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This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult., Comment: 8 pages, 2 figures

Type B superconformal quantum mechanical sigma models are of physical interest as they arise in the description of D-brane bound states forming an AdS$_2$ throat. In this work we discuss the applicability of localization methods to compute the superconformal index in these theories, despite the fact that their target spaces are generically singular. Similar in spirit to recent works on type A models, we propose to work on a suitably resolved target space to compute a regularized index. While this regularized index correctly captures the actual index unambiguously in models of physical interest, we do uncover a subtlety in more pathological examples. This occurs in situations where the supercharge is not essentially-selfadjoint, in which case the index becomes ambiguous and depends on the chosen selfadjoint extension. We also discuss the special class of models with K\"ahler target spaces, which can accommodate both type A and type B models, and show that the type B index is a particular limit of the type A index. For Calabi-Yau cones, the type B index coincides with the Hilbert series of the unresolved space., Comment: 47 pages, 2 figures, 3 appendices

GRB 191019A was a long Gamma-ray burst (GRB) lasting about 65 s and, as such, originally thought to be linked to a core-collapse supernova. However, even though follow-up observations identified the optical counterpart close to the bright nucleus of a nearby ancient galaxy (z=0.248), no associated supernova was found. This led to the suggestion that the burst was caused by the merger of two compact stellar objects, likely in a dense circumnuclear environment. By using a recently developed diagnostic tool based on prompt emission temporal properties, we noticed that GRB 191019A falls among those long GRBs which are associated with compact mergers and with evidence of kilonova light. We thus re-analyzed unpublished GROND multi-color (g'r'i'z'JHK_s) data obtained between 0.4 and 15 days post trigger. Image subtraction confirmed the optical counterpart in all four optical bands, with GROND tracking its fading until 1.5 days post-burst. Incorporating publicly available Swift-XRT data, a joint fit of an afterglow plus a kilonova model revealed a better match than an afterglow-only scenario. The resulting kilonova properties resemble those of AT2017gfo associated with the binary neutron star merger GW170817, with a total ejected mass of about 0.06 solar mass. Contrary to previous findings inferring a high-density circumburst environment (n0=10^7-10^8 cm^-3), our analysis finds standard conditions (n0 = 1 cm^-3), suggesting the long duration of GRB 191019A was intrinsic rather than due to jet interaction with a dense external medium., Comment: 21 pages, 7 figures, accepted for publication in ApJ

Let $\overline{\mathcal{M}}_{0,n+1}$ be the moduli space of genus zero stable curves with $(n+1)$-marked points. The collection $\overline{\mathcal{M}}=\{\overline{\mathcal{M}}_{0,n+1}\}_{n\geq 2}$ forms an operad in the category of complex projective varieties; its homology $Hycom= H_*(\overline{\mathcal{M}})$ is called the Hypercommutative operad. In this paper we construct a chain model for the hypercommutative operad, i.e. an operad of chain complexes $C_*^{dual}(\overline{\mathcal{M}})$ which is weakly equivalent to the operad of singular chains $C_*(\overline{\mathcal{M}})$. We prove that $C_*^{dual}(\overline{\mathcal{M}})$ is the linear dual of the bar construction $B(grav)$, where $grav$ is a chain model of the gravity operad based on cacti without basepoint. This shows that the Gravity and Hypercommutative operad are Koszul dual also at the chain level, refining a previous result of Getzler. The construction is topological, since $C_*^{dual}(\overline{\mathcal{M}})(n)$ is the cellular complex associated to a regular CW-decomposition of $\overline{\mathcal{M}}_{0,n+1}$., Comment: 52 pages, 25 figures, comments are welcome!

The cross-correlation of cosmic voids with the lensing convergence ($\kappa$) map of the Cosmic Microwave Background (CMB) fluctuations provides a powerful tool to refine our understanding of the cosmological model. However, several studies have reported a moderate tension between the lensing imprint of cosmic voids on the observed CMB and the simulated $\mathrm{\Lambda}$CDM signal. To address this "lensing-is-low" tension and to obtain new, precise measurements, we exploit the large DESI Legacy Survey Luminous Red Galaxy (LRG) dataset, covering approximately 19,500 $\deg^2$ of the sky and including about 10 million LRGs at $z < 1.05$. Our $\mathrm{\Lambda}$CDM template was created using the Buzzard mocks, which we specifically calibrated to match the clustering properties of the observed galaxy sample by exploiting more than one million DESI spectra. We identified our catalogs of 3D voids in the range $0.35 < z < 0.95$, dividing the sample into bins according to the redshift and $\lambda_\mathrm{v}$ values of the voids. We report a 14$\sigma$ detection of the lensing signal, with $A_\kappa = 1.016 \pm 0.054$, which increases to 17$\sigma$ when considering the void-in-void ($A_\kappa = 0.944 \pm 0.064$) and the void-in-cloud ($A_\kappa = 0.975 \pm 0.060$) populations individually, the highest detection significance for studies of this kind. We observe a full agreement between the observations and $\mathrm{\Lambda}$CDM predictions across all redshift bins, sky regions, and void populations considered. In addition to these findings, our analysis highlights the importance of matching sparseness and redshift error distributions between mocks and observations, as well as the role of $\lambda_\mathrm{v}$ in enhancing the signal-to-noise ratio., Comment: 19 pages, 13 figures

The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen target at $\approx$ 230~MeV/nucleon. The energy level scheme of $^{52}$K was built using single $\gamma$ and $\gamma$-$\gamma$ coincidence spectra. The spins and parities of the excited states were established based on momentum distributions of the fragment after the knockout reaction and based on exclusive cross sections. The results were compared to state-of-the-art shell model calculations with the SDPF-Umod interaction and ab initio IMSRG calculations with chiral effective field theory nucleon-nucleon and three-nucleon forces.

Constraint Satisfaction Problems (CSPs) present significant challenges to artificial intelligence due to their intricate constraints and the necessity for precise solutions. Existing symbolic solvers are often slow, and prior research has shown that Large Language Models (LLMs) alone struggle with CSPs because of their complexity. To bridge this gap, we build upon the existing SOFAI architecture (or SOFAI-v1), which adapts Daniel Kahneman's ''Thinking, Fast and Slow'' cognitive model to AI. Our enhanced architecture, SOFAI-v2, integrates refined metacognitive governance mechanisms to improve adaptability across complex domains, specifically tailored for solving CSPs like graph coloring. SOFAI-v2 combines a fast System 1 (S1) based on LLMs with a deliberative System 2 (S2) governed by a metacognition module. S1's initial solutions, often limited by non-adherence to constraints, are enhanced through metacognitive governance, which provides targeted feedback and examples to adapt S1 to CSP requirements. If S1 fails to solve the problem, metacognition strategically invokes S2, ensuring accurate and reliable solutions. With empirical results, we show that SOFAI-v2 for graph coloring problems achieves a 16.98% increased success rate and is 32.42% faster than symbolic solvers., Comment: 18 Pages, 18 Figures, 3 Tables

Interfaces between twisted 2D materials host a wealth of physical phenomena originating from the long-scale periodicity associated with the resulting moire structure. Besides twisting, an alternative route to create structures with comparably long or even longer periodicities is inducing a differential strain between adjacent layers in a van der Waals (vdW) material. Despite recent theoretical efforts analyzing its benefits, this route has not yet been implemented experimentally. Here we report evidence for the simultaneous presence of ferromagnetic and antiferromagnetic regions in CrBr3 _a hallmark of moire magnetism_ from the observation of an unexpected magnetoconductance in CrBr3 tunnel barriers with ferromagnetic Fe3GeTe2 and graphene electrodes. The observed magnetoconductance evolves with temperature and magnetic field as the magnetoconductance measured in small angle CrBr3 twisted junctions, in which moire magnetism occurs. Consistent with Raman measurements and theoretical modeling, we attribute the phenomenon to the presence of a differential strain in the CrBr3 multilayer, which locally modifies the stacking and the interlayer exchange between adjacent CrBr3 layers, resulting in spatially modulated spin textures. Our conclusions indicate that inducing differential strain in vdW multilayers is a viable strategy to create moire-like superlattices, which in the future may offer in-situ continuous tunability even at low temperatures.

Jupiter Trojans preserve primitive formation characteristics due to their collisionless stable orbits. Determination of their shapes and size-frequency distribution constrains the collisional evolution of their parent population which also originated the Kuiper Belt. We started a program to find precise sizes/shapes for Trojans, combining stellar occultations and DAMIT 3D shape models. We report results for Diomedes, by fitting its dimensionless 3D model to 3 chords of a stellar occultation observed in 2020, using iterative $\chi^{2}$ procedures. The pole coordinates, rotation period, volume-equivalent radius and geometric albedo were: $\lambda$ = 153.73$^{o}$ $\pm$ 2.5$^{o}$, $\beta$ = 12.69$^{o}$ $\pm$ 2.6$^{o}$, $P$ = 24.4984 $\pm$ 0.0002 h, $R_{eq}$ = 59.4 $\pm$ 0.3 km and $p_{V}$ = 0.030 $\pm$ 0.004. A precise position was obtained too., Comment: 14 pages, 6 figures, 7 tables

We present the discovery of the fourth persistent radio source (PRS) associated with a fast radio burst (FRB). Following previous indications of a candidate PRS associated with FRB20240114A, we performed deep VLBA observations at 5 GHz to test the presence of a compact radio source within the uncertainty position of this FRB ($\pm$200 mas). We detect a component $\sim$50 mas northwards the nominal position provided by the PRECISE collaboration. The corresponding radio luminosity, together with the Faraday rotation measure provided by previous observations of the FRB, locate this PRS in the expected region of the $L$ vs |RM| relation for the nebular model, further supporting it. The comparison of the measured flux density with the respect to the values collected at lower frequency by previous studies, indicates a steepening of the radio spectrum in the 1-3 GHz range and the presence of a possible synchrotron peak at $\sim$1 GHz. Optical observations performed with the LBT could reveal that the FRB and its PRS lie at $\sim$1 kpc from the centre of the host galaxy, which is a dwarf sub-solar metallicity starburst galaxy with SFR $\sim 1 M_\odot\;\mathrm{yr^{-1}}$ and stellar mass $M\sim10^8 M_\odot$., Comment: Submitted to A&A

Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications. We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly. Furthermore, we discuss how such techniques can be combined or adapted when appropriate, highlighting their advantages and underlying rationale. We also provide a succinct summary of personalization tasks investigated in existing research, along with the evaluation metrics commonly used. Additionally, we summarize the datasets that are useful for benchmarking personalized MLLMs. Finally, we outline critical open challenges. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the development of personalized multimodal large language models.

Protein evolution involves mutations occurring across a wide range of time scales [Di Bari et al., PNAS 121, e2406807121 (2024)]. In analogy with other disordered systems, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution, simulated using a data-driven epistatic landscape. By applying spatio-temporal correlation functions inspired by statistical physics, we disentangle fluctuations originating from the ancestral protein sequence from those driven by stochastic mutations along independent evolutionary paths. Our analysis shows that, in diverse protein families, fluctuations from the ancestral sequence predominate at shorter time scales. This allows us to identify a time scale over which ancestral sequence information persists, enabling its reconstruction. We link this persistence to the strength of epistatic interactions: ancestral sequences with stronger epistatic signatures impact evolutionary trajectories over extended periods. At longer time scales, however, ancestral influence fades as epistatically constrained sites evolve collectively. To confirm this idea, we apply a standard ancestral sequence reconstruction algorithm and verify that the time-dependent recovery error is influenced by the properties of the ancestor itself.

Ultrafast X-ray spectroscopy has been revolutionized in recent years due to the advent of fourth-generation X-ray facilities. In solid-state materials, core excitons determine the energy and line shape of absorption features in core-level spectroscopies such as X-ray absorption spectroscopy. The screening of core excitons is an inherent many-body process that can reveal insight into charge-transfer excitations and electronic correlations. Under non-equilibrium conditions such as after photoexcitation, however, core-exciton screening is still not fully understood. Here we demonstrate the dynamic Coulomb screening of core excitons induced by photoexcited carriers by employing X-ray transient absorption (XTA) spectroscopy with picosecond time resolution. Our interpretation is supported by state-of-the-art ab initio theory, combining constrained and real-time time-dependent density functional theory with many-body perturbation theory. Using ZnO as an archetypal wide band-gap semiconductor, we show that the Coulomb screening by photoexcited carriers at the Zn K-edge leads to a decrease in the core-exciton binding energy, which depends nonlinearly on both the excitation density and the distribution of photoexcited carriers in reciprocal space. The effect of Coulomb screening dominates over Pauli blocking in the XTA spectra. We show that dynamic core-exciton screening is also observed at other X-ray absorption edges and theoretically predict the effect of core-exciton screening on the femtosecond time scale for the case of ZnO, a major step towards hard X-ray excitonics. The results have implications for the interpretation of ultrafast X-ray spectra in general and their use in tracking charge carrier dynamics in complex materials on atomic length scales., Comment: 43 pages, 37 figures

Density functional theory with linear combination of atomic orbitals (LCAO) basis sets is useful for studying large atomic systems, especially when it comes to computationally highly demanding time-dependent dynamics. We have implemented the Ehrenfest molecular dynamics (ED) method with the approximate approach of Tomfohr and Sankey within the projector augmented-wave code GPAW. We apply this method to small molecules as well as larger periodic systems, and elucidate its limits, advantages, and disadvantages in comparison to the existing implementation of Ehrenfest dynamics with a real-space grid representation. For modest atomic velocities, LCAO-ED shows satisfactory accuracy at a much reduced computational cost. This method will be particularly useful for modeling ion irradiation processes that require large amounts of vacuum in the simulation cell.

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ~2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the ``standard'' reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove nonlinearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses., Comment: 51 pages, 28 figures. Supporting publication of DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts., Comment: 23 pages, 14 Figures

Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to score or classify multiple visualizations choices to recommend the most effective ones, as per the statistics. However, state-of-the art models rely on very large number of expensive statistics and therefore using such models on large datasets become infeasible due to prohibitively large computational time, limiting the effectiveness of such techniques to most real world complex and large datasets. In this paper, we propose a novel reinforcement-learning (RL) based framework that takes a given vis-rec model and a time-budget from the user and identifies the best set of input statistics that would be most effective while generating the visual insights within a given time budget, using the given model. Using two state-of-the-art vis-rec models applied on three large real-world datasets, we show the effectiveness of our technique in significantly reducing time-to visualize with very small amount of introduced error. Our approach is about 10X times faster compared to the baseline approaches that introduce similar amounts of error., Comment: Accepted at PAKDD 2024 (Oral)

An increasing number of web articles engage the reader with the feeling of being immersed in the data space. However, the exact characteristics of spatial immersion in the context of visual storytelling remain vague. For example, what are the common design patterns of data stories with spatial immersion? How do they affect the reader's experience? To gain a deeper understanding of the subject, we collected 23 distinct data stories with spatial immersion, and identified six design patterns, such as cinematic camera shots and transitions, intuitive data representations, realism, naturally moving elements, direct manipulation of camera or visualization, and dynamic dimension. Subsequently, we designed four data stories and conducted a crowdsourced user study comparing three design variations (static, animated, and immersive). Our results suggest that data stories with the design patterns for spatial immersion are more interesting and persuasive than static or animated ones, but no single condition was deemed more understandable or trustworthy.

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cases involving Software Engineering tasks carried out via multimodal GPTs prompted with a mix of diagrams and natural language., Comment: 9 pages, working paper

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter $f_{\rm NL}^{\rm loc}$ via the large-scale scale-dependent bias in the power spectrum using $1,631,716$ LRGs ($0.6 < z < 1.1$) and $1,189,129$ QSOs ($0.8 < z < 3.1$). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative correction of the window function. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure $f_{\rm NL}^{\rm loc} = {-3.6}_{-9.1}^{+9.0}$ at $68\%$ confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to $f_{\rm NL}^{\rm loc} = 3.5_{-7.4}^{+10.7}$ at $68\%$ confidence. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of $2.3$.

Lead halide perovskite nanocrystals (LHP-NCs) embedded in a plastic matrix are highly promising for a variety of photonic technologies and are quickly gaining attention as ultrafast, radiation-resistant nanoscintillators for radiation detection. However, advancements in LHP-NC-based photonics are hindered by their well-known thermal instability, which makes them unsuitable for industrial thermally activated mass polymerization processes - crucial for creating polystyrene-based scintillating nanocomposites. In this study, we address this challenge by presenting the first thermal nanocomposite scintillators made from CsPbBr3 NCs passivated with fluorinated ligands that remain attached to the particles surfaces even at high temperatures, enabling their integration into mass-cured polyvinyl toluene without compromising optical properties. Consequently, these nanocomposites demonstrate scintillation light yields reaching 10,400 photons/MeV, sub-nanosecond scintillation kinetics, and remarkable radiation resilience, able to withstand gamma radiation doses of up to 1 MGy. This performance not only meets but also exceeds the scintillation of plastic scintillators, despite the radiation-induced damage to the host matrix.

The physical size of the sound horizon at recombination is a powerful source of information for early-time measurements of the Hubble constant $H_0$, and many proposed solutions to the Hubble tension therefore involve modifications to this scale. In light of this, there has been growing interest in measuring $H_0$ independently of the sound horizon. We present the first such measurement to use data from the Dark Energy Spectroscopic Instrument (DESI), jointly analyzing the full-shape galaxy power spectra of DESI luminous red galaxies, emission line galaxies, quasars, and the bright galaxy sample, in a total of six redshift bins. Information from the sound horizon scale is removed from our constraints via a rescaling procedure at the power spectrum level, with our sound horizon-marginalized measurement being driven instead primarily by the matter-radiation equality scale. This measurement is then combined with additional sound horizon-free information from Planck+ACT CMB lensing, uncalibrated type Ia supernovae, and the DESI Lyman-$\alpha$ forest. We agnostically combine with the DESY5, Pantheon+, and Union3 supernova datasets, with our tightest respective constraints being $H_0=66.7^{+1.7}_{-1.9},~67.9^{+1.9}_{-2.1},$ and $67.8^{+2.0}_{-2.2}$ km s-1 Mpc-1. This corresponds to a sub-3% sound horizon-free constraint of the Hubble constant, and is the most precise measurement of its kind to date. Even without including information from the sound horizon, our measurement is still in 2.2-3.0$\sigma$ tension with SH0ES. Additionally, the consistency between our result and other measurements that do rely on the sound horizon scale provides no evidence for new early-Universe physics (e.g. early dark energy). Future DESI data releases will allow unprecedented measurements of $H_0$ and place strong constraints on models that use beyond-$\Lambda$CDM physics to ameliorate the Hubble tension., Comment: 17+7 pages; 5 figures. Submitted to JCAP

Comets offer valuable insights into the early Solar System's conditions and processes. Stellar occultations enables detailed study of cometary nuclei typically hidden by their coma. Observing the star's light passing through the coma helps infer dust's optical depth near the nucleus and determine dust opacity detection limits. 29P/Schwassmann-Wachmann 1, a Centaur with a diameter of approximately 60 km, lies in a region transitioning from Centaurs to Jupiter-Family comets. Our study presents the first-ever observed occultation by 29P, allowing in the future a more refined orbit and thus better predictions for other occultations. The light curve reveals a solid-body detection lasting $3.65\pm0.05$ seconds, corresponding to a chord length of approximately 54 km. This provides a lower limit for the object's radius, measured at $27.0\pm0.7$ km. We identified features on both sides of the main-body occultation around 1,700 km from the nucleus in the sky plane for which upper limits on apparent opacity and equivalent width were determined. Gradual dimming within 23 km of the nucleus during ingress only is interpreted as a localised dust cloud/jet above the surface, with an optical depth of approximately $\tau \sim 0.18$., Comment: 16 pages. 7 figures. 1 table

The mean free path of ionizing photons for neutral hydrogen ($\lambda_\mathrm{mfp}^{912}$) is a crucial quantity in modelling the ionization state of the intergalactic medium (IGM) and the extragalactic ultraviolet background (EUVB), and is widely used in hydrodynamical simulations of galaxies and reionization. We construct the largest quasar spectrum dataset to date -- 12,595 $\mathrm{S/N}>3$ spectra -- using the Y1 observation of Dark Energy Spectroscopic Instrument (DESI) to make the most precise model-independent measurement of the mean free path at $3.2\leq z\leq 4.6$. By stacking the spectra in 17 redshift bins and modelling the Lyman continuum profile, we get a redshift evolution $\lambda_\mathrm{mfp}^{912}\propto(1+z)^{-4.27}$ at $2\leq z\leq 5$, which is much shallower than previous estimates. We then explore the sources of systematic bias, including the choice of intrinsic quasar continuum, the consideration of Lyman series opacity and Lyman limit opacity evolution and the definition of $\lambda_\mathrm{mfp}^{912}$. Combining our results with estimates of $\lambda_\mathrm{mfp}^{912}$ at higher redshifts, we conclude at high confidence that the evolution in $\lambda_\mathrm{mfp}^{912}$ steepens at $z \approx 5$. We interpret this inflection as the transition from the end of HI reionization to a fully ionized plasma which characterizes the intergalactic medium of the past $\sim10$ billion years., Comment: 19 pages, 12 figures, submitted to ApJL. Comments are welcome!

In galaxy survey analysis, the observed clustering statistics do not directly match theoretical predictions but rather have been processed by a window function that arises from the survey geometry including the sky footprint, redshift-dependent background number density and systematic weights. While window convolution of the power spectrum is well studied, for the bispectrum with a larger number of degrees of freedom, it poses a significant numerical and computational challenge. In this work, we consider the effect of the survey window in the tripolar spherical harmonic decomposition of the bispectrum and lay down a formal procedure for their convolution via a series expansion of configuration-space three-point correlation functions, which was first proposed by Sugiyama et al. (2019). We then provide a linear algebra formulation of the full window convolution, where an unwindowed bispectrum model vector can be directly premultiplied by a window matrix specific to each survey geometry. To validate the pipeline, we focus on the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) luminous red galaxy (LRG) sample in the South Galactic Cap (SGC) in the redshift bin $0.4 \leqslant z \leqslant 0.6$. We first perform convergence checks on the measurement of the window function from discrete random catalogues, and then investigate the convergence of the window convolution series expansion truncated at a finite of number of terms as well as the performance of the window matrix. This work highlights the differences in window convolution between the power spectrum and bispectrum, and provides a streamlined pipeline for the latter for current surveys such as DESI and the Euclid mission., Comment: 30 pages, 12 figures, for submission to JCAP

In networked systems, the interplay between the dynamics of individual subsystems and their network interactions has been found to generate multistability in various contexts. Despite its ubiquity, the specific mechanisms and ingredients that give rise to multistability from such interplay remain poorly understood. In a network of coupled excitable units, we show that this interplay generating multistability occurs through a competition between the units' transient dynamics and their coupling. Specifically, the diffusive coupling between the units manages to reinject them in the excitability region of their individual state space and effectively trap them there. We show that this trapping mechanism leads to the coexistence of multiple types of oscillations: periodic, quasiperiodic, and even chaotic, although the units separately do not oscillate. Interestingly, we show that the attractors emerge through different types of bifurcations - in particular, the periodic attractors emerge through either saddle-node of limit cycles bifurcations or homoclinic bifurcations - but in all cases the reinjection mechanism is present., Comment: Submitted to Chaos

The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full Shape, two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on en ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full Shape analyses, which motivates the use of a corrected mock covariance for DESI's Full Shape analysis., Comment: 23 pages, 5 figures 7 tables, submitted to JCAP

We present cosmological constraints on deviations from general relativity (GR) from the first-year of clustering observations from the Dark Energy Spectroscopic Instrument (DESI) in combination with other datasets. We first consider the $\mu(a,k)$-$\Sigma(a,k)$ modified gravity (MG) parametrization (as well as $\eta(a,k)$) in flat $\Lambda$CDM and $w_0 w_a$CDM backgrounds. Using a functional form for time-only evolution gives $\mu_0= 0.11^{+0.44}_{-0.54}$ from DESI(FS+BAO)+BBN and a wide prior on $n_{s}$. Using DESI(FS+BAO)+CMB+DESY3+DESY5-SN, we obtain $\mu_0 = 0.05\pm 0.22$ and $\Sigma_0 = 0.009\pm 0.045$ in the $\Lambda$CDM background. In $w_0 w_a$CDM, we obtain $\mu_0 =-0.24^{+0.32}_{-0.28}$ and $\Sigma_0 = 0.006\pm 0.043$, consistent with GR, and we still find a preference of the data for dynamical dark energy with $w_0>-1$ and $w_a<0$. We then use binned forms in the two backgrounds starting with two bins in redshift and then combining them with two bins in scale for a total of 4 and 8 MG parameters, respectively. All MG parameters are found consistent with GR. We also find that the tension reported for $\Sigma_0$ with GR when using Planck PR3 goes away when we use the recent LoLLiPoP+HiLLiPoP likelihoods. As noted previously, this seems to indicate that the tension is related to the CMB lensing anomaly in PR3 which is also alleviated when using these likelihoods. We then constrain the class of Horndeski theory in the effective field theory of dark energy. We consider both EFT-basis and $\alpha$-basis. Assuming a power law parametrization for the function $\Omega$, which controls non-minimal coupling, we obtain $\Omega_0 = 0.0120^{+0.0021}_{-0.013}$ and $s_0 = 0.99^{+0.54}_{-0.20}$ from DESI(FS+BAO)+DESY5SN+CMB in a $\Lambda$CDM background. Similar results are obtained when using the $\alpha$-basis, where we constrain $c_M<1.24$, and are all consistent with GR. [Abridged.], Comment: 52 pages, 10 figures. This DESI Collaboration Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present an in-depth analysis of the fiber assignment incompleteness in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). This incompleteness is caused by the restricted mobility of the robotic fiber positioner in the DESI focal plane, which limits the number of galaxies that can be observed at the same time, especially at small angular separations. As a result, the observed clustering amplitude is suppressed in a scale-dependent manner, which, if not addressed, can severely impact the inference of cosmological parameters. We discuss the methods adopted for simulating fiber assignment on mocks and data. In particular, we introduce the fast fiber assignment (FFA) emulator, which was employed to obtain the power spectrum covariance adopted for the DR1 full-shape analysis. We present the mitigation techniques, organised in two classes: measurement stage and model stage. We then use high fidelity mocks as a reference to quantify both the accuracy of the FFA emulator and the effectiveness of the different measurement-stage mitigation techniques. This complements the studies conducted in a parallel paper for the model-stage techniques, namely the $\theta$-cut approach. We find that pairwise inverse probability (PIP) weights with angular upweighting recover the "true" clustering in all the cases considered, in both Fourier and configuration space. Notably, we present the first ever power spectrum measurement with PIP weights from real data., Comment: 42 pages, 19 figures

Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as imaging systematics. We present the imaging systematics mitigation applied to the DESI Data Release 1 (DR1) large-scale structure catalogs used in the DESI 2024 cosmological analyses. We also explore extensions of the fiducial treatment. This includes a combined approach, through forward image simulations in conjunction with neural network-based regression, to obtain an angular selection function that mitigates the imaging systematics observed in the DESI DR1 ELGs target density. We further derive a line-of-sight selection function from the forward model that removes the strong redshift dependence between imaging systematics and low redshift ELGs. Combining both angular and redshift-dependent systematics, we construct a 3D selection function and assess the impact of all selection functions on clustering statistics. We quantify differences between these extended treatments and the fiducial treatment in terms of the measured 2-point statistics. We find that the results are generally consistent with the fiducial treatment and conclude that the differences are far less than the imaging systematics uncertainty included in DESI 2024 full-shape measurements. We extend our investigation to the ELGs at $0.6

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

We analyse the robustness of the DESI 2024 cosmological inference from fits to the full shape of the galaxy power spectrum to uncertainties in the Halo Occupation Distribution (HOD) model of the galaxy-halo connection and the choice of priors on nuisance parameters. We assess variations in the recovered cosmological parameters across a range of mocks populated with different HOD models and find that shifts are often greater than 20% of the expected statistical uncertainties from the DESI data. We encapsulate the effect of such shifts in terms of a systematic covariance term, $\mathsf{C}_{\rm HOD}$, and an additional diagonal contribution quantifying the impact of our choice of nuisance parameter priors on the ability of the effective field theory (EFT) model to correctly recover the cosmological parameters of the simulations. These two covariance contributions are designed to be added to the usual covariance term, $\mathsf{C}_{\rm stat}$, describing the statistical uncertainty in the power spectrum measurement, in order to fairly represent these sources of systematic uncertainty. This approach is more general and robust to choices of model free parameters or additional external datasets used in cosmological fits than the alternative approach of adding systematic uncertainties at the level of the recovered marginalised parameter posteriors. We compare the approaches within the context of a fixed $\Lambda$CDM model and demonstrate that our method gives conservative estimates of the systematic uncertainty that nevertheless have little impact on the final posteriors obtained from DESI data., Comment: This DESI Collaboration Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 26 pages, 10 figures

We report on tip-enhanced Raman scattering (TERS) of H2 and D2 molecules physisorbed within a plasmonic picocavity at a cryogenic temperature (10 K). The intense Raman peaks resulting from the rotational and vibrational transitions are observed at sub-nanometer gap distances of the junction formed by a Ag tip and Ag(111) surface. We clarify that the predominant contribution of the electromagnetic field enhancement of the picocavity to the detection of a single hydrogen molecule. The gap-distance dependent TERS reveals not only the evolution of the picocavity field, but also the interaction between the molecule and tip/surface, which exhibit nontrivial isotope effects. A significant red-shift and peak broadening of the H-H stretching as the gap distance decreases, while the D-D stretching mode is unaffected. A combination of density functional theory and reduced-dimension models reveals that a distinct anharmonicity in the mode potential of H2 is one cause of the anomalous red-shift, whereas D2 has less anharmonicity due to the geometric isotope effect.

Coupled cluster theory in the standard formulation is unable to correctly describe conical intersections among states of the same symmetry. This limitation has restricted the practical application of an otherwise highly accurate electronic structure model, particularly in nonadiabatic dynamics. Recently, the intersection problem among the excited states was fully characterized and resolved. However, intersections with the ground state remain an open challenge, and addressing this problem is our objective here. We present a generalized coupled cluster framework that correctly accounts for the geometric phase effect and avoids bifurcations of the solutions to the ground state equations. Several applications are presented that demonstrate the correct description of ground state conical intersections. We also propose how the framework can be used for other electronic-structure methods., Comment: 45 pages, 9 figures

In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table, or text to the user for the specific question. For this, we conduct a user study where users are shown a question and asked what they would prefer to see and used the data to establish that a user's personal traits does influence the data outputs that they prefer. Understanding how user characteristics impact a user's preferences is critical to creating data tools with a better user experience. Additionally, we investigate to what degree an LLM can be used to replicate a user's preference with and without user preference data. Overall, these findings have significant implications pertaining to the development of data tools and the replication of human preferences using LLMs. Furthermore, this work demonstrates the potential use of LLMs to replicate user preference data which has major implications for future user modeling and personalization research.

The adaptation to users' preferences and the ability to infer and interpret humans' beliefs and intents, which is known as the Theory of Mind (ToM), are two crucial aspects for achieving effective human-robot collaboration. Despite its importance, very few studies have investigated the impact of adaptive robots with ToM abilities. In this work, we present an exploratory comparative study to investigate how social robots equipped with ToM abilities impact users' performance and perception. We design a two-layer architecture. The Q-learning agent on the first layer learns the robot's higher-level behaviour. On the second layer, a heuristic-based ToM infers the user's intended strategy and is responsible for implementing the robot's assistance, as well as providing the motivation behind its choice. We conducted a user study in a real-world setting, involving 56 participants who interacted with either an adaptive robot capable of ToM, or with a robot lacking such abilities. Our findings suggest that participants in the ToM condition performed better, accepted the robot's assistance more often, and perceived its ability to adapt, predict and recognise their intents to a higher degree. Our preliminary insights could inform future research and pave the way for designing more complex computation architectures for adaptive behaviour with ToM capabilities.

Large Language Models (LLMs) have significantly advanced code generation but often require substantial resources and tend to over-generalize, limiting their efficiency for specific tasks. Fine-tuning smaller, open-source LLMs presents a viable alternative; however, it typically lags behind cutting-edge models due to supervised fine-tuning's reliance solely on correct code examples, which restricts the model's ability to learn from its own mistakes and adapt to diverse programming challenges. To bridge this gap, we introduce CodeLutra, a novel framework that enhances low-performing LLMs by leveraging both successful and failed code generation attempts. Unlike conventional fine-tuning, CodeLutra employs an iterative preference learning mechanism to compare correct and incorrect solutions as well as maximize the likelihood of correct codes. Through continuous iterative refinement, CodeLutra enables smaller LLMs to match or surpass GPT-4's performance in various code generation tasks without relying on vast external datasets or larger auxiliary models. On a challenging data analysis task, using just 500 samples improved Llama-3-8B's accuracy from 28.2% to 48.6%, approaching GPT-4's performance. These results highlight CodeLutra's potential to close the gap between open-source and closed-source models, making it a promising approach in the field of code generation., Comment: 18 pages, 4 figures

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems often results in an exponential overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems; however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry groups into the simulation of many-body systems on quantum computers. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and ab initio electronic structure in quantum chemistry. We execute a symmetry-adapted quantum subroutine for small molecules in first quantization on noisy hardware, and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreducible representations. In addition, we present a discussion of major open problems regarding the use of symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries for practical quantum advantage. The broad applicability and the efficiency of the proposed symmetry-adapted subroutine holds the promise for exponential speedup in quantum simulation of many-body systems., Comment: 26 pages, 18 figures

The purpose of this paper is to characterize one-dimensional local domains, or more in general reduced, in terms of its Macaulay's inverse system. This leads to study almost finitely generated modules in the divided power ring. We specialize the results to a numerical semigroup ring by computing explicitly its inverse system. In the graded case we characterize reduced arithmetically Gorenstein $0$-dimensional schemes.

We present two cases of Ly$\alpha$ changing-look (CL) quasars (J1306 and J1512) along with two additional candidates (J1511 and J1602), all discovered serendipitously at $z >2$ through the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). It is the first time to capture CL events in Ly$\alpha$ at high redshift, which is crucial for understanding underlying mechanisms driving the CL phenomenon and the evolution of high-redshift quasars and galaxies. The variability of all four sources is confirmed by the significant change of amplitude in the $r$ band ($|r_{\rm DESI}-r_{\rm SDSS}| >0.5 \ \rm mag$). We find that the accretion rate in the dim state for these CL objects corresponds to a relatively low value ($\mathscr{\dot M} \approx 2\times10^{-3}$), which suggests that the inner region of the accretion disk might be in transition between the Advection Dominated Accretion Flow ($\mathscr{\dot M}<10^{-3}\sim 10^{-2}$) and the canonical accretion disk (optically thick, geometrically thin). However, unlike in C {\sc iv} CL quasars in which broad Ly$\alpha$ remained, the broad C {\sc iv} may still persist after a CL event occurs in Ly$\alpha$, making the physical origin of the CL and ionization mechanism event more puzzling and interesting.

Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly-scoped environments, we argue that it presents two major challenges when deploying LLM agents in real-world scenarios: (1) selecting from a fixed set of actions significantly restricts the planning and acting capabilities of LLM agents, and (2) this approach requires substantial human effort to enumerate and implement all possible actions, which becomes impractical in complex environments with a vast number of potential actions. In this work, we propose an LLM agent framework that enables the dynamic creation and composition of actions in an online manner. In this framework, the agent interacts with the environment by generating and executing programs written in a general-purpose programming language at each step. Furthermore, generated actions are accumulated over time for future reuse. Our extensive experiments on the GAIA benchmark demonstrate that this framework offers significantly greater flexibility and outperforms previous methods. Notably, it allows an LLM agent to recover in scenarios where no relevant action exists in the predefined set or when existing actions fail due to unforeseen edge cases. At the time of writing, we hold the top position on the GAIA public leaderboard. Our code can be found in \href{https://github.com/adobe-research/dynasaur}{https://github.com/adobe-research/dynasaur}., Comment: 15 pages, 8 figures

Large multimodal models (LMMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page, visually-rich documents. Traditional methods using document parsers for retrieval-augmented generation suffer from performance and efficiency limitations, while directly presenting all pages to LMMs leads to inefficiencies, especially with lengthy documents. In this work, we present a novel framework named LoRA-Contextualizing Adaptation of Large multimodal models (LoCAL), which broadens the capabilities of any LMM to support long-document understanding. We demonstrate that LMMs can effectively serve as multimodal retrievers, fetching relevant pages to answer user questions based on these pages. LoCAL is implemented with two specific LMM adapters: one for evidence page retrieval and another for question answering. Empirical results show state-of-the-art performance on public benchmarks, demonstrating the effectiveness of LoCAL., Comment: Currently Under Review

Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality makes them particularly suited to run on current quantum processors, the task of verification at non-trivial scales is complicated for non-integrable systems. Here, we study a special class of maximally chaotic circuits known as dual unitary circuits -- exhibiting unitarity in both space and time -- that are known to have exact analytical solutions for certain correlation functions. With advances in noise learning and the implementation of novel error mitigation methods, we show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators. We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point, and compare our results to classical approximations with tensor networks. These results cement error-mitigated digital quantum simulation on pre-fault-tolerant quantum processors as a trustworthy platform for the exploration and discovery of novel emergent quantum many-body phases., Comment: 7 + 23 pages, 3 + 17 figures, 0 + 2 tables

Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence of QA datasets that provide fine-grained reasoning structures. To address this gap, we introduce the Graph Reasoning-Structured Question Answering Dataset (GRS-QA), which includes both semantic contexts and reasoning structures for QA pairs. Unlike existing M-QA datasets, where different reasoning structures are entangled together, GRS-QA explicitly captures intricate reasoning pathways by constructing reasoning graphs, where nodes represent textual contexts and edges denote logical flows. These reasoning graphs of different structures enable a fine-grained evaluation of LLM reasoning capabilities across various reasoning structures. Our empirical analysis reveals that LLMs perform differently when handling questions with varying reasoning structures. This finding facilitates the exploration of textual structures as compared with semantics., Comment: 15 pages, 24 figures, 10 tables

We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog with an overlapping SDSS void catalog, we find generally consistent void properties but significant differences in the void volume overlap, which we attribute to differences in the galaxy selection and survey masks. These catalogs are suitable for studying the variation in galaxy properties with cosmic environment and for cosmological studies., Comment: 17 pages, 6 figures

Using early data from the Dark Energy Spectroscopic Instrument (DESI) survey, we search for AGN signatures in 410,757 line-emitting galaxies. By employing the BPT emission-line ratio diagnostic diagram, we identify AGN in 75,928/296,261 ($\approx$25.6%) high-mass ($\log (M_{\star}/\rm M_{\odot}) >$ 9.5) and 2,444/114,496 ($\approx$2.1%) dwarf ($\log (M_{\star}/\rm M_{\odot}) \leq$ 9.5) galaxies. Of these AGN candidates, 4,181 sources exhibit a broad H$\alpha$ component, allowing us to estimate their BH masses via virial techniques. This study more than triples the census of dwarf AGN as well as that of intermediate-mass black hole (IMBH; $M_{\rm BH} \le 10^6~\rm M_{\odot}$) candidates, spanning a broad discovery space in stellar mass (7 $< \log (M_{\star}/\rm M_{\odot}) <$ 12) and redshift (0.001 $< \rm z <$ 0.45). The observed AGN fraction in dwarf galaxies ($\approx$2.1%) is nearly four times higher than prior estimates, primarily due to DESI's smaller fiber size, which enables the detection of lower luminosity dwarf AGN candidates. We also extend the $M_{\rm BH}$ - $M_{\star}$ scaling relation down to $\log (M_{\star}/\rm M_{\odot}) \approx$ 8.5 and $\log (M_{\rm BH}/M_{\odot}) \approx$ 4.4, with our results aligning well with previous low-redshift studies. The large statistical sample of dwarf AGN candidates from current and future DESI releases will be invaluable for enhancing our understanding of galaxy evolution at the low-mass end of the galaxy mass function., Comment: 35 pages, 22 figures, Submitted to AAS Journals, Comments are welcome

This paper introduces a graph-based algorithm for solving single-item, single-location inventory lot-sizing problems under non-stationary stochastic demand using the $(R_t, S_t)$ policy and a penalty cost scheme. The proposed method relaxes the original mixed-integer linear programming (MILP) model by eliminating non-negative order quantity constraints and formulating it as a shortest-path problem on a weighted directed acyclic graph. A repetitive augmentation procedure is proposed to resolve any infeasibility in the solution. This procedure consists of three stages: (1) filtration, (2) repeated augmentation by redirecting, reconnecting, and duplicating between newly introduced and existing nodes to adjust the graph and eliminate negative replenishment orders, and (3) re-optimising. The effectiveness and computational efficiency of the proposed approach are assessed through extensive experiments on 1,620 test instances across various demand patterns and parameter settings. The results show that 195 instances required augmentation, mainly those with high penalty costs, low fixed ordering costs, large demand variability, and extended planning horizons. The efficiency of the algorithm for instances with extended planning horizon scenarios demonstrates its suitability for use in real-world scenarios.