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Combined dark matter search towards dwarf spheroidal galaxies with Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS
No full textInternational audienceDwarf spheroidal galaxies (dSphs) are excellent targets for indirect dark matter (DM) searches using gamma-ray telescopes because they are thought to have high DM content and a low astrophysical background. The sensitivity of these searches is improved by combining the observations of dSphs made by different gamma-ray telescopes. We present the results of a combined search by the most sensitive currently operating gamma-ray telescopes, namely: the satellite-borne Fermi-LAT telescope; the ground-based imaging atmospheric Cherenkov telescope arrays H.E.S.S., MAGIC, and VERITAS; and the HAWC water Cherenkov detector. Individual datasets were analyzed using a common statistical approach. Results were subsequently combined via a global joint likelihood analysis. We obtain constraints on the velocity-weighted cross section for DM self-annihilation as a function of the DM particle mass. This five-instrument combination allows the derivation of up to 2-3 times more constraining upper limits on than the individual results over a wide mass range spanning from 5 GeV to 100 TeV. Depending on the DM content modeling, the 95% confidence level observed limits reach 10 cms and 10 cms, respectively, in the annihilation channel for a DM mass of 2 TeV
Optimisation of the vertex detector and measurement of Higgs decays to second-generation quarks at the CEPC
No full textInternational audienceThe vertex detector is crucial for precision measurements of the Higgs boson at the electron-positron Higgs factory. Benchmarked with and measurements in the channel, we perform an optimisation study on the inner radius and spatial resolution of the vertex detector using the Jet Origin Identification (JOI) framework, which determines the parton flavor of jets using advanced Artificial Intelligence (AI) algorithm. We observe that, compared to the reference detector configuration, halving the inner radius and spatial resolution improves the transverse and longitudinal impact parameter resolution approximately by a factor of two, while increasing the accuracy and significance of the measurement by 4% and 8%, respectively. Conversely, doubling these parameters results in comparable degradation, with variations in the inner radius being the dominant factor. Our results provide guidance for detector design and highlight promising prospects for identifying the decay mode at future Higgs factories
Search for nonresonant new physics signals in high-mass dilepton events produced in association with b-tagged jets in proton-proton collisions at = 13 TeV
No full textInternational audienceA search for nonresonant new physics phenomena in high-mass dilepton events produced in association with b-tagged jets is performed using proton-proton collision data collected in 20162018 by the CMS experiment at the CERN LHC, at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138 fb. The analysis considers two effective field theory models with dimension-six operators; involving four-fermion contact interactions between two leptons (, electrons or muons) and b or s quarks (bb and bs). Two lepton flavor combinations (ee and ) are required and events are classified as having 0, 1, and 2 b-tagged jets in the final state. No significant excess is observed over the standard model backgrounds. Upper limits are set on the production cross section of the new physics signals. These translate into lower limits on the energy scale of 6.9 to 9.0 TeV in the bb model, depending on model parameters, and on the ratio of energy scale and effective coupling, , of 2.0 to 2.6 TeV in the bs model. The latter represent the most stringent limits on this model to date. Lepton flavor universality is also tested by comparing the dielectron and dimuon mass spectra for different b-tagged jet multiplicities. No significant deviation from the standard model expectation of unity is observed
Functionalization of Silicon Surfaces Using SI-ATRP and Click Chemistry for Anchoring Asymmetric Catalysts
No full textInternational audienceA novel approach for the surface-initiated atom transfer radical polymerization (SI-ATRP) of methoxyethyl methacrylate (MEMA) and 3-azidopropyl methacrylate (AZMA) on macroporous silicon substrates and their postfunctionalization by click chemistry with asymmetric catalysts is presented. Crystalline silicon was first used to monitor the multistep functionalization by quantitative IR-ATR spectroscopy. The attachment of an alkynyl FTIR marker on crystalline silicon demonstrated the effectiveness of the methodology, which was then applied onto macroporous silicon to anchor an enantiopure chromium-salen complex as a first step toward the development of new supported asymmetric organometallic catalysts on silicon-based materials. SEM and EDS measurements clearly show good homogeneity of the polymer growth through the porous layers with a uniform distribution of the catalysts (even deep inside the pores). The successful functionalization of macroporous silicon has confirmed the transferability of the technique to porous materials, highlighting its potential for application to even larger surface area substrates in future catalytic studies
Deformable Polygonal Flow Matching with Informed Priors and Hierarchical Graph Constraints
No full textInternational audienceThis paper presents a novel method, called Deformable Polygonal Flow Matching (DPFM), for the generation of polygonal arrangements such as jigsaw puzzles and floor plans. DPFM is a Flow Matching framework that enables the generation process to deform, rotate, and translate polygons while decoupling these transformation, allowing to toggle them individually. Able to combine the spatial reasoning capabilities of arrangement models with the flexibility of position-based models, it covers a wide range of applications within a unified formulation, from noiseless puzzle solving using rigid alignments to unconstrained floor plan generation. We represent data using a hierarchical graph composed of a topological subgraph encoding connectivity information and semantics (such as room types for floor plans), and a geometrical subgraph encoding the 1D polygonal loop of each shape. DPFM also leverages Flow Matching’s arbitrary prior distributions for geometric constraints by designing priors with domain knowledge. Rather than starting the generation process from uninformed distributions, the generation is constrained through the informed priors at the initialization stage. The qualitative and quantitative evaluations of our method, ran on the RPLAN and jigsaw puzzle datasets,demonstrate strong performance. DPFM outperforms task-specific methods, becoming the new state-of-the-art for 2D arrangement generation. Our results show that DPFM is able to solve novel tasks, such as puzzle denoising, where pieces are reconstructed from noisy versions and arranged into a valid puzzle in parallel
Extreme precipitation changes in relation to urbanization
No full textInternational audienceThe rising frequency of extreme precipitation is a major concern linked to climate change, commonly associated with increased atmospheric water vapor due to global warming. In densely populated areas, intense rainfall has particularly severe impacts, with urbanization amplifying extreme weather through changes in land surface and local atmospheric conditions. As attribution science increasingly informs climate policy, it is crucial to discern the extent to which shifts in extreme event probability stem from global versus local anthropogenic drivers. This study analyzes multi-decadal daily precipitation records alongside urbanization indices. In line with previous research, results show a general rise in extreme rainfall frequency, with more intense events exhibiting a larger increase. Analysis of population and urban development metrics reveals that the increase is notably smaller in rural areas, suggesting that the rise attributable to local urban development is of the same order of magnitude as that resulting from global warming. This result is shown to be associated with the urban amplification of convective updraft intensification.</div
Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models
No full textDeep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and are prone to overfitting on datasets with short sequences.In this paper, we explore a new paradigm for knowledge tracing by leveraging tabular foundation models (TFMs).Unlike traditional methods that require offline training on a fixed training set, our approach performs real-time "live" knowledge tracing in an online way.The core of our method lies in a two-way attention mechanism:while attention knowledge tracing models only attend across earlier time steps,TFMs simultaneously attend across both time steps and interactions of other students in the training set.They align testing sequences with relevant training sequences at inference time, therefore skipping the training step entirely.We demonstrate, using several datasets of increasing size, that our method achieves competitive predictive performance with up to 273x speedups, in a setting where more student interactions are observed over time
Development of an early warning method incorporating pre-supernova neutrino light curves
No full textInternational audienceMassive stars () emit neutrinos known as pre-supernova (pre-SN) neutrinos through thermal and nuclear interactions for cooling the stellar core during the final stage of stellar evolution. Real-time monitoring of their pre-SN neutrino interaction rate offers a crucial opportunity to issue an early warning to a core-collapse supernova. Some neutrino detectors, including KamLAND and Super-Kamiokande already operate pre-SN alarm systems based on a statistically significant excess of the observed event rate over the expected background. To improve alarm sensitivity, we propose an alarm method which incorporates the time evolution of the observed pre-SN neutrino event rate. The method uses a log likelihood ratio test that references multiple theoretical stellar-evolution models and treats the core collapse time as a nuisance parameter to be profiled over. The performance of the proposed method was evaluated using simulated data for the KamLAND, Super-Kamiokande with dissolved Gadolinium (SK-Gd) and their combined analysis. The results demonstrate a significant improvement in the warning time compared to the conventional rate-only method, while maintaining the same false alarm rate
An all-topology two-fluid model for two-phase flows derived through Hamilton's Stationary Action Principle
No full textInternational audienceWe present a novel multi-fluid model for compressible two-phase flows. The model is derived through a newly developed Stationary Action Principle framework. It is fully closed and introduces a new interfacial quantity, the interfacial work. The closures for the interfacial quantities are provided by the variational principle. They are physically sound and well-defined for all types of flow topologies. The model is shown to be hyperbolic, symmetrizable, and admits an entropy conservation law. Its non-conservative products yield uniquely defined jump conditions which are provided. As such, it allows for the proper treatment of weak solutions. In the multi-dimensional setting, the model presents lift forces which are discussed. The model constitutes a sound basis for future numerical simulations
A statistical approach to unveil phytoplankton adaptation to ocean fronts
No full textInternational audienceAbstract. Fine-scale oceanic fronts are ubiquitous and ephemeral physical features that separate contrasting water masses, creating significant heterogeneity in the physical seascape and plankton distributions. Because phytoplankton community composition (PCC) is a key driver of marine ecosystem functioning, understanding the extent to which fine-scale fronts influence PCC is a critical challenge. However, studying PCC across and within fronts is particularly difficult due to data scarcity and high biophysical variability. We developed a tailored statistical model to characterize PCC within an oceanic front we studied in the Mediterranean Sea. We modeled the frontal community as a finite mixture model with three components: two communities of adjacent water masses and a potential front-adapted community. Each component was further considered as a discrete mixture of an unknown number of multivariate Gaussian sub-components. First, we used an Expectation–Maximization algorithm to estimate the Gaussian parameters and determine the optimal number of sub-components based on in situ datasets of the PCC within a frontal zone and its adjacent water masses. Second, a hierarchical Bayesian approach was applied to estimate the weight of all components within the frontal dataset. Our analysis suggests that within the front a new community component, distinct from those in adjacent water masses, accounts for 70 % of the frontal community, indicating that a specific phytoplankton community can emerge in fine-scale oceanic fronts. Despite the limited number of frontal observations, our Bayesian modelling approach provides statistical evidence of the front's influence on phytoplankton community composition, effectively overcoming data scarcity and high variability