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We present the optical spectroscopic evolution of SN~2023ixf seen in sub-night cadence spectra from 1.18 to 14 days after explosion. We identify high-ionization emission features, signatures of interaction with material surrounding the progenitor star, that fade over the first 7 days, with rapid evolution between spectra observed within the same night. We compare the emission lines present and their relative strength to those of other supernovae with early interaction, finding a close match to SN~2020pni and SN~2017ahn in the first spectrum and SN~2014G at later epochs. To physically interpret our observations we compare them to CMFGEN models with confined, dense circumstellar material around a red supergiant progenitor from the literature. We find that very few models reproduce the blended \NC{} emission lines observed in the first few spectra and their rapid disappearance thereafter, making this a unique diagnostic. From the best models, we find a mass-loss rate of $10^{-3}-10^{-2}$ \mlunit{}, which far exceeds the mass-loss rate for any steady wind, especially for a red supergiant in the initial mass range of the detected progenitor. These mass-loss rates are, however, similar to rates inferred for other supernovae with early circumstellar interaction. Using the phase when the narrow emission features disappear, we calculate an outer dense radius of circumstellar material $R_\mathrm{CSM, out}\sim5\times10^{14}~\mathrm{cm}$ and a mean circumstellar material density of $\rho=5.6\times10^{-14}~\mathrm{g\,cm^{-3}}$. This is consistent with the lower limit on the outer radius of the circumstellar material we calculate from the peak \Halpha{} emission flux, $R_\text{CSM, out}\gtrsim9\times10^{13}~\mathrm{cm}$., Comment: Submitted to ApJL

The distribution of small-scale magnetic fields in stellar photospheres is an important ingredient in our understanding of the magnetism of low mass stars. Their spatial distribution connects the field generated in the stellar interior with the outer corona and the large scale field, and thereby affects the space weather of planets. Unfortunately, we lack techniques that can locate them on most low-mass stars. One strategy is to localize field concentrations using the flares that occur in their vicinity. We explore a new method that adapts the spot simulation software fleck to study the modulation of flaring times as a function of active latitude. We use empirical relations to construct flare light curves similar to those available from Kepler and the Transiting Exoplanet Survey Satellite (TESS), search them for flares, and use the waiting times between flares to determine the location of active latitudes. We find that the mean and standard deviation of the waiting time distribution provide a unique diagnostic of flaring latitudes as a function of the number of active regions. Latitudes are best recovered when stars have three or less active regions that flare repeatedly, and active latitude widths below 20 deg; when either increases, the information about the active latitude location is gradually lost. We demonstrate our technique on a sample of flaring G dwarfs observed with the Kepler satellite, and furthermore suggest that combining ensemble methods for spots and flares could overcome the limitations of each individual technique for the localization of surface magnetic fields., 15 pages, 10 figures. Accepted to MNRAS. Repository with source code: https://github.com/ekaterinailin/flare-locations-ensembles-science Repository with data: https://zenodo.org/record/7996929

We explore the transit timing variations (TTVs) of the young (22 Myr) nearby AU Mic planetary system. For AU Mic b, we introduce three Spitzer (4.5 $μ$m) transits, five TESS transits, 11 LCO transits, one PEST transit, one Brierfield transit, and two transit timing measurements from Rossiter-McLaughlin observations; for AU Mic c, we introduce three TESS transits. We present two independent TTV analyses. First, we use EXOFASTv2 to jointly model the Spitzer and ground-based transits and to obtain the midpoint transit times. We then construct an O--C diagram and model the TTVs with Exo-Striker. Second, we reproduce our results with an independent photodynamical analysis. We recover a TTV mass for AU Mic c of 10.8$^{+2.3}_{-2.2}$ M$_{E}$. We compare the TTV-derived constraints to a recent radial-velocity (RV) mass determination. We also observe excess TTVs that do not appear to be consistent with the dynamical interactions of b and c alone, and do not appear to be due to spots or flares. Thus, we present a hypothetical non-transiting "middle-d" candidate exoplanet that is consistent with the observed TTVs, the candidate RV signal, and would establish the AU Mic system as a compact resonant multi-planet chain in a 4:6:9 period commensurability. These results demonstrate that the AU Mic planetary system is dynamically interacting producing detectable TTVs, and the implied orbital dynamics may inform the formation mechanisms for this young system. We recommend future RV and TTV observations of AU Mic b and c to further constrain the masses and to confirm the existence of possible additional planet(s)., Corrected typos; revised Section 3, 4, and 5 to reflect reanalysis, results unchanged. Submitted to AAS Journals Nov 11th, 2020; favorable referee report received Jan 3rd; final draft accepted for publication in the AJ Apr 19th

General circulation models (GCMs) provide context for interpreting multi-wavelength, multi-phase data of the atmospheres of tidally locked exoplanets. In the current study, the non-hydrostatic THOR GCM is coupled with the HELIOS radiative transfer solver for the first time, supported by an equilibrium chemistry solver (FastChem), opacity calculator (HELIOS-K) and Mie scattering code (LX-MIE). To accurately treat the scattering of radiation by medium-sized to large aerosols/condensates, improved two-stream radiative transfer is implemented within a GCM for the first time. Multiple scattering is implemented using a Thomas algorithm formulation of the two-stream flux solutions, which decreases the computational time by about 2 orders of magnitude compared to the iterative method used in past versions of HELIOS. As a case study, we present four GCMs of the hot Jupiter WASP-43b, where we compare the temperature, velocity, entropy, and streamfunction, as well as the synthetic spectra and phase curves, of runs using regular versus improved two-stream radiative transfer and isothermal versus non-isothermal layers. While the global climate is qualitatively robust, the synthetic spectra and phase curves are sensitive to these details. A THOR+HELIOS WASP-43b GCM (horizontal resolution of about 4 degrees on the sphere and with 40 radial points) with multi-wavelength radiative transfer (30 k-table bins) running for 3000 Earth days (864,000 time steps) takes about 19-26 days to complete depending on the type of GPU., Comment: 31 pages, 24 figures, accepted for publication at MNRAS

Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited., The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project. © 2022. The Author(s). Published by the American Astronomical Society., We acknowledge the Gordon and Betty Moore Foundation for their continued financial support. This work is partially supported by NASA under grant No. 80NSSC22K0347 issued through the NASA ROSES program. This work is partially supported by the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. We also thank NumFOCUS and the Python Software Foundation for financial support. J.A.A.-M. acknowledges funding support from Macquarie University through the International Macquarie University Research Excellence Scholarship (“iMQRES”). A.B. was supported by the Lendület Program of the Hungarian Academy of Sciences, project No. LP2018-7, and the KKP-137523 “SeismoLab” Élvonal grant of the Hungarian Research, Development and Innovation Office (NKFIH). M.B. gratefully acknowledges support from the ANID BASAL project FB210003 and the FONDECYT regular grant 1211000. F.D.E. acknowledges funding through the H2020 ERC Consolidator grant 683184, the ERC Advanced grant 695671 “QUENCH” and support from the Science and Technology Facilities Council (STFC). S.G. made contributions to Astropy as part of the Google Summer of Code 2021. S.G. acknowledges some intellectual support from the CCAD-UNC-NOVA. Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation (grant agreement No.: DNRF106). N.K. acknowledges support from the MIT Pappalardo fellowship. K.A.O. acknowledges support from the European Research Council (ERC) through Advanced Investigator grant to C.S. Frenk, DMIDAS (GA 786910). C.P. is supported by the Canadian Space Agency under a contract with NRC Herzberg Astronomy and Astrophysics. S.P. has been supported by Spanish MINECO-FEDER grant RTI2018-096188-B-I00 J.P.G. acknowledges funding support from Spanish public funds for research from project PID2019-107061GB-C63 from the “Programas Estatales de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad,” as well as from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). N.S. acknowledges support from the National Science Foundation through the Graduate Research Fellowship Program under grant 1842402. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Supported by the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). Parts of this research were supported by the Australian Research Council Centre of Excellence for All-Sky Astro-physics in 3 Dimensions (ASTRO 3D), through project number CE170100013. D.S. is supported by STFC grant ST/S000240/1. N.S. acknowledges the support of the Science and Engineering Research Council of Canada (NSERC) Canadian Graduate Scholarship—Doctoral Program, [funding reference numbers CGSD547212020].

Pandora is a SmallSat mission designed to study the atmospheres of exoplanets, and was selected as part of NASA's Astrophysics Pioneers Program. Transmission spectroscopy of transiting exoplanets provides our best opportunity to identify the makeup of planetary atmospheres in the coming decade. Stellar brightness variations due to star spots, however, can impact these measurements and contaminate the observed spectra. Pandora's goal is to disentangle star and planet signals in transmission spectra to reliably determine exoplanet atmosphere compositions. Pandora will collect long-duration photometric observations with a visible-light channel and simultaneous spectra with a near-IR channel. The broad-wavelength coverage will provide constraints on the spot and faculae covering fractions of low-mass exoplanet host stars and the impact of these active regions on exoplanetary transmission spectra. Pandora will subsequently identify exoplanets with hydrogen- or water-dominated atmospheres, and robustly determine which planets are covered by clouds and hazes. Pandora will observe at least 20 exoplanets with sizes ranging from Earth-size to Jupiter-size and host stars spanning mid-K to late-M spectral types. The project is made possible by leveraging investments in other projects, including an all-aluminum 0.45-meter Cassegrain telescope design, and a NIR sensor chip assembly from the James Webb Space Telescope. The mission will last five years from initial formulation to closeout, with one-year of science operations. Launch is planned for the mid-2020s as a secondary payload in Sun-synchronous low-Earth orbit. By design, Pandora has a diverse team, with over half of the mission leadership roles filled by early career scientists and engineers, demonstrating the high value of SmallSats for developing the next generation of space mission leaders., Proceedings of the Small Satellite Conference, Science/Mission Payloads, SSC21-VI-02 (2021)

"exoplanet" is a toolkit for probabilistic modeling of astronomical time series data, with a focus on observations of exoplanets, using PyMC3 (Salvatier et al., 2016). PyMC3 is a flexible and high-performance model-building language and inference engine that scales well to problems with a large number of parameters. "exoplanet" extends PyMC3's modeling language to support many of the custom functions and probability distributions required when fitting exoplanet datasets or other astronomical time series. While it has been used for other applications, such as the study of stellar variability, the primary purpose of "exoplanet" is the characterization of exoplanets or multiple star systems using time-series photometry, astrometry, and/or radial velocity. In particular, the typical use case would be to use one or more of these datasets to place constraints on the physical and orbital parameters of the system, such as planet mass or orbital period, while simultaneously taking into account the effects of stellar variability., Published in the Journal of Open Source Software. Comments (still) welcome. Software available at https://docs.exoplanet.codes

We have collected transit times for the TRAPPIST-1 system with the Spitzer Space Telescope over four years. We add to these ground-based, HST and K2 transit time measurements, and revisit an N-body dynamical analysis of the seven-planet system using our complete set of times from which we refine the mass ratios of the planets to the star. We next carry out a photodynamical analysis of the Spitzer light curves to derive the density of the host star and the planet densities. We find that all seven planets' densities may be described with a single rocky mass-radius relation which is depleted in iron relative to Earth, with Fe 21 wt% versus 32 wt% for Earth, and otherwise Earth-like in composition. Alternatively, the planets may have an Earth-like composition, but enhanced in light elements, such as a surface water layer or a core-free structure with oxidized iron in the mantle. We measure planet masses to a precision of 3-5%, equivalent to a radial-velocity (RV) precision of 2.5 cm/sec, or two orders of magnitude more precise than current RV capabilities. We find the eccentricities of the planets are very small; the orbits are extremely coplanar; and the system is stable on 10 Myr timescales. We find evidence of infrequent timing outliers which we cannot explain with an eighth planet; we instead account for the outliers using a robust likelihood function. We forecast JWST timing observations, and speculate on possible implications of the planet densities for the formation, migration and evolution of the planet system., Final version to be published in the Planetary Sciences Journal. 56 pages, 30 figures. Data from the paper and a complete table of forecast JWST times may be found at https://github.com/ericagol/TRAPPIST1_Spitzer/

Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that lead to degenerate solutions. We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves. Geometry-preserving time-series to image transformations of the light curves serve as inputs to a ResNet-18 based architecture which is trained through transfer learning. The McQuillan catalog of published rotation periods is used as ansatz to groundtruth. We benchmark the performance of our method against a random forest regressor, a 1D CNN, and the Auto-Correlation Function (ACF) - the current standard to estimate rotation periods. Despite limiting our input to fewer data points (1k), our model yields more accurate results and runs 350 times faster than ACF runs on the same number of data points and 10,000 times faster than ACF runs on 65k data points. With only minimal feature engineering our approach has impressive accuracy, motivating the application of deep learning to regress stellar parameters on an even larger scale, Comment: Accepted for the Machine Learning and the Physical Sciences Workshop, NeurIPS 2020

The 2020s will be the most data-rich decade of astronomy in history. As the scale and complexity of our surveys increase, the problem of scheduling becomes more critical. We must develop high-quality scheduling approaches, implement them as open-source software, and begin linking the typically separate stages of observation and data analysis., 10 pages; Astro2020 APC White Paper

Transmission spectra probe the atmospheres of transiting exoplanets, but these observations are also subject to signals introduced by magnetic active regions on host stars. Here we outline scientific opportunities in the next decade for providing useful constraints on stellar photospheres and inform interpretations of transmission spectra of the smallest ($R, Science white paper submitted in response to the the U.S. National Academies of Science, Engineering, and Medicine's call for community input to the Astro2020 Decadal Survey; 9 pages, 3 figures

The Transiting Exoplanet Survey Satellite (TESS, launched early 2018) is expected to find a multitude of new transiting planet candidates around the nearest and brightest stars. Timely high-precision follow-up observations from the ground are essential in confirming and further characterizing the planet candidates that TESS will find. However, achieving extreme photometric precisions from the ground is challenging, as ground-based telescopes are subject to numerous deleterious atmospheric effects. Beam-shaping diffusers are emerging as a low-cost technology to achieve hitherto unachievable differential photometric precisions from the ground. These diffusers mold the focal plane image of a star into a broad and stable top-hat shape, minimizing photometric errors due to non-uniform pixel response, atmospheric seeing effects, imperfect guiding, and telescope-induced variable aberrations seen in defocusing. In this paper, we expand on our previous work (Stefansson et al. 2017; Stefansson et al. 2018 [submitted]), providing a further detailed discussion of key guidelines when sizing a diffuser for use on a telescope. Furthermore, we present our open source Python package iDiffuse which can calculate the expected PSF size of a diffuser in a telescope system, along with its expected on-sky diffuser-assisted photometric precision for a host star of a given magnitude. We use iDiffuse to show that most ($\sim$80\%) of the planet hosts that TESS will find will be scintillation limited in transit observations from the ground. Although iDiffuse has primarily been developed to plan challenging transit observations using the diffuser on the ARCTIC imager on the ARC 3.5m Telescope at Apache Point observatory, iDiffuse is modular and can be easily extended to calculate the expected diffuser-assisted photometric precisions on other telescopes., 16 pages, 7 Figures, Submitted to SPIE, Proceedings of the 2018 Conference on Astronomical Telescopes and Instrumentation

TRAPPIST-1 hosts seven Earth-sized planets transiting an M8 star. We observed mid-transit times of each of the inner two planets with the Astrophysical Research Consortium (ARC) 3.5 m Telescope at Apache Point Observatory (APO) to help constrain the planet masses with transit timing variations, and we outline a procedure for analyzing transit observations of late-M stars with APO. The transit times of TRAPPIST-1 b and c are $\mathrm{BJD}_{\mathrm{TDB}} = 2457580.87634^{+0.00034}_{-0.00034}$ and $2457558.89477^{+0.00080}_{-0.00085}$, respectively, which will help constrain the planet masses., 1 figure, 2 pages, accepted to RNAAS

We present the results of an abundance analysis of three G and two K dwarfs in the Praesepe open cluster based on high-resolution, moderate signal-to-noise-ratio spectra obtained with the ARC 3.5-m Telescope at Apache Point Observatory. Using a Principle Component Analysis and the BACCHUS automated spectral analysis code, we determined stellar parameters and abundances of up to 24 elements for each of our targets, which range in temperature from 6000 to 4600 K. The average derived iron abundance for the three G stars is 0.17+/-0.07 dex, consistent with the 0.12+/-0.04 dex derived by Boesgaard et al. (2013) for their sample of 11 solar-type Praesepe members, which included these G stars. To investigate the efficacy of using automated routines to derive the abundances of cooler main-sequence stars, we compared the abundances of the K dwarfs to those of the G dwarfs. Our abundances are consistent to, 11 pages (plus 29 page long table), 3 figures; accepted by ApJ