Search

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) tags neutrons via their capture on gadolinium or hydrogen, which release $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the key features and the first results of the XENONnT NV when operated with demineralized water in the initial phase of the experiment. Its efficiency for detecting neutrons is $(82\pm 1)\,\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of $(53\pm 3)\,\%$ for the tagging of WIMP-like neutron signals, inside a tagging time window of $250\,\mathrm{\mu s}$ between TPC and NV, leading to a livetime loss of $1.6\,\%$ during the first science run of XENONnT.

Orphan gamma-ray burst afterglows are good candidates to learn more about the GRB physics and progenitors or for the development of multi-messenger analysis with gravitational waves. Our objective is to identify orphan afterglows in Rubin LSST data, by using the characteristic features of their light curves. In this work, we generated a population of short GRBs based on the Swift SBAT4 catalogue, and we simulated their off-axis afterglow light curves with afterglowpy. We then used the rubin_sim package to simulate observations of these orphan afterglows with Rubin LSST and proceeded with the characterisation of orphan light curves by extracting a number of parameters. The same parameters are computed for the ELAsTiCC (Extended LSST Astronomical Time-series Classification Challenge) data set, a simulated alert stream of the Rubin LSST data. We then started to develop a machine learning filter able to discriminate orphan-like events among all the variable objects. We present here the performance of our filter as implemented in the Fink broker and tested on the ELAsTiCC data set and our own Rubin pseudo-observation simulations., Comment: Contribution to the 2024 Very High Energy Phenomena in the Universe session of the 58th Rencontres de Moriond

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within 10-20\,MeV/$c^2$ and electron absorption of axion-like particles and dark photons for $m_\chi$ below 0.186\,keV/$c^2$., Comment: 9 pages, 5 figures

To date there is little publicly available scientific data on Unidentified Aerial Phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal ground-based observatory to continuously monitor the sky and conduct a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave infrared FLIR Boson 640 cameras. Their calibration includes a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance-Broadcast (ADS-B) data. We establish a first baseline for the system performance over five months of field operation, using a real-world dataset derived from ADS-B data, synthetic 3-D trajectories, and a hand-labelled real-world dataset. We report acceptance rates (e.g. viewable airplanes that are recorded) and detection efficiencies (e.g. recorded airplanes which are successfully detected) for a variety of weather conditions, range and aircraft size. We reconstruct $\sim$500,000 trajectories of aerial objects from this commissioning period. A toy outlier search focused on large sinuosity of the 2-D reconstructed trajectories flags about 16% of trajectories as outliers. After manual review, 144 trajectories remain ambiguous: they are likely mundane objects but cannot be elucidated at this stage of development without distance and kinematics estimation or other sensor modalities. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers count for the five-month interval at a 95% confidence level. This likelihood-based method to evaluate significance is applicable to all of our future outlier searches.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

A fundamental challenge in modern physics is bridging the gap between an exact description of few-body microscopic systems and the emergent description of many-body macroscopic systems. This gap may be bridged by tracing the properties of well-controlled intermediate-size mesoscopic systems. Here, we study mesoscopic signatures of a spatial self-organization phase transition in deterministically prepared arrays of between 10 and 22 atoms inside an optical cavity. Through precise engineering of the atom-cavity interactions, we reveal how critical behavior depends on atom number. We identify characteristic dynamical behaviors related to symmetry breaking and system size in the self-organized regime, and observe a finite optomechanical susceptibility at the critical point. This work opens the door to probing particle-number- and time-resolved properties of phase transitions in mesoscopic systems., Comment: 21 pages, 9 figures

This report summarizes what has happened in the mini-workshops entitled ''My Earth in 180 minutes'' organized during the lunch break at the SF2A 2024 conference in Marseille. The project showcased an innovative serious game designed to raise awareness of greenhouse gas (GHG) emissions in astronomical research laboratories. Participants, organized into teams, simulate strategies to reduce their carbon footprints by 50\%, focusing on key astronomical activities such as space instrumentation, data analysis, and laboratory work. The sessions highlight the challenges of achieving significant emissions reductions without disrupting core research activities, such as telescope observations. While the serious game facilitates important discussions on sustainable practices, the results point to the need for broader engagement, adaptation to different cultural contexts, and institutional support. The project highlights the importance of integrating climate action into the academic environment and suggests potential future directions for expanding its impact., Comment: SF2A 2024, Jun 2024, Marseille, France

We introduce "visual writing", an approach to writing stories by manipulating visuals instead of words. Visual writing relies on editable visual representations of time, entities, events, and locations to offer representations more suited to specific editing tasks. We propose a taxonomy for these representations and implement a prototype software supporting the visual writing workflow. The system allows writers to edit the story by alternating between modifying the text and manipulating visual representations to edit entities, actions, locations, and order of events. We evaluate this workflow with eight creative writers and find visual writing can help find specific passages, keep track of story elements, specify edits, and explore story variations in a way that encourages creativity.

We propose the use of collective states of matter as a resource for the deterministic generation of quantum states of light, which are fundamental for quantum information technologies. Our minimal model consists of three emitters coupled to a half-waveguide, i.e., a one-dimensional waveguide terminated by a mirror. Photon-mediated interactions between the emitters result in the emergence of bright and dark states. The dark states form a decoherence-free subspace, protected from dissipation. Local driving of the emitters and control of their resonance frequencies allows to perform arbitrary quantum gates within the decoherence-free subspace. Coupling to bright states facilitates photon emission, thereby enabling the realization of quantum gates between light and matter. We demonstrate that sequential application of these gates leads to the generation of photonic entangled states, such as Greenberger-Horne-Zeilinger and one- and two-dimensional cluster states.

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time., Comment: 10 Figures, 3 Tables, 23 Pages (incl. references)

The detection of B-modes in the CMB polarization pattern is a major issue in modern cosmology and must therefore be handled with analytical methods that produce reliable results. We describe a method that uses the frequency dependency of the QUBIC synthesized beam to perform component separation at the map-making stage, to obtain more precise results. We aim to demonstrate the feasibility of component separation during the map-making stage in time domain space. This new technique leads to a more accurate description of the data and reduces the biases in cosmological analysis. The method uses a library for highly parallel computation which facilitates the programming and permits the description of experiments as easily manipulated operators. These operators can be combined to obtain a joint analysis using several experiments leading to maximized precision. The results show that the method works well and permits end-to-end analysis for the CMB experiments, and in particular, for QUBIC. The method includes astrophysical foregrounds, and also systematic effects like gain variation in the detectors. We developed a software pipeline that produces uncertainties on tensor-to-scalar ratio at the level of $\sigma(r) \sim 0.023$ using only QUBIC simulated data., Comment: 15 pages, 13 figures

We search for dark matter (DM) with a mass [3,12] $\mathrm{GeV} / c^2$ using an exposure of 3.51 $\mathrm{t} \times \mathrm{y}$ with the XENONnT experiment. We consider spin-independent, spin-dependent, momentum-dependent, mirror DM, and self-interacting DM with a light mediator coupling to Standard Model particles. Using a lowered energy threshold compared to the previous WIMP search, a blind analysis of [0.5, 5.0] $\mathrm{keV}$ nuclear recoil events reveals no significant signal excess over the background. XENONnT excludes spin-independent DM-nucleon cross sections $>2.5 \times 10^{-45} \mathrm{~cm}^2$ at $90 \%$ confidence level for 6 $\mathrm{GeV} / c^2$ DM. The solar ${ }^8 \mathrm{B}$ neutrino coherent elastic neutrino-nucleus scattering background accounts for approximately half of the background in the signal region. In the considered mass range, the DM sensitivity approaches the 'neutrino fog', the limitation where neutrinos produce a signal that is indistinguishable from that of light DM-xenon nucleus scattering.

We explore how interactions inspired by drawing software can help edit text. Making an analogy between visual and text editing, we consider words as pixels, sentences as regions, and tones as colours. For instance, direct manipulations move, shorten, expand, and reorder text; tools change number, tense, and grammar; colours map to tones explored along three dimensions in a tone picker; and layers help organize and version text. This analogy also leads to new workflows, such as boolean operations on text fragments to construct more elaborated text. A study shows participants were more successful at editing text and preferred using the proposed interface over existing solutions. Broadly, our work highlights the potential of interaction analogies to rethink existing workflows, while capitalizing on familiar features.

Jupiter's icy moon Ganymede has a tenuous exosphere produced by sputtering and possibly sublimation of water ice. To date, only atomic hydrogen and oxygen have been directly detected in this exosphere. Here, we present observations of Ganymede's CO$_2$ exosphere obtained with the James Webb Space Telescope. CO$_2$ gas is observed over different terrain types, mainly over those exposed to intense Jovian plasma irradiation, as well as over some bright or dark terrains. Despite warm surface temperatures, the CO$_2$ abundance over equatorial subsolar regions is low. CO$_2$ vapor has the highest abundance over the north polar cap of the leading hemisphere, reaching a surface pressure of 1 pbar. From modeling we show that the local enhancement observed near 12 h local time in this region can be explained by the presence of cold traps enabling CO$_2$ adsorption. However, whether the release mechanism in this high-latitude region is sputtering or sublimation remains unclear. The north polar cap of the leading hemisphere also has unique surface-ice properties, probably linked to the presence of the large atmospheric CO2 excess over this region. These CO2 molecules might have been initially released in the atmosphere after the radiolysis of CO$_2$ precursors, or from the sputtering of CO$_2$ embedded in the H$_2$O ice bedrock. Dark terrains (regiones), more widespread on the north versus south polar regions, possibly harbor CO$_2$ precursors. CO$_2$ molecules would then be redistributed via cold trapping on ice-rich terrains of the polar cap and be diurnally released and redeposited on these terrains. Ganymede's CO$_2$ exosphere highlights the complexity of surface-atmosphere interactions on Jupiter's icy Galilean moons., Comment: 21 pages, 21 figures, Accepted as a Letter in Astronomy and Astrophysics

Autonomous precision navigation to land onto the Moon relies on vision sensors. Computer vision algorithms are designed, trained and tested using synthetic simulations. High quality terrain models have been produced by Moon orbiters developed by several nations, with resolutions ranging from tens or hundreds of meters globally down to few meters locally. The SurRender software is a powerful simulator able to exploit the full potential of these datasets in raytracing. New interfaces include tools to fuse multi-resolution DEMs and procedural texture generation. A global model of the Moon at 20m resolution was integrated representing several terabytes of data which SurRender can render continuously and in real-time. This simulator will be a precious asset for the development of future missions., Comment: 8 pages, 6 figures, Proceedings of the 73rd International Astronautical Congress (IAC), Paris, France, 18-22 November 2022. Contribution reference: IAC-22,A3,IP,44,x71795

The XENONnT experiment, located at the INFN Laboratori Nazionali del Gran Sasso, Italy, features a 5.9 tonne liquid xenon time projection chamber surrounded by an instrumented neutron veto, all of which is housed within a muon veto water tank. Due to extensive shielding and advanced purification to mitigate natural radioactivity, an exceptionally low background level of (15.8 $\pm$ 1.3) events/(tonne$\cdot$year$\cdot$keV) in the (1, 30) keV region is reached in the inner part of the TPC. XENONnT is thus sensitive to a wide range of rare phenomena related to Dark Matter and Neutrino interactions, both within and beyond the Standard Model of particle physics, with a focus on the direct detection of Dark Matter in the form of weakly interacting massive particles (WIMPs). From May 2021 to December 2021, XENONnT accumulated data in rare-event search mode with a total exposure of one tonne $\cdot$ year. This paper provides a detailed description of the signal reconstruction methods, event selection procedure, and detector response calibration, as well as an overview of the detector performance in this time frame. This work establishes the foundational framework for the `blind analysis' methodology we are using when reporting XENONnT physics results., Comment: 27 pages, 23 figures

We present the first measurement of nuclear recoils from solar $^8$B neutrinos via coherent elastic neutrino-nucleus scattering with the XENONnT dark matter experiment. The central detector of XENONnT is a low-background, two-phase time projection chamber with a 5.9 t sensitive liquid xenon target. A blind analysis with an exposure of 3.51 t$\times$yr resulted in 37 observed events above 0.5 keV, with ($26.4^{+1.4}_{-1.3}$) events expected from backgrounds. The background-only hypothesis is rejected with a statistical significance of 2.73 $\sigma$. The measured $^8$B solar neutrino flux of $(4.7_{-2.3}^{+3.6})\times 10^6 \mathrm{cm}^{-2}\mathrm{s}^{-1}$ is consistent with results from the Sudbury Neutrino Observatory. The measured neutrino flux-weighted CE$\nu$NS cross section on Xe of $(1.1^{+0.8}_{-0.5})\times10^{-39} \mathrm{cm}^2$ is consistent with the Standard Model prediction. This is the first direct measurement of nuclear recoils from solar neutrinos with a dark matter detector.

This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.

We present a theoretical framework for 'collective transition quenching', a quantum many-body dissipative phenomenon that occurs in systems with multiple collective decay channels. Despite the competition, interactions suppress all but the dominant decay transition, leading to a 'winner takes all' dynamic where the system primarily settles into the dominant ground state. We prove that, in the presence of permutation symmetry, this problem is exactly solvable for any number of competing channels. Additionally, we develop an approximate model for the dynamics by mapping the evolution into a continuity equation for a fluid, and show analytically that the dominant transition ratio converges to unity with increasing system size as a power-law, for any branching ratio. This near-deterministic preparation of the dominant ground state has broad applicability. As an example we discuss a protocol for molecular photoassociation where collective dynamics effectively acts as a catalyst, amplifying the yield in a particular final state. Our results open new avenues for many-body strategies in the preparation and control of quantum systems., Comment: 5 pages, 3 figures

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run., Comment: 32 pages, 19 figures

In this paper, we revisit the Dressing Field Method (DFM) in the context of Quantum (Gauge) Field Theories (QFT). In order to adapt this method to the functional path integral formalism of QFT, we depart from the usual differential geometry approach used so far to study the DFM which also allows to tackle the infinite dimension of the field spaces. Our main result is that gauge fixing is an instance of the application of the DFM. The Faddeev-Popov gauge fixing procedure and the so-called unitary gauge are revisited in light of this result., Comment: 19 pages

When training deep learning models, the performance depends largely on the selected hyperparameters. However, hyperparameter optimization (HPO) is often one of the most expensive parts of model design. Classical HPO methods treat this as a black-box optimization problem. However, gray-box HPO methods, which incorporate more information about the setup, have emerged as a promising direction for more efficient optimization. For example, using intermediate loss evaluations to terminate bad selections. In this work, we propose an HPO method for neural networks using logged checkpoints of the trained weights to guide future hyperparameter selections. Our method, Forecasting Model Search (FMS), embeds weights into a Gaussian process deep kernel surrogate model, using a permutation-invariant graph metanetwork to be data-efficient with the logged network weights. To facilitate reproducibility and further research, we open-source our code at https://github.com/NVlabs/forecasting-model-search., Comment: See the project website at https://research.nvidia.com/labs/toronto-ai/FMS/

Load balancing and auto scaling are at the core of scalable, contemporary systems, addressing dynamic resource allocation and service rate adjustments in response to workload changes. This paper introduces a novel model and algorithms for tuning load balancers coupled with auto scalers, considering bursty traffic arriving at finite queues. We begin by presenting the problem as a weakly coupled Markov Decision Processes (MDP), solvable via a linear program (LP). However, as the number of control variables of such LP grows combinatorially, we introduce a more tractable relaxed LP formulation, and extend it to tackle the problem of online parameter learning and policy optimization using a two-timescale algorithm based on the LP Lagrangian.

The XENONnT experiment searches for weakly-interacting massive particle (WIMP) dark matter scattering off a xenon nucleus. In particular, XENONnT uses a dual-phase time projection chamber with a 5.9-tonne liquid xenon target, detecting both scintillation and ionization signals to reconstruct the energy, position, and type of recoil. A blind search for nuclear recoil WIMPs with an exposure of 1.1 tonne-years yielded no signal excess over background expectations, from which competitive exclusion limits were derived on WIMP-nucleon elastic scatter cross sections, for WIMP masses ranging from 6 GeV/$c^2$ up to the TeV/$c^2$ scale. This work details the modeling and statistical methods employed in this search. By means of calibration data, we model the detector response, which is then used to derive background and signal models. The construction and validation of these models is discussed, alongside additional purely data-driven backgrounds. We also describe the statistical inference framework, including the definition of the likelihood function and the construction of confidence intervals., Comment: 20 pages, 10 figures

For several years, the metastable helium triplet line has been successfully used as a tracer to probe atmospheric escape in transiting exoplanets. This absorption in the near-infrared (1083.3 nm) can be observed from the ground using high-resolution spectroscopy, providing new constraints on the mass-loss rate and the temperature characterizing the upper atmosphere of close-in exoplanets. The aim of this work is to search for the He triplet signature in 15 transiting exoplanets -- ranging from super-Earths to ultrahot Jupiters -- observed with SPIRou, a high-resolution (R~70 000) near-infrared spectropolarimeter at the CFHT, in order to bring new constraints or to improve existing ones regarding atmospheric escape through a homogeneous study. We developed a full data processing and analysis pipeline to correct for the residual telluric and stellar contributions. We then used two different 1D models based on the Parker-wind equations and nonlocal thermodynamic equilibrium (NLTE) radiative transfer to interpret the observational results. We confirm published He triplet detections for HAT-P-11 b, HD 189733 b, and WASP-69 b. We tentatively detect the signature of escaping He in HD 209458 b, GJ 3470 b, and WASP-76 b. We report new constraints on the mass-loss rate and temperature for our three detections and set upper limits for the tentative and nondetections. We notably report improved constraints on the mass-loss rate and temperature of the escaping gas for TOI-1807 b, and report a nondetection for the debated atmospheric escape in GJ 1214 b. We also conducted the first search for the He signature in GJ 486 b since its discovery and report a nondetection of the He triplet. Finally, we studied the impact of important model assumptions on our retrieved parameters, notably the limitations of 1D models and the influence of the H/He ratio on the derived constraints., Comment: 33 pages, 14 figures

We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. Vibe-Eval is open-ended and challenging with dual objectives: (i) vibe checking multimodal chat models for day-to-day tasks and (ii) rigorously testing and probing the capabilities of present frontier models. Notably, our hard set contains >50% questions that all frontier models answer incorrectly. We explore the nuances of designing, evaluating, and ranking models on ultra challenging prompts. We also discuss trade-offs between human and automatic evaluation, and show that automatic model evaluation using Reka Core roughly correlates to human judgment. We offer free API access for the purpose of lightweight evaluation and plan to conduct formal human evaluations for public models that perform well on the Vibe-Eval's automatic scores. We release the evaluation code and data, see https://github.com/reka-ai/reka-vibe-eval

We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of training some of these models and provides comprehensive evaluation results. We show that Reka Edge and Reka Flash are not only state-of-the-art but also outperform many much larger models, delivering outsized values for their respective compute class. Meanwhile, our most capable and largest model, Reka Core, approaches the best frontier models on both automatic evaluations and blind human evaluations. On image question answering benchmarks (e.g. MMMU, VQAv2), Core performs competitively to GPT4-V. Meanwhile, on multimodal chat, Core ranks as the second most preferred model under a blind third-party human evaluation setup, outperforming other models such as Claude 3 Opus. On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e.g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation. On video question answering (Perception-Test), Core outperforms Gemini Ultra. Models are shipped in production at http://chat.reka.ai . A showcase of non cherry picked qualitative examples can also be found at http://showcase.reka.ai .

This work performs the convergence analysis of the polytopal nodal discretisation of contact-mechanics (with Tresca friction) recently introduced in [18] in the framework of poro-elastic models in fractured porous media. The scheme is based on a mixed formulation, using face-wise constant approximations of the Lagrange multipliers along the fracture network and a fully discrete first order nodal approximation of the displacement field. The displacement field is enriched with additional bubble degrees of freedom along the fractures to ensure the inf-sup stability with the Lagrange multiplier space. It is presented in a fully discrete formulation, which makes its study more straightforward, but also has a Virtual Element interpretation. The analysis establishes an abstract error estimate accounting for the fully discrete framework and the non-conformity of the discretisation. A first order error estimate is deduced for sufficiently smooth solutions both for the gradient of the displacement field and the Lagrange multiplier. A key difficulty of the numerical analysis is the proof of a discrete inf-sup condition, which is based on a non-standard $H^{-1/2}$-norm (to deal with fracture networks) and involves the jump of the displacements, not their traces. The analysis also requires the proof of a discrete Korn inequality for the discrete displacement field which takes into account fracture networks. Numerical experiments based on analytical solutions confirm our theoretical findings

Measuring the abundances of C- and O-bearing species in exoplanet atmospheres enables us to constrain the C/O ratio, that contains indications about the planet formation history. With a wavelength coverage going from 0.95 to 2.5 microns, the high-resolution (R$\sim$70 000) spectropolarimeter SPIRou can detect spectral lines of major bearers of C and O in exoplanets. Here we present our study of SPIRou transmission spectra of WASP-76 b acquired for the ATMOSPHERIX program. We applied the publicly available data analysis pipeline developed within the ATMOSPHERIX consortium, analysing the data using 1-D models created with the petitRADTRANS code, with and without a grey cloud deck. We report the detection of H$_2$O and CO at a Doppler shift of around -6 km.s$^{-1}$, consistent with previous observations of the planet. Finding a deep cloud deck to be favoured, we measured in mass mixing ratio (MMR) log(H$_2$O)$_{MMR}$ = -4.52 $\pm$ 0.77 and log(CO)$_{MMR}$ = -3.09 $\pm$ 1.05 consistent with a sub-solar metallicity to more than 1$\sigma$. We report 3$\sigma$ upper limits for the abundances of C$_2$H$_2$, HCN and OH. We estimated a C/O ratio of 0.94 $\pm$ 0.39 ($\sim$ 1.7 $\pm$ 0.7 x solar, with errors indicated corresponding to the 2$\sigma$ values) for the limbs of WASP-76 b at the pressures probed by SPIRou. We used 1-D ATMO forward models to verify the validity of our estimation. Comparing them to our abundance estimations of H$_2$O and CO, as well as our upper limits for C$_2$H$_2$, HCN and OH, we found that our results were consistent with a C/O ratio between 1 and 2 x solar, and hence with our C/O estimation. Finally, we found indications of asymmetry for both H$_2$O and CO when investigating the dynamics of their signatures, pointing to a complex scenario involving possibly both a temperature difference between limbs and clouds being behind the asymmetry this planet is best known for., Comment: 20 pages, 19 figures, accepted for publication in A&A

This paper details the first application of a software tagging algorithm to reduce radon-induced backgrounds in liquid noble element time projection chambers, such as XENON1T and XENONnT. The convection velocity field in XENON1T was mapped out using $^{222}\text{Rn}$ and $^{218}\text{Po}$ events, and the root-mean-square convection speed was measured to be $0.30 \pm 0.01$ cm/s. Given this velocity field, $^{214}\text{Pb}$ background events can be tagged when they are followed by $^{214}\text{Bi}$ and $^{214}\text{Po}$ decays, or preceded by $^{218}\text{Po}$ decays. This was achieved by evolving a point cloud in the direction of a measured convection velocity field, and searching for $^{214}\text{Bi}$ and $^{214}\text{Po}$ decays or $^{218}\text{Po}$ decays within a volume defined by the point cloud. In XENON1T, this tagging system achieved a $^{214}\text{Pb}$ background reduction of $6.2^{+0.4}_{-0.9}\%$ with an exposure loss of $1.8\pm 0.2 \%$, despite the timescales of convection being smaller than the relevant decay times. We show that the performance can be improved in XENONnT, and that the performance of such a software-tagging approach can be expected to be further improved in a diffusion-limited scenario. Finally, a similar method might be useful to tag the cosmogenic $^{137}\text{Xe}$ background, which is relevant to the search for neutrinoless double-beta decay., Comment: 17 pages, 19 figures

Consistent range-hashing is a technique used in distributed systems, either directly or as a subroutine for consistent hashing, commonly to realize an even and stable data distribution over a variable number of resources. We introduce FlipHash, a consistent range-hashing algorithm with constant time complexity and low memory requirements. Like Jump Consistent Hash, FlipHash is intended for applications where resources can be indexed sequentially. Under this condition, it ensures that keys are hashed evenly across resources and that changing the number of resources only causes keys to be remapped from a removed resource or to an added one, but never shuffled across persisted ones. FlipHash differentiates itself with its low computational cost, achieving constant-time complexity. We show that FlipHash beats Jump Consistent Hash's cost, which is logarithmic in the number of resources, both theoretically and in experiments over practical settings., Comment: 16 pages, 3 figures, 4 tables

Large, distributed data streams are now ubiquitous. High-accuracy sketches with low memory overhead have become the de facto method for analyzing this data. For instance, if we wish to group data by some label and report the largest counts using fixed memory, we need to turn to mergeable heavy hitter sketches that can provide highly accurate approximate counts. Similarly, if we wish to keep track of the number of distinct items in a single set spread across several streams using fixed memory, we can turn to mergeable count distinct sketches that can provide highly accurate set cardinalities. If we were to try to keep track of the cardinality of multiple sets and report only on the largest ones, maintaining individual count distinct sketches for each set can grow unwieldy, especially if the number of sets is not known in advance. We consider the natural combination of the heavy hitters problem with the count distinct problem, the heavy distinct hitters problem: given a stream of $(\ell, x)$ pairs, find all the labels $\ell$ that are paired with a large number of distinct items $x$ using only constant memory. No previous work on heavy distinct hitters has managed to be of practical use in the large, distributed data stream setting. We propose a new algorithm, the Sampling Space-Saving Set Sketch, which combines sketching and sampling techniques and has all the desired properties for size, speed, accuracy, mergeability, and invertibility. We compare our algorithm to several existing solutions to the heavy distinct hitters problem, and provide experimental results across several data sets showing the superiority of the new sketch., Comment: 12 pages, 5 figures

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

In this study, we explore mixed-dimensional Thermo-Hydro-Mechanical (THM) models in fractured porous media accounting for Coulomb frictional contact at matrix fracture interfaces. The simulation of such models plays an important role in many applications such as hydraulic stimulation in deep geothermal systems and assessing induced seismic risks in CO2 storage. We first extend to the mixed-dimensional framework the thermodynamically consistent THM models derived in [16] based on first and second principles of thermodynamics. Two formulations of the energy equation will be considered based either on energy conservation or on the entropy balance, assuming a vanishing thermo-poro-elastic dissipation. Our focus is on space time discretisations preserving energy estimates for both types of formulations and for a general single phase fluid thermodynamical model. This is achieved by a Finite Volume discretisation of the non-isothermal flow based on coercive fluxes and a tailored discretisation of the non-conservative convective terms. It is combined with a mixed Finite Element formulation of the contact-mechanical model with face-wise constant Lagrange multipliers accounting for the surface tractions, which preserves the dissipative properties of the contact terms. The discretisations of both THM formulations are investigated and compared in terms of convergence, accuracy and robustness on 2D test cases. It includes a Discrete Fracture Matrix model with a convection dominated thermal regime, and either a weakly compressible liquid or a highly compressible gas thermodynamical model.

We consider a non-isothermal compositional gas liquid model for the simulation of well operations in geothermal processes. The model accounts for phase transitions assumed to be at thermodynamical equilibrium and is based on an hydrodynamical Drift Flux Model (DFM) combined with a No Pressure Wave approximation of the momentum equation. The focus of this work is on the design of a robust discretization accounting for slanted and multibranch wells with the ability to simulate both transient behavior such as well opening as well as coupled simulations at the time scale of the reservoir. It is based on a staggered finite volume scheme in space combined with a fully implicit Euler time integration. The construction of consistent and stable numerical fluxes is a key feature for a robust numerical method. It is achieved by combining a monotone flux approximation for the phase superficial velocities with an upwind approximation of the phase molar fractions, density and enthalpy. In order to facilitate the coupling of the well and reservoir models, the Newton linearization accounts for the elimination of the hydrodynamical unknowns leading to Jacobian systems using the same primary unknowns than those of the reservoir model. The efficiency of our approach is investigated on both stand alone well test cases without and with cross flow, and on a fully coupled well-reservoir simulation.

The precision in reconstructing events detected in a dual-phase time projection chamber depends on an homogeneous and well understood electric field within the liquid target. In the XENONnT TPC the field homogeneity is achieved through a double-array field cage, consisting of two nested arrays of field shaping rings connected by an easily accessible resistor chain. Rather than being connected to the gate electrode, the topmost field shaping ring is independently biased, adding a degree of freedom to tune the electric field during operation. Two-dimensional finite element simulations were used to optimize the field cage, as well as its operation. Simulation results were compared to ${}^{83m}\mathrm{Kr}$ calibration data. This comparison indicates an accumulation of charge on the panels of the TPC which is constant over time, as no evolution of the reconstructed position distribution of events is observed. The simulated electric field was then used to correct the charge signal for the field dependence of the charge yield. This correction resolves the inconsistent measurement of the drift electron lifetime when using different calibrations sources and different field cage tuning voltages.

The PANCAKE facility is the world's largest liquid xenon test platform. Inside its cryostat with an internal diameter of 2.75 m, components for the next generation of liquid xenon experiments, such as DARWIN or XLZD, will be tested at their full scale. This is essential to ensure their successful operation. This work describes the facility, including its cryostat, cooling systems, xenon handling infrastructure, and its monitoring and instrumentation. The inner vessel has a flat floor, which allows the full diameter to be used with a modest amount of xenon. This is a novel approach for such a large cryostat and is of interest for future large-scale experiments, where a standard torispherical head would require tonnes of additional xenon. Our current xenon inventory of 400 kg allows a liquid depth of about 2 cm in the inner cryostat vessel. We also describe the commissioning of the facility, which is now ready for component testing., Comment: 17 pages, 8 figures

Machine learning techniques, such as deep learning and ensemble methods, are widely used in various domains due to their ability to handle complex real-world tasks. However, their black-box nature has raised multiple concerns about the fairness, trustworthiness, and transparency of computer-assisted decision-making. This has led to the emergence of local post-hoc explainability methods, which offer explanations for individual decisions made by black-box algorithms. Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework. Despite these strengths, Kernel SHAP suffers from high instability: different executions of the method with the same inputs can lead to significantly different explanations, which diminishes the relevance of the explanations. The contribution of this paper is two-fold. On the one hand, we show that Kernel SHAP's instability is caused by its stochastic neighbor selection procedure, which we adapt to achieve full stability without compromising explanation fidelity. On the other hand, we show that by restricting the neighbors generation to perturbations of size 1 -- which we call the coalitions of Layer 1 -- we obtain a novel feature-attribution method that is fully stable, computationally efficient, and still meaningful.

The objective of this article is to address the discretisation of fractured/faulted poromechanical models using 3D polyhedral meshes in order to cope with the geometrical complexity of faulted geological models. A polytopal scheme is proposed for contact-mechanics, based on a mixed formulation combining a fully discrete space and suitable reconstruction operators for the displacement field with a face-wise constant approximation of the Lagrange multiplier accounting for the surface tractions along the fracture/fault network. To ensure the inf--sup stability of the mixed formulation, a bubble-like degree of freedom is included in the discrete space of displacements (and taken into account in the reconstruction operators). It is proved that this fully discrete scheme for the displacement is equivalent to a low-order Virtual Element scheme, with a bubble enrichment of the VEM space. This $\mathbb{P}^1$-bubble VEM--$\mathbb{P}^0$ mixed discretization is combined with an Hybrid Finite Volume scheme for the Darcy flow. All together, the proposed approach is adapted to complex geometry accounting for network of planar faults/fractures including corners, tips and intersections; it leads to efficient semi-smooth Newton solvers for the contact-mechanics and preserve the dissipative properties of the fully coupled model. Our approach is investigated in terms of convergence and robustness on several 2D and 3D test cases using either analytical or numerical reference solutions both for the stand alone static contact mechanical model and the fully coupled poromechanical model.

Atoms are not two-level systems, and their rich internal structure often leads to complex phenomena in the presence of light. Here, we analyze off-resonant light scattering including the full hyperfine and magnetic structure. We find a set of frequency detunings where the induced atomic dipole is the same irrespective of the Zeeman state, and where two-photon transitions that alter the atomic state turn off. For alkali atoms and alkaline-earth ions, if the hyperfine splitting is dominated by the magnetic dipole moment contribution, these detunings approximately coincide. Therefore, at a given ``magical'' detuning, all Zeeman states in a hyperfine manifold behave almost identically, and can be traced out to good approximation. This feature prevents state decoherence due to light scattering, which impacts quantum optics experiments and quantum information applications., Comment: 12+9 pages, 4+1 figures, 2 tables. This version is in line with the published version

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred as "building blocks of complex networks". Their well-defined structures and small sizes also enables the testing of their functions in synthetic and natural biological experiments. The statistical inference of network motifs is however fraught with difficulties, from defining and sampling the right null model to accounting for the large number of possible motifs and their potential correlations in statistical testing. Here we develop a framework for motif mining based on lossless network compression using subgraph contractions. The minimum description length principle allows us to select the most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. The approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. This provides an alternative definition of motif significance which guarantees more robust statistical inference. Our approach overcomes the common problems in classic testing-based motif analysis. We apply our methodology to perform comparative connectomics by evaluating the compressibility and the circuit motifs of a range of synaptic-resolution neural connectomes., Comment: Published in PLoS Computational Biology: Methods

The problem of Novel Class Discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD has recently received a lot of attention from the community, it is often solved on computer vision problems and under unrealistic conditions. In particular, the number of novel classes is usually assumed to be known in advance, and their labels are sometimes used to tune hyperparameters. Methods that rely on these assumptions are not applicable in real-world scenarios. In this work, we focus on solving NCD in tabular data when no prior knowledge of the novel classes is available. To this end, we propose to tune the hyperparameters of NCD methods by adapting the $k$-fold cross-validation process and hiding some of the known classes in each fold. Since we have found that methods with too many hyperparameters are likely to overfit these hidden classes, we define a simple deep NCD model. This method is composed of only the essential elements necessary for the NCD problem and performs impressively well under realistic conditions. Furthermore, we find that the latent space of this method can be used to reliably estimate the number of novel classes. Additionally, we adapt two unsupervised clustering algorithms ($k$-means and Spectral Clustering) to leverage the knowledge of the known classes. Extensive experiments are conducted on 7 tabular datasets and demonstrate the effectiveness of the proposed method and hyperparameter tuning process, and show that the NCD problem can be solved without relying on knowledge from the novel classes., Comment: 30 pages, including 7 pages of annexes

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems.

Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a general consensus that for the short to medium term, there is a requirement for a human supervisor to handle the edge cases that inevitably arise. Given this requirement, it is essential that the state of the vehicle operator is monitored to ensure they are contributing to the vehicle's safe operation. This paper introduces a dual-source approach integrating data from an infrared camera facing the vehicle operator and vehicle perception systems to produce a metric for driver alertness in order to promote and ensure safe operator behaviour. The infrared camera detects the driver's head, enabling the calculation of head orientation, which is relevant as the head typically moves according to the individual's focus of attention. By incorporating environmental data from the perception system, it becomes possible to determine whether the vehicle operator observes objects in the surroundings. Experiments were conducted using data collected in Sydney, Australia, simulating AV operations in an urban environment. Our results demonstrate that the proposed system effectively determines a metric for the attention levels of the vehicle operator, enabling interventions such as warnings or reducing autonomous functionality as appropriate. This comprehensive solution shows promise in contributing to ADAS and AVs' overall safety and efficiency in a real-world setting.

We characterize and demonstrate how the principles of direct manipulation can improve interaction with large language models. This includes: continuous representation of generated objects of interest; reuse of prompt syntax in a toolbar of commands; manipulable outputs to compose or control the effect of prompts; and undo mechanisms. This idea is exemplified in DirectGPT, a user interface layer on top of ChatGPT that works by transforming direct manipulation actions to engineered prompts. A study shows participants were 50% faster and relied on 50% fewer and 72% shorter prompts to edit text, code, and vector images compared to baseline ChatGPT. Our work contributes a validated approach to integrate LLMs into traditional software using direct manipulation. Data, code, and demo available at https://osf.io/3wt6s.

We perform a blind search for particle signals in the XENON1T dark matter detector that occur close in time to gravitational wave signals in the LIGO and Virgo observatories. No particle signal is observed in the nuclear recoil, electronic recoil, CE$\nu$NS, and S2-only channels within $\pm$ 500 seconds of observations of the gravitational wave signals GW170104, GW170729, GW170817, GW170818, and GW170823. We use this null result to constrain mono-energetic neutrinos and Beyond Standard Model particles emitted in the closest coalescence GW170817, a binary neutron star merger. We set new upper limits on the fluence (time-integrated flux) of coincident neutrinos down to 17 keV at 90% confidence level. Furthermore, we constrain the product of coincident fluence and cross section of Beyond Standard Model particles to be less than $10^{-29}$ cm$^2$/cm$^2$ in the [5.5-210] keV energy range at 90% confidence level.

Multiple viable theoretical models predict heavy dark matter particles with a mass close to the Planck mass, a range relatively unexplored by current experimental measurements. We use 219.4 days of data collected with the XENON1T experiment to conduct a blind search for signals from Multiply-Interacting Massive Particles (MIMPs). Their unique track signature allows a targeted analysis with only 0.05 expected background events from muons. Following unblinding, we observe no signal candidate events. This work places strong constraints on spin-independent interactions of dark matter particles with a mass between 1$\times$10$^{12}\,$GeV/c$^2$ and 2$\times$10$^{17}\,$GeV/c$^2$. In addition, we present the first exclusion limits on spin-dependent MIMP-neutron and MIMP-proton cross-sections for dark matter particles with masses close to the Planck scale., Comment: 7 pages, 6 figures

We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data., Comment: 11 pages, 8 figures

In a companion paper, we introduced a publicly-available pipeline to characterise exoplanet atmospheres through high-resolution spectroscopy. In this paper, we use this pipeline to study the biases and degeneracies that arise in atmospheric characterisation of exoplanets in near-infrared ground-based transmission spectroscopy. We inject synthetic planetary transits into sequences of SPIRou spectra of the well known M dwarf star Gl 15 A, and study the effects of different assumptions on the retrieval. We focus on (i) mass and radius uncertainties, (ii) non isothermal vertical profiles and (iii) identification and retrieval of multiple species. We show that the uncertainties on mass and radius should be accounted for in retrievals and that depth-dependent temperature information can be derived from high-resolution transmission spectroscopy data. Finally, we discuss the impact of selecting wavelength orders in the retrieval and the issues that arise when trying to identify a single species in a multi-species atmospheric model. This analysis allows us to understand better the results obtained through transmission spectroscopy and their limitations in preparation to the analysis of actual SPIRou data., Comment: Accepted in MNRAS