Search

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using \texttt{cloudy}, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic \hii{} regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [{\sc O\,iii}]$\lambda5007$/H$\beta$, [{\sc N\,ii}]$\lambda6583$/H$\alpha$, ([{\sc S\,ii}]$\lambda6717$+[{\sc S\,ii}]$\lambda6731$)/H$\alpha$. We also tested the impact of the addition of the [{\sc O\,ii}]$\lambda3726,3729$/[{\sc O\,iii}]$\lambda5007$ line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints., Comment: 17 pages; 17 figures; Accepted to RASTI

Light plays an important role in human well-being. However, most computer vision tasks treat pixels without considering their relationship to physical luminance. To address this shortcoming, we introduce the Laval Photometric Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of high dynamic range 360{\deg} panoramas. Our key contribution is the calibration of an existing, uncalibrated HDR Dataset. We do so by accurately capturing RAW bracketed exposures simultaneously with a professional photometric measurement device (chroma meter) for multiple scenes across a variety of lighting conditions. Using the resulting measurements, we establish the calibration coefficients to be applied to the HDR images. The resulting dataset is a rich representation of indoor scenes which displays a wide range of illuminance and color, and varied types of light sources. We exploit the dataset to introduce three novel tasks, where: per-pixel luminance, per-pixel color and planar illuminance can be predicted from a single input image. Finally, we also capture another smaller photometric dataset with a commercial 360{\deg} camera, to experiment on generalization across cameras. We are optimistic that the release of our datasets and associated code will spark interest in physically accurate light estimation within the community. Dataset and code are available at https://lvsn.github.io/beyondthepixel/.

After the reported discovery of room-temperature superconductivity at only 1GPa in nitrogendoped lutetium trihydride [1] and the resulting heated discussions, there is an urgent need to reproduce the results and synthesis of this compound. Here, we report the synthesis of stable trigonal LuH3 by hydrogenating pure lutetium which was subsequently pressurised to 2GPa in a dilute-N2/He-rich pressure medium. Raman spectroscopy and X-ray diffraction were used to characterise the structures throughout. After depressurising, energy-dispersive and wavelength-dispersive X-ray spectroscopies were used to characterise the final compound. Though our compound exhibits similar structural behaviour to the Dasenbrock-Gammon et al. sample [1], we do not observe any nitrogen within the structure of the recovered sample at ambient pressure. We observe two cubic structures under pressure that may simultaneously explain the X-ray diffraction and Raman spectra observed: the first corresponds well to Fm-3m LuH2+x, whilst the latter is a novel Ia-3 - type structure., Comment: 8 pages, 3 figures

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess traits that make it unsuitable for standard emission-based deep learning representations such as point clouds. Radar point clouds tend to be sparse and therefore information extraction is not efficient. To overcome this, more traditional digital signal processing pipelines were adapted to form inputs residing directly in the frequency domain via Fast Fourier Transforms. Commonly, three transformations were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This too has drawbacks, namely the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We explore the possibility of operating on raw radar inputs from analog to digital converters via the utilization of complex transformation layers. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, i.e. relatively low and high numbers of transmitters and receivers, while obtaining on par or better results than the state-of-the-art.

Monitoring the rock-physics properties of the subsurface is of great importance for reservoir management. For either oil and gas applications or CO2 storage, seismic data are a valuable source of information for tracking changes in elastic properties which can be related to fluids saturation and pressure changes within the reservoir. Changes in elastic properties can be estimated with time-lapse full-waveform inversion. Monitoring rock-physics properties, such as saturation, with time-lapse full-waveform inversion is usually a two-step process: first, elastic properties are estimated with full-waveform inversion, then, the rock-physics properties are estimated with rock-physics inversion. However, multiparameter time-lapse full-waveform inversion is prone to crosstalk between parameter classes across different vintages. This leads to leakage from one parameter class to another, which, in turn, can introduce large errors in the estimated rock-physics parameters. To avoid inaccuracies caused by crosstalk and the two-step inversion strategy, we reformulate time-lapse full-waveform inversion to estimate directly the changes in the rock-physics properties. Using Gassmann's model, we adopt a new parameterization containing porosity, clay content, and water saturation. In the context of reservoir monitoring, changes are assumed to be induced by fluid substitution only. The porosity and clay content can thus be kept constant during time-lapse inversion. We compare this parameterization with the usual density-velocity parameterization for different benchmark models. Results indicate that the proposed parameterization eliminates crosstalk between parameters of different vintages, leading to more accurate estimation of saturation changes. We also show that using the parameterization based on porosity, clay content, and water saturation, the elastic changes can be monitored more accurately.

PICO bubble chambers have exceptional sensitivity to inelastic dark matter-nucleus interactions due to a combination of their extended nuclear recoil energy detection window from a few keV to $O$(100 keV) or more and the use of iodine as a heavy target. Inelastic dark matter-nucleus scattering is interesting for studying the properties of dark matter, where many theoretical scenarios have been developed. This study reports the results of a search for dark matter inelastic scattering with the PICO-60 bubble chambers. The analysis reported here comprises physics runs from PICO-60 bubble chambers using CF$_{3}$I and C$_{3}$F$_{8}$. The CF$_{3}$I run consisted of 36.8 kg of CF$_{3}$I reaching an exposure of 3415 kg-day operating at thermodynamic thresholds between 7 and 20 keV. The C$_{3}$F$_{8}$ runs consisted of 52 kg of C$_{3}$F$_{8}$ reaching exposures of 1404 kg-day and 1167 kg-day running at thermodynamic thresholds of 2.45 keV and 3.29 keV, respectively. The analysis disfavors various scenarios, in a wide region of parameter space, that provide a feasible explanation of the signal observed by DAMA, assuming an inelastic interaction, considering that the PICO CF$_{3}$I bubble chamber used iodine as the target material., Comment: 7 pages, 3 figures

We present the conceptual design and the physics potential of DarkSPHERE, a proposed 3 m in diameter spherical proportional counter electroformed underground at the Boulby Underground Laboratory. This effort builds on the R&D performed and experience acquired by the NEWS-G Collaboration. DarkSPHERE is primarily designed to search for nuclear recoils from light dark matter in the 0.05--10 GeV mass range. Electroforming the spherical shell and the implementation of a shield based on pure water ensures a background level below 0.01 dru. These, combined with the proposed helium-isobutane gas mixture, will provide sensitivity to the spin-independent nucleon cross-section of $2\times 10^{-41} (2\times 10^{-43})$ cm$^2$ for a dark matter mass of $0.1 (1)$ GeV. The use of a hydrogen-rich gas mixture with a natural abundance of $^{13}$C provides sensitivity to spin-dependent nucleon cross-sections more than two orders of magnitude below existing constraints for dark matter lighter than 1 GeV. The characteristics of the detector also make it suitable for searches of other dark matter signatures, including scattering of MeV-scale dark matter with electrons, and super-heavy dark matter with masses around the Planck scale that leave extended ionisation tracks in the detector., Comment: 19 pages, 14 figures

We present a loop-by-loop method for computing the differential equations of Feynman integrals using the recently developed dual form formalism. We give explicit prescriptions for the loop-by-loop fibration of multi-loop dual forms. Then, we test our formalism on a simple, but non-trivial, example: the two-loop three-mass elliptic sunrise family of integrals. We obtain an epsilon-form differential equation within the correct function space in a sequence of relatively simple algebraic steps. In particular, none of these steps relies on the analysis of $q$-series. Then, we discuss interesting properties satisfied by our dual basis as well as its simple relation to the known epsilon-form basis of Feynman integrands. The underlying K3-geometry of the three-loop four-mass sunrise integral is also discussed. Finally, we speculate on how to construct a "good" loop-by-loop basis at three-loop., Comment: 57+9 pages, 9 figures; JHEP version

This report summarizes the findings of the CF1 Topical Subgroup to Snowmass 2021, which was focused on particle dark matter. One of the most important scientific goals of the next decade is to reveal the nature of dark matter (DM). To accomplish this goal, we must delve deep, to cover high priority targets including weakly-interacting massive particles (WIMPs), and search wide, to explore as much motivated DM parameter space as possible. A diverse, continuous portfolio of experiments at large, medium, and small scales that includes both direct and indirect detection techniques maximizes the probability of discovering particle DM. Detailed calibrations and modeling of signal and background processes are required to make a convincing discovery. In the event that a candidate particle is found through different means, for example at a particle collider, the program described in this report is also essential to show that it is consistent with the actual cosmological DM. The US has a leading role in both direct and indirect detection dark matter experiments -- to maintain this leading role, it is imperative to continue funding major experiments and support a robust R\&D program., Comment: Submitted 30 pages, 11 figures, many references, Report of the CF1 Topical Group for Snowmass 2021

Particle detectors making use of noble elements in gaseous, liquid, or solid phases are prevalent in neutrino and dark matter experiments and are also used to a lesser extent in collider-based particle physics experiments. These experiments take advantage of both the very large, ultra-pure target volumes achievable and the multiple observable signal pathways possible in noble-element based particle detectors. As these experiments seek to increase their sensitivity, novel and improved technologies will be needed to enhance the precision of their measurements and to broaden the reach of their physics programs. The areas of R&D in noble element instrumentation that have been identified by the HEP community in the Snowmass process are highlighted by five key messages: IF08-1) Enhance and combine existing modalities (scintillation and electron drift) to increase signal-to-noise and reconstruction fidelity; IF08-2) Develop new modalities for signal detection in noble elements, including methods based on ion drift, metastable fluids, solid-phase detectors and dissolved targets. Collaborative and blue-sky R&D should also be supported to enable advances in this area; IF08-3) Improve the understanding of detector microphysics and calibrate detector response in new signal regimes; IF08-4) Address challenges in scaling technologies, including material purification, background mitigation, large-area readout, and magnetization; and IF08-5) Train the next generation of researchers, using fast-turnaround instrumentation projects to provide the design-through-result training no longer possible in very-large-scale experiments. This topical group report identifies and documents recent developments and future needs for noble element detector technologies. In addition, we highlight the opportunity that this area of research provides for continued training of the next generation of scientists., Comment: 20 pages, 1 figure

For a sample of 4378 nearby spiral and S0 galaxies, Yu and Ho (2020) used Fourier analysis of Sloan Digital Sky Survey images to show that the strengths of the spiral arms and the pitch angles of the arms are inversely correlated with central concentration. In the current study, we search for trends in the Yu and Ho (2020) spiral arm parameters with environment and specific star formation rate (sSFR). When comparing galaxies with similar concentrations, we do not find a significant difference in the arm strengths or pitch angles of spiral galaxies in clusters compared to field galaxies. When differences in concentration are taken into account, we also find no significant difference in the parameter f3 for cluster spirals compared to field spirals, where f3 is the normalized m = 3 Fourier amplitude. When concentration is held fixed, both arm strength and pitch angle are correlated with sSFR, but f3 is not. These relations support the suggestion by Davis et al. (2015) of a `fundamental plane' of spiral structure involving pitch angle, bulge stellar mass, and gas surface density. We discuss these results in terms of theories of spiral arm production and quenching in galaxies. To aid comparison with earlier studies based on Galaxy Zoo, we explore how the Yu and Ho (2020) parameters relate to similar parameters measured by Galaxy Zoo (i.e., f3 vs. number of arms, pitch angle vs. winding parameter, and concentration vs. bulge class)., Comment: AJ, in press