arXiv subjects

Anirban Majumdar

Publications and source records attributed to Anirban Majumdar.

At least 19 recordsLinked to original sources

arXiv2026

Can Elastic Neutrino Scattering Account for the LZ230616 Event?

Recently, the LUX-ZEPLIN (LZ) Collaboration reported an isolated nuclear-recoil event at $248\pm 23_\mathrm{stat}\pm 23_\mathrm{syst}~\mathrm{keV_{nr}}$, in a region where the expected background is very small and conventional elastic dark matter (DM)-nucleus scattering cannot readily account for such a localized feature. We investigate whether LZ230616 could instead originate from coherent elastic neutrino-nucleus scattering (CE$ν$NS). We consider neutrino-nucleus interactions within and beyond the Standard Model, together with exotic neutrino fluxes from DM annihilation ($χχ\to ν\barν$) or decay ($χ\to ν\barν$) into neutrino pairs and from primordial black hole (PBH) evaporation. We show that kinematic considerations, the accompanying low energy recoil spectrum, and existing constraints exclude a viable interpretation of LZ230616 in terms of elastic neutrino-nucleus scattering for all scenarios considered.

hep-ph↗

arXiv2026

Atmospheric neutrino up-scattering explanation of LZ 2026 excess

The recent observation of an isolated nuclear recoil at $248\pm 23\pm 23$ keV energy by LUX-ZEPLIN (LZ) experiment has motivated the community to look for a new physics explanation, as the Standard model background estimation fails to accomodate that. Most of the existing literature hitherto considers a galactic halo dark matter with a heavier partner. In this work, we traverse the alternate route of atmospheric neutrino ($ν$) up-scattering, thus producing a massive beyond standard model (BSM) particle $χ$. The kinematic requirement of such a scattering poses a cut off in the lower recoil energies providing an explanation of the unique isolated event at such a higher recoil energy. Such up-scattering with the nucleons ($\nuc$), $ν\nuc\to χ\nuc$ can be naturally realized in sterile neutrino models, though we keep our analysis generic without specifying $χ$. We identify the region of parameter space that can produce such an isolated event assuming a scalar mediator with mass $m_ϕ$ and coupling $y_{χ,q}$. For example, with $m_χ\sim1$ GeV, and $\sqrt{y_χy_q}/m_ϕ=2 \times 10^{-2}$ GeV$^{-1}$ can satisfy such an excess of events while remaining allowed by other existing constraints as well.

hep-ph↗

arXiv2026

Algorithms for Robbins' Problem using Markov Decision Processes

In this paper, we consider Robbins' problem, which is a full information variant of the well-known secretary selection problem. In this version of the problem, the goal is to minimize the expected rank of the selected candidate among $n$ that are interviewed sequentially, and a decision to select or not the $m^{th}$ candidate needs to be taken right after the interview (so without seeing the last $n-m$ candidates and without recall). We first show how to model instances of Robbins' problem as infinite Markov Decision Processes (MDPs). Then we propose several finite-state abstractions of these MDPs that allow us to approximate the value of the problem for fixed $n$. While it is known that the full memory of past candidates' values is necessary for optimal expected rank minimization, making the analysis of the problem challenging, we highlight simple memory structures that are sufficient for obtaining near-optimal selection strategies. Additionally, we provide approximate values for Robbins' problem for numbers of candidates $n$ up to 100 for which no good approximations were previously known (the exact value is only known for instances where $n \leq 4$ and numerical approximations were for small values of $n$ not exceeding one digit), for all $n : 5 \leq n \leq 100$, we give better approximation than what was previously known.

cs.GT↗

arXiv2026

Precision Tests of SM and new physics with the COHERENT Ge-mini and TEXONO data

A comprehensive numerical analysis of the latest germanium based CE$ν$NS data from the COHERENT Ge-mini and TEXONO experiments has been conducted to test the Standard Model (SM) and search for new physics. By combining CE$ν$NS and E$ν$NS signals with a consistent treatment of detector effects and systematic uncertainties, we obtain a low energy determination of the weak mixing angle from COHERENT Ge-mini, in agreement with the SM prediction. We derive novel constraints on neutrino electromagnetic properties, including the magnetic moment, millicharge, charge radius, and anapole moment, with TEXONO providing particularly competitive bounds. Inclusion of E$ν$NS, significantly improves the sensitivity to the neutrino millicharge by up to three orders of magnitude. We also investigated light scalar and vector mediators, finding striking complementarity between reactor and stopped pion sources across different mediator mass regimes. Finally, we put bounds on sterile neutral leptons production through transition dipole, scalar, and vector portals, probing masses from the sub MeV to tens of MeV scale. Our results demonstrate that current germanium based CE$ν$NS experiments provide a powerful low energy laboratory for precision electroweak tests and complementary probes of a broad class of physics beyond the SM.

hep-ph↗

arXiv2026

Cosmic-ray-electron boosted light dark matter: Implications of LZ 2025 data

Current multiton detectors put stringent constraints on the GeV-scale galactic dark matter, pushing the allowed cross section almost toward the neutrino fog, yet remain mostly insensitive to the light dark matter. Cosmic rays can upscatter the nonrelativistic halo dark matter particles, making a subpopulation of them gain sufficient kinetic energy to be discernible in current direct search experiments. In this work, we explore this alternate strategy to probe sub-MeV electrophilic dark matter boosted by cosmic rays with the latest data of LZ 2025 (WS2024 run). We also incorporate the attenuation effect on the boosted dark matter flux during its propagation through the Earth and perform a full numerical treatment to obtain the resulting event rate. Our result shows LZ 2025 data improve the constraint on the MeV scale dark matter by almost $\sim\mathcal{O}(1)$ compared to the previous XENONnT limit for the energy-independent cross section. Using realistic energy-dependent cross sections, we also analyze such a scenario, where the associated mediator mass plays a crucial role in governing the event rate and hence the expected limits too. With energy-dependent cross sections, our obtained limits also remain stronger than the existing constraints from the XENONnT experiment. Even compared to the limits from neutrino detectors with much larger target masses, LZ 2025 can place stringent constraints in certain regions of the mediator parameter space, particularly in the light-mediator regime, excluding previously unexplored regions.

hep-ph↗

arXiv2026

Primordial black holes as cosmic accelerators of light dark matter: Novel direct detection constraints

Current multi-tonne-scale dark matter (DM) detectors are largely incapable of detecting light dark matter from the Galactic halo due to the energy threshold limitations of their recoil measurements. However, primordial black holes (PBHs) can evaporate via Hawking radiation to particles whose energies are set by the black hole temperature. Consequently, weakly interacting light dark matter (or dark radiation) particles produced in this manner can reach the Earth with sufficient flux and kinetic energy above the experimental thresholds. This opens up a novel avenue to probe the light dark sector in terrestrial experiments. In this work, we explore this possibility by considering fermionic DM produced through PBH evaporation and investigating its electron recoil signatures in direct detection experiments. We analyze both energy independent (constant) and energy dependent (scalar and vector mediated) DM-electron interactions, highlighting the strong dependence of the recoil spectra on the underlying Lorentz structure of the interaction. In addition, we also account for the attenuation effects due to the loss of kinetic energy while DM traverses through Earth's crust, which can significantly modify the incoming DM flux. Incorporating these effects carefully, we place constraints on light DM using the electron recoil data from XENONnT, LZ, and PandaX-4T. Finally, we also discuss the detection prospects of such dark matter in current and future generation neutrino detectors, such as Super-Kamiokande and Hyper-Kamiokande.

hep-ph↗

arXiv2026

New light mediators and the neutrino fog: Implications from XENONnT nuclear recoil data

Current ton-scale, xenon-based dark matter (DM) direct detection experiments have now reached the sensitivity required to observe solar neutrinos, marking the onset of the so-called neutrino fog. In this work, we explore how this fog is modified when either neutrinos or DM interact with nuclei through a new scalar, vector or axial-vector interaction, considering both heavy and light mediators. Using the latest nuclear-recoil data from XENONnT, which show indications of coherent elastic neutrino-nucleus scattering from $^8$B solar neutrinos, we derive new strong bounds on couplings of light mediators. We find that these limits are significantly more stringent when the mediator couples to DM, rather than when new physics affects only neutrino interactions. Building on these results, we recompute the expected neutrino fog and compare it with the corresponding constraints on spin-independent and spin-dependent DM-nucleon interactions. We show that the morphology of the neutrino fog can be markedly modified if either neutrinos or DM interact with nuclei through light mediators, even in light of these recent constraints.

hep-ph↗

arXiv2026

Synthesizing POMDP Policies: Sampling Meets Model-checking via Learning

Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods scale well, they lack formal correctness guarantees, making them unsuitable for safety-critical applications. Conversely, formal synthesis techniques provide correctness-by-construction but often struggle with scalability, as general POMDP synthesis is undecidable. To bridge this gap, we propose a synthesis framework that integrates sampling, automata learning, and model-checking. Inspired by Angluin's $L^*$ algorithm, our approach utilizes sampling as a membership oracle and model-checking as an equivalence oracle. This enables the synthesis of finite-state controllers with formal guarantees, provided the sampling-induced policy is regular. We establish a relative completeness result for this framework. Experimental results from our prototypical implementation demonstrate that this method successfully solves threshold-safety problems that remain challenging for existing formal synthesis tools. We believe our algorithm serves as a valuable component in a portfolio approach to tackling the inherent difficulty of POMDP synthesis problems.

cs.AI↗

arXiv2026

About Time: Model-free Reinforcement Learning with Timed Reward Machines

Reward specification plays a central role in reinforcement learning (RL), guiding the agent's behavior. To express non-Markovian rewards, formalisms such as reward machines have been introduced to capture dependencies on histories. However, traditional reward machines lack the ability to model precise timing constraints, limiting their use in time-sensitive applications. In this paper, we propose timed reward machines (TRMs), which are an extension of reward machines that incorporate timing constraints into the reward structure. TRMs enable more expressive specifications with tunable reward logic, for example, imposing costs for delays and granting rewards for timely actions. We study model-free RL frameworks (i.e., tabular Q-learning) for learning optimal policies with TRMs under digital and real-time semantics. Our algorithms integrate the TRM into learning via abstractions of timed automata, and employ counterfactual-imagining heuristics that exploit the structure of the TRM to improve the search. Experimentally, we demonstrate that our algorithm learns policies that achieve high rewards while satisfying the timing constraints specified by the TRM on popular RL benchmarks. Moreover, we conduct comparative studies of performance under different TRM semantics, along with ablations that highlight the benefits of counterfactual-imagining.

cs.AI↗

arXiv2025

Scalable Learning of One-Counter Automata via State-Merging Algorithms

We propose One-counter Positive Negative Inference (OPNI), a passive learning algorithm for deterministic real-time one-counter automata (DROCA). Inspired by the RPNI algorithm for regular languages, OPNI constructs a DROCA consistent with any given valid sample set. We further present a method for combining OPNI with active learning of DROCA, and provide an implementation of the approach. Our experimental results demonstrate that this approach scales more effectively than existing state-of-the-art algorithms. We also evaluate the performance of the proposed approach for learning visibly one-counter automata.

cs.FL↗

arXiv2025

Learning Event-recording Automata Passively

This paper presents a state-merging algorithm for learning timed languages definable by Event-Recording Automata (ERA) using positive and negative samples in the form of symbolic timed words. Our algorithm, LEAP (Learning Event-recording Automata Passively), constructs a possibly nondeterministic ERA from such samples based on merging techniques. We prove that determining whether two ERA states can be merged while preserving sample consistency is an NP-complete problem, and address this with a practical SMT-based solution. Our implementation demonstrates the algorithm's effectiveness through examples. We also show that every ERA-definable language can be inferred using our algorithm with a suitable sample.

cs.FL↗

arXiv2025

Probing conventional and new physics at the ESS with coherent elastic neutrino-nucleus scattering

We explore the potential of the European Spallation Source (ESS) in probing physics within and beyond the Standard Model (SM), based on future measurements of coherent elastic neutrino-nucleus scattering (CE$ν$NS). We consider two SM physics cases, namely the weak mixing angle and the nuclear radius. Regarding physics beyond the SM, we focus on neutrino generalized interactions (NGIs) and on various aspects of sterile neutrino and sterile neutral lepton phenomenology. For this, we explore the violation of lepton unitarity, active-sterile oscillations as well as interesting upscattering channels such as the sterile dipole portal and the production of sterile neutral leptons via NGIs. The projected ESS sensitivities are estimated by performing a statistical analysis considering the various CE$ν$NS detectors and expected backgrounds. We find that the enhanced statistics achievable in view of the highly intense ESS neutrino beam, will offer a drastic improvement in the current constraints obtained from existing CE$ν$NS measurements. Finally, we discuss how the ESS has the potential to provide the leading CE$ν$NS-based constraints, complementing also further experimental probes and astrophysical observations.

hep-ph↗

arXiv2025

Constraining low scale dark hypercharge symmetry at spallation, reactor and Dark Matter direct detection experiments

Coherent elastic neutrino-nucleus (CE$ν$NS) and elastic neutrino-electron scattering (E$ν$ES) data are exploited to constrain ``chiral'' $U(1)_{X}$ gauged models with light vector mediator mass. These models fall under a distinct class of new symmetries called dark hypercharge symmetries. A key feature is the fact that the $Z'$ boson can couple to all Standard Model fermions at tree level, with the $U(1)_X$ charges determined by the requirement of anomaly cancellation. Notably, the charges of leptons and quarks can differ significantly depending on the specific anomaly cancellation solution. As a result, different models exhibit distinct phenomenological signatures and can be constrained through various experiments. In this work, we analyze the recent data from the COHERENT experiment, along with results from dark matter (DM) direct detection experiments such as XENONnT, LUX-ZEPLIN, and PandaX-4T, and place new constraints on three benchmark models. Additionally, we set constraints from a performed analysis of TEXONO data and discuss the prospects of improvement in view of the next-generation DM direct detection DARWIN experiment.

hep-ph↗

arXiv2025

Probing Standard Model and Beyond with Reactor CE$ν$NS Data of CONUS+ experiment

We explore the potential of reactor antineutrino-induced Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) data from the CONUS+ experiment to investigate both the Standard Model (SM) and Beyond Standard Model (BSM) scenarios. Alongside CE$ν$NS, Elastic Neutrino-Electron Scattering (E$ν$ES) events are also included in our analysis, enabling more stringent constraints on new physics. Within the SM, we examine the weak mixing angle as a precision test of the electroweak sector. For BSM scenarios, we constrain the parameter space of light mediators arising from neutrino generalized interactions (NGI), while also setting limits on the electromagnetic properties of neutrinos, including their charge radius, millicharge, and magnetic moment.

hep-ph↗

arXiv2024

Greybox Learning of Languages Recognizable by Event-Recording Automata

In this paper, we revisit the active learning of timed languages recognizable by event-recording automata. Our framework employs a method known as greybox learning, which enables the learning of event-recording automata with a minimal number of control states. This approach avoids learning the region automaton associated with the language, contrasting with existing methods. We have implemented our greybox learning algorithm with various heuristics to maintain low computational complexity. The efficacy of our approach is demonstrated through several examples.

cs.FL↗

arXiv2024

XENONnT and LUX-ZEPLIN constraints on DSNB-boosted dark matter

We consider a scenario in which dark matter particles are accelerated to semi-relativistic velocities through their scattering with the Diffuse Supernova Neutrino Background. Such a subdominant, but more energetic dark matter component can be then detected via its scattering on the electrons and nucleons inside direct detection experiments. This opens up the possibility to probe the sub-GeV mass range, a region of parameter space that is usually not accessible at such facilities. We analyze current data from the XENONnT and LUX-ZEPLIN experiments and we obtain novel constraints on the scattering cross sections of sub-GeV boosted dark matter with both nucleons and electrons. We also highlight the importance of carefully taking into account Earth's attenuation effects as well as the finite nuclear size into the analysis. By comparing our results to other existing constraints, we show that these effects lead to improved and more robust constraints.

hep-ph↗

arXiv2024

Static and Dynamic Synthesis of Bengali and Devanagari Signatures

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.

cs.CV↗

arXiv2023

Bi-Objective Lexicographic Optimization in Markov Decision Processes with Related Objectives

We consider lexicographic bi-objective problems on Markov Decision Processes (MDPs), where we optimize one objective while guaranteeing optimality of another. We propose a two-stage technique for solving such problems when the objectives are related (in a way that we formalize). We instantiate our technique for two natural pairs of objectives: minimizing the (conditional) expected number of steps to a target while guaranteeing the optimal probability of reaching it; and maximizing the (conditional) expected average reward while guaranteeing an optimal probability of staying safe (w.r.t. some safe set of states). For the first combination of objectives, which covers the classical frozen lake environment from reinforcement learning, we also report on experiments performed using a prototype implementation of our algorithm and compare it with what can be obtained from state-of-the-art probabilistic model checkers solving optimal reachability.

cs.GT↗