Search

The accurate and fast estimation of velocity models is crucial in seismic imaging. Conventional methods, like Tomography and Full-Waveform Inversion (FWI), obtain appropriate velocity models; however, they require intense and specialized human supervision and consume much time and computational resources. In recent years, some works investigated deep learning(DL) algorithms to obtain the velocity model directly from shots or migrated angle panels, obtaining encouraging predictions of synthetic models. This paper proposes a new flow to increase the complexity of velocity models recovered with DL. Inspired by the conventional geophysical velocity model building methods, instead of predicting the entire model in one step, we predict the velocity model iteratively. We implement the iterative nature of the process when, for each iteration, we train the DL algorithm to determine the velocity model with a certain level of precision/resolution for the next iteration; we name this process as Deep-Tomography. Starting from an initial model that roughly approaches the true model, the Deep-Tomography is able to predict an appropriate final model, even in complete unseen data, like the Marmousi model., Comment: 27 pages, 9 figures. First manuscript version submitted to Geophysical Journal International in February 2022

Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images., Comment: Manuscript published at Geophysics

We present the second public data release (DR2) from the DECam Local Volume Exploration survey (DELVE). DELVE DR2 combines new DECam observations with archival DECam data from the Dark Energy Survey, the DECam Legacy Survey, and other DECam community programs. DELVE DR2 consists of ~160,000 exposures that cover >21,000 deg^2 of the high Galactic latitude (|b| > 10 deg) sky in four broadband optical/near-infrared filters (g, r, i, z). DELVE DR2 provides point-source and automatic aperture photometry for ~2.5 billion astronomical sources with a median 5{\sigma} point-source depth of g=24.3, r=23.9, i=23.5, and z=22.8 mag. A region of ~17,000 deg^2 has been imaged in all four filters, providing four-band photometric measurements for ~618 million astronomical sources. DELVE DR2 covers more than four times the area of the previous DELVE data release and contains roughly five times as many astronomical objects. DELVE DR2 is publicly available via the NOIRLab Astro Data Lab science platform., Comment: 23 pages, 10 figures, 5 tables; to be submitted to AAS Journals; public data release at https://datalab.noirlab.edu/delve/. arXiv admin note: substantial text overlap with arXiv:2103.07476

We report the discovery of Pegasus IV, an ultra-faint dwarf galaxy found in archival data from the Dark Energy Camera processed by the DECam Local Volume Exploration Survey. Pegasus IV is a compact, ultra-faint stellar system ($r_{1/2} = 41^{+8}_{-6}$ pc; $M_V = -4.25 \pm 0.2$ mag) located at a heliocentric distance of $90^{+4}_{-6}$ kpc. Based on spectra of seven non-variable member stars observed with Magellan/IMACS, we confidently resolve Pegasus IV's velocity dispersion, measuring $\sigma_{v} = 3.3^{+1.7}_{-1.1} \text{ km s}^{-1}$ (after excluding three velocity outliers); this implies a mass-to-light ratio of $M_{1/2}/L_{V,1/2} = 167^{+224}_{-99} M_{\odot}/L_{\odot}$ for the system. From the five stars with the highest signal-to-noise spectra, we also measure a systemic metallicity of $\rm [Fe/H] = -2.67^{+0.25}_{-0.29}$ dex, making Pegasus IV one of the most metal-poor ultra-faint dwarfs. We tentatively resolve a non-zero metallicity dispersion for the system. These measurements provide strong evidence that Pegasus IV is a dark-matter-dominated dwarf galaxy, rather than a star cluster. We measure Pegasus IV's proper motion using data from Gaia Early Data Release 3, finding ($\mu_{\alpha*}, \mu_{\delta}) = (0.33\pm 0.07, -0.21 \pm 0.08) \text{ mas yr}^{-1}$. When combined with our measured systemic velocity, this proper motion suggests that Pegasus IV is on an elliptical, retrograde orbit, and is currently near its orbital apocenter. Lastly, we identify three potential RR Lyrae variable stars within Pegasus IV, including one candidate member located more than ten half-light radii away from the system's centroid. The discovery of yet another ultra-faint dwarf galaxy strongly suggests that the census of Milky Way satellites is still incomplete, even within 100 kpc., Comment: 22 pages, 6 figures + 2 page Appendix; submitting to AAS journals

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical objects' rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analyzed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific Strong Lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named Strong Gravitational Lensing Challenge (SGLC). This work presents the Deep Learning strategies and methodology used to design the highest-scoring algorithm in the II SGLC. We discuss the approach used for this dataset, the choice for a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a dataset representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable and accurate analyses of strong lenses with deep learning frameworks., Comment: 14 pages, 12 figures

In this paper we will list a few important goals that need to be addressed in the next decade, also taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant $H_0$, the $\sigma_8$--$S_8$ tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the $5.0\,\sigma$ tension between the {\it Planck} CMB estimate of the Hubble constant $H_0$ and the SH0ES collaboration measurements. After showing the $H_0$ evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the {\it Planck} CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density $\Omega_m$, and the amplitude or rate of the growth of structure ($\sigma_8,f\sigma_8$). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the $H_0$--$S_8$ tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals.[Abridged], Comment: Contribution to Snowmass 2021. 224 pages, 27 figures. Accepted for publication in JHEAp

We show that the derived category of a curve is embedded into the derived category of the moduli space of vector bundles on the curve of coprime rank and degree. We also generalize the semiorthogonal decomposition constructed by Narasimhan and Belmans-Mukhopadhyay. Finally, we produce a one-dimensional family of ACM bundles over the moduli space., Comment: We merge our article arXiv:2106.04857 and the previous version of arXiv:2201.10033 into this new version

We investigate two algebra of curves on a topological surface with punctures - the cluster algebra of surfaces defined by Fomin, Shapiro, and Thurston, and the generalized skein algebra constructed by Roger and Yang. By establishing their compatibility, we resolve Roger-Yang's conjecture on the deformation quantization of the decorated Teichmuller space. We also obtain several structural results on the cluster algebra of surfaces. The cluster algebra of a positive genus surface is not finitely generated, and it differs from its upper cluster algebra., Comment: 32 pages, a main result (Theorem C) has been strengthened. Comments are welcome!

Entanglement and the R\'enyi entropies for Dirac fermions on 2 dimensional torus in the presence of chemical potential, current source, and topological Wilson loop are unified in a single framework by exhausting all the ingredients of the electromagnetic vertex operators of $\mathbb{Z}_n$ orbifold conformal field theory. We employ different normalizations for different topological sectors to organize various topological phase transitions in the context of entanglement entropy. Pictorial representations for the topological transitions are given for the $n=2$ R\'enyi entropy. Our analytic computations reveal numerous novelties and provide resolutions for existing issues. We have settled to provide non-singular entanglement entropies that are also continuous across the topological sectors. Surprisingly, in infinite space, these entropies become exact and depend only on the Wilson loop. On a circle, we resolve to find the entropies subtly depend on the chemical potential at zero temperature, which is useful for probing the ground state energy levels of quantum systems., Comment: 7+3 pages, 7 figures. Here entanglement entropy is continuous across the Wilson loop topological transitions, while arXiv:1705.01859 realizes its discontinuous version

In this paper, we describe a new method to select globular cluster (GC) candidates, including galaxy subtraction with unsharp masking, template fitting techniques and the inclusion of Gaia's proper motions. We report the use of the 12-band photometric system of S-PLUS to determine radial velocities and stellar populations of GCs around nearby galaxies. Specifically, we assess the effectiveness of identifying GCs around nearby and massive galaxies (D $< 20$ Mpc and $\sigma > 200$ km/s) in a multi-band survey such as S-PLUS by using spectroscopically confirmed GCs and literature GC candidate lists around the bright central galaxy in the Fornax cluster, NGC 1399 (D = 19 Mpc), and the isolated lenticular galaxy NGC 3115 (D = 9.4 Mpc). Despite the shallow survey depth, that limits the present work to $r < 21.3$ mag, we measure reliable photometry and perform robust SED fitting for a sample of 115 GCs around NGC 1399 and 42 GCs around NGC 3115, recovering radial velocities, ages, and metallicities for the GC populations., Comment: Accepted for publication in MNRAS. 11 pages, 5 figures, 2 tables

The growing availability of the data collected from smart manufacturing is changing the paradigms of production monitoring and control. The increasing complexity and content of the wafer manufacturing process in addition to the time-varying unexpected disturbances and uncertainties, make it infeasible to do the control process with model-based approaches. As a result, data-driven soft-sensing modeling has become more prevalent in wafer process diagnostics. Recently, deep learning has been utilized in soft sensing system with promising performance on highly nonlinear and dynamic time-series data. Despite its successes in soft-sensing systems, however, the underlying logic of the deep learning framework is hard to understand. In this paper, we propose a deep learning-based model for defective wafer detection using a highly imbalanced dataset. To understand how the proposed model works, the deep visualization approach is applied. Additionally, the model is then fine-tuned guided by the deep visualization. Extensive experiments are performed to validate the effectiveness of the proposed system. The results provide an interpretation of how the model works and an instructive fine-tuning method based on the interpretation.

Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing. While playing an essential role in facilitating real-time monitoring and control, the data-driven soft-sensors in industries have provided a competitive edge when augmented with deep learning approaches for wafer fault-diagnostics. Despite the success of deep learning methods across various domains, they tend to suffer from bad performance on multi-variate soft-sensing data domains. To mitigate this, we propose a soft-sensing ConFormer (CONvolutional transFORMER) for wafer fault-diagnostic classification task which primarily consists of multi-head convolution modules that reap the benefits of fast and light-weight operations of convolutions, and also the ability to learn the robust representations through multi-head design alike transformers. Another key issue is that traditional learning paradigms tend to suffer from low performance on noisy and highly-imbalanced soft-sensing data. To address this, we augment our soft-sensing ConFormer model with a curriculum learning-based loss function, which effectively learns easy samples in the early phase of training and difficult ones later. To further demonstrate the utility of our proposed architecture, we performed extensive experiments on various toolsets of Seagate Technology's wafer manufacturing process which are shared openly along with this work. To the best of our knowledge, this is the first time that curriculum learning-based soft-sensing ConFormer architecture has been proposed for soft-sensing data and our results show strong promise for future use in soft-sensing research domain.

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.

We present a new constraint on the Hubble constant $H_0$ using a sample of well-localized gravitational wave (GW) events detected during the first three LIGO/Virgo observing runs as dark standard sirens. In the case of dark standard sirens, a unique host galaxy is not identified, and the redshift information comes from the distribution of potential host galaxies. From the third LIGO/Virgo observing run detections, we add the asymmetric-mass binary black hole GW190412, the high-confidence GW candidates S191204r, S200129m, and S200311bg to the sample of dark standard sirens analyzed. Our sample contains the top $20\%$ (based on localization) GW events and candidates to date with significant coverage by the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey. We combine the $H_0$ posterior for eight dark siren events, finding $H_0 = 79.8^{+19.1}_{-12.8}~{\rm km~s^{-1}~Mpc^{-1}}$ ($68\%$ Highest Density Interval) for a prior in $H_0$ uniform between $[20,140]~{\rm km~s^{-1}~Mpc^{-1}}$. This result shows that a combination of 8 well-localized dark sirens combined with an appropriate galaxy catalog is able to provide an $H_0$ constraint that is competitive ($\sim 20\%$ versus $18\%$ precision) with a single bright standard siren analysis (i.e. assuming the electromagnetic counterpart) using GW170817. When combining the posterior with that from GW170817, we obtain $H_0 = 72.77^{+11.0}_{-7.55}~{\rm km~s^{-1}~Mpc^{-1}}$. This result is broadly consistent with recent $H_0$ estimates from both the Cosmic Microwave Background and Supernovae., Comment: 14 pages, 5 figures

With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more complex, yet, the data sets remain limited: researchers are fitting million-parameter models with hundreds of data samples, which is insufficient to exercise the effectiveness of their models and thus often fail to perform when implemented in industrial applications. To solve this long-lasting problem, we are providing large scale, high dimensional time series manufacturing sensor data from Seagate Technology to the public. We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets. Transformer is used because, it has outperformed state-of-the-art techniques in Natural Language Processing, and since then has also performed well in the direct application to computer vision without introduction of image-specific inductive biases. We observe the similarity of a sentence structure to the sensor readings and process the multi-variable sensor readings in a time series in a similar manner of sentences in natural language. The high-dimensional time-series data is formatted into the same shape of embedded sentences and fed into the transformer model. The results show that transformer model outperforms the benchmark models in soft sensing field based on auto-encoder and long short-term memory (LSTM) models. To the best of our knowledge, we are the first team in academia or industry to benchmark the performance of original transformer model with large-scale numerical soft sensing data.

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ($\sigma_\text{NMAD}$) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r-auto magnitudes between 16 and 21., Comment: 49 pages, 9 figures. Accepted for publication in Astronomy and Computing. Data products are available in https://splus.cloud/ website

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond., Comment: 32 pages, 18 figures, accepted for publication by ApJ

IEEE BigData 2021 Cup: Soft Sensing at Scale is a data mining competition organized by Seagate Technology, in association with the IEEE BigData 2021 conference. The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques. In this paper we go into the details of the challenge and describe the data set provided to participants. We define the metrics of interest, baseline models, and describe approaches we found meaningful which may be a good starting point for further analysis. We discuss the results obtained with our approaches and give insights on what potential challenges participants may run into. Students, researchers, and anyone interested in working on a major industrial problem are welcome to participate in the challenge!, Comment: 4 pages, 4 figures, for IEEE Big Data Cup challenge 2021