Your PRISE Universe, 2021, at Harvard University, Tuesday, August 3, 2021:
A discussion of visualization, glue, glupyter, the Path to Newton, and the Prediction Project, with the Harvard PRISE (Program for Research in Science and Engineering) students, summer 2021. (AKA "Distinguished Speaker Lecture")
Chilloquium , at (Online only), Tuesday, May 4, 2021:
A discussion with Harvard's Society of Physics Students (unscripted). The small number of slides that were used to guide the discussion are posted here to make the links  embedded in them available. 
Jorma Harju, Jaime E. Pineda, Anton I. Vasyunin, Paola Caselli, Stella S. R. Offner, Alyssa A. Goodman, and alia. 2019. “Efficient methanol desorption in shear instability.” arXiv, 1903, 11298. Publisher's VersionAbstract
We present ALMA maps of the starless molecular cloud core Ophiuchus/H-MM1 in the lines of deuterated ammonia (ortho-NH2D), methanol (CH3OH), and sulphur monoxide (SO). The dense core is seen in NH2D emission, whereas the CH3OH and SO distributions form a halo surrounding the core. Because methanol is formed on grain surfaces, its emission highlights regions where desorption from grains is particularly efficient. Methanol and sulphur monoxide are most abundant in a narrow zone that follows the eastern side of the core. This side is sheltered from the stronger external radiation field coming from the west. We show that photodissociation on the illuminated side can give rise to an asymmetric methanol distribution, but that the stark contrast observed in H-MM1 is hard to explain without assuming enhanced desorption on the shaded side. The region of the brightest emission has a wavy structure that rolls up at one end. This is the signature of Kelvin-Helmholtz instability occurring in sheared flows. We suggest that in this zone, methanol and sulphur are released as a result of grain-grain collisions induced by shear vorticity.
Hope How-Huan Chen, Jaime E. Pineda, Stella S. R. Offner, Alyssa A. Goodman, and alia. 2019. “Droplets II: Internal Velocity Structures and Potential Rotational Motions in Coherent Cores.” arXiv, 1908, 04367. Publisher's VersionAbstract
We present an analysis of the internal velocity structures of the newly identified sub-0.1 pc coherent structures, droplets, in L1688 and B18. By fitting 2D linear velocity fields to the observed maps of velocity centroids, we determine the magnitudes of linear velocity gradients and examine the potential rotational motions that could lead to the observed velocity gradients. The results show that the droplets follow the same power-law relation between the velocity gradient and size found for larger-scale dense cores. Assuming that rotational motion giving rise to the observed velocity gradient in each core is a solid-body rotation of a rotating body with a uniform density, we derive the "net rotational motions" of the droplets. We find a ratio between rotational and gravitational energies, β, of ∼0.046 for the droplets, and when including both droplets and larger-scale dense cores, we find β∼0.039. We then examine the alignment between the velocity gradient and the major axis of each droplet, using methods adapted from the histogram of relative orientations (HRO) introduced by Soler et al. (2013). We find no definitive correlation between the directions of velocity gradients and the elongations of the cores. Lastly, we discuss physical processes other than rotation that may give rise to the observed velocity field.
Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, Arya Farahi, Doug Finkbeiner, Shy Genel, Alyssa Goodman, and alia. 2019. “The Role of Machine Learning in the Next Decade of Cosmology.” Bulletin of the American Astronomical Society, 51, 14. Publisher's VersionAbstract
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
C. Zucker, J. S. Speagle, E. Schlafly, G. M. Green, D. P. Finkbeiner, A. Goodman, and J. Alves. 2019. “VizieR Online Data Catalog: Distances to molecular clouds in SFR (Zucker+, 2020).” VizieR Online Data Catalog, J/A+A/633/A51.Abstract

Distances to ~60 star-forming regions in Reipurth (2008, Star Formation Handbook, vols I and II) have been computed using stellar photometry and Gaia DR2 parallax measurements. Usually, several distance estimates are taken across each cloud.


For each sightline, the median distance (d50) is provided, plus the 16th and 84th percentiles on the distance probability distribution function. There is an additional systematic uncertainty, which is unknown but estimated to be ~5% in distance for clouds <1.5kpc, ~10% in distance for clouds >1.5kpc, and ~7% in distance for the southern clouds Lupus, Chamaeleon, and Corona Australis. These should be added in quadrature with the statistical uncertainties reported in the table. In addition to the distances, ancillary model parameters used in our fit are also included (e.g. the amount of foreground extinction "f"). See Section 3.2.1 and Section 3.2.2 in Zucker et al. (2019ApJ...879..125Z) for a complete description of model parameters.