arXiv subjects

Soumya Roy

Publications and source records attributed to Soumya Roy.

At least 19 recordsLinked to original sources

arXiv2026

Multi-Wavelength Diagnostics of Pre-Flare Evolution with Aditya-L1: From the Solar Chromosphere to the Corona

The pre-flare phase of solar flares provides important insight into the processes that drive active regions toward instability. We investigate chromospheric pre-flare activity using observations from the Solar Ultraviolet Imaging Telescope (SUIT) onboard Aditya-L1, complemented with X-ray measurements from High Energy L1 Orbiting X-ray Spectrometer (HEL1OS) and Solar Low Energy X-ray Spectrometer (SoLEXS). We analyse seven M- and X-class flares, focusing on spatially resolved Mg II h (2803~\AA) observations from SUIT. We identify 102 pre-flare transients within regions of interest prior to flare onset. These transients are detected in the Mg II h channel, with no counterparts in continuum filters, confirming their chromospheric origin. In most cases, the transients are co-spatial with polarity inversion lines (PILs) and the eventual flaring region. Approximately 28~\% of transients have X-ray counterparts in HEL1OS (10-30~keV); The Spectrometer Telescope for Imaging X-rays (STIX) spectral analysis reveals non-thermal emission in a subset, indicating that some transients are small-scale flare-like events. A hot X-ray onset is identified in four cases. For the remaining three cases, the signal-to-noise ratio above the background is insufficient to determine whether a hot-onset phase is present. The peak-flux distribution of the transients follows a broken power law with indices $\alpha_1 = 1.64^{+0.59}_{-0.57}$ and $\alpha_2 = 3.12^{+0.64}_{-0.61}$, with the higher-energy slope consistent with the Ly-$\alpha$ flare distribution. These results suggest that chromospheric pre-flare transients represent small-scale magnetic energy-release events that contribute to the progressive destabilisation of active regions prior to major flare onset.

astro-ph.SR↗

arXiv2026

Energy Evolution from the Chromosphere to the Heliosphere in the 2021 October 28 Solar Eruption

We perform a detailed study of the energetics for a well-observed solar eruption and flare that occurred on 28 October 2021. This event included a GOES class X1.0 flare, a global EUV wave, and a coronal mass ejection that reached speeds of >2000 km/s. The event was observed from a variety of spacecraft in NASA's Heliophysics System Observatory, including multiple missions near Earth, STEREO-A off the Sun-Earth line, and Solar Orbiter, near the Sun-Earth line at about 0.8 au. Using remote sensing, in situ observations, and in some cases scaling laws based on previous observations, we characterize the following quantities: free magnetic energy, energy in non-thermal electrons, energy in non-thermal ions, bolometric energy, energy deposited in the chromosphere, thermal energy radiated in the flare loops, energy dissipated by the EUV wave, CME kinetic and gravitational potential energy, CME energy flux in the heliosphere, and the energy partition in the CME shock. We find that the total energy released during the event is consistent with estimates of the pre-event stored magnetic energy, and the CME kinetic + potential energy dominates the energy partition.

astro-ph.SR↗

arXiv2026

FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inference

Flow-matching models deliver state-of-the-art fidelity in image and video generation, but the inherent sequential denoising process renders them slower. Existing acceleration methods like distillation, trajectory truncation, and consistency approaches are static, require retraining, and often fail to generalize across tasks. We propose FastFlow, a plug-and-play adaptive inference framework that accelerates generation in flow matching models. FastFlow identifies denoising steps that produce only minor adjustments to the denoising path and approximates them without using the full neural network models used for velocity predictions. The approximation utilizes finite-difference velocity estimates from prior predictions to efficiently extrapolate future states, enabling faster advancements along the denoising path at zero compute cost. This enables skipping computation at intermediary steps. We model the decision of how many steps to safely skip before requiring a full model computation as a multi-armed bandit problem. The bandit learns the optimal skips to balance speed with performance. FastFlow integrates seamlessly with existing pipelines and generalizes across image generation, video generation, and editing tasks. Experiments demonstrate a speedup of over 2.6x while maintaining high-quality outputs. The source code for this work can be found at https://github.com/Div290/FastFlow.

cs.CV↗

arXiv2025

Rethinking Test Time Scaling for Flow-Matching Generative Models

The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While existing trajectory level exploration methods improve the effectiveness of test-time scaling for standard diffusion models, they are largely incompatible with modern flow matching models, which use deterministic sampling. This imposes significant computational overhead on local trajectory search, making the trade-offs less favorable compared to global search. However, global search strategies like trajectory pruning face two critical challenges: the sharp, low-diversity distributions characteristic of scaled flow models that restrict the candidate search space, and the bias of reward models in the early denoising process. To overcome these limitations, we propose Repel, a token-level mechanism that encourages sample diversity, and NARF, a noise-aware reward fine-tuning strategy to obtain more accurate reward ranking at early denoising stages. Together, these promote more effective test-time scaling resource allocation. Overall, we name our pipeline as \textbf{DOG-Trim}: \textbf{D}iversity enhanced \textbf{O}rder aligned \textbf{G}lobal flow Trimming. The experiments demonstrate that, under the same compute cost, our approach achieves around twice the performance improvement relative to the scaling-free baseline compared to the best existing method. Github: https://github.com/TerrysLearning/DOGTrimTTS.

cs.CV↗

arXiv2025

Disentangling Thermal and Abundance Characteristics in a Solar Flare Using SDO/AIA, Hinode/XRT, and MinXSS-1 Observations

We investigate the thermal properties of a solar flare by the observations of soft X-ray Telescope (XRT) on board Hinode and the Atmospheric Image Assembly on board the Solar Dynamics Observatory (SDO/AIA). Our analysis reveals a tenuous but hot plasma cloud located above the flare loops. The flare loops and plasma cloud exhibit independent temperature profiles as a function of time, implying different heating mechanisms are present in these structures. We interpret the plasma cloud as an eruptive filament directly heated in the corona via magnetic reconnection that occurs when a rising filament interacts with this eruptive filament. Previous disk-integrated solar soft X-ray spectral measurements of this flare using the Miniature X-ray Solar Spectrometer CubeSat-1 (MinXSS-1) inferred coronal abundances at the flare peak. However, MinXSS synthetic spectra from AIA observations show that the plasma cloud is too tenuous to be detected in MinXSS-1 and thus the coronal abundances of low FIP elements must be in emission from the flare loops. Furthermore, we demonstrate the non-negligible differences in differential emission measures (DEMs) between coronal and photospheric abundance models. These results highlight the importance of instruments capable of simultaneous spectral and spatial diagnostics during large-scale solar events. Such coordinated measurements would enable more accurate thermal and compositional analyses and deeper insight into the underlying physical mechanisms.

astro-ph.SR↗

arXiv2025

TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning

Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table retrieval and schema interaction, leading to critical performance bottlenecks. To address this limitation, we propose TaTToo, a novel table-grounded PRM framework that (i) reasons explicitly over tabular reasoning steps and (ii) integrates tool-based verification to provide precise reward supervision. Concretely, we first design a scalable data curation pipeline that constructs over 60k high-quality step-level annotations by integrating table verification rationales with tool-based executions. Building on the collected data, we train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning with tool-grounded reward shaping to align our model with table-based verification. We provide a comprehensive evaluation of the policy improvement induced by our newly designed PRM. Across 5 challenging tabular reasoning benchmarks covering numerical reasoning, fact-checking, and data analysis, TaTToo improves downstream policy LRMs by 30.9% at inference, surpasses strong PRM baselines such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong generalizability across diverse TTS strategies.

cs.AI↗

arXiv2025

Unraveling the Secrets of the lower Solar Atmosphere: One year of Operation of the Solar Ultraviolet Imaging Telescope (SUIT) on board Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument onboard Aditya--L1, the first solar space observatory of the Indian Space Research Organization (ISRO), India, launched on September 2, 2023. SUIT is designed to image the Sun in the 200--400 nm wavelength band in eight narrowband and three broadband filters. SUIT's science goals start with observing the solar atmosphere and large-scale continuum variations, the physics of solar flares in the NUV region, and many more. The paper elucidates the functioning of the instrument, software packages developed for easier calibration, analysis, and feedback, calibration routines, and the regular maintenance activity of SUIT during the first year of its operation. The paper also presents the various operations undergone by, numerous program sequences orchestrated to achieve the science requirements, and highlights some remarkable observations made during the first year of observations with SUIT.

astro-ph.SR↗

arXiv2025

Bayesian reliability acceptance sampling plan sampling plans under adaptive accelerated type-II censored competing risk data

In recent times, products have become increasingly complex and highly reliable, so failures typically occur after long periods of operation under normal conditions and may arise from multiple causes. This paper employs simple step-stress partial accelerated life testing (SSSPALT) within the competing risks framework to determine the Bayesian reliability acceptance sampling plan (BRASP) under type-II censoring. Elevating the stress during the life test incurs an additional cost that increases the cost of the life test. In this context, an adaptive scenario is also considered in that sampling plan. The adaptive scenario is as follows: the stress is increased after a certain time if the number of failures up to that point is less than a pre-specified number of failures. The Bayes decision function and Bayes risk are derived for the general loss function. An optimal BRASP under that adaptive SSSPALT is obtained for the quadratic loss function by minimizing Bayes risk. An algorithm is provided to determine the optimal proposed BRASP. Further, comparative studies are conducted between the proposed BRASP, the conventional non-accelerated BRASP, and the conventional accelerated BRASP under type-II censoring to evaluate the effectiveness of the proposed approach. Finally, the methodology is illustrated using real data.

stat.ME↗

arXiv2025

NOVO: Unlearning-Compliant Vision Transformers

Machine unlearning (MUL) refers to the problem of making a pre-trained model selectively forget some training instances or class(es) while retaining performance on the remaining dataset. Existing MUL research involves fine-tuning using a forget and/or retain set, making it expensive and/or impractical, and often causing performance degradation in the unlearned model. We introduce {\pname}, an unlearning-aware vision transformer-based architecture that can directly perform unlearning for future unlearning requests without any fine-tuning over the requested set. The proposed model is trained by simulating unlearning during the training process itself. It involves randomly separating class(es)/sub-class(es) present in each mini-batch into two disjoint sets: a proxy forget-set and a retain-set, and the model is optimized so that it is unable to predict the forget-set. Forgetting is achieved by withdrawing keys, making unlearning on-the-fly and avoiding performance degradation. The model is trained jointly with learnable keys and original weights, ensuring withholding a key irreversibly erases information, validated by membership inference attack scores. Extensive experiments on various datasets, architectures, and resolutions confirm {\pname}'s superiority over both fine-tuning-free and fine-tuning-based methods.

cs.CV↗

arXiv2025

SPACE-SUIT: An Artificial Intelligence Based Chromospheric Feature Extractor and Classifier for SUIT

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses YOLO, a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform "self-validation" by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.

astro-ph.SR↗

arXiv2025

X-class flare on Dec 31, 2023, observed by the Solar Ultraviolet Imaging Telescope on board Aditya-L1

We present the multi-wavelength study of the ejection of a plasma blob from the limb flare SOL2023-12-31T21:36:00 from NOAA 13536 observed by the Solar Ultraviolet Imaging Telescope (SUIT) on board Aditya-L1. We use SUIT observations along with those from Atmospheric Imaging Assembly (AIA) on board SDO and Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter to infer the kinematics and thermal nature of the ejected blob and its connection to the associated flare. The observations show that the flare was comprised of two eruptions. The blob was ejected during the first eruption and later accelerated to velocities over 1500 km/s measured at a maximum projected height of ~ 178 Mm from the Sun's surface. The acceleration of the ejected plasma blob is co-temporal with the bursty appearance of the hard X-ray light curve recorded by STIX. Radio spectrogram observations from STEREO-A/WAVES and RSTN reveal type III bursts at the same time, indicative of magnetic reconnection. DEM analysis using AIA observations suggests the plasma blob is comprised of cooler and denser plasma in comparison to the ambient corona. To the best of our knowledge, this is the first observation of such a plasma blob in the NUV, providing crucial measurements for eruption thermodynamics.

astro-ph.SR↗

arXiv2025

Test and Calibration of the Solar Ultraviolet Imaging Telescope (SUIT) on board Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) on board the AdityaL1 mission observes the Sun in the 200-400 nm wavelength range. This paper presents the results of various on ground and on board tests and their comparison with the specifications. Moreover, we also present the scheme for data calibration. We demonstrate that the test results are compliant with the specified figures, except the spatial resolution. Such discrepancy will limit the photometric measurements only, at a scale of 2.2" instead of 1.4" as originally envisioned. The results obtained here show that SUIT observations open up a new window for solar observations.

astro-ph.IM↗

arXiv2025

Photometric Calibration & Spectral Validation of the Solar Ultraviolet Imaging Telescope onboard Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) is one of the seven payloads on board Aditya-L1 mission of the Indian Space Research Organization (ISRO). SUIT provides full and partial disk images of the Sun in the 200-400 nm wavelength range. This would help us probe the solar atmosphere at different heights and understand the mass and energy transfer process between its layers. For the first time, SUIT will also help us measure spatially resolved solar spectral irradiance at this wavelength band, which is significant for studying the sun-climate relationships. To perform these studies, it is necessary to photometrically calibrate the payload and validate the spectral coverage of the various bandpasses. We perform the photometric calibration and spectral validation of 8 bandpasses using light of known intensity and spectral coverage. For photometric calibration, the telescope throughput is modeled using sun-as-a-star spectrum from SOLSTICE and SOLSPEC. The modeled throughput is compared with in-lab measurements taken with light of known intensity. The ratio of measured photoelectrons gathered with the modeled prediction agree within 20%. For spectral validation, readings are taken across the transmission spectrum of each filter, keeping adjacent readings independent of each other. The relative intensity measured at each wavelength is seen to trace the modeled telescope bandpass for that filter. These tests could not be performed for filters with bandpasses operating below 250 nm (NB01, BB01 and BB02), primarily due to heavy atmospheric attenuation in these wavelengths leading to decreased SNR of the data. The experimentally measured results agree closely with the modeled values, validating SUIT's optical performance and presenting the reliability of the developed throughput model.

astro-ph.IM↗

arXiv2025

Near and Mid UltraViolet Observations of X-6.3 flare on 22nd February 2024 recorded by the Solar Ultraviolet Imaging Telescope on board Aditya-L1

Solar flares are regularly observed in extreme ultraviolet (EUV), soft X-rays (SXR), and hard X-rays (HXR). However, those in near and mid-UV are sparse. The Solar Ultraviolet Imaging Telescope (SUIT) onboard the Aditya-L1, launched on 2nd September, 2023 provides regular observations in the 200-400 nm wavelength range through eleven filters. Here, we report the observation of the X6.3 flare on Feb 22, 2024 using eight narrow band (NB) filters of SUIT. We have also used co-spatiotemporal observations from SDO/AIA, Solar Orbiter/STIX, GONG H$α$, Aditya-L1/SoLEXS and GOES. We obtained light curves over the flaring region from AIA 1600, 1700 Å and GONG H$α$ and compared them with the disk-integrated lightcurve obtained from GOES and SoLEXS SXR and STIX HXR. We find that the flare peaks in SUIT NB01, NB03, NB04, and NB08 filters simultaneously with HXR, 1600, and 1700 Å along with the peak temperature obtained from SoLEXS. In contrast, in NB02 and NB05, the flare peaks $\sim$ 2 minutes later than the HXR peak, while in NB06 and NB07, the flare peaks $\sim$ 3 minutes after the GOES soft X-ray peak. To the best of our knowledge, this is the first observation of a flare in these wavelengths (except in NB03, NB04 and NB05). Moreover, for the first time, we show the presence of a bright kernel in NB02. These results demonstrate the capabilities of SUIT observations in flare studies.

astro-ph.SR↗

arXiv2025

Reliability Acceptance Sampling Plans under Progressive Type-I Interval Censoring Schemes in Presence of Dependent Competing Risks

We discuss the development of reliability acceptance sampling plans under progressive Type-I interval censoring schemes in the presence of competing causes of failure. We consider a general framework to accommodate the presence of independent or dependent competing risks and derive the expression for the Fisher information matrix under this framework. We also discuss the asymptotic properties of the maximum likelihood estimators, which are essential in obtaining the sampling plans. Subsequently, we specialize in a frailty model, which allows us to accommodate the dependence among the potential causes of failure. The frailty model provides an independent competing risks model as a limiting case. We then present the traditional sampling plans for both independent and dependent competing risks models using producer and consumer risks. We also consider the design of optimal PIC-I schemes in this context and use a c optimal design criterion, which helps us to obtain more useful reliability acceptance sampling plans in the presence of budgetary constraints. We conduct a comprehensive numerical experiment to examine the impact of the level of dependence among the potential failure times on the resulting sampling plans. We demonstrate an application of the developed methodology using a real-life example and perform a simulation study to study the finite sample properties of the developed sampling plans. The methodology developed in this article has the potential to improve the design of optimal censoring schemes in the presence of competing risks while taking into account budgetary constraints.

stat.AP↗

arXiv2025

The Solar Ultraviolet Imaging Telescope on board Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products.

astro-ph.SR↗

arXiv2024

Science Filter Characterization of the Solar Ultraviolet Imaging Telescope (SUIT) on board Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) on board the Aditya-L1 mission is designed to observe the Sun across 200-400 nm wavelength. The telescope used 16 dichroic filters tuned at specific wavelengths in various combinations to achieve its science goals. For accurate measurements and interpretation, it is important to characterize these filters for spectral variations as a function of spatial location and tilt angle. Moreover, we also measured out-of-band and in-band transmission characteristics with respect to the inband transmissions. In this paper, we present the experimental setup, test methodology, and the analyzed results. Our findings reveal that the transmission properties of all filters meet the expected performance for spatial variation of transmission and the transmission band at a specific tilt angle. The out-of-band transmission for all filters is below 1% with respect to in-band, except for filters BB01 and NB01. These results confirm the capabilities of SUIT to effectively capture critical solar features in the anticipated layer of the solar atmosphere.

astro-ph.SR↗

arXiv2024

Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective

In the internet era, almost every business entity is trying to have its digital footprint in digital media and other social media platforms. For these entities, word of mouse is also very important. Particularly, this is quite crucial for the hospitality sector dealing with hotels, restaurants etc. Consumers do read other consumers reviews before making final decisions. This is where it becomes very important to understand which aspects are affecting most in the minds of the consumers while giving their ratings. The current study focuses on the consumer reviews of Indian hotels to extract aspects important for final ratings. The study involves gathering data using web scraping methods, analyzing the texts using Latent Dirichlet Allocation for topic extraction and sentiment analysis for aspect-specific sentiment mapping. Finally, it incorporates Random Forest to understand the importance of the aspects in predicting the final rating of a user.

cs.CL↗