arXiv subjects

Abhay Deshpande

Publications and source records attributed to Abhay Deshpande.

At least 19 recordsLinked to original sources

arXiv2026

Producing and Studying Rare Isotopes in $e+A$ Collisions at the Electron-Ion Collider

The Electron--Ion Collider (EIC) offers a unique environment to study kinematically controlled lepton--nucleus ($e{+}A$) reactions, where a primary hard scattering is followed by an intranuclear cascade and the subsequent statistical de-excitation of the nuclear remnant. Utilizing the \soft{BeAGLE} model, we demonstrate that event-by-event fluctuations in nucleon removal and energy deposition populate a diverse ensemble of excited remnants. Furthermore, we show that varying the target mass systematically shifts the distribution of these remnants across the $(N, Z)$ plane. Although this excited prefragment remnant is not directly observable, its properties are shown to be strongly correlated with final-state fragments; specifically, the largest nuclear residue and the intensity of evaporation yield serve as effective experimental proxies for event-level remnant characterization. We also evaluate photon observables essential for nuclear spectroscopy. While various photon sources overlap significantly in pseudorapidity, we find that in the nucleus-rest frame, the low-energy spectrum is dominated by de-excitation $γ$ rays and exhibits distinct discrete structures. These findings motivate an EIC research program that correlates rare-isotope production and de-excitation radiation with well-defined initial conditions, providing a collider-based approach to nuclear spectroscopy that is complementary to existing fixed-target facilities.

nucl-th↗

arXiv2026

Continuous coherent spin-frequency metrology in storage rings via resonant beam-driven detection

Precision measurements in storage rings are increasingly limited by the ability to monitor collective spin dynamics coherently over long time scales. Existing polarimetry techniques rely on destructive scattering processes that preclude continuous, non-intercepting tracking of spin evolution and constrain both statistical sensitivity and systematic control. Here we introduce a non-destructive, phase-coherent polarimetry method in which the stored beam polarization is treated as a continuous dynamical observable rather than a quantity inferred from scattering events. Spin-dependent electromagnetic fields generated by a polarized relativistic beam establish a symmetry-selected differential signal on pickup electrodes. This signal is transduced into a narrowband phase modulation of a high-Q resonator interrogated with a coherent probe, while dominant charge-induced backgrounds are rejected through geometric symmetry, helicity reversal, and synchronous demodulation. Controlled spin precession (spin-wheel operation) provides a stable phase reference enabling phase-coherent detection of slow spin evolution. Combined with optimized lattice symmetry and beam cooling, this approach can substantially extend the usable spin coherence time, with values approaching 10^5 s appearing realistic within existing accelerator technology. The resulting readout supports optimal slope-based estimation with T^{-3/2} statistical scaling while eliminating the efficiency penalties inherent to scattering-based polarimetry. For storage-ring EDM experiments, this combination enables sensitivity approaching the level expected within the Standard Model. More broadly, the method establishes a general phase-coherent architecture for collective spin measurements in storage rings, adapting resonant sensing concepts from axion dark-matter searches to charged-particle precision experiments.

hep-ex↗

arXiv2026

MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation

A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific fine-tuning to bridge the gap between simulated and physical environments. We challenge that assumption. With sufficiently large-scale and diverse simulated synthetic training data, we show that zero-shot transfer to the real world is not only possible, but effective for both static and mobile manipulation. We introduce MolmoBot-Engine, a fully open-source pipeline for procedural data generation across robots, tasks, and diverse simulated environments in MolmoSpaces. With it, we release MolmoBot-Data, a dataset of 1.8 million expert trajectories for articulated object manipulation and pick-and-place tasks. We train three policy classes: MolmoBot, a Molmo2-based multi-frame vision-language model with a flow-matching action head; MolmoBot-Pi0, which replicates the $π_0$ architecture to enable direct comparison; and MolmoBot-SPOC, a lightweight policy suitable for edge deployment and amenable to RL fine-tuning. We evaluate on two robotic platforms: the Franka FR3 for tabletop manipulation tasks and the Rainbow Robotics RB-Y1 mobile manipulator for door opening, drawer manipulation, cabinet interaction, and mobile pick-and-place. Without any real-world fine-tuning, our policies achieve zero-shot transfer to unseen objects and environments. On tabletop pick-and-place, MolmoBot achieves a success rate of 79.2% in real world evaluations across 4 settings, outperforming $π_{0.5}$ at 39.2%. Our results demonstrate that procedural environment generation combined with diverse articulated assets can produce robust manipulation policies that generalize broadly to the real world. Technical website: https://allenai.github.io/MolmoBot

cs.RO↗

arXiv2026

MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation

Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that characterize real environments are vast and underrepresented in existing robot benchmarks. Measuring this level of generalization requires infrastructure at a scale and diversity that physical evaluation alone cannot provide. We introduce MolmoSpaces, a fully open ecosystem to support large-scale benchmarking of robot policies. MolmoSpaces consists of over 230k diverse indoor environments, ranging from handcrafted household scenes to procedurally generated multiroom houses, populated with 130k richly annotated object assets, including 48k manipulable objects with 42M stable grasps. Crucially, these environments are simulator-agnostic, supporting popular options such as MuJoCo, Isaac, and ManiSkill. The ecosystem supports the full spectrum of embodied tasks: static and mobile manipulation, navigation, and multiroom long-horizon tasks requiring coordinated perception, planning, and interaction across entire indoor environments. We also design MolmoSpaces-Bench, a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects. Our experiments show MolmoSpaces-Bench exhibits strong sim-to-real correlation (R = 0.96, \r{ho} = 0.98), confirm newer and stronger zero-shot policies outperform earlier versions in our benchmarks, and identify key sensitivities to prompt phrasing, initial joint positions, and camera occlusion. Through MolmoSpaces and its open-source assets and tooling, we provide a foundation for scalable data generation, policy training, and benchmark creation for robot learning research.

cs.RO↗

arXiv2025

Realizing the Scientific Program with Polarized Ion Beams at EIC

Polarized ion beams at the Electron Ion Collider are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, we summarize the science case and identify polarized $^2$H, $^3$He, $^6$Li and $^7$Li ion beams as critical technology that will enable experiments which address the most important science. Further, we discuss the required ion polarimetry and spin manipulation in EIC. The current EIC accelerator design is presented. We identify a significant R\&D effort involving both national laboratories and universities that is required over about a decade to realize the polarized ion beams and estimate (based on previous experience) that it will require about 20 FTE over 10 years (or a total of about 200 FTE-years) of personnel, including graduate students, postdoctoral researchers, technicians and engineers. Attracting, educating and training a new generation of physicists in experimental spin techniques will be essential for successful realization. AI/ML is seen as having significant potential for both acceleration of R\&D and amplification of discovery in optimal realization of this unique quantum technology on a cutting-edge collider. The R\&D effort is synergistic with research in atomic physics and fusion energy science.

nucl-ex↗

arXiv2025

GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation

We present GrasMolmo, a generalizable open-vocabulary task-oriented grasping (TOG) model. GraspMolmo predicts semantically appropriate, stable grasps conditioned on a natural language instruction and a single RGB-D frame. For instance, given "pour me some tea", GraspMolmo selects a grasp on a teapot handle rather than its body. Unlike prior TOG methods, which are limited by small datasets, simplistic language, and uncluttered scenes, GraspMolmo learns from PRISM, a novel large-scale synthetic dataset of 379k samples featuring cluttered environments and diverse, realistic task descriptions. We fine-tune the Molmo visual-language model on this data, enabling GraspMolmo to generalize to novel open-vocabulary instructions and objects. In challenging real-world evaluations, GraspMolmo achieves state-of-the-art results, with a 70% prediction success on complex tasks, compared to the 35% achieved by the next best alternative. GraspMolmo also successfully demonstrates the ability to predict semantically correct bimanual grasps zero-shot. We release our synthetic dataset, code, model, and benchmarks to accelerate research in task-semantic robotic manipulation, which, along with videos, are available at https://abhaybd.github.io/GraspMolmo/.

cs.RO↗

arXiv2025

Entanglement as a probe of hadronization

Recently, it was discovered that the proton structure at high energies exhibits maximal entanglement. This leads to a simple relation between the proton's parton distributions and the entropy of hadrons produced in high-energy inelastic interactions that has been experimentally confirmed. In this letter, we extend this approach to the production of jets. Here, the maximal entanglement predicts a relation between the jet fragmentation function and the entropy of hadrons produced in jet fragmentation. We test this relation using the ATLAS Collaboration data on jet production at the Large Hadron Collider and find good agreement between the prediction based on maximal entanglement within the jet and the data. This study represents the first use of the quantum entanglement framework in the experimental study of the hadronization process, offering a new perspective on the transition from perturbative to non-perturbative QCD. Our results open the door to a more comprehensive understanding of the quantum nature of hadronization.

hep-ph↗

arXiv2024

Search for baryon junctions in e+A collisions at the Electron Ion Collider

Constituent quarks in a nucleon are the essential elements in the standard ``quark model" associated with the electric charge, spin, mass, and baryon number of a nucleon. Quantum Chromodynamics (QCD) describes nucleon as a composite object containing current quarks (valence quarks and sea (anti-)quarks) and gluons. These subatomic elements and their interactions are known to contribute in complex ways to the overall nucleon spin and mass. In the early development of QCD theory in the 1970s, an alternative hypothesis postulated that the baryon number might manifest itself through a non-perturbative configuration of gluon fields forming a Y-shaped topology known as the gluon junction. In this work, we propose to test such hypothesis by measuring (i) the Regge intercept of the net-baryon distributions for $e$+($p$)Au collisions, (ii) baryon and charge transport in the isobaric ratio between $e$+Ru and $e$+Zr collisions, and (iii) target flavor dependence of proton and antiproton yields at large rapidity, transported from the hydrogen and deuterium targets in $e+p$(d) collisions. Our study indicates that these measurements at the EIC can help determine what carries the baryon number.

hep-ph↗

arXiv2024

Nuclear Dependence of Beam Normal Single Spin Asymmetry in Elastic Scattering from Nuclei

We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculations based on two-photon exchange suggest no nuclear dependence at this kinematics, the results of 208Pb from Jefferson Lab show a striking disagreement from both theoretical predictions and light nuclei measurements. The proposed measurements will provide new data for intermediate to heavy nuclei where no data exists for $Z \geq$ 20 in the kinematics of previous high-energy experiments. It will allow one to investigate the missing contributions that are not accounted in the current theoretical models.

nucl-ex↗

arXiv2024

Centrality definition in e+A collisions at the Electron-Ion Collider

In this work, we investigate the feasibility of defining centrality in electron-ion collisions at the Electron-Ion Collider (EIC) by examining the correlation between the impact parameter and several observables, including total energy, total transverse momentum, and total number of particles. Using the BeAGLE Monte Carlo generator, we simulate e+Au and e+Ru collisions at different energies and analyze the correlation between the impact parameter and these observables across different kinematic regions. Our findings indicate that the correlation is weak in the central rapidity region but becomes stronger in the forward and far-forward rapidity regions. However, the correlation is not sufficiently robust to allow for precise centrality determination. We conclude that defining centrality in electron-ion collisions is more challenging than in ion-ion collisions, necessitating further studies to develop a robust centrality definition for the EIC.

hep-ph↗

arXiv2024

Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels

We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in mitigating this issue for real-world fine manipulation tasks. CCIL generates corrective labels by learning a locally continuous dynamics model from demonstrations to guide the agent back toward expert states. Through extensive experiments on peg insertion and fine grasping, we provide the first empirical validation that CCIL can significantly improve imitation learning performance despite discontinuities present in contact-rich manipulation. We find that: (1) real-world manipulation exhibits sufficient local smoothness to apply CCIL, (2) generated corrective labels are most beneficial in low-data regimes, and (3) label filtering based on estimated dynamics model error enables performance gains. To effectively apply CCIL to robotic domains, we offer a practical instantiation of the framework and insights into design choices and hyperparameter selection. Our work demonstrates CCIL's practicality for alleviating compounding errors in imitation learning on physical robots.

cs.RO↗

arXiv2024

A study of nuclear structure of light nuclei at the Electron-Ion Collider

Understanding the substructure of atomic nuclei, particularly the clustering of nucleons inside them, is essential for comprehending nuclear dynamics. Various cluster configurations can emerge depending on excitation energy, the number and types of core clusters, and the presence of excess neutrons. Despite the prevalence of tightly bound cluster formations in low-lying states, understanding the correlation between clusters and their formation mechanisms remains incomplete. This exploring study investigates nuclear clustering at the Electron-Ion Collider (EIC) using simulations based on the modified BeAGLE model. By simulating collisions involving $e$+$^{9}$Be, $e$+$^{12}$C, and $e$+$^{16}$O nuclei, we find that the average energy of particles $\langle E \rangle$ and the system size ratios of particles at forward rapidity exhibit sensitivity to alpha clustering and its various configurations. These findings offer valuable insights into the dynamics of nuclear clustering and its implications for future studies at the EIC.

nucl-th↗

arXiv2024

CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning

We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond access to expert data. The key insight is to leverage local continuity in the environment dynamics to generate corrective labels. Our method first constructs a dynamics model from the expert demonstration, encouraging local Lipschitz continuity in the learned model. In locally continuous regions, this model allows us to generate corrective labels within the neighborhood of the demonstrations but beyond the actual set of states and actions in the dataset. Training on this augmented data enhances the agent's ability to recover from perturbations and deal with compounding errors. We demonstrate the effectiveness of our generated labels through experiments in a variety of robotics domains in simulation that have distinct forms of continuity and discontinuity, including classic control problems, drone flying, navigation with high-dimensional sensor observations, legged locomotion, and tabletop manipulation.

cs.RO↗

arXiv2023

Extraction of the strong coupling with HERA and EIC inclusive data

Sensitivity to the strong coupling $α_S(M^2_Z)$ is investigated using existing Deep Inelastic Scattering data from HERA in combination with projected future measurements from the Electron Ion Collider (EIC) in a next-to-next-to-leading order QCD analysis. A potentially world-leading level of precision is achievable when combining simulated inclusive neutral current EIC data with inclusive charged and neutral current measurements from HERA, with or without the addition of HERA inclusive jet and dijet data. The result can be obtained with substantially less than one year of projected EIC data at the lower end of the EIC centre-of-mass energy range. Some questions remain over the magnitude of uncertainties due to missing higher orders in the theoretical framework.

hep-ph↗

arXiv2023

Cherry-Picking with Reinforcement Learning : Robust Dynamic Grasping in Unstable Conditions

Grasping small objects surrounded by unstable or non-rigid material plays a crucial role in applications such as surgery, harvesting, construction, disaster recovery, and assisted feeding. This task is especially difficult when fine manipulation is required in the presence of sensor noise and perception errors; errors inevitably trigger dynamic motion, which is challenging to model precisely. Circumventing the difficulty to build accurate models for contacts and dynamics, data-driven methods like reinforcement learning (RL) can optimize task performance via trial and error, reducing the need for accurate models of contacts and dynamics. Applying RL methods to real robots, however, has been hindered by factors such as prohibitively high sample complexity or the high training infrastructure cost for providing resets on hardware. This work presents CherryBot, an RL system that uses chopsticks for fine manipulation that surpasses human reactiveness for some dynamic grasping tasks. By integrating imprecise simulators, suboptimal demonstrations and external state estimation, we study how to make a real-world robot learning system sample efficient and general while reducing the human effort required for supervision. Our system shows continual improvement through 30 minutes of real-world interaction: through reactive retry, it achieves an almost 100% success rate on the demanding task of using chopsticks to grasp small objects swinging in the air. We demonstrate the reactiveness, robustness and generalizability of CherryBot to varying object shapes and dynamics (e.g., external disturbances like wind and human perturbations). Videos are available at https://goodcherrybot.github.io/.

cs.RO↗

arXiv2022

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection.

hep-ph↗

arXiv2022

Snowmass 2021 White Paper: Charged lepton flavor violation in the tau sector

Charged lepton flavor violation has long been recognized as unambiguous signature of New Physics. Here we describe the physics capabilities and discovery potential of New Physics models with charged lepton flavor violation in the tau sector as its experimental signature. Current experimental status from the B-Factory experiments BaBar, Belle and Belle II, and future prospects at Super Tau Charm Factory, LHC, EIC and FCC-ee experiments to discover New Physics via charged lepton flavor violation in the tau sector are discussed in detail. Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)

hep-ph↗

arXiv2022

Experimental study to optimise the treatment efficacy of pharmaceutical effluents by combining electron beam irradiation with conventional techniques

The inability of conventional methods to completely remove the contaminants from pharmaceutical effluents led us to study the effect of Electron Beam (EB) irradiation on real pharmaceutical wastewater. In this paper, the samples from different stages of existing treatment facilities of industry are irradiated with varying doses from 25 to 200 kGy. The study aimed to find a suitable combination of EB and conventional treatments for efficient degradation of complex pharmaceutical effluent. It has been successfully demonstrated that electron beam irradiation when combined with conventional techniques like coagulation before or after the irradiation improves the efficiency of the process, resulting in lower Chemical Oxygen Demand (COD). In this investigation, the maximum COD reduction was found to be around 65 percent.

physics.bio-ph↗