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Abstract Volcanic stratigraphy reconstruction is traditionally based on qualitative facies analysis complemented by geochemical analyses. Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. This technique, based on a simple photograph of a rock (or core) sample, is complementary to existing methods and allows another strategy to identify and compare volcanic rocks for stratigraphic correlation. Phenocryst size distributions display overall homogeneity within one volcanic body but may vary considerably between igneous bodies. Restricted to shallow intrusions and volcanic lavas, this concept allows for stratigraphic fingerprinting of volcanic rocks in poorly exposed, up to moderately altered, and/or complexly tectonized formations. We built an automated image analysis workflow using machine-learning for crystal segmentation, followed by statistical analysis of physical descriptors to compare and match the size distribution of feldspar phenocrysts. The workflow comprises three instance segmentation models for pre-processing the images, automated scale measurement and grain segmentation using Mask R-CNN. This avoids the laborious and time-consuming task of manual picking by image analysis, and allows for a rapid, unbiased and quantitative approach to determine crystal size distribution (CSD). Our volcanic architecture reconstruction of multiple dacite bodies in the mineralized Cambrian Mt Read Volcanics in Tasmania, Australia, is independently validated by bulk-rock chemical analyses of key samples. This volcanic stratigraphy method can be applied to a large variety of igneous rocks and is complementary to geochemical analyses and qualitative crystal content assessment.

We present here the DECam Ecliptic Exploration Project (DEEP), a 3 yr NOAO/NOIRLab Survey that was allocated 46.5 nights to discover and measure the properties of thousands of trans-Neptunian objects (TNOs) to magnitudes as faint as VR ∼ 27 mag, corresponding to sizes as small as 20 km diameter. In this paper we present the science goals of this project, the experimental design of our survey, and a technical demonstration of our approach. The core of our project is “digital tracking,” in which all collected images are combined at a range of motion vectors to detect unknown TNOs that are fainter than the single exposure depth of VR ∼ 23 mag. Through this approach, we reach a depth that is approximately 2.5 mag fainter than the standard LSST “wide fast deep” nominal survey depth of 24.5 mag. DEEP will more than double the number of known TNOs with observational arcs of 24 hr or more, and increase by a factor of 10 or more the number of known small (

We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project’s B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real detections. This enables us to characterize the survey’s completeness as a function of apparent magnitudes and on-sky rates of motion. We study the statistically optimal functional form for the magnitude, and develop a methodology that can estimate the magnitude and rate efficiencies for all survey’s pointing groups simultaneously. We have determined that our peak completeness is on average 80% in each pointing group, and our magnitude drops to 25% of this value at m _25 = 26.22. We describe the freely available survey simulation software and its methodology. We conclude by using it to infer that our effective search area for objects at 40 au is 14.8 deg ^2 , and that our lack of dynamically cold distant objects means that there at most 8 × 10 ^3 objects with 60 < a < 80 au and absolute magnitudes H ≤ 8.

We present the DECam Ecliptic Exploration Project (DEEP) survey strategy, including observing cadence for orbit determination, exposure times, field pointings and filter choices. The overall goal of the survey is to discover and characterize the orbits of a few thousand Trans-Neptunian objects (TNOs) using the Dark Energy Camera (DECam) on the Cerro Tololo Inter-American Observatory Blanco 4 m telescope. The experiment is designed to collect a very deep series of exposures totaling a few hours on sky for each of several 2.7 square degree DECam fields-of-view to achieve approximate depths of magnitude 26.2 using a wide V R filter that encompasses both the V and R bandpasses. In the first year, several nights were combined to achieve a sky area of about 34 square degrees. In subsequent years, the fields have been re-visited to allow TNOs to be tracked for orbit determination. When complete, DEEP will be the largest survey of the outer solar system ever undertaken in terms of newly discovered object numbers, and the most prolific at producing multiyear orbital information for the population of minor planets beyond Neptune at 30 au.

We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project. Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, using two-to-four-hour exposure sets, and the detections were subsequently linked across multiple observing seasons. This procedure allows us to find objects with magnitudes m _VR ≈ 26. The object discovery processing also included a comprehensive population of objects injected into the images, with a recovery and linking rate of at least 94%. The final orbits were obtained using a specialized orbit-fitting procedure that accounts for the positional errors derived from the digital tracking procedure. Our results include robust orbits and magnitudes for classical TNOs with absolute magnitudes H ∼ 10, as well as a dynamically detached object found at 76 au (semimajor axis a ≈ 77 au). We find a disagreement between our population of classical TNOs and the CFEPS-L7 three-component model for the Kuiper Belt.

We present the methods and results from the discovery and photometric measurement of 26 bright VR > 24 trans-Neptunian objects (TNOs) during the first year (2019–20) of the DECam Ecliptic Exploration Project (DEEP). The DEEP survey is an observational TNO survey with wide sky coverage, high sensitivity, and a fast photometric cadence. We apply a computer vision technique known as a progressive probabilistic Hough transform to identify linearly moving transient sources within DEEP photometric catalogs. After subsequent visual vetting, we provide a photometric and astrometric catalog of our TNOs. By modeling the partial lightcurve amplitude distribution of the DEEP TNOs using Monte Carlo techniques, we find our data to be most consistent with an average TNO axis ratio b / a < 0.5, implying a population dominated by non-spherical objects. Based on ellipsoidal gravitational stability arguments, we find our data to be consistent with a TNO population containing a high fraction of contact binaries or other extremely non-spherical objects. We also discuss our data as evidence that the expected binarity fraction of TNOs may be size-dependent.

The DECam Ecliptic Exploration Project (DEEP) is a deep survey of the trans-Neptunian solar system being carried out on the 4 m Blanco telescope at the Cerro Tololo Inter-American Observatory in Chile using the Dark Energy Camera (DECam). By using a shift-and-stack technique to achieve a mean limiting magnitude of r ∼ 26.2, DEEP achieves an unprecedented combination of survey area and depth, enabling quantitative leaps forward in our understanding of the Kuiper Belt populations. This work reports results from an analysis of 20, 3 deg ^2 DECam fields along the invariable plane. We characterize the efficiency and false-positive rates for our moving-object detection pipeline, and use this information to construct a Bayesian signal probability for each detected source. This procedure allows us to treat all of our Kuiper Belt object (KBO) detections statistically, simultaneously accounting for efficiency and false positives. We detect approximately 2300 candidate sources with KBO-like motion with signal-to-noise ratios > 6.5. We use a subset of these objects to compute the luminosity function of the Kuiper Belt as a whole, as well as the cold classical (CC) population. We also investigate the absolute magnitude ( H ) distribution of the CCs, and find consistency with both an exponentially tapered power law, which is predicted by streaming instability models of planetesimal formation, and a rolling power law. Finally, we provide an updated mass estimate for the CC Kuiper Belt of ${M}_{{CC}}({H}_{r}\lt 12)\,=\,{0.0017}_{-0.0004}^{+0.0010}{M}_{\oplus }$ , assuming albedo p = 0.15 and density ρ = 1 g cm ^−3 .

The Solar system Notification Alert Processing System ( snaps ) is a Zwicky Transient Facility (ZTF) and Rubin Observatory alert broker that will send alerts to the community regarding interesting events in the solar system. snaps is actively monitoring solar system objects and one of its functions is to compare objects (primarily main belt asteroids) to one another to find those that are outliers relative to the population. In this paper, we use the SNAPShot1 data set, which contains 31,693 objects from ZTF, and derive outlier scores for each of these objects. snaps employs an unsupervised approach; consequently, to derive outlier rankings for each object, we propose four different outlier metrics such that we can explore variants of the outlier scores and add confidence to the outlier rankings. We also provide outlier scores for each object in each permutation of 15 feature spaces, between two and 15 features, which yields 32,752 total feature spaces. We show that we can derive population outlier rankings each month at Rubin Observatory scale using four Nvidia A100 GPUs, and present several avenues of scientific investigation that can be explored using population outlier detection.

Superfast rotators (SFRs) are small solar system objects that rotate faster than generally possible for a cohesionless rubble pile. Their rotational characteristics allow us to make inferences about their interior structure and composition. Here, we present the methods and results from a preliminary search for SFRs in the DECam Ecliptic Exploration Project (DEEP) data set. We find three SFRs from a sample of 686 main-belt asteroids, implying an occurrence rate of ${0.4}_{-0.3}^{+0.1}$ %—a higher incidence rate than has been measured by previous studies. We suggest that this high occurrence rate is due to the small sub-kilometer size regime to which DEEP has access: the objects searched here were as small as ∼500 m. We compute the minimum required cohesive strength for each of these SFRs and discuss the implications of these strengths in the context of likely evolution mechanisms. We find that all three of these SFRs require strengths that are more than that of weak regolith but consistent with many cohesive asteroid strengths reported in the literature. Across the full DEEP data set, we have identified ∼70,000 Main-Belt Asteroids and expect ∼300 SFRs—a result that will be assessed in a future paper.

Deriving high-quality light curves for asteroids and other periodic sources from survey data is challenging owing to many factors, including the sparsely sampled observational record and diurnal aliasing, which is a signature imparted into the periodic signal of a source that is a function of the observing schedule of ground-based telescopes. In this paper we examine the utility of combining asteroid observational records from the Zwicky Transient Facility and the Transiting Exoplanet Survey Satellite, which are the ground- and space-based facilities, respectively, to determine to what degree the data from the space-based facility can suppress diurnal aliases. Furthermore, we examine several optimizations that are used to derive the rotation periods of asteroids, which we then compare to the reported rotation periods in the literature. Through this analysis we find that we can reliably derive the rotation periods for ∼85% of our sample of 222 objects that are also reported in the literature and that the remaining ∼15% are difficult to reliably derive, as many are asteroids that are insufficiently elongated, which produces a light curve with an insufficient amplitude and, consequently, an incorrect rotation period. We also investigate a binary classification method that biases against reporting incorrect rotation periods. We conclude the paper by assessing the utility of using other ground- or space-based facilities as companion telescopes to the forthcoming Rubin Observatory.

This study explored how frequent gamblers perceive gambling marketing and the role they feel it has in their gambling behaviour. Ten frequent gamblers participated in semi-structured interviews oriented around their experiences of gambling marketing. An interpretative phenomenological analysis of the data led to three overarching themes: exploiting gambling marketing for personal gain; gambling marketing as a test of self-control; and safer gambling messages marketing perceived as ineffective. These themes encapsulated participants' views of gambling marketing as something they could take advantage of to increase their own gambling success. Marketing was also perceived as a test of self-control among self-identified experienced gamblers, although identified as a risk to those who are considered more vulnerable. Finally, safer gambling messages included within marketing was considered ineffective due to perceived insincerity and being seen as an 'afterthought' by marketers. In support of previous research, the current investigation highlights concerning narratives around self-control and perceived risk, as encapsulated within gambling marketing, and these are evident in the perceptions of frequent gamblers. Given gamblers' perceived lack of effectiveness of current safer gambling messages within marketing, future research should explore new avenues for safer gambling promotion.

Waiting lists in mental health services are currently considered too long. Lengthy waits of up to 18 weeks are commonly reported in the UK. Waiting lists have long been associated with a variety of negative psychological responses, however there is little understanding behind young adults’ personal experiences of such delays within mental health services. The current study aimed to explore young adults’ experiences of waiting lists in mental healthcare in the UK. Seven young adults were interviewed in the current study (aged 19–22). Interpretive phenomenological analysis was utilised to explore participant accounts. Three super-ordinate themes were generated: Reliance on Alternative Methods of Support; Inability to Function Sufficiently; and Emergence of Negative Beliefs, Emotions and Thoughts. Participants primarily reported a variety of negative psychological and behavioural consequences associated with waiting lists in mental health services, as well as exacerbated existing physical and psychological health issues. In accordance with the limited number of previous studies, waiting lists are considered by patients to be barriers to mental health support and intervention. Future direction is advised.

We present here the design, architecture, and first data release for the Solar System Notification Alert Processing System (SNAPS). SNAPS is a solar system broker that ingests alert data from all-sky surveys. At present, we ingest data from the Zwicky Transient Facility (ZTF) public survey, and we will ingest data from the forthcoming Legacy Survey of Space and Time (LSST) when it comes online. SNAPS is an official LSST downstream broker. In this paper we present the SNAPS design goals and requirements. We describe the details of our automatic pipeline processing in which the physical properties of asteroids are derived. We present SNAPShot1, our first data release, which contains 5,458,459 observations of 31,693 asteroids observed by ZTF from 2018 July to 2020 May. By comparing a number of derived properties for this ensemble to previously published results for overlapping objects we show that our automatic processing is highly reliable. We present a short list of science results, among many that will be enabled by our SNAPS catalog: (1) we demonstrate that there are no known asteroids with very short periods and high amplitudes, which clearly indicates that in general asteroids in the size range 0.3–20 km are strengthless; (2) we find no difference in the period distributions of Jupiter Trojan asteroids, implying that the L4 and L5 clouds have different shape distributions; and (3) we highlight several individual asteroids of interest. Finally, we describe future work for SNAPS and our ability to operate at LSST scale.