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Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text modality information. Moreover, although Multimodal Large Language Models (MLLMs) capable of processing multi-modal inputs have emerged, how to equip MLLMs with multi-modal recommendation capabilities remains largely unexplored. To this end, in this paper, we propose the Multimodal Large Language Model-enhanced Multimodaln Sequential Recommendation (MLLM-MSR) model. To capture the dynamic user preference, we design a two-stage user preference summarization method. Specifically, we first utilize an MLLM-based item-summarizer to extract image feature given an item and convert the image into text. Then, we employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences based on an LLM-based user-summarizer. Finally, to enable the MLLM for multi-modal recommendation task, we propose to fine-tune a MLLM-based recommender using Supervised Fine-Tuning (SFT) techniques. Extensive evaluations across various datasets validate the effectiveness of MLLM-MSR, showcasing its superior ability to capture and adapt to the evolving dynamics of user preferences.

We report nucleosynthetic results for both $^{44}$Ti and nickel isotopes for eighteen three-dimensional (3D) core-collapse supernova (CCSN) simulations extended to $\sim$20 seconds after bounce. We find that many of our long-term models are able to achieve $^{44}$Ti/$^{56}$Ni ratios similar to that observed in Cassiopeia A, and modern supernova models can synthesize up to $2\times10^{-4}M_\odot$ of $^{44}$Ti. Neutrino-driven winds and the fact that there can be simultaneous accretion and explosion in 3D models of core-collapse supernovae play central roles in its production. We conclude that the $^{44}$Ti underproduction problem in previous CCSN models is no longer an issue. In addition, we discuss the production of both $^{57}$Ni and stable nickel/iron ratios and compare our results to observations of SN1987A and the Crab., Comment: 15 pages, 7 figures. Accepted to ApJ

Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically follow a binary matching paradigm that ignores the global consistency between record relationships. In this paper, we investigate various methodologies for LLM-based entity matching that incorporate record interactions from different perspectives. Specifically, we comprehensively compare three representative strategies: matching, comparing, and selecting, and analyze their respective advantages and challenges in diverse scenarios. Based on our findings, we further design a compound entity matching framework (ComEM) that leverages the composition of multiple strategies and LLMs. ComEM benefits from the advantages of different sides and achieves improvements in both effectiveness and efficiency. Experimental results on 8 ER datasets and 9 LLMs verify the superiority of incorporating record interactions through the selecting strategy, as well as the further cost-effectiveness brought by ComEM., Comment: Code is available at https://github.com/tshu-w/LLM4EM

We here focus on the behavior of supernovae that technically explode in 1D (spherical symmetry). When simulated in 3D, however, the outcomes of representative progenitors of this class are quite different in almost all relevant quantities. In 3D, the explosion energies can be two to ten times higher, and there are correspondingly large differences in the $^{56}$Ni yields. These differences between the 3D and 1D simulations reflect in part the relative delay to explosion of the latter and in the former the presence of proto-neutron star convection that boosts the driving neutrino luminosities by as much as $\sim$50\% at later times. In addition, we find that the ejecta in 3D models are more neutron-rich, resulting in significant weak r-process and $^{48}$Ca yields. Furthermore, we find that in 3D the core is an interesting, though subdominant, source of acoustic power. In summary, we find that though a model might be found theoretically to explode in 1D, one must perform supernova simulations in 3D to capture most of the associated observables. The differences between 1D and 3D models are just too large to ignore., Comment: 15 pages, 9 figures. Accepted to ApJ

Guidance commands of flight vehicles are a series of data sets with fixed time intervals, thus guidance design constitutes a sequential decision problem and satisfies the basic conditions for using deep reinforcement learning (DRL). In this paper, we consider the scenario where the escape flight vehicle (EFV) generates guidance commands based on DRL and the pursuit flight vehicle (PFV) generates guidance commands based on the proportional navigation method. For the EFV, the objective of the guidance design entails progressively maximizing the residual velocity, subject to the constraint imposed by the given evasion distance. Thus an irregular dynamic max-min problem of extremely large-scale is formulated, where the time instant when the optimal solution can be attained is uncertain and the optimum solution depends on all the intermediate guidance commands generated before. For solving this problem, a two-step strategy is conceived. In the first step, we use the proximal policy optimization (PPO) algorithm to generate the guidance commands of the EFV. The results obtained by PPO in the global search space are coarse, despite the fact that the reward function, the neural network parameters and the learning rate are designed elaborately. Therefore, in the second step, we propose to invoke the evolution strategy (ES) based algorithm, which uses the result of PPO as the initial value, to further improve the quality of the solution by searching in the local space. Simulation results demonstrate that the proposed guidance design method based on the PPO algorithm is capable of achieving a residual velocity of 67.24 m/s, higher than the residual velocities achieved by the benchmark soft actor-critic and deep deterministic policy gradient algorithms. Furthermore, the proposed ES-enhanced PPO algorithm outperforms the PPO algorithm by 2.7\%, achieving a residual velocity of 69.04 m/s., Comment: 13 pages, 13 figures, accepted to appear on IEEE Access, Mar. 2024

Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information. Current studies are limited to specific tasks like information retrieval or semantic matching, where the claim-reference relationships are unique and fixed, while the referential knowledge linking (RKL) in real-world can be much more diverse and complex. In this paper, we propose universal referential knowledge linking (URL), which aims to resolve diversified referential knowledge linking tasks by one unified model. To this end, we propose a LLM-driven task-instructed representation compression, as well as a multi-view learning approach, in order to effectively adapt the instruction following and semantic understanding abilities of LLMs to referential knowledge linking. Furthermore, we also construct a new benchmark to evaluate ability of models on referential knowledge linking tasks across different scenarios. Experiments demonstrate that universal RKL is challenging for existing approaches, while the proposed framework can effectively resolve the task across various scenarios, and therefore outperforms previous approaches by a large margin.

Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking. However, previous advanced self-supervised dense blocking approaches require domain-specific training on the target domain, which limits the benefits and rapid adaptation of these methods. To address this issue, we propose UniBlocker, a dense blocker that is pre-trained on a domain-independent, easily-obtainable tabular corpus using self-supervised contrastive learning. By conducting domain-independent pre-training, UniBlocker can be adapted to various downstream blocking scenarios without requiring domain-specific fine-tuning. To evaluate the universality of our entity blocker, we also construct a new benchmark covering a wide range of blocking tasks from multiple domains and scenarios. Our experiments show that the proposed UniBlocker, without any domain-specific learning, significantly outperforms previous self- and unsupervised dense blocking methods and is comparable and complementary to the state-of-the-art sparse blocking methods., Comment: Code and data are available at this https://github.com/tshu-w/Uniblocker

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Open Domain Question Answering (ODQA) task as a point of entry, our findings reveal a potential digital "Spiral of Silence" effect, with LLM-generated text consistently outperforming human-authored content in search rankings, thereby diminishing the presence and impact of human contributions online. This trend risks creating an imbalanced information ecosystem, where the unchecked proliferation of erroneous LLM-generated content may result in the marginalization of accurate information. We urge the academic community to take heed of this potential issue, ensuring a diverse and authentic digital information landscape., Comment: Accepted to ACL2024

In this paper, we derive correlations between core-collapse supernova observables and progenitor core structures that emerge from our suite of twenty state-of-the-art 3D core-collapse supernova simulations carried to late times. This is the largest such collection of 3D supernova models ever generated and allows one to witness and derive testable patterns that might otherwise be obscured when studying one or a few models in isolation. From this panoramic perspective, we have discovered correlations between explosion energy, neutron star gravitational birth masses, $^{56}$Ni and $\alpha$-rich freeze-out yields, and pulsar kicks and theoretically important correlations with the compactness parameter of progenitor structure. We find a correlation between explosion energy and progenitor mantle binding energy, suggesting that such explosions are self-regulating. We also find a testable correlation between explosion energy and measures of explosion asymmetry, such as the ejecta energy and mass dipoles. While the correlations between two observables are roughly independent of the progenitor ZAMS mass, the many correlations we derive with compactness can not unambiguously be tied to a particular progenitor ZAMS mass. This relationship depends upon the compactness/ZAMS mass mapping associated with the massive star progenitor models employed. Therefore, our derived correlations between compactness and observables may be more robust than with ZAMS mass, but can nevertheless be used in the future once massive star modeling has converged., Comment: Accepted to the Astrophysical Journal Letters

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries. While recent advances in large language models (LLMs) have improved the zero-shot text-to-SQL paradigm, existing methods face scalability challenges when dealing with massive, dynamically changing databases. This paper introduces DBCopilot, a framework that addresses these challenges by employing a compact and flexible copilot model for routing across massive databases. Specifically, DBCopilot decouples the text-to-SQL process into schema routing and SQL generation, leveraging a lightweight sequence-to-sequence neural network-based router to formulate database connections and navigate natural language questions through databases and tables. The routed schemas and questions are then fed into LLMs for efficient SQL generation. Furthermore, DBCopilot also introduced a reverse schema-to-question generation paradigm, which can learn and adapt the router over massive databases automatically without requiring manual intervention. Experimental results demonstrate that DBCopilot is a scalable and effective solution for real-world text-to-SQL tasks, providing a significant advancement in handling large-scale schemas., Comment: Code and data are available at https://github.com/tshu-w/DBCopilot

Using twenty long-term 3D core-collapse supernova simulations, we find that lower compactness progenitors that explode quasi-spherically due to the short delay to explosion experience smaller neutron star recoil kicks in the $\sim$100$-$200 km s$^{-1}$ range, while higher compactness progenitors that explode later and more aspherically leave neutron stars with kicks in the $\sim$300$-$1000 km s$^{-1}$ range. In addition, we find that these two classes are correlated with the gravitational mass of the neutron star. This correlation suggests that the survival of binary neutron star systems may in part be due to their lower kick speeds. We also find a correlation of the kick with both the mass dipole of the ejecta and the explosion energy. Furthermore, one channel of black hole birth leaves masses of $\sim$10 $M_{\odot}$, is not accompanied by a neutrino-driven explosion, and experiences small kicks. A second is through a vigorous explosion that leaves behind a black hole with a mass of $\sim$3.0 $M_{\odot}$ kicked to high speeds. We find that the induced spins of nascent neutron stars range from seconds to $\sim$10 milliseconds, {but do not yet see a significant spin/kick correlation for pulsars.} We suggest that if an initial spin biases the explosion direction, a spin/kick correlation {would be} a common byproduct of the neutrino mechanism of core-collapse supernovae. Finally, the induced spin in explosive black hole formation is likely large and in the collapsar range. This new 3D model suite provides a greatly expanded perspective and appears to explain some observed pulsar properties by default., Comment: 16 pages, 7 figures. Accepted to ApJ

We study in detail the ejecta conditions and theoretical nucleosynthetic results for 18 three-dimensional core-collapse supernova (CCSN) simulations done by F{\sc ornax}. {Most simulations are carried out to at least 3 seconds after bounce, which allows us to follow their longer-term behaviors.} We find that multi-dimensional effects introduce many complexities into ejecta conditions. We see stochastic electron fraction evolution, complex peak temperature distributions and histories, and long-tail distributions of the time spent within nucleosynthetic temperature ranges. These all lead to substantial variation in CCSN nucleosynthetic yields and differences with 1D results. We discuss the production of lighter $\alpha$-nuclei, radioactive isotopes, heavier elements, and a few isotopes of special interest. Comparing pre-CCSN and CCSN contributions, we find that a significant fraction of elements between roughly Si and Ge are generically produced in CCSNe. We find that $^{44}$Ti exhibits an extended production timescale compared to $^{56}$Ni, which may explain its different distribution and higher than previously predicted abundances in supernova remnants such as Cas A and SN1987A. We also discuss the morphology of the ejected elements. This study highlights the high-level diversity of ejecta conditions and nucleosynthetic results in 3D CCSN simulations and emphasizes the need for additional long-term {($\sim$10 seconds)} 3D simulations to properly address such complexities., Comment: 33 pages, 20 figures. Accepted to ApJ. Tracer data, progenitor profiles, and ejecta nucleosynthetic abundances are available at https://doi.org/10.5281/zenodo.10498615

We have simulated the collapse and evolution of the core of a solar-metallicity 40-M$_{\odot}$ star and find that it explodes vigorously by the neutrino mechanism. This despite its very high "compactness". Within $\sim$1.5 seconds of explosion, a black hole forms. The explosion is very asymmetrical and has a total explosion energy of $\sim$1.6$\times$10$^{51}$ ergs. At black hole formation, its baryon mass is $\sim$2.434 M$_{\odot}$ and gravitational mass is 2.286 M$_{\odot}$. Seven seconds after black hole formation an additional $\sim$0.2 M$_{\odot}$ is accreted, leaving a black hole baryon mass of $\sim$2.63 M$_{\odot}$. A disk forms around the proto-neutron star, from which a pair of neutrino-driven jets emanates. These jets accelerate some of the matter up to speeds of $\sim$45,000 km s$^{-1}$ and contain matter with entropies of $\sim$50. The large spatial asymmetry in the explosion results in a residual black hole recoil speed of $\sim$1000 km s$^{-1}$. This novel black-hole formation channel now joins the other black-hole formation channel between $\sim$12 and $\sim$15 M$_{\odot}$ discovered previously and implies that the black-hole/neutron-star birth ratio for solar-metallicity stars could be $\sim$20\%. However, one channel leaves black holes in perhaps the $\sim$5-15 M$_{\odot}$ range with low kick speeds, while the other leaves black holes in perhaps the $\sim$2.5-3.0 M$_{\odot}$ mass range with high kick speeds. However, even $\sim$8.8 seconds after core bounce the newly-formed black hole is still accreting at a rate of $\sim$2$\times$10$^{-2}$ M$_{\odot}$ s$^{-1}$ and whether the black hole eventually achieves a significantly larger mass over time is yet to be determined., Comment: 23 pages, 15 figures. Accepted to the Astrophysical Journal

In this paper, we analyze the neutrino-driven winds that emerge in twelve unprecedentedly long-duration 3D core-collapse supernova simulations done using the code Fornax. The twelve models cover progenitors with ZAMS mass between 9 and 60 solar masses. In all our models, we see transonic outflows that are at least two times as fast as the surrounding ejecta and that originate generically from a PNS surface atmosphere that is turbulent and rotating. We find that winds are common features of 3D simulations, even if there is anisotropic early fallback. We find that the basic dynamical properties of 3D winds behave qualitatively similarly to those inferred in the past using simpler 1D models, but that the shape of the emergent wind can be deformed, very aspherical, and channeled by its environment. The thermal properties of winds for less massive progenitors very approximately recapitulate the 1D stationary solutions, while for more massive progenitors they deviate significantly due to aspherical fallback. The $Y_e$ temporal evolution in winds is stochastic, and there can be some neutron-rich phases. Though no strong r-process is seen in any model, a weak r-process can be produced and isotopes up to $^{90}$Zr are synthesized in some models. Finally, we find that there is at most a few percent of a solar mass in the integrated wind component, while the energy carried by the wind itself can be as much as 10-20% of the total explosion energy., Comment: 21 pages, 13 figures. Accepted by the Astrophysical Journal

Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities. In recent years, large-scale pre-trained language models have shown remarkable memorizing ability. On the contrary, vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting problem. To investigate such a retentive-forgetful contradiction and understand the memory mechanism of language models, we conduct thorough experiments by controlling the target knowledge types, the learning strategies and the learning schedules. We find that: 1) Vanilla language models are forgetful; 2) Pre-training leads to retentive language models; 3) Knowledge relevance and diversification significantly influence the memory formation. These conclusions are useful for understanding the abilities of pre-trained language models and shed light on designing and evaluating new learning and inference algorithms of language models., Comment: Accepted by LREC-COLING 2024

We calculate the gravitational-wave (GW) signatures of detailed 3D core-collapse supernova simulations spanning a range of massive stars. Most of the simulations are carried out to times late enough to capture more than 95% of the total GW emission. We find that the f/g-mode and f-mode of proto-neutron star oscillations carry away most of the GW power. The f-mode frequency inexorably rises as the proto-neutron star (PNS) core shrinks. We demonstrate that the GW emission is excited mostly by accretion plumes onto the PNS that energize modal oscillations and also high-frequency (``haze") emission correlated with the phase of violent accretion. The duration of the major phase of emission varies with exploding progenitor and there is a strong correlation between the total GW energy radiated and the compactness of the progenitor. Moreover, the total GW emissions vary by as much as three orders of magnitude from star to star. For black-hole formation, the GW signal tapers off slowly and does not manifest the haze seen for the exploding models. For such failed models, we also witness the emergence of a spiral shock motion that modulates the GW emission at a frequency near $\sim$100 Hertz that slowly increases as the stalled shock sinks. We find significant angular anisotropy of both the high- and low-frequency (memory) GW emissions, though the latter have very little power., Comment: accepted to PRD

The two-moment method is widely used to approximate the full neutrino transport equation in core-collapse supernova (CCSN) simulations, and different closures lead to subtle differences in the simulation results. In this paper, we compare the effects of closure choices on various physical quantities in 1D and 2D time-dependent CCSN simulations with our multi-group radiation hydrodynamics code Fornax. We find that choices of the 3rd-order closure relations influence the time-dependent simulations only slightly. Choices of the 2nd-order closure relation have larger consequences than choices of the 3rd-order closure do, but these are still small compared to the remaining variations due to ambiguities in some physical inputs such as the nuclear equation of state. We also find that deviations in Eddington factors are not monotonically related to deviations in physical quantities, which means that simply comparing the Eddington factors does not inform one concerning which closure is better., Comment: 28 pages, 17 figures. Accepted by ApJ

Calibrating with detailed 2D core-collapse supernova simulations, we derive a simple core-collapse supernova explosion condition based solely upon the terminal density profiles of state-of-the-art stellar evolution calculations of the progenitor massive stars. This condition captures the vast majority of the behavior of the one hundred 2D state-of-the-art models we performed to gauge its usefulness. The goal is to predict, without resort to detailed simulation, the explodability of a given massive star. We find that the simple maximum fractional ram pressure jump discriminant we define works well ~90% of the time and we speculate on the origin of the few false positives and false negatives we witness. The maximum ram pressure jump generally occurs at the time of accretion of the silicon/oxygen interface, but not always. Our results depend upon the fidelity with which the current implementation of our code Fornax adheres to Nature and issues concerning the neutrino-matter interaction, the nuclear equation of state, the possible effects of neutrino oscillations, grid resolution, the possible role of rotation and magnetic fields, and the accuracy of the numerical algorithms employed remain to be resolved. Nevertheless, the explodability condition we obtain is simple to implement, shows promise that it might be further generalized while still employing data from only the unstable Chandrasekhar progenitors, and is a more credible and robust simple explosion predictor than can currently be found in the literature., Comment: 20 pages, 17 figures. Accepted by MNRAS

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark construction process, which is inconsistent with the nature of entity matching and therefore leads to biased evaluations of current EM approaches. To this end, we build a new EM corpus and re-construct EM benchmarks to challenge critical assumptions implicit in the previous benchmark construction process by step-wisely changing the restricted entities, balanced labels, and single-modal records in previous benchmarks into open entities, imbalanced labels, and multimodal records in an open environment. Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching. The constructed benchmarks and code are publicly released, Comment: Accepted to IJCAI2022

Murchikova et al 2019 discovered a disk of cool ionized gas within 20,000 Schwarzschild radii of the Milky Way's Galactic Center black hole Sagittarius A*. They further demonstrated that the ionizing photon flux in the region is enough to keep the disk ionized, but there is not ample excess of this radiation. This raised the possibility that some neutral gas could also be in the region shielded within the cool ionized clumps. Here we present ALMA observations of a broad 1.3 millimeter hydrogen recombination line H30alpha: n = 31 -> 30, conducted during the flyby of the S0-2 star by Sgr A*. We report that the velocity-integrated H30alpha line flux two month prior to the S0-2 pericenter passage is about 20% larger than it was one month prior to the passage. The S0-2 is a strong source of ionizing radiation moving at several thousand kilometers per second during the approach. Such a source is capable of ionising parcels of neural gas along its trajectory, resulting in variation of the recombination line spectra from epoch to epoch. We conclude that there are at least (6.6 +- 3.3) x 10^{-6} Msun of neutral gas within 20,000 Schwarzschild radii of Sgr A*., Comment: 16 pages, 4 figures, comments are welcomed

Resource allocation problems are often approached with linear programming techniques. But many concrete allocation problems in the experimental and observational sciences cannot or should not be expressed in the form of linear objective functions. Even if the objective is linear, its parameters may not be known beforehand because they depend on the results of the experiment for which the allocation is to be determined. To address these challenges, we present a bipartite Graph Neural Network architecture for trainable resource allocation strategies. Items of value and constraints form the two sets of graph nodes, which are connected by edges corresponding to possible allocations. The GNN is trained on simulations or past problem occurrences to maximize any user-supplied, scientifically motivated objective function, augmented by an infeasibility penalty. The amount of feasibility violation can be tuned in relation to any available slack in the system. We apply this method to optimize the astronomical target selection strategy for the highly multiplexed Subaru Prime Focus Spectrograph instrument, where it shows superior results to direct gradient descent optimization and extends the capabilities of the currently employed solver which uses linear objective functions. The development of this method enables fast adjustment and deployment of allocation strategies, statistical analyses of allocation patterns, and fully differentiable, science-driven solutions for resource allocation problems., Comment: The GNN code used in this paper is available at https://github.com/tianshu-wang/PFS-GNN-bipartite

We introduce Rabia, a simple and high performance framework for implementing state-machine replication (SMR) within a datacenter. The main innovation of Rabia is in using randomization to simplify the design. Rabia provides the following two features: (i) It does not need any fail-over protocol and supports trivial auxiliary protocols like log compaction, snapshotting, and reconfiguration, components that are often considered the most challenging when developing SMR systems; and (ii) It provides high performance, up to 1.5x higher throughput than the closest competitor (i.e., EPaxos) in a favorable setup (same availability zone with three replicas) and is comparable with a larger number of replicas or when deployed in multiple availability zones., Comment: Full version of the SOSP21 paper

In order to exhume the buried signatures of "missing planetary caustics" in the KMTNet data, we conducted a systematic anomaly search to the residuals from point-source point-lens fits, based on a modified version of the KMTNet EventFinder algorithm. This search reveals the lowest mass-ratio planetary caustic to date in the microlensing event OGLE-2019-BLG-1053, for which the planetary signal had not been noticed before. The planetary system has a planet-host mass ratio of $q = (1.25 \pm 0.13) \times 10^{-5}$. A Bayesian analysis yields estimates of the mass of the host star, $M_{\rm host} = 0.61_{-0.24}^{+0.29}~M_\odot$, the mass of its planet, $M_{\rm planet} = 2.48_{-0.98}^{+1.19}~M_{\oplus}$, the projected planet-host separation, $a_\perp = 3.4_{-0.5}^{+0.5}$ au, and the lens distance of $D_{\rm L} = 6.8_{-0.9}^{+0.6}$ kpc. The discovery of this very low mass-ratio planet illustrates the utility of our method and opens a new window for a large and homogeneous sample to study the microlensing planet-host mass-ratio function down to $q \sim 10^{-5}$., Comment: published by AJ

We present a method to compute the magnification of a finite source star lensed by a triple lens system based on the image boundary (contour integration) method. We describe a new procedure to obtain continuous image boundaries from solutions of the tenth-order polynomial obtained from the lens equation. Contour integration is then applied to calculate the image areas within the image boundaries, which yields the magnification of a source with uniform brightness. We extend the magnification calculation to limb-darkened stars approximated with a linear profile. In principle, this method works for all multiple lens systems, not just triple lenses. We also include an adaptive sampling and interpolation method for calculating densely covered light curves. The C++ source code and a corresponding Python interface are publicly available., Comment: 13 pages, 11 figures, accepted by MNRAS

Based on the Kompaneets approximation, we develop a robust methodology to calculate spectral redistribution via inelastic neutrino-nucleon scattering in the context of core-collapse supernova simulations. The resulting equations conserve lepton number to machine precision and scale linearly, not quadratically, with number of energy groups. The formalism also provides an elegant means to derive the rate of energy transfer to matter which, as it must, automatically goes to zero when the neutrino radiation field is in thermal equilibrium. Furthermore, we derive the next-higher-order in {\epsilon}/mc2 correction to the neutrino Kompaneets equation. Unlike other Kompaneets schema, ours also generalizes to the case of anisotropic angular distributions, while retaining the conservative form that is a hallmark of the classical Kompaneets equation. Our formalism enables immediate incorporation into supernova codes that follow the spectral angular moments of the neutrino radiation fields., Comment: 13 pages, 4 figures. Accepted on June 19 by Physical Review D

We report the discovery and the analysis of the short (tE < 5 days) planetary microlensing event, OGLE-2015-BLG-1771. The event was discovered by the Optical Gravitational Lensing Experiment (OGLE), and the planetary anomaly (at I ~ 19) was captured by The Korea Microlensing Telescope Network (KMTNet). The event has three surviving planetary models that explain the observed light curves, with planet-host mass ratio q \~ 5.4 * 10^{-3}, 4.5 * 10^{-3} and 4.5 * 10^{-2}, respectively. The first model is the best-fit model, while the second model is disfavored by \Delta\chi^2 ~ 3. The last model is strongly disfavored by \Delta\chi^2 ~ 15 but not ruled out. A Bayesian analysis using a Galactic model indicates that the first two models are probably composed of a Saturn-mass planet orbiting a late M dwarf, while the third one could consist of a super-Jovian planet and a mid-mass brown dwarf. The source-lens relative proper motion is mu_rel ~ 9 mas/yr, so the source and lens could be resolved by current adaptive-optics (AO) instruments in 2021 if the lens is luminous., Comment: 7 figures and 3 tables; submitted to AAS Journal