Search

Empirically, the total number (or total mass) of globular clusters bound in a single galactic system correlates with the viral mass of the system. The form of this relation and its intrinsic scatter are potentially valuable constraints on theories of globular cluster formation and galaxy evolution. In this work, we use the DESI Legacy Imaging Survey to make a large-scale, homogeneous estimate of GC abundance around 707 galaxies at distances $\lesssim 30\,\mathrm{Mpc}$ with luminosities $8 \leq \log_{10}L/\mathrm{L}_\odot \leq 11.5$. The combination of depth and sky coverage in DESI-LS allow us to extend the techniques used by previous ground-based photometric GC surveys to a larger and potentially more representative sample of galaxies. We find average GC counts and radial profiles that are broadly consistent with the literature on individual galaxies, including good agreement with the distribution of GCs in the Milky Way, demonstrating the viability of DESI-LS images for this purpose. We find a relation between GC counts and virial mass in agreement with previous estimates based on heterogenous datasets, except at the lowest masses we probe, where we find a larger scatter in the number of cluster candidates and a slightly higher average count., Comment: 26 pages, 17 figures, submitted to ApJ

Stellar tidal streams are a key tracer of galaxy evolution and have the potential to provide an indirect means for tracing dark matter. For the Local Group, many diffuse substructures have been identified and their link to galaxy evolution has been traced. However, an analysis of a larger sample is required to better probe the frequency and characteristics of these streams to verify the predictions of the Lambda-CDM model and its implementation in cosmological simulations. For that purpose, we are carrying out the first systematic survey of faint stellar debris from tidally disrupted dwarf satellites around nearby galaxies up to a distance of 100 Mpc. In this paper, we present a catalogue with the results of the first harvest of stellar tidal streams found by visual inspection in deep images of ~ 700 galaxies from the Dark Energy Survey (DES). We include a photometric characterisation of the streams obtained by measuring their surface brightnesses and colours. We found a total of 63 streams in our sample at distances between 40 and 100 Mpc, including 59 which were not previously reported. We measured their average surface brightness for the grz bands, to be 28.35+/-0.20, 27.81+/-0.13 and 27.62+/-0.09 mag arcsec^-2, respectively. By applying a statistical analysis to our findings, we obtained a stream detection frequency of 9.1% +/- 1.1% , in agreement with previous studies. We identified stream progenitors in 5-14% of our stream sample, depending on the confidence level. The first catalogue of streams in the Local Universe presented here will be complemented by future stream surveys within the Stellar Stream Legacy Survey. In this work we have learnt that the faintest measured stream surface brightness can be significantly brighter than the surface brightness limit of an image measured at pixel level, mainly due to correlated noise present in the images., Comment: 20 pages with 16 figures, plus Appendices: 27 pages in total

We present ~ 126 new spectroscopically identified members of the GD-1 tidal stream obtained with the 5000-fiber Dark Energy Spectroscopic Instrument (DESI). We confirm the existence of a ``cocoon'' which is broad (FWHM~2.932deg~460pc) and kinematically hot (velocity dispersion, sigma~5-8km/s) component that surrounds a narrower (FWHM~0.353deg~55pc) and colder (sigma~ 2.2-2.6km/s) thin stream component (based on a median per star velocity precision of 2.7km/s). The cocoon extends over at least a ~ 20deg segment of the stream observed by DESI. The thin and cocoon components have similar mean values of [Fe/H]: -2.54+/- 0.04dex and -2.45+/-0.06dex suggestive of a common origin. The data are consistent with the following scenarios for the origin of the cocoon. The progenitor of the GD-1 stream was an accreted globular cluster (GC) and: (a) the cocoon was produced by pre-accretion tidal stripping of the GC while it was still inside its parent dwarf galaxy; (b) the cocoon is debris from the parent dwarf galaxy; (c) an initially thin GC tidal stream was heated by impacts from dark subhalos in the Milky Way; (d) an initially thin GC stream was heated by a massive Sagittarius dwarf galaxy; or a combination of some these. In the first two cases the velocity dispersion and mean metallicity are consistent with the parent dwarf galaxy having a halo mass of ~0^9\msun. Future DESI spectroscopy and detailed modeling may enable us to distinguish between these possible origins., Comment: Submitted to ApJ, 23 pages, 13 figures 4 tables

The Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS) will explore the assembly history of the Milky Way by characterising remnants of ancient dwarf galaxy accretion events and improving constraints on the distribution of dark matter in the outer halo. We present mock catalogues that reproduce the selection criteria of MWS and the format of the final MWS data set. These catalogues can be used to test methods for quantifying the properties of stellar halo substructure and reconstructing the Milky Way's accretion history with the MWS data, including the effects of halo-to-halo variance. The mock catalogues are based on a phase-space kernel expansion technique applied to star particles in the Auriga suite of six high-resolution $\Lambda$CDM magneto-hydrodynamic zoom-in simulations. They include photometric properties (and associated errors) used in DESI target selection and the outputs of the MWS spectral analysis pipeline (radial velocity, metallicity, surface gravity, and temperature). They also include information from the underlying simulation, such as the total gravitational potential and information on the progenitors of accreted halo stars. We discuss how the subset of halo stars observable by MWS in these simulations corresponds to their true content and properties. These mock Milky Ways have rich accretion histories, resulting in a large number of substructures that span the whole stellar halo out to large distances and have substantial overlap in the space of orbital energy and angular momentum., Comment: Accepted for publication in MNRAS, 31 pages, 27 figues, 7 tables. The mock catalogues are available at https://data.desi.lbl.gov/public/papers/mws/auridesi/v1

The dark matter subhalos orbiting in a galactic halo perturb the orbits of stars in thin stellar streams. Over time the random velocities in the streams develop non-Gaussian wings. The rate of velocity increase is approximately a random walk at a rate proportional to the number of subhalos, primarily those in the mass range $\approx 10^{6-7} M_\odot$. The distribution of random velocities in long, thin, streams is measured in simulated Milky Way-like halos that develop in representative WDM and CDM cosmologies. The radial velocity distributions are well modeled as the sum of a Gaussian and an exponential. The resulting MCMC fits find Gaussian cores of 1-2 km/sec and exponential wings that increase from 3 km/sec for 5.5 keV WDM, 4 km/sec for 7 keV WDM, to 6 km/sec for a CDM halo. The observational prospects to use stream measurements to constrain the nature of galactic dark matter are discussed., Comment: ApJ accepted

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Robotics and automation offer massive accelerations for solving intractable, multivariate scientific problems such as materials discovery, but the available search spaces can be dauntingly large. Bayesian optimization (BO) has emerged as a popular sample-efficient optimization engine, thriving in tasks where no analytic form of the target function/property is known. Here, we exploit expert human knowledge in the form of hypotheses to direct Bayesian searches more quickly to promising regions of chemical space. Previous methods have used underlying distributions derived from existing experimental measurements, which is unfeasible for new, unexplored scientific tasks. Also, such distributions cannot capture intricate hypotheses. Our proposed method, which we call HypBO, uses expert human hypotheses to generate improved seed samples. Unpromising seeds are automatically discounted, while promising seeds are used to augment the surrogate model data, thus achieving better-informed sampling. This process continues in a global versus local search fashion, organized in a bilevel optimization framework. We validate the performance of our method on a range of synthetic functions and demonstrate its practical utility on a real chemical design task where the use of expert hypotheses accelerates the search performance significantly.

Performing a large volume of experiments in Chemistry labs creates repetitive actions costing researchers time, automating these routines is highly desirable. Previous experiments in robotic chemistry have performed high numbers of experiments autonomously, however, these processes rely on automated machines in all stages from solid or liquid addition to analysis of the final product. In these systems every transition between machine requires the robotic chemist to pick and place glass vials, however, this is currently performed using open loop methods which require all equipment being used by the robot to be in well defined known locations. We seek to begin closing the loop in this vial handling process in a way which also fosters human-robot collaboration in the chemistry lab environment. To do this the robot must be able to detect valid placement positions for the vials it is collecting, and reliably insert them into the detected locations. We create a single modality visual method for estimating placement locations to provide a baseline before introducing two additional methods of feedback (force and tactile feedback). Our visual method uses a combination of classic computer vision methods and a CNN discriminator to detect possible insertion points, then a vial is grasped and positioned above an insertion point and the multi-modal methods guide the final insertion movements using an efficient search pattern. Through our experiments we show the baseline insertion rate of 48.78% improves to 89.55% with the addition of our "force and vision" multi-modal feedback method., Comment: 8 pages, 5 figures, accepted by CASE2023

Wide-area deep imaging surveys have discovered large numbers of extremely low surface brightness dwarf galaxies, which challenge galaxy formation theory and, potentially, offer new constraints on the nature of dark matter. Here we discuss one as-yet unexplored formation mechanism that may account for a fraction of low surface brightness dwarfs. We call this the `ghost galaxy' scenario. In this scenario, inefficient radiative cooling prevents star formation in the `main branch' of the merger tree of a low mass dark matter halo, such that almost all its stellar mass is acquired through mergers with less massive (but nevertheless star-forming) progenitors. Present-day systems formed in this way would be `ghostly' isolated stellar halos with no central galaxy. We use merger trees based on the Extended Press-Schechter formalism and the COCO cosmological N-body simulation to demonstrate that mass assembly histories of this kind can occur for low-mass halos in Lambda-CDM, but they are rare. They are most probable in isolated halos of present-day mass ~4x10^9 M_sun, occurring for ~5 per cent of all halos of that mass under standard assumptions about the timing and effect of cosmic reionization. The stellar masses of star-forming progenitors in these systems are highly uncertain; abundance-matching arguments imply a bimodal present-day mass function having a brighter population (median M_star ~3x10^6 M_sun) consistent with the tail of the observed luminosity function of ultra-diffuse galaxies. This suggests observable analogues of these systems may await discovery. We find that a stronger ionizing background (globally or locally) produces brighter and more extended ghost galaxies., Comment: 19 pages, 13 figures, ApJ in press

The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This opportunity arises in the midst of numerous ground- and space-based surveys that will provide extensive spectroscopy and imaging together covering the entire sky (such as Rubin/LSST, Euclid, UNIONS, SPHEREx, DESI, SDSS-V, GALAH, 4MOST, WEAVE, MOONS, PFS, UVEX, NEO Surveyor, etc.). Roman can uniquely provide uniform high-spatial-resolution (~0.1 arcsec) imaging over the entire sky, vastly expanding the science reach and precision of all of these near-term and future surveys. This imaging will not only enhance other surveys, but also facilitate completely new science. By imaging the full sky over two epochs, Roman can measure the proper motions for stars across the entire Milky Way, probing 100 times fainter than Gaia out to the very edge of the Galaxy. Here, we propose NANCY: a completely public, all-sky survey that will create a high-value legacy dataset benefiting innumerable ongoing and forthcoming studies of the universe. NANCY is a pure expression of Roman's potential: it images the entire sky, at high spatial resolution, in a broad infrared bandpass that collects as many photons as possible. The majority of all ongoing astronomical surveys would benefit from incorporating observations of NANCY into their analyses, whether these surveys focus on nearby stars, the Milky Way, near-field cosmology, or the broader universe., Comment: Submitted to the call for white papers for the Roman Core Community Survey (June 16th, 2023), and to the Bulletin of the AAS

We provide a survey of non-stationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including non-stationary kernel adaptations, partition and local GPs, and spatial warpings through deep Gaussian processes. We also overview publicly available software implementations and conclude with a bake-off involving an 8-dimensional satellite drag computer experiment. Code for this example is provided in a public git repository., Comment: 13 pages, 5 figures

The use of laboratory robotics for autonomous experiments offers an attractive route to alleviate scientists from tedious tasks while accelerating material discovery for topical issues such as climate change and pharmaceuticals. While some experimental workflows can already benefit from automation, sample preparation is still carried out manually due to the high level of motor function and dexterity required when dealing with different tools, chemicals, and glassware. A fundamental workflow in chemical fields is crystallisation, where one application is polymorph screening, i.e., obtaining a three dimensional molecular structure from a crystal. For this process, it is of utmost importance to recover as much of the sample as possible since synthesising molecules is both costly in time and money. To this aim, chemists scrape vials to retrieve sample contents prior to imaging plate transfer. Automating this process is challenging as it goes beyond robotic insertion tasks due to a fundamental requirement of having to execute fine-granular movements within a constrained environment (sample vial). Motivated by how human chemists carry out this process of scraping powder from vials, our work proposes a model-free reinforcement learning method for learning a scraping policy, leading to a fully autonomous sample scraping procedure. We first create a scenario-specific simulation environment with a Panda Franka Emika robot using a laboratory scraper that is inserted into a simulated vial, to demonstrate how a scraping policy can be learned successfully in simulation. We then train and evaluate our method on a real robotic manipulator in laboratory settings, and show that our method can autonomously scrape powder across various setups., Comment: 8 pages, 7 figures

Radial colour gradients within galaxies arise from gradients of stellar age, metallicity and dust reddening. Large samples of colour gradients from wide-area imaging surveys can complement smaller integral-field spectroscopy datasets and can be used to constrain galaxy formation models. Here we measure colour gradients for low-redshift galaxies (z<0.1) using photometry from the DESI Legacy Imaging Survey DR9. Our sample comprises ~93,000 galaxies with spectroscopic redshifts and ~574,000 galaxies with photometric redshifts. We focus on gradients across a radial range 0.5 Re to Re, which corresponds to the inner disk of typical late type systems at low redshift. This region has been the focus of previous statistical studies of colour gradients and has recently been explored by spectroscopic surveys such as MaNGA. We find the colour gradients of most galaxies in our sample are negative (redder towards the centre), consistent with the literature. We investigate empirical relationships between colour gradient, average $g-r$ and $r-z$ colour, $M_r$, $M_\star$, and sSFR. Trends of gradient strength with $M_r$ ($M_\star$) show an inflection around $M_r\sim-21$ ($\log_{10} \, M_\star/\mathrm{M_\odot}\sim10.5$). Below this mass, colour gradients become steeper with increasing $M_\star$, whereas colour gradients in more massive galaxies become shallower. We find that positive gradients (bluer stars at smaller radii) are typical for galaxies of $M_{\star}\sim10^{8}\,\mathrm{M_\odot}$. We compare our results to age and metallicity gradients in two datasets derived from fits of different stellar population libraries to MaNGA spectra, but find no clear consensus explanation for the trends we observe. Both MaNGA datasets seem to imply a significant contribution from dust reddening, in particular, to explain the flatness of colour gradients along the red sequence., Comment: 26 pages, 26 figures, Submitted to MNRAS, data of ~93,000 galaxies with spectroscopic redshift and ~574,000 galaxies with photometric redshift is available at https://osf.io/q8x76/?view_only=3dc4faf80aae40e186ec358ad261d490

Context. Stellar tidal streams are the result of tidal interactions between a central galaxy and lower mass systems like satellite galaxies or globular clusters. For the Local Group, many diffuse substructures have been identified and their link to the galaxy evolution has been traced. However it cannot be assumed that the Milky Way or M31 are representative of their galaxy class, and a larger sample of analogue galaxies beyond the Local Group is required to be able to generalise the underlying theory. Aims. We want to characterise photometrically the stellar streams around Milky Way analogues in the local Universe with the goal to deepen our understanding of the interaction between host and satellite galaxies, and ultimately of the galaxy formation and evolution processes. Methods. In the present work we identified and analysed stellar tidal streams around Milky Way analogue galaxies from the SAGA sample, using deep images of the DESI Legacy Imaging Surveys (for this sample, we obtain a range of r-band surface brightness limit between 27.8 and 29 mag / arcsec2). We measure the surface brightness and colours of the detected streams using GNU Astronomy Utilities software. Results. We identified 16 new stellar tidal streams around Milky Way analogue galaxies at distances between 25 and 40 Mpc. Applying statistical analysis to our findings for the SAGA II galaxy sample, we obtained a frequency of 12.2% +/- 2.4% for stellar streams. We measured surface brightness and colours of the detected streams, and the comparison to the dwarf satellite galaxies population around galaxies belonging to the same SAGA sample shows that the mean colour of the streams is 0.20 mag redder than that of the SAGA satellites; also, the streams are, in average, 0.057 +/- 0.021 mag redder that their progenitor, for those cases when a likely progenitor could be identified., Comment: 5 pages, 3 figures, 2 tables

In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies (ELGs), luminous red galaxies (LRGs) and quasars (QSOs). In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey (BGS) and the Milky Way Survey (MWS). DESI also observes a selection of "secondary" targets for bespoke science goals. This paper gives an overview of the publicly available pipeline (desitarget) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID (TARGETID) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe "supporting" DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data., Comment: AJ, accepted, 27 pages, 4 figures, 10 tables, one of a suite of 8 papers detailing targeting for DESI. Minor textual updates to better match the final, accepted version. Also added two missing co-authors

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory. Over the next 5 yr DESI MWS will observe approximately seven million stars at Galactic latitudes |b|>20 degrees, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines for deriving radial velocities, atmospheric parameters, and chemical abundances. We use ~500,000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to ~1 km/s and [Fe/H] accurate to ~0.2 dex for typical stars in our main sample. We find the stellar parameter distributions from ~100 sq. deg of SV observations with >90% completeness on our main sample are in good agreement with expectations from mock catalogs and previous surveys., Comment: Accepted for publication in ApJ, 44 pages, 25 figures, 4 tables, one of a suite of 8 papers detailing targeting for DESI; v2 added links to data shown in figures, added citations to other DESI papers, corrected author list and minor typos; v3 fixed minor errors in Fig. 6 and clarified associated text; v4 updated to include minor changes in response to review

Over the next five years, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage-IV dark energy galaxy survey. At $z < 0.6$, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the Universe during the dark energy dominated epoch with redshifts of >10 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter., Comment: AJ, submitted, 34 pages, 22 figures, one of a suite of 8 papers detailing targeting for DESI

Automated laboratory experiments have the potential to propel new discoveries, while increasing reproducibility and improving scientists' safety when handling dangerous materials. However, many automated laboratory workflows have not fully leveraged the remarkable advancements in robotics and digital lab equipment. As a result, most robotic systems used in the labs are programmed specifically for a single experiment, often relying on proprietary architectures or using unconventional hardware. In this work, we tackle this problem by proposing a novel robotic system architecture specifically designed with and for chemists, which allows the scientist to easily reconfigure their setup for new experiments. Specifically, the system's strength is its ability to combine together heterogeneous robotic platforms with standard laboratory equipment to create different experimental setups. Finally, we show how the architecture can be used for specific laboratory experiments through case studies such as solubility screening and crystallisation., Comment: 7 pages, 5 figures, accepted for presentation at 2022 International Conference on Robotics and Automation (ICRA2022)

To understand how the evolution of grain size distribution in galaxies affects observed dust properties, we apply a post-processing dust evolution model to galaxy merger trees from the IllustrisTNG cosmological hydrodynamical simulation. Our dust model includes stellar dust production, sputtering in hot gas, dust growth by accretion and coagulation in the dense interstellar medium (ISM), and shattering in the diffuse ISM. We decompose the grain size distribution into different dust species depending on the elemental abundances and the dense ISM fraction given by the simulation. In our previous work, we focused on Milky Way (MW) analogs and reproduced the observed MW extinction curve. In this study, we compute dust spectral energy distributions (SEDs) for the MW analogues. Our simulated SEDs broadly reproduce the observed MW SED within their dispersion and so does the observational data of nearby galaxies, although they tend to underpredict the MW SED at short wavelengths where emission is dominated by polycyclic aromatic hydrocarbons (PAHs). We find that metallicity and dense gas fraction are the most critical factors for the SED shape, through their influence on coagulation and shattering.The overall success of our models in reproducing the MW SED further justifies the dust evolution processes included in the model and predicts the dispersion in the SEDs caused by the variety in the assembly history. We also show that the most significant increase in the dust SED occurs between redshifts $z\sim 3$ and 2 in the progenitors of the simulated MW-like galaxies., Comment: 10 pages, 8 figure, accepted for publication in MNRAS

Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Two DGP regimes have emerged in recent literature. A "big data" regime, prevalent in machine learning, favors approximate, optimization-based inference for fast, high-fidelity prediction. A "small data" regime, preferred for computer surrogate modeling, deploys posterior integration for enhanced uncertainty quantification (UQ). We aim to bridge this gap by expanding the capabilities of Bayesian DGP posterior inference through the incorporation of the Vecchia approximation, allowing linear computational scaling without compromising accuracy or UQ. We are motivated by surrogate modeling of simulation campaigns with upwards of 100,000 runs - a size too large for previous fully-Bayesian implementations - and demonstrate prediction and UQ superior to that of "big data" competitors. All methods are implemented in the "deepgp" package on CRAN., Comment: 17 pages, 6 figures

Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups., Comment: 7 pages, 4 figures

Despite efforts over several decades, direct-detection experiments have not yet led to the discovery of the dark matter (DM) particle. This has led to increasing interest in alternatives to the Lambda CDM (LCDM) paradigm and alternative DM scenarios (including fuzzy DM, warm DM, self-interacting DM, etc.). In many of these scenarios, DM particles cannot be detected directly and constraints on their properties can ONLY be arrived at using astrophysical observations. The Dark Energy Spectroscopic Instrument (DESI) is currently one of the most powerful instruments for wide-field surveys. The synergy of DESI with ESA's Gaia satellite and future observing facilities will yield datasets of unprecedented size and coverage that will enable constraints on DM over a wide range of physical and mass scales and across redshifts. DESI will obtain spectra of the Lyman-alpha forest out to z~5 by detecting about 1 million QSO spectra that will put constraints on clustering of the low-density intergalactic gas and DM halos at high redshift. DESI will obtain radial velocities of 10 million stars in the Milky Way (MW) and Local Group satellites enabling us to constrain their global DM distributions, as well as the DM distribution on smaller scales. The paradigm of cosmological structure formation has been extensively tested with simulations. However, the majority of simulations to date have focused on collisionless CDM. Simulations with alternatives to CDM have recently been gaining ground but are still in their infancy. While there are numerous publicly available large-box and zoom-in simulations in the LCDM framework, there are no comparable publicly available WDM, SIDM, FDM simulations. DOE support for a public simulation suite will enable a more cohesive community effort to compare observations from DESI (and other surveys) with numerical predictions and will greatly impact DM science., Comment: Contributed white paper to Snowmass 2021, CF03; minor revisions

This paper extends the recently obtained complete and continuous map of the Lattice Isometry Space (LISP) to the practical case of dimension 3. A periodic 3-dimensional lattice is an infinite set of all integer linear combinations of basis vectors in Euclidean 3-space. Motivated by crystal structures determined in a rigid form, we study lattices up to rigid motion or isometry, which is a composition of translations, rotations and reflections. The resulting space LISP consists of infinitely many isometry classes of lattices. In dimension 3, we parameterise this continuous space LISP by six coordinates and introduce new metrics satisfying the metric axioms and continuity under all perturbations. This parameterisation helps to visualise hundreds of thousands of real crystal lattices from the Cambridge Structural Database for the first time., Comment: 26 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:2109.10885

This paper develops geographic-style maps containing 2D lattices in all known crystals parameterised by recent complete invariants. Motivated by rigid crystal structures, lattices are considered up to rigid motion and uniform scaling. The resulting space of 2D lattices is a square with identified edges or a sphere without one point. The new continuous maps show all Bravais classes as low-dimensional subspaces, visualise hundreds of thousands of real crystal lattices from the Cambridge Structural Database, and motivate the development of continuous and invariant-based crystallography., Comment: 24 pages, 14 figures. The second version (the latest pdf at http://kurlin.org/projects/periodic-geometry-topology/lattices2Dmap.pdf) focuses on all 2D lattices extracted from 870K+ crystals in the Cambridge Structural Database. The mathematical details are in the paper arxiv:2201.05150, whose latest version is at http://kurlin.org/projects/periodic-geometry-topology/lattices2Dmaths.pdf

Crystal Structure Prediction (CSP) aims to discover solid crystalline materials by optimizing periodic arrangements of atoms, ions or molecules. CSP takes weeks of supercomputer time because of slow energy minimizations for millions of simulated crystals. The lattice energy is a key physical property, which determines thermodynamic stability of a crystal but has no simple analytic expression. Past machine learning approaches to predict the lattice energy used slow crystal descriptors depending on manually chosen parameters. The new area of Periodic Geometry offers much faster isometry invariants that are also continuous under perturbations of atoms. Our experiments on simulated crystals confirm that a small distance between the new invariants guarantees a small difference of energies. We compare several kernel methods for invariant-based predictions of energy and achieve the mean absolute error of less than 5kJ/mole or 0.05eV/atom on a dataset of 5679 crystals., Comment: To appear in the proceedings of DACOMSIN (Data and Computation for Materials Science and Innovation) 2021, https://en.misis.ru/university/colleges/sound/thermochemistry/dacomsin/