arXiv subjects

Abhishek Das

Publications and source records attributed to Abhishek Das.

At least 19 recordsLinked to original sources

arXiv2026

Multimessenger Constraints on Production Sites of High-Energy Neutrinos from NGC 1068

The detection of high-energy neutrino signals from the nearby Seyfert galaxy NGC 1068 provides us with a unique opportunity to explore nonthermal processes near the center of supermassive black holes. Using the IceCube and Fermi-LAT data, we present general multimessenger constraints on the energetics of cosmic rays and the extent of the neutrino emission region, considering not only photohadronic (photomeson and photopair production) but also hadronuclear ($pp$) processes. Compared to the photohadronic scenario, the hadronuclear scenario can alleviate constraints on the emission region, yielding $R\lesssim30-70R_S$ for low-$β$ plasma, where the magnetic pressure dominates the gas pressure, and $R\lesssim5-50R_S$ for high-$β$ plasma. While our results support the previous conclusion that the photohadronic scenario favors a compact corona with $R\sim3-10R_S$, these suggest the relevance of further investigations into $pp$ neutrino contributions. When the the cosmic-ray spectrum is extended down to a minimum energy of 1 GeV, which is natural in light of particle acceleration mechanisms, we find that the required cosmic-ray luminosity exceeds the X-ray luminosity for a spectral index of $s_{\rm CR}\gtrsim2$, which challenges some shock acceleration models. We also show that the beta decay scenario is unlikely even if the magnetic field is as strong as the maximum allowed by the Eddington luminosity. Given that NGC 1068 can be established as a neutrino source, our results will provide evidence for the standard hadronic scenario, including magnetically powered corona models having hard spectra with $s_{\rm CR}\lesssim2$.

astro-ph.HE↗

arXiv2026

A Newton Identity and Finite-Rank Reconstruction for the Queer Lie Superalgebra

We establish a Newton-type identity for the queer Lie superalgebra $\mathfrak q_N$, relating Sergeev's odd cyclic central elements to Nazarov's one-row Capelli elements. The identity is obtained by comparing Ivanov's generating function for factorial Schur $Q$-functions with the queer Perelomov-Popov product of Grigoryev and Nazarov. Its coefficient expansion yields a triangular change of generators between the odd cyclic and odd one-row families. In particular, the odd one-row Capelli elements generate the center, while the even one-row elements are redundant. In fixed rank, we derive determinantal relations and a generic reconstruction theorem. The basic cyclic Hankel determinant is identified with a resultant and factored into the failure-of-strong-typicality and shifted-resonance factors. After localization at this determinant, the center is generated by the first $2N$ odd cyclic elements; consequently, generic central characters are determined by their values on these elements.

math.CO↗

arXiv2026

Physical origin of very-high-energy gamma rays from the low-luminosity active galactic nucleus NGC 4278 and implications for neutrino observations

Relativistic jets in active galactic nuclei (AGNs) are known to accelerate particles to extreme energies, yet the physical origin of very-high-energy (VHE) emission from low-luminosity (LL) AGNs remains unclear. NGC 4278, a nearby LL AGN, has recently been identified as a VHE source following detections by LHAASO. In this work, we present a multiwavelength and multimessenger analysis to investigate the origin of this emission. Swift X-Ray Telescope monitoring reveals a quasi-quiescent state characterized by the low X-ray flux. Modeling the broadband spectral energy distribution with the leptohadronic code AMES, we find that a standard one-zone synchrotron self-Compton model underpredicts the VHE flux, unless a relatively high Doppler factor ($δ\gtrsim 6$) is invoked. Alternatively, an external inverse-Compton (EIC) scenario-scattering seed photons from a radiatively inefficient accretion flow-provides a good description of the broadband emission with modest jet power and Doppler factor. We further explore neutrino production within a leptohadronic framework. The EIC model in the quasi-quiescent state yields the largest predicted number of muon neutrinos, reaching $N_{ν_μ} \sim 0.001$ over 15 yr of IceCube observations (assuming that 0.1\% of the Eddington luminosity is converted into high-energy protons). Future multimessenger observations are essential to unveil the details of the high-energy processes of NGC 4278.

astro-ph.HE↗

arXiv2026

Interpreting Swift and NuSTAR Observations of the Low-Luminosity Active Galactic Nucleus NGC 4278 with Radiatively Inefficient Accretion Flows and Implications for Neutrino Emission

We report the first NuSTAR hard X-ray observations of the low-luminosity active galactic nucleus NGC 4278. The source is clearly detected beyond 10 keV with a hard X-ray spectrum consistent with a power law of photon index between 2.2 and 2.5 without evidence for a high-energy cutoff. The X-ray flux is low compared to the active state in 2021, but exhibits variability by a factor of ~2 on a timescale of a month. We discuss the origin of the hard X-ray emission and explore its connection to gamma rays and high-energy neutrinos. We explain the X-ray data, including both quiescent and active states, using a radiatively inefficient accretion flow (RIAF) model with a variable accretion rate. We also show that TeV gamma rays cannot escape from the RIAF disk, and very high-energy gamma rays observed in LHAASO are likely to originate from outer regions such as jets and winds, which is consistent with our results favoring a magnetically arrested disk. We also discuss hidden neutrino emission from RIAFs together with possible connections to coronae of active galactic nuclei with standard, radiatively efficient disks.

astro-ph.HE↗

arXiv2026

Optimization of circular cavities via guided-mode expansion method based inverse design

Spin-photon interfaces, realized by coupling optically active spin systems to photonic cavities, are essential for quantum networking and quantum information processing. Implementing such an interface for polarization-encoded photons requires a cavity that supports arbitrary polarization, provides efficient optical access through its far-field mode, and maintains sufficiently high quality factors to enable high cooperativity with the system's optical transitions. However, inherent trade-offs between the Q-factor and far-field emission mode make the simultaneous optimization of these parameters toward the realization of spin-photon interfaces challenging. In this work, we implement a gradient-based inverse-design framework using guided-mode expansion with automatic differentiation to obtain the geometrical features of a circular ring cavity that supports arbitrary polarization while simultaneously optimizing the cavity quality factor and far-field mode profile. The resulting optimized non-periodic cavity achieves a quality factor of approximately $9,000$, about an order-of-magnitude higher than that of a periodic ("bullseye") cavity while preserving a Gaussian-like far-field emission pattern. Furthermore, by varying the cavity geometry within a $\pm 6$ nm fabrication tolerance, we demonstrate the robustness of the design against fabrication errors and identify the innermost ring width and central disk radius as the parameters with the greatest impact on the quality factor and far-field mode. These results establish guided mode expansion-based inverse design as a powerful and computationally efficient approach for developing high-cooperativity spin-photon interfaces for quantum photonic applications.

quant-ph↗

arXiv2026

Lung Cancer Detection Using Deep Learning

Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15% of cases occur in non-smokers. In 2020, approximately 2 million people were affected globally, resulting in 1.5 million deaths. The survival rate, at around 20%, lags behind other cancers, partly due to late-stage symptom manifestation. Necessitates early and accurate detection for effective treatment. Performance metrics such as accuracy, precision, recall (sensitivity), and F1-score are computed to provide a comprehensive evaluation of each model's capabilities. By comparing these metrics, this study offers insights into the strengths and limitations of each approach, contributing to the advancement of lung cancer detection techniques. In this paper, we are going to discuss the methodologies of lung cancer detection using different deep learning algorithms - InceptionV3, MobileNetV2, VGG16, ResNet152 - are explored for their efficacy in classifying lung cancer cases. Our Proposed Model algorithm based is a 16 layers architecture based on CNN model. Our Proposed model exhibits several key highlights that contribute to its novelty. By integrating multiple layer types such as convolutional, pooling, flatten, dropout, fully connected and dense layers, the model leverages the strengths of each layer to enhance its predictive capabilities. Novelty of our proposed model is that its accuracy is increasing consistently with the increasing no of epochs. We have tested the model performance up to epoch no 30. Our proposed model also overcome the overfitting problem.

cs.CV↗

arXiv2026

Quantum interference in a twisted high-Tc SQUID senses emergent interfacial order

Engineering artificial systems by twisting and stacking van der Waals materials has proven to be an excellent platform for exploring emergent quantum phenomena that can be significantly different from the constituents. Recent advances in the fabrication of high-quality twisted interfaces provide a unique opportunity to study the little-explored interfacial superconducting order in twisted cuprate superconductors. In our work, we fabricate superconducting quantum interference devices (SQUID) that utilize the twisted interface of $\mathrm{Bi_2Sr_2CaCu_2O_{8+δ}}$, a high-Tc cuprate superconductor. By measuring the magnetic field modulation of switching current and differential resistance, we find a $\mathrmπ$ phase difference between the two Josephson junction arms of the SQUID reflecting chiral superconducting order -- a crucial aspect inaccessible to single Josephson junction devices of the past. Our observations also indicate co-tunneling of the Cooper pairs and a time-reversal symmetry-broken emergent superconducting order. Additionally, these SQUIDs are well suited for use as state-of-the-art flux sensors close to 77 K, achieving a flux noise sensitivity of $\sim$1.5 $\mathrm{μΦ_0/\sqrt{Hz}}$. Stabilizing new superconducting orders using twisted interfaces and probing them using quantum interference opens new avenues to understanding the microscopic origin of unconventional superconductors. Our SQUID architecture is suitable for investigating the charge transport mechanisms and the symmetry of superconducting order at the interfaces of other systems, reflecting the broad applicability beyond cuprate superconductors.

cond-mat.supr-con↗

arXiv2026

UMA: A Family of Universal Models for Atoms

The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts. We develop empirical scaling laws to help understand how to increase model capacity alongside dataset size to achieve the best accuracy. The UMA small and medium models utilize a novel architectural design we refer to as mixture of linear experts that enables increasing model capacity without sacrificing speed. For example, UMA-medium has 1.4B parameters but only ~50M active parameters per atomic structure. We evaluate UMA models on a diverse set of applications across multiple domains and find that, remarkably, a single model without any fine-tuning can perform similarly or better than specialized models. We are releasing the UMA code, weights, and associated data to accelerate computational workflows and enable the community to continue to build increasingly capable AI models.

cs.LG↗

arXiv2026

Investigation of the Electronic Structure and Spin-State Crossover in LaCoO3 Using Photoemission Spectroscopy

Photoemission spectroscopy is a powerful technique for studying electronic structure and spin-state transitions, as it reveals changes in the orbital configuration accompanying a spin-state crossover. In this report, we combine excitation-energy-, temperature-, and geometry-dependent photoemission measurements to probe the electronic structure of LaCoO3 across its thermally driven spin-state transition. By systematically comparing valence-band spectra across a wide photon-energy window - from surface-sensitive soft x-ray photoemission spectroscopy (SXPS) to bulk-sensitive hard x-ray photoemission spectroscopy (HAXPES) - we identify the Co 3d-derived feature (A) along with the O 2p-dominated features (B and C), and explain their relative evolution in terms of photon-energy-dependent photo-ionization cross-section ratios. The thermally induced spin-state crossover is demonstrated using temperature-dependent SXPS valence-band spectra, which show a progressive suppression of the feature A with heating. Geometry-dependent HAXPES measurements further clarify how the signature of the spin-state transition in LaCoO3 is intricately linked to the orbital-selective response of the t2g and eg states. Additionally, angular-dependent photo-ionization cross-section analysis provides a consistent description of the polarization dependence observed in HAXPES. Finally, configuration-interaction analysis of the Co 2p core-level spectra reveals that LaCoO3 evolves from a predominantly low-spin ground state at low temperature to a mixed low-spin/high-spin configuration at elevated temperatures, with the high-spin fraction reaching about 30 percent at 400 K. The temperature evolution of the core-level line shape thus establishes Co 2p photoemission as a sensitive quantitative probe of spin-state transitions in LaCoO3.

cond-mat.str-el↗

arXiv2025

A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation

This article explores a novel approach to time series forecasting applied to the context of Chennai's climate data. Our methodology comprises two distinct established time series models, leveraging their strengths in handling seasonality and periods. Notably, a new algorithm is developed to compute the period of the time series using unsupervised machine learning and spline interpolation techniques. Through a meticulous ensembling process that combines these two models, we achieve optimized forecasts. This research contributes to advancing forecasting techniques and offers valuable insights into climate data analysis.

stat.AP↗

arXiv2024

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Understanding the interactions of atoms such as forces in 3D atomistic systems is fundamental to many applications like molecular dynamics and catalyst design. However, simulating these interactions requires compute-intensive ab initio calculations and thus results in limited data for training neural networks. In this paper, we propose to use denoising non-equilibrium structures (DeNS) as an auxiliary task to better leverage training data and improve performance. For training with DeNS, we first corrupt a 3D structure by adding noise to its 3D coordinates and then predict the noise. Different from previous works on denoising, which are limited to equilibrium structures, the proposed method generalizes denoising to a much larger set of non-equilibrium structures. The main difference is that a non-equilibrium structure does not correspond to local energy minima and has non-zero forces, and therefore it can have many possible atomic positions compared to an equilibrium structure. This makes denoising non-equilibrium structures an ill-posed problem since the target of denoising is not uniquely defined. Our key insight is to additionally encode the forces of the original non-equilibrium structure to specify which non-equilibrium structure we are denoising. Concretely, given a corrupted non-equilibrium structure and the forces of the original one, we predict the non-equilibrium structure satisfying the input forces instead of any arbitrary structures. Since DeNS requires encoding forces, DeNS favors equivariant networks, which can easily incorporate forces and other higher-order tensors in node embeddings. We study the effectiveness of training equivariant networks with DeNS on OC20, OC22 and MD17 datasets and demonstrate that DeNS can achieve new state-of-the-art results on OC20 and OC22 and significantly improve training efficiency on MD17.

cs.LG↗

arXiv2024

HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation

We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object Goal Navigation (ObjectNav) benchmarks. Leveraging the HM3DSem dataset, HM3D-OVON incorporates over 15k annotated instances of household objects across 379 distinct categories, derived from photo-realistic 3D scans of real-world environments. In contrast to earlier ObjectNav datasets, which limit goal objects to a predefined set of 6-20 categories, HM3D-OVON facilitates the training and evaluation of models with an open-set of goals defined through free-form language at test-time. Through this open-vocabulary formulation, HM3D-OVON encourages progress towards learning visuo-semantic navigation behaviors that are capable of searching for any object specified by text in an open-vocabulary manner. Additionally, we systematically evaluate and compare several different types of approaches on HM3D-OVON. We find that HM3D-OVON can be used to train an open-vocabulary ObjectNav agent that achieves both higher performance and is more robust to localization and actuation noise than the state-of-the-art ObjectNav approach. We hope that our benchmark and baseline results will drive interest in developing embodied agents that can navigate real-world spaces to find household objects specified through free-form language, taking a step towards more flexible and human-like semantic visual navigation. Code and videos available at: naoki.io/ovon.

cs.AI↗

arXiv2024

Distribution Learning for Molecular Regression

Using "soft" targets to improve model performance has been shown to be effective in classification settings, but the usage of soft targets for regression is a much less studied topic in machine learning. The existing literature on the usage of soft targets for regression fails to properly assess the method's limitations, and empirical evaluation is quite limited. In this work, we assess the strengths and drawbacks of existing methods when applied to molecular property regression tasks. Our assessment outlines key biases present in existing methods and proposes methods to address them, evaluated through careful ablation studies. We leverage these insights to propose Distributional Mixture of Experts (DMoE): A model-independent, and data-independent method for regression which trains a model to predict probability distributions of its targets. Our proposed loss function combines the cross entropy between predicted and target distributions and the L1 distance between their expected values to produce a loss function that is robust to the outlined biases. We evaluate the performance of DMoE on different molecular property prediction datasets -- Open Catalyst (OC20), MD17, and QM9 -- across different backbone model architectures -- SchNet, GemNet, and Graphormer. Our results demonstrate that the proposed method is a promising alternative to classical regression for molecular property prediction tasks, showing improvements over baselines on all datasets and architectures.

cs.LG↗

arXiv2024

Revealing the Production Mechanism of High-Energy Neutrinos from NGC 1068

The detection of high-energy neutrino signals from the nearby Seyfert galaxy NGC 1068 provides us with an opportunity to study nonthermal processes near the center of supermassive black holes. Using the IceCube and latest Fermi-LAT data, we present general multimessenger constraints on the energetics of cosmic rays and the size of neutrino emission regions. In the photohadronic scenario, the required cosmic-ray luminosity should be larger than about 1-10 percent of the Eddington luminosity, and the emission radius should be less than about 15 Schwarzschild radii in low-beta plasma and less than about 3 Schwarzschild radii in high-beta plasma. The leptonic scenario overshoots the NuSTAR or Fermi-LAT data for any emission radii we consider, and the required gamma-ray luminosity is much larger than the Eddington luminosity. The beta decay scenario also violates not only the energetics requirement but also gamma-ray constraints especially when the Bethe-Heitler and photomeson production processes are consistently considered. Our results rule out the leptonic and beta decay scenarios in a nearly model-independent manner, and support hadronic mechanisms in magnetically-powered coronae if NGC 1068 is a source of high-energy neutrinos.

astro-ph.HE↗

arXiv2024

On tensor products of representations of Lie superalgebras

We consider typical finite dimensional complex irreducible representations of a basic classical simple Lie superalgebra, and give a sufficient condition on when unique factorization of finite tensor products of such representations hold. We also prove unique factorization of tensor products of singly atypical finite dimensional irreducible modules for $\mathfrak{sl}(m+1,n+1)$, $\mathfrak{osp}(2,2n)$, $G(3)$ and $F(4)$ under an additional assumption. This result is a Lie superalgebra analogue of Rajan's fundamental result \cite{MR2123935} on unique factorization of tensor products for finite dimensional complex simple Lie algebras.

math.RT↗

arXiv2024

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace $SO(3)$ convolutions with eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements -- attention re-normalization, separable $S^2$ activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on large-scale OC20 dataset by up to $9\%$ on forces, $4\%$ on energies, offers better speed-accuracy trade-offs, and $2\times$ reduction in DFT calculations needed for computing adsorption energies. Additionally, EquiformerV2 trained on only OC22 dataset outperforms GemNet-OC trained on both OC20 and OC22 datasets, achieving much better data efficiency. Finally, we compare EquiformerV2 with Equiformer on QM9 and OC20 S2EF-2M datasets to better understand the performance gain brought by higher degrees.

cs.LG↗

arXiv2023

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly from ambient air. Metal-organic frameworks (MOFs) have been widely studied as potentially customizable adsorbents for DAC. However, discovering promising MOF sorbents for DAC is challenging because of the vast chemical space to explore and the need to understand materials as functions of humidity and temperature. We explore a computational approach benefiting from recent innovations in machine learning (ML) and present a dataset named Open DAC 2023 (ODAC23) consisting of more than 38M density functional theory (DFT) calculations on more than 8,400 MOF materials containing adsorbed $CO_2$ and/or $H_2O$. ODAC23 is by far the largest dataset of MOF adsorption calculations at the DFT level of accuracy currently available. In addition to probing properties of adsorbed molecules, the dataset is a rich source of information on structural relaxation of MOFs, which will be useful in many contexts beyond specific applications for DAC. A large number of MOFs with promising properties for DAC are identified directly in ODAC23. We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level. This open-source dataset and our initial ML models will provide an important baseline for future efforts to identify MOFs for a wide range of applications, including DAC.

cond-mat.mtrl-sci↗

arXiv2023

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a 2000x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 100,000 unique configurations.

cond-mat.mtrl-sci↗