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The inner hundred parsecs of the Milky Way hosts the nearest supermassive black hole, largest reservoir of dense gas, greatest stellar density, hundreds of massive main and post main sequence stars, and the highest volume density of supernovae in the Galaxy. As the nearest environment in which it is possible to simultaneously observe many of the extreme processes shaping the Universe, it is one of the most well-studied regions in astrophysics. Due to its proximity, we can study the center of our Galaxy on scales down to a few hundred AU, a hundred times better than in similar Local Group galaxies and thousands of times better than in the nearest active galaxies. The Galactic Center (GC) is therefore of outstanding astrophysical interest. However, in spite of intense observational work over the past decades, there are still fundamental things unknown about the GC. JWST has the unique capability to provide us with the necessary, game-changing data. In this White Paper, we advocate for a JWST NIRCam survey that aims at solving central questions, that we have identified as a community: i) the 3D structure and kinematics of gas and stars; ii) ancient star formation and its relation with the overall history of the Milky Way, as well as recent star formation and its implications for the overall energetics of our galaxy's nucleus; and iii) the (non-)universality of star formation and the stellar initial mass function. We advocate for a large-area, multi-epoch, multi-wavelength NIRCam survey of the inner 100\,pc of the Galaxy in the form of a Treasury GO JWST Large Program that is open to the community. We describe how this survey will derive the physical and kinematic properties of ~10,000,000 stars, how this will solve the key unknowns and provide a valuable resource for the community with long-lasting legacy value., Comment: This White Paper will be updated when required (e.g. new authors joining, editing of content). Most recent update: 24 Oct 2023

Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input. The latter is particularly difficult when the goal is not given as a category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception module needs to learn a comparison strategy requiring to solve an underlying visual correspondence problem. This has been shown to be difficult from reward alone or with standard auxiliary tasks. We address this problem through a sequence of two pretext tasks, which serve as a prior for what we argue is one of the main bottleneck in perception, extremely wide-baseline relative pose estimation and visibility prediction in complex scenes. The first pretext task, cross-view completion is a proxy for the underlying visual correspondence problem, while the second task addresses goal detection and finding directly. We propose a new dual encoder with a large-capacity binocular ViT model and show that correspondence solutions naturally emerge from the training signals. Experiments show significant improvements and SOTA performance on the two benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics and height differ between observation and goal.

We present a new measurement of the positive muon magnetic anomaly, $a_\mu \equiv (g_\mu - 2)/2$, from the Fermilab Muon $g\!-\!2$ Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, $\tilde{\omega}'^{}_p$, and of the anomalous precession frequency corrected for beam dynamics effects, $\omega_a$. From the ratio $\omega_a / \tilde{\omega}'^{}_p$, together with precisely determined external parameters, we determine $a_\mu = 116\,592\,057(25) \times 10^{-11}$ (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain $a_\mu\text{(FNAL)} = 116\,592\,055(24) \times 10^{-11}$ (0.20 ppm). The new experimental world average is $a_\mu (\text{Exp}) = 116\,592\,059(22)\times 10^{-11}$ (0.19 ppm), which represents a factor of 2 improvement in precision., Comment: 8 pages, 3 figures

Metallicity gradients refer to the sloped radial profile of metallicities of gas and stars and are commonly seen in disk galaxies. A well-defined metallicity gradient of the Galactic disk is observed particularly well with classical Cepheids, which are good stellar tracers thanks to their period-luminosity relation allowing precise distance estimation and other advantages. However, the measurement of the inner-disk gradient has been impeded by the incompleteness of previous samples of Cepheids and limitations of optical spectroscopy in observing highly reddened objects. Here we report the metallicities of 16 Cepheids measured with high-resolution spectra in the near-infrared YJ bands. These Cepheids are located at 3-5.6 kpc in the Galactocentric distance, R(GC), and reveal the metallicity gradient in this range for the first time. Their metallicities are mostly between 0.1 and 0.3 dex in [Fe/H] and more or less follow the extrapolation of the metallicity gradient found in the outer part, R(GC) larger than 6.5 kpc. The gradient in the inner disk may be shallower or even flat, but the small sample does not allow to determine the slope precisely. More extensive spectroscopic observations would also be necessary for studying minor populations, if any, with higher or lower metallicities that were reported in previous literature. In addition, three-dimensional velocities of our inner-disk Cepheids show the kinematic pattern that indicates non-circular orbits caused by the Galactic bar, which is consistent with the patterns reported in recent studies on high-mass star-forming regions and red giant branch stars., Comment: Accepted for publication in ApJ. 10 pages, 2 figures, and 2 tables

Classical Cepheids (CCs) are solid distance indicators and tracers of young stellar populations. Our aim is to provide iron, oxygen, and sulfur abundances for the largest and most homogeneous sample of Galactic CCs ever analyzed. The current sample covers a wide range in Galactocentric distances (RG), pulsation modes and periods. High-resolution and high S/N spectra collected with different spectrographs were adopted to estimate the atmospheric parameters. Individual distances are based on Gaia trigonometric parallaxes or on near-infrared Period-Luminosity relations. We found that Fe and alpha-element radial gradients based on CCs display a well-defined change in the slope for RG larger than 12 kpc. Radial gradients based on open clusters, covering a wide range in age, display similar trends, meaning that the flattening in the outer disk is an intrinsic feature of the radial gradients since it is independent of age. Empirical evidence indicates that the radial gradient for S is steeper than for Fe. The difference in the slope is a factor of two in the linear fit. We also found that S is, on average, under-abundant compared with O. We performed a detailed comparison with Galactic chemical evolution models and we found that a constant Star Formation Efficiency for RG larger than 12 kpc takes account for the flattening in both Fe and alpha-elements. To further constrain the impact that predicted S yields for massive stars have on radial gradients, we adopted a "toy model" and we found that the flattening in the outermost regions requires a decrease of a factor of four in the current S predictions. Sulfur photospheric abundances, compared with other alpha-elements, have the key advantage of being a volatile element. Therefore, stellar S abundances can be directly compared with nebular S abundances in external galaxies., Comment: 21 pages, 13 figures, 3 tables; to be published in the A&A journal

Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7--4 trillion euros are laundered annually and go undetected. We propose The GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. This setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous approaches for graph representation learning have focused on either sampling k-hop neighborhoods, akin to breadth-first search, or random walks, akin to depth-first search. However, these methods are computationally expensive and unsuitable for real-time, low-latency inference on dynamic graphs. To overcome these limitations, we propose graph-sprints a general purpose feature extraction framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with state-of-the-art, higher latency models. To achieve this, a streaming, low latency approximation to the random-walk based features is proposed. In our framework, time-aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges. We evaluate our proposed approach on three open-source datasets and two in-house datasets, and compare with three state-of-the-art algorithms (TGN-attn, TGN-ID, Jodie). We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets). Simultaneously, graph-sprints significantly reduce inference latencies, achieving close to an order of magnitude speed-up in our experimental setting., Comment: 9 pages, 5 figures, 7 tables

Owing to their unique tunable optical properties, chalcogenide phase change materials are increasingly being investigated for optics and photonics applications. However, in situ characterization of their phase transition characteristics is a capability that remains inaccessible to many researchers. In this article, we introduce a multi-functional silicon microheater platform capable of in situ measurement of structural, kinetic, optical, and thermal properties of these materials. The platform can be fabricated leveraging industry-standard silicon foundry manufacturing processes. We fully open-sourced this platform, including complete hardware design and associated software codes., Comment: 13 pages main text, 5 figures +1 (supplementary)

The technique of line depth ratios (LDR) is one of the methods to determine the effective temperature of a star. They are crucial in the spectroscopic studies of variable stars like Cepheids since no simultaneous photometry is usually available. A good number of LDR-temperature relations are already available in the optical domain, here we want to expand the number of relations available in the near-infrared in order to fully exploit the capabilities of current and upcoming near-infrared spectrographs. We used 115 simultaneous spectroscopic observations in the optical and the near-infrared for six Cepheids and optical line depth ratios to find new pairs of lines sensitive to temperature and to calibrate LDR-temperature relations in the near-infrared spectral range. We have derived 87 temperature calibrations valid in the [4800-6500] K range of temperatures. The typical uncertainty for a given relation is 60-70 K, and combining many of them provides a final precision within 30-50 K. We found a discrepancy between temperatures derived from optical or near-infrared LDR for pulsations phases close to phi ~ 0.0 and we discuss the possible causes for these differences. Line depth ratios in the near-infrared will allow us to spectroscopically investigate highly reddened Cepheids in the Galactic centre or in the far side of the disk., Comment: 17 pages, 10 figures, 4 tables. Published in MNRAS

Recent discovery of emergent magnetism in van der Waals magnetic materials (vdWMM) has broadened the material space for developing spintronic devices for energy-efficient computation. While there has been appreciable progress in vdWMM discovery, with strong perpendicular magnetic anisotropy (PMA) and Curie temperatures exceeding room temperature, a solution for non-volatile, deterministic switching of vdWMMs at room temperature has been missing, limiting the prospects of their adoption into commercial spintronic devices. Here, we report the first demonstration of current-controlled non-volatile, deterministic magnetization switching in a vdW magnetic material at room temperature. We have achieved spin-orbit torque (SOT) switching of the PMA vdW magnet Fe3GaTe2 using a Pt spin-Hall layer up to 320 K, with a threshold switching current density as low as $J_{sw} = 1.69\times10^6 A/cm^2$ at room temperature. We have also quantitatively estimated the anti-damping-like SOT efficiency of our Fe3GaTe2/Pt bilayer system to be $\xi_{DL}$ = 0.093, using second harmonic Hall voltage measurement technique. These results mark a crucial step in making vdW magnetic materials a viable choice for the development of scalable, future spintronic devices.

Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretation, whereas classical robotics addresses this with scene reconstruction resulting in some form of map, usually estimated with geometry and sensor models and/or learning. In this work we propose to learn an actionable representation of the scene independently of the targeted downstream task and without explicitly optimizing reconstruction. The learned representation is optimized by a blind auxiliary agent trained to navigate with it on multiple short sub episodes branching out from a waypoint and, most importantly, without any direct visual observation. We argue and show that the blindness property is important and forces the (trained) latent representation to be the only means for planning. With probing experiments we show that the learned representation optimizes navigability and not reconstruction. On downstream tasks we show that it is robust to changes in distribution, in particular the sim2real gap, which we evaluate with a real physical robot in a real office building, significantly improving performance.

Wide-field and deep DECam multi-band photometry, combined with HST data for the core of the Galactic globular cluster NGC 2808, allowed us to study the distribution of various stellar sub-populations and stars in different evolutionary phases out to the cluster tidal radius. We used the C_ugi = (u-g)-(g-i) index to identify three chemically distinct sub-populations along the red giant branch and compared their spatial distributions. The most light-element enriched sub-population (P3) is more centrally concentrated; however, it shows a more extended distribution in the external regions of the cluster compared to the primordial (P1) and intermediate (P2) composition populations. Furthermore, the P3 sub-population centroid is off-center relative to those of the P1 and P2 groups. We also analyzed the spatial distribution of horizontal branch stars and found that the relative fraction of red horizontal branch stars increases for radial distances larger than ~ 1.5' while that of the blue and hotter stars decreases. These new observations, combined with literature spectroscopic measurements, suggest that the red horizontal branch stars are the progeny of all the stellar sub-populations in NGC 2808, i.e. primordial and light-element enhanced, while the blue stars are possibly the result of a combination of the "hot-flasher" and the "helium-enhanced" scenarios. A similar distribution of different red giant branch sub-populations and horizontal branch stars was also found for the most massive Galactic globular cluster, omega Cen, based on combined DECam and HST data, which suggests the two may share a similar origin., Comment: Accepted for publication in AJ; 22 pages; 16 figures; the full version of table 2 will be available in electronic form with the published version

RR Lyrae (RRL) are old, low-mass radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators, since they obey to well defined period-luminosity relations in the near-infrared regime. Their photometric identification is not trivial, indeed, RRL samples can be contaminated by eclipsing binaries, especially in large datasets produced by fully automatic pipelines. Interpretable machine-learning approaches for separating eclipsing binaries from RRL are thus needed. Ideally, they should be able to achieve high precision in identifying RRL while generalizing to new data from different instruments. In this paper, we train a simple logistic regression classifier on Catalina Sky Survey (CSS) light curves. It achieves a precision of 87% at 78% recall for the RRL class on unseen CSS light curves. It generalizes on out-of-sample data (ASAS/ASAS-SN light curves) with a precision of 85% at 96% recall. We also considered a L1-regularized version of our classifier, which reaches 90% sparsity in the light-curve features with a limited trade-off in accuracy on our CSS validation set and -- remarkably -- also on the ASAS/ASAS-SN light curve test set. Logistic regression is natively interpretable, and regularization allows us to point out the parts of the light curves that matter the most in classification. We thus achieved both good generalization and full interpretability., Comment: To appear on The Astrophysical Journal. 13 pages, 8 figures, 3 tables