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  1. ABSTRACT

    We present multi-epoch spectropolarimetry and spectra for a sample of 14 Type IIn supernovae (SNe IIn). We find that after correcting for likely interstellar polarization, SNe IIn commonly show intrinsic continuum polarization of 1–3 per cent at the time of peak optical luminosity, although a few show weaker or negligible polarization. While some SNe IIn have even stronger polarization at early times, their polarization tends to drop smoothly over several hundred days after peak. We find a tendency for the intrinsic polarization to be stronger at bluer wavelengths, especially at early times. While polarization from an electron scattering region is expected to be grey, scattering of SN light by dusty circumstellar material (CSM) may induce such a wavelength-dependent polarization. For most SNe IIn, changes in polarization degree and wavelength dependence are not accompanied by changes in the position angle, requiring that asymmetric pre-SN mass loss had a persistent geometry. While 2–3 per cent polarization is typical, about 30 per cent of SNe IIn have very low or undetected polarization. Under the simplifying assumption that all SN IIn progenitors have axisymmetric CSM (i.e. disc/torus/bipolar), then the distribution of polarization values we observe is consistent with similarly asymmetric CSM seen from a distribution of random viewing angles. This asymmetry has very important implications for understanding the origin of pre-SN mass loss in SNe IIn, suggesting that it was shaped by binary interaction.

     
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  2. The data generated during additive manufacturing (AM) practice can be used to train machine learning (ML) tools to reduce defects, optimize mechanical properties, or increase efficiency. In addition to the size of the repository, emerging research shows that other characteristics of the data also impact suitability of the data for AM-ML application. What should be done in cases for which the data in too small, too homogeneous, or otherwise insufficient? Data augmentation techniques present a solution, offering automated methods for increasing the quality of data. However, many of these techniques were developed for machine vision tasks, and hence their suitability for AM data has not been verified. In this study, several data augmentation techniques are applied to synthetic design repositories to characterize if and to what degree they enhance their performance as ML training sets. We discuss the comparative advantage of these data augmentation techniques across several canonical AM-ML tasks. 
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    Free, publicly-accessible full text available October 1, 2024
  3. ABSTRACT

    We present multi-epoch spectropolarimetry of Type IIn supernova SN2017hcc, 16–391 d after explosion. Continuum polarization up to 6 per cent is observed during the first epoch, making SN 2017hcc the most intrinsically polarized SN ever reported at visible wavelengths. During the first 29 d, when the polarization is strongest, the continuum polarization exhibits wavelength dependence that rises toward the blue, then becomes wavelength independent by day 45. The polarization drops rapidly during the first month, even as the flux is still climbing to peak brightness. None the less, unusually high polarization is maintained until day 68, at which point the polarization declines to levels comparable to those of previous well-studied SNe IIn. Only minor changes in position angle (PA) are measured throughout the evolution. The blue slope of the polarized continuum and polarized line emission during the first month suggests that an aspherical distribution of dust grains in pre-shock circumstellar material (CSM) is echoing the SN IIn spectrum and strongly influencing the polarization, while the subsequent decline during the wavelength-independent phase appears consistent with electron scattering near the SN/CSM interface. The persistence of the PA between these two phases suggests that the pre-existing CSM responsible for the dust scattering at early times is part of the same geometric structure as the electron-scattering region that dominates the polarization at later times. SN 2017hcc appears to be yet another, but more extreme, case of aspherical yet well-ordered CSM in Type IIn SNe, possibly resulting from pre-SN mass-loss shaped by a binary progenitor system.

     
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  4. Abstract

    We present high-cadence photometric and spectroscopic observations of SN 2023axu, a classical Type II supernova with an absoluteV-band peak magnitude of –17.2 ± 0.1 mag. SN 2023axu was discovered by the Distance Less Than 40 Mpc (DLT40) survey within 1 day of the last nondetection in the nearby galaxy NGC 2283 at 13.7 Mpc. We modeled the early light curve using a recently updated shock cooling model that includes the effects of line blanketing and found the explosion epoch to be MJD 59971.48 ± 0.03 and the probable progenitor to be a red supergiant. The shock cooling model underpredicts the overall UV data, which point to a possible interaction with circumstellar material. This interpretation is further supported by spectral behavior. We see a ledge feature around 4600 Å in the very early spectra (+1.1 and +1.5 days after the explosion), which can be a sign of circumstellar interaction. The signs of circumstellar material are further bolstered by the presence of absorption features blueward of Hαand Hβat day >40, which is also generally attributed to circumstellar interaction. Our analysis shows the need for high-cadence early photometric and spectroscopic data to decipher the mass-loss history of the progenitor.

     
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  5. Abstract

    We present the optical spectroscopic evolution of SN 2023ixf seen in subnight cadence spectra from 1.18 to 15 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 (RSG) progenitor from the literature. We find that very few models reproduce the blended Niii(λλ4634.0,4640.6)/Ciii(λλ4647.5,4650.0) 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 10−3–10−2Myr−1, which far exceeds the mass-loss rate for any steady wind, especially for an RSG 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 materialRCSM,out≈ 5 × 1014cm, and a mean circumstellar material density ofρ= 5.6 × 10−14g cm−3. This is consistent with the lower limit on the outer radius of the circumstellar material we calculate from the peak Hαemission flux,RCSM,out≳ 9 × 1013cm.

     
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    Free, publicly-accessible full text available October 1, 2024
  6. Abstract Background The Learning Assistant (LA) model with its subsequent support and training has evidenced significant gains for undergraduate STEM learning and persistence, especially in high-stakes courses like Calculus. Yet, when a swift and unexpected transition occurs from face-to-face to online, remote learning of the LA environment, it is unknown how LAs are able to maintain their motivation (competence, autonomy, and relatedness), adapt to these new challenges, and sustain their student-centered efforts. This study used Self-Determination Theory (SDT) to model theoretical aspects of LAs’ motivations (persistence and performance) both before and after changes were made in delivery of a Calculus II course at Texas Tech University due to COVID-19 interruptions. Results Analysis of weekly written reflections, a focus group session, and a post-course questionnaire of 13 Calculus II LAs throughout Spring semester of 2020 showed that LAs’ reports of competence proportionally decreased when they transitioned online, which was followed by a moderate proportional increase in reports of autonomy (actions they took to adapt to distance instruction) and a dramatic proportional increase in reports of relatedness (to build structures for maintaining communication and building community with undergraduate students). Conclusions Relatedness emerged as the most salient factor from SDT to maintain LA self-determination due to the COVID-19 facilitated interruption to course delivery in a high-stakes undergraduate STEM course. Given that online learning continues during the pandemic and is likely to continue after, this research provides an understanding to how LAs responded to this event and the mounting importance of relatedness when LAs are working with undergraduate STEM learners. Programmatic recommendations are given for enhancing LA preparation including selecting LAs for autonomy and relatedness factors (in addition to competence), modeling mentoring for remote learners, and coaching in best practices for online instruction. 
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  7. In higher education, Learning Assistants (LAs)—a relatively recent evolution grounded in peer mentorship models—are gaining popularity in classrooms as universities strive to meet the needs of undergraduate learners. Unlike Teaching Assistants, LAs are undergraduate students who receive continuous training from faculty mentors in content-area coaching and pedagogical skills. As near-peers, they assist assigned groups of undergraduates (students) during class. Research on LAs suggests that they are significant in mitigating high Drop-Fail-Withdrawal rates of large enrollment undergraduate science, technology, engineering, mathematics, and medical (STEMM) courses. However, there is a dearth of description regarding the learning between LAs and STEMM faculty mentors. This paper reports on perspectives of faculty mentors and their cooperating LAs in regard to their learning relationships during a Calculus II at a research-oriented university during Spring of 2020. Using an exploratory-descriptive qualitative design, faculty (oral responses) and LAs (written responses) reflected on their relationship. Content analysis (coding) resulted in four salient categories (by faculty and LA percentages, respectively) in: Showing Care and Fostering Relationships (47%, 23%); Honing Pedagogical Skills (27%, 36%); Being Prepared for Class and Students (23%, 28%); and Developing Content Knowledge in Calculus (3%, 13%). Benefits of LAs to faculty and ways to commence LA programs at institutions are also discussed. 
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  8. The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy. 
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  9. Abstract

    We present deep, nebular-phase spectropolarimetry of the Type II-P/L SN 2013ej, obtained 167 days after explosion with the European Southern Observatory’s Very Large Telescope. The polarized flux spectrum appears as a nearly perfect (92% correlation), redshifted (by ∼4000 km s−1) replica of the total flux spectrum. Such a striking correspondence has never been observed before in nebular-phase supernova spectropolarimetry, although data capable of revealing it have heretofore been only rarely obtained. Through comparison with 2D polarized radiative transfer simulations of stellar explosions, we demonstrate that localized ionization produced by the decay of a high-velocity, spatially confined clump of radioactive56Ni—synthesized by and launched as part of the explosion with final radial velocity exceeding 4500 km s−1—can reproduce the observations through enhanced electron scattering. Additional data taken earlier in the nebular phase (day 134) yield a similarly strong correlation (84%) and redshift, whereas photospheric-phase epochs that sample days 8 through 97 do not. This suggests that the primary polarization signatures of the high-velocity scattering source only come to dominate once the thick, initially opaque hydrogen envelope has turned sufficiently transparent. This detection in an otherwise fairly typical core-collapse supernova adds to the growing body of evidence supporting strong asymmetries across nature’s most common types of stellar explosions, and establishes the power of polarized flux—and the specific information encoded by it in line photons at nebular epochs—as a vital tool in such investigations going forward.

     
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  10. null (Ed.)