arXiv subjects

Piyush Sharma

Publications and source records attributed to Piyush Sharma.

At least 19 recordsLinked to original sources

arXiv2025

Evidence for in situ particle energization during the May-2024 event based on ASPEX instrument on board Aditya-L1

The interaction between interplanetary Coronal Mass Ejection (ICME) structures can alter the geo-effectiveness of the ICME events in myriad ways. Many aspects of these interaction processes are not well-understood till date. Using the energy spectra measured in two mutually orthogonal top hat analyzers (THA 1 and 2), which are part of the Solar Wind Ion Spectrometer (SWIS) subsystem of the Aditya Solar Wind Particle EXperiment (ASPEX) on board India's Aditya L1 mission, we gain insights into intricate features of ICME ICME interactions during May 2024 solar event. We report here an unprecedented two-orthogonal-plane perspective of ICME ICME interactions for the first time from the L1 point. The investigation reveals a special interaction region formed by the propagation of the forward shock driven by complex ejecta in the preceding ICME. The interaction causes the formation of a downstream region spanning over 13 hours, which propagates in the interplanetary medium. The observations reveal that this region serves as a site for proton and alpha particle energization, and the particles within this region get distributed from one plane to the other. The presence of forward shock and particle energization is confirmed by the energetic particle flux measurements by the SupraThermal and Energetic Particle Spectrometer (STEPS) of ASPEX. These observations provide an unprecedented perspective on how solar wind ions become energized and distributed in an ICME-ICME interaction region.

astro-ph.SR↗

arXiv2025

A new kind of $A_{\p}$-matrix for mixed graphs

Nikiforov\cite{nikiforov2017merging} introduced the concept of the $A_{\p}$-matrix as a convex linear combination of a graph's adjacency matrix and its diagonal matrix of vertex degrees. In this paper, we introduce a new variant of the $A_{\p}$-matrix, which is a linear combination of the Hermitian adjacency matrix of the second kind, $H^{\om}$, and the degree-diagonal matrix. This study provides new insights into the $A_{\p}$-matrix as a Hermitian matrix, leading to novel bounds in spectral graph theory.

math.SP↗

arXiv2025

One Year of ASPEX-STEPS Operation: Characteristic Features, Observations and Science Potential

The SupraThermal and Energetic Particle Spectrometer (STEPS), a subsystem of the Aditya Solar wind Particle EXperiment (ASPEX) onboard India's Aditya-L1 satellite, is designed to study different aspects of energetic particles in the interplanetary medium from the Sun-Earth L1 point using six detector units oriented in different directions. This article presents details of the one-year operation (08 January 2024 - 28 February 2025) of the AL1-ASPEX-STEPS after the insertion of the satellite into the final halo orbit around the L1 point with emphasis on performance, science observations, and scientific potentials. Four out of six AL1-ASPEX-STEPS units exhibit a stable detector response throughout the observation period, confirming operational robustness. This work also includes the temporal variation of particle fluxes, spectra of ions during selected quiet times and transient events, and cross-comparisons with existing instruments at the L1 point. A strong correlation (with coefficient of determination, R2 ~ 0.9) is observed in the cross-comparison study, establishing the reliability of the AL1- ASPEX-STEPS observations. AL1-ASPEX-STEPS also captures different forms of energetic ion spectra similar to those observed by previous missions. These results underscore the instrument's potential to contribute significantly to the study of energetic particle acceleration, transport, and long-term space weather monitoring from the Sun-Earth L1 vantage point.

physics.space-ph↗

arXiv2025

One year of ASPEX-SWIS operation -- Characteristic features, observations and science potential

The Aditya-L1 mission, India's first dedicated solar observatory positioned at the first Lagrange point (L1) of the Sun-Earth system, carries the Solar Wind Ion Spectrometer (SWIS) as part of the ASPEX payload suite. Even before settling into its Halo orbit, SWIS has been providing nearly continuous in-situ measurements of solar wind ion spectra. Moments of the velocity distribution functions (VDFs) have been calculated to derive key solar wind parameters such as density, bulk speed, and temperature. In this study, we assess the performance of SWIS (hereafter referred to as AL1-ASPEX-SWIS) by comparing its measurements with contemporaneous data from the Wind and DSCOVR missions. In this study, we assess the performance of SWIS (hereafter referred to as AL1-ASPEX-SWIS) by comparing its measurements with contemporaneous data from the Wind and DSCOVR missions. A detailed case study of the interplanetary coronal mass ejection (ICME) event on August 7, 2024, is presented, where sharp changes in bulk speed, thermal speed, and number density were found to be well-aligned with independent observations-confirming the instrument's ability to capture dynamic solar wind features. Spectral analysis of kinetic fluctuations revealed a well-defined inertial range with a spectral slope consistent with magnetohydrodynamic (MHD) turbulence. Furthermore, a 17-month statistical comparison (from January 2024 to May 2025) shows a strong correlation in bulk velocity (R2 = 0.94 with Wind), with expected variations in thermal speed and density arising from differences between instruments. These findings demonstrate the scientific value of AL1-ASPEX-SWIS for monitoring both transient solar events and long-term solar wind conditions.

astro-ph.SR↗

arXiv2025

Multi-directional investigations on quiet time suprathermal ions measured by ASPEX-STEPS on-board Aditya L1

The origin, acceleration and anisotropy of suprathermal ions in the interplanetary medium during quiet periods have remained poorly understood issues in solar wind physics. To address these aspects, we derive the spectral indices for the quiet time suprathermal ions based on the measurements by the four directionally separated sensors that are part of the Supra-Thermal and Energetic Particle Spectrometer (STEPS) of Aditya Solar Wind Particle EXperiment (ASPEX) on-board Aditya L1 spacecraft. Three out of four STEPS sensors Parker Spiral (PS), Inter-Mediate (IM), Earth Pointing (EP) are in one plane (nearly aligned with the ecliptic plane) while the fourth sensor North Pointing (NP) is in a mutually orthogonal plane. The energy ranges covered by the PS, IM, EP and NP sensors are 0.36-1.32 MeV, 0.14-1.22 MeV, 0.39-1.33 MeV and 0.12-1.23 MeV respectively. The quiet intervals are identified during January November, 2024 and the derived spectral indices (differential directional flux versus energy) are found to be in the range of 2.0 for all directions in the time scale of a few days revealing isotropic nature of their distribution. Further analysis of elemental abundance ratios (3He/4He, Fe/O, and C/O) during the same quiet intervals obtained from the Ultra-Low Energy Isotope Spectrometer (ULEIS) on board the Advanced Composition Explorer (ACE) spacecraft suggests possible contributions from the leftover ions from the previous impulsive (Solar flares) and gradual events (CMEs) in the quiet time suprathermal ion pool.

physics.space-ph↗

arXiv2025

Energetic ($<$ 2 MeV) ion fluxes measured by ASPEX-STEPS on board Aditya-L1 during its earth-bound phase

During its earth-bound phase of the Aditya-L1 spacecraft of India, the Supra-Thermal and Energetic Particle Spectrometer (STEPS) of the Aditya Solar wind Particle EXperiment (ASPEX) was operated whenever the orbit was above 52000 km during 11 - 19 September 2023. This phase of operation provided measurements of energetic ions (with energies 0.1--2 MeV) in the magnetosphere, magnetosheath, and interplanetary medium. Three interplanetary coronal mass ejections (ICME) hit the magnetosphere during this period. This provided opportunity to examine the relative roles of ICME-generated solar energetic particles (SEPs) and substorm generated energetic ions on the magnetosphere. We approach this objective by detailed spectral analyses of energetic ion fluxes measured by two units of ASPEX-STEPS. We identify three distinctly different conditions of the north-south component of the interplanetary magnetic field (IMF $B_z$ = 0, $>$ 0, and $<$ 0) and use the derived spectral indices to understand this relative role. By combining these with the simultaneous energetic ion flux variations from the Advanced Composition Explorer (ACE) around the Sun-Earth first Lagrangian (L1) point and the Geostationary Operational Environmental Satellite (GOES) in the Earth's magnetosphere, we show that the polarity of IMF $B_z$ influences the energetic ion spectra in the magnetosphere by modulating the interplay of the ICME-generated SEP with the energetic particles generated inside the magnetosphere by substorms. Interestingly, ASPEX-STEPS observations also indicate towards directional anisotropy based on spectral indices. This suggests spatially inhomogeneous mixing of energetic ions coming from different source processes.

physics.space-ph↗

arXiv2025

Tailored Vapor Deposition Unlocks Large-Grain, Wafer-Scale Epitaxial Growth of 2D Magnetic CrCl3

Two-dimensional magnetic materials (2D-MM) are an exciting playground for fundamental research, and for spintronics and quantum sensing. However, their large-grain large-area synthesis using scalable vapour deposition methods is still an unsolved challenge. Here, we develop a tailored approach for centimetre-scale growth of semiconducting 2D-MM CrCl3 films on mica substrate, via physical vapour transport deposition. A controlled synthesis protocol, enabled via innovations concerning light management, very-high carrier-gas flow, precursor flux, and oxygen/moisture removal, is critical for wafer-scale growth. Optical, stoichiometric, structural, and magnetic characterization identify crystalline, phase-pure 2D-MM CrCl3. Substrate temperature tunes thickness of films from few-layers to tens of nanometres. Further, selective-area growth and large-area transfer are demonstrated. Substrate-dependent growth features are explained by density functional theory and state-of-the-art machine learning interatomic potential-based atomic-scale simulations. This scalable vapour deposition approach can be applied for growth of several 2D-MM, and low growth temperature (~500 C) will enable creation of hybrid heterostructures.

cond-mat.mtrl-sci↗

arXiv2024

Science with the Daksha High Energy Transients Mission

We present the science case for the proposed Daksha high energy transients mission. Daksha will comprise of two satellites covering the entire sky from 1~keV to $>1$~MeV. The primary objectives of the mission are to discover and characterize electromagnetic counterparts to gravitational wave source; and to study Gamma Ray Bursts (GRBs). Daksha is a versatile all-sky monitor that can address a wide variety of science cases. With its broadband spectral response, high sensitivity, and continuous all-sky coverage, it will discover fainter and rarer sources than any other existing or proposed mission. Daksha can make key strides in GRB research with polarization studies, prompt soft spectroscopy, and fine time-resolved spectral studies. Daksha will provide continuous monitoring of X-ray pulsars. It will detect magnetar outbursts and high energy counterparts to Fast Radio Bursts. Using Earth occultation to measure source fluxes, the two satellites together will obtain daily flux measurements of bright hard X-ray sources including active galactic nuclei, X-ray binaries, and slow transients like Novae. Correlation studies between the two satellites can be used to probe primordial black holes through lensing. Daksha will have a set of detectors continuously pointing towards the Sun, providing excellent hard X-ray monitoring data. Closer to home, the high sensitivity and time resolution of Daksha can be leveraged for the characterization of Terrestrial Gamma-ray Flashes.

astro-ph.HE↗

arXiv2024

Daksha: On Alert for High Energy Transients

We present Daksha, a proposed high energy transients mission for the study of electromagnetic counterparts of gravitational wave sources, and gamma ray bursts. Daksha will comprise of two satellites in low earth equatorial orbits, on opposite sides of earth. Each satellite will carry three types of detectors to cover the entire sky in an energy range from 1 keV to >1 MeV. Any transients detected on-board will be announced publicly within minutes of discovery. All photon data will be downloaded in ground station passes to obtain source positions, spectra, and light curves. In addition, Daksha will address a wide range of science cases including monitoring X-ray pulsars, studies of magnetars, solar flares, searches for fast radio burst counterparts, routine monitoring of bright persistent high energy sources, terrestrial gamma-ray flashes, and probing primordial black hole abundances through lensing. In this paper, we discuss the technical capabilities of Daksha, while the detailed science case is discussed in a separate paper.

astro-ph.IM↗

arXiv2022

Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models

Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data. However, recent modeling approaches to IC often fall short in terms of performance in this case, because they assume a clean annotated dataset (as opposed to the noisier alt-text--based annotations), and employ an end-to-end generation approach, which often lacks both controllability and interpretability. We address these problems by breaking down the task into two simpler, more controllable tasks -- skeleton prediction and skeleton-based caption generation. Specifically, we show that selecting content words as skeletons} helps in generating improved and denoised captions when leveraging rich yet noisy alt-text--based uncurated datasets. We also show that the predicted English skeletons can be further cross-lingually leveraged to generate non-English captions, and present experimental results covering caption generation in French, Italian, German, Spanish and Hindi. We also show that skeleton-based prediction allows for better control of certain caption properties, such as length, content, and gender expression, providing a handle to perform human-in-the-loop semi-automatic corrections.

cs.CL↗

arXiv2021

Understanding Guided Image Captioning Performance across Domains

Image captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativeness, and information overload. On the other hand, VQA models generally lack the ability to provide long descriptive answers, while expecting the textual question to be quite precise. We present a method to control the concepts that an image caption should focus on, using an additional input called the guiding text that refers to either groundable or ungroundable concepts in the image. Our model consists of a Transformer-based multimodal encoder that uses the guiding text together with global and object-level image features to derive early-fusion representations used to generate the guided caption. While models trained on Visual Genome data have an in-domain advantage of fitting well when guided with automatic object labels, we find that guided captioning models trained on Conceptual Captions generalize better on out-of-domain images and guiding texts. Our human-evaluation results indicate that attempting in-the-wild guided image captioning requires access to large, unrestricted-domain training datasets, and that increased style diversity (even without increasing the number of unique tokens) is a key factor for improved performance.

cs.CV↗

arXiv2021

COSMic: A Coherence-Aware Generation Metric for Image Descriptions

Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image$\unicode{x2013}$description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness$\unicode{x2014}$its ability to predict human ratings of output captions$\unicode{x2014}$on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.

cs.CL↗

arXiv2021

Quality Estimation for Image Captions Based on Large-scale Human Evaluations

Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and without access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on previously unseen images. For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings. We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task. Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out low-quality image captions, thereby improving the user experience from captioning systems.

cs.CL↗

arXiv2021

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pre-training data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [Sharma et al. 2018] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.

cs.CV↗

arXiv2020

Clue: Cross-modal Coherence Modeling for Caption Generation

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image--caption coherence relations, we annotate 10,000 instances from publicly-available image--caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.

cs.CL↗

arXiv2020

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.

cs.CL↗

arXiv2019

Reinforcing an Image Caption Generator Using Off-Line Human Feedback

Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation is a few overall statistics over the evaluation dataset. In this paper, we show that the signal from instance-level human caption ratings can be leveraged to improve captioning models, even when the amount of caption ratings is several orders of magnitude less than the caption training data. We employ a policy gradient method to maximize the human ratings as rewards in an off-policy reinforcement learning setting, where policy gradients are estimated by samples from a distribution that focuses on the captions in a caption ratings dataset. Our empirical evidence indicates that the proposed method learns to generalize the human raters' judgments to a previously unseen set of images, as judged by a different set of human judges, and additionally on a different, multi-dimensional side-by-side human evaluation procedure.

cs.CV↗

arXiv2019

Neural Naturalist: Generating Fine-Grained Image Comparisons

We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., "heart-shaped face," "squat body"). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance---drawn from a novel stratified sampling approach---with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.

cs.CL↗