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Register a new userHow Interstellar Chemistry (and Astrochemistry More Generally) Became Useful
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In 1986 Alex Dalgarno published a paper entitled Is Interstellar Chemistry Useful? By the middle 1970s, and perhaps even earlier, Alex had hoped that astronomical molecules would prove to: possess significant diagnostic utility; control many of the environments in which they exist; stimulate a wide variety of physicists and chemists who are at least as fascinated by the mechanisms forming and removing the molecules as by astronomy. His own research efforts have contributed greatly to the realization of that hope. This paper contains a few examples of: how molecules are used to diagnose large-scale dynamics in astronomical sources including star forming regions and supernovae; the ways in which molecular processes control the evolution of astronomical objects such as dense cores destined to become stars and very evolved giant stars; theoretical and laboratory investigations that elucidate the processes producing and removing astronomical molecules and allow their detection.
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For several decades a portrait of Johannes Kepler has been widely circulating among professional astronomers, scientific and academic institutions, and the general public. Despite its provenance and identification having been questioned in the early part of the last century, this painting has reached iconic status. We review its history from its first mention in the literature in the 1870s to a published but virtually unknown judgment of competent art experts of the 1920s that the work is in fact an early nineteenth century forgery. We display the painting in context with other more secure portraits and suggest that if it is based on anything, the painting may derive from the well known portrait from life of Michael Mastlin. This correction takes on certain urgency since 2021 is the 450th anniversary of Keplers birth.
The interstellar medium is characterized by a rich and diverse chemistry. Many of its complex organic molecules are proposed to form through radical chemistry in icy grain mantles. Radicals form readily when interstellar ices (composed of water and other volatiles) are exposed to UV photons and other sources of dissociative radiation, and, if sufficiently mobile, the radicals can react to form larger, more complex molecules. The resulting complex organic molecules (COMs) accompany star and planet formation, and may eventually seed the origins of life on nascent planets. Experiments of increasing sophistication have demonstrated that known interstellar COMs as well as the prebiotically interesting amino acids can form through ice photochemistry. We review these experiments and discuss the qualitative and quantitative kinetic and mechanistic constraints they have provided. We finally compare the effects of UV radiation with those of three other potential sources of radical production and chemistry in interstellar ices: electrons, ions and X-rays.
Astronomy provides a laboratory for extreme physics, a window into environments at extremes of distance, temperature and density that often cant be reproduced in Earth laboratories, or at least not right away. A surprising amount of the science we understand today started out as solutions to problems in astronomy. Some of this science was key in the development of many technologies which we enjoy today. This paper describes some of these connections between astronomy and technology and their history.
We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the treatment effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice and (iv) the $L_2$ is preferred to the $bar epsilon$-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.
We report the detection of linear and cyclic isomers of C3H and C3H2 towards various starless cores and review the corresponding chemical pathways involving neutral (C3Hx with x=1,2) and ionic (C3Hx+ with x = 1,2,3) isomers. We highlight the role of the branching ratio of electronic Dissociative Recombination (DR) reactions of C3H2+ and C3H3+ isomers showing that the statistical treatment of the relaxation of C3H* and C3H2* produced in these DR reactions may explain the relative c,l-C3H and c,l-C3H2 abundances. We have also introduced in the model the third isomer of C3H2 (HCCCH). The observed cyclic-to-linear C3H2 ratio vary from 110 + or - 30 for molecular clouds with a total density around 1e4 molecules.cm-3 to 30 + or - 10 for molecular clouds with a total density around 4e5 molecules.cm-3, a trend well reproduced with our updated model. The higher ratio for low molecular cloud densities is mainly determined by the importance of the H + l-C3H2 -> H + c-C3H2 and H + t-C3H2 -> H + c-C3H2 isomerization reactions.
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