation of state ensembles, one without and one with a first-order phase transition, and examine how $f$- and $p$-modes might help us differentiate them. We find ensemble specific exclusion regions in the $65\%$ and $95\%$ confidence contours of the frequency-damping time relations. The exclusion regions become more prominent for the higher-order oscillation modes. However, these modes have higher frequencies, which are beyond the detection capabilities of present gravitational wave detectors. The quasi-universal relations of dimensionless quantities prove to be ineffective in differentiating the equation of state ensembles, as they obscure the details of the equation of state.

gr-qc↗

arXiv2024

Generalization Bounds for Dependent Data using Online-to-Batch Conversion

In this work, we upper bound the generalization error of batch learning algorithms trained on samples drawn from a mixing stochastic process (i.e., a dependent data source) both in expectation and with high probability. Unlike previous results by Mohri et al. (2010) and Fu et al. (2023), our work does not require any stability assumptions on the batch learner, which allows us to derive upper bounds for any batch learning algorithm trained on dependent data. This is made possible due to our use of the Online-to-Batch ( OTB ) conversion framework, which allows us to shift the burden of stability from the batch learner to an artificially constructed online learner. We show that our bounds are equal to the bounds in the i.i.d. setting up to a term that depends on the decay rate of the underlying mixing stochastic process. Central to our analysis is a new notion of algorithmic stability for online learning algorithms based on Wasserstein distances of order one. Furthermore, we prove that the EWA algorithm, a textbook family of online learning algorithms, satisfies our new notion of stability. Following this, we instantiate our bounds using the EWA algorithm.

cs.LG↗

arXiv2024

Effects of Reduced Interlayer Interactions on the K-point Excitons of MoS$_2$ Nanoscrolls

Transition metal dichalcogenide (TMD) nanoscrolls (NS) exhibit significant photoluminescence (PL) signals despite their multilayer structure, which cannot be explained by the strained multilayer description of NS. Here, we investigate the interlayer interactions in NS to address this discrepancy. The reduction of interlayer interactions in NS is attributed to two factors: (1) the symmetry-broken mixed stacking order between neighbouring layers due to misalignment, and (2) the high inhomogeneity in the strain landscape resulting from the unique Archimedean spiral-like geometry with positive eccentricity. These were confirmed through transmission electron microscopy, field emission scanning electron microscopy and atomic force microscopy. To probe the effect of reduction of interlayer interactions in multilayered MoS$_2$ nanoscrolls, low-temperature PL spectroscopy was employed investigating the behaviour of K-point excitons. The effects of reduced interlayer interactions on exciton-phonon coupling (EXPC), exciton energy, and exciton oscillator strength are discussed, providing insights into the unique properties of TMD nanoscrolls.

cond-mat.mes-hall↗

arXiv2024

Deciphering Accretion-Driven Starquakes in Recycled Millisecond Pulsars using Gravitational Waves

Recycled millisecond pulsars are susceptible to starquakes as they are continuously accreting matter from their binary companion. A starquake happens when the rotational frequency of the star crosses its breaking frequency. In this study, we perform a model analysis of an accreting neutron star suffering a starquake. We analyze two models: a spherical star with accreting mountains and a deformed star with accreting mountains. We find that as the star crosses the breaking frequency and suffers a starquake there is a sudden change in the continuous gravitational wave signal arriving from them. It is interesting to note that the amplitude of the gravitational wave signals increases suddenly for the spherical star. In contrast, for the deformed star, the amplitude of the continuous gravitational wave signal decreases suddenly. This sudden change in the continuous gravitational wave signal in recycled millisecond pulsars can be a unique signature for such pulsars undergoing a starquake.

astro-ph.HE↗

arXiv2023

Tensile strain induced brightening of momentum forbidden dark exciton in WS$_2$

Transition-metal dichalcogenides (TMDs) host tightly bound quasi-particles called excitons. Based on spin and momentum selection rules, these excitons can be either optically bright or dark. In tungsten-based TMDs, momentum-forbidden dark exciton is the energy ground state and therefore it strongly affect the emission properties. In this work, we brighten the momentum forbidden dark exciton by placing WS$_2$ on top of nanotextured substrates which put the WS$_2$ layer under tensile strain, modifying electronic bandstructure. This enables phonon assisted scattering of exciton between momentum valleys, thereby brightening momentum forbidden dark excitons. Our results will pave the way to design ultrasensitive strain sensing devices based on TMDs.

cond-mat.mes-hall↗

arXiv2023

Quantum Solutions to the Privacy vs. Utility Tradeoff

In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membership inference attacks on generative models. Our architecture can be used on top of any existing classical or quantum generative models. We argue that the use of quantum gates associated with unitary operators provides inherent advantages compared to standard Differential Privacy based techniques for establishing guaranteed security from all polynomial-time adversaries.

quant-ph↗