Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection

The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (Local Outlier Factor and One Class Support Vector Machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use ROC curves to quantify and compare the performance of the two ML techniques.Comment: Submitted to AAS Journals, 30 pages, 14 figure

Identifying the Group-Theoretic Structure of Machine-Learned Symmetries

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building.Comment: 10 pages, 8 figures, 2 table

Discovering Sparse Representations of Lie Groups with Machine Learning

Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter, we extend this technique to derive sparse representations of arbitrary Lie algebras. We show that our method reproduces the canonical (sparse) representations of the generators of the Lorentz group, as well as the $U(n)$ and $SU(n)$ families of Lie groups. This approach is completely general and can be used to find the infinitesimal generators for any Lie group.Comment: 14 pages, 6 figure

Amenorrhea and pituitary human chorionic gonadotrophin production in a 38-year-old presenting as pregnancy of unknown location: case report and review of literature

Background: Amenorrhea and extraplacental production of serum human chorionic gonadotropin (hCG), particularly in young women, can mimic a pregnancy of unknown location. Elevated serum hCG in the absence of pregnancy can pose a diagnostic dilemma and has led to potentially harmful and unwarranted interventions including chemotherapeutic agents like methotrexate or have led to delay in necessary medical interventions in women. We report a case to demonstrate that amenorrhea and extraplacental human chorionic gonadotropin (hCG) production in young women can mimic a pregnancy of unknown location. Furthermore, we performed a critical review of literature on pituitary hCG production. Case: A 38-year-old woman with a diagnosis of Silver-Russell syndrome, a unicornuate uterus, history of right oophorectomy for a benign serous cystadenoma and a desire for pregnancy presenting with a provisional diagnosis of pregnancy of unknown location.After performing a thorough review of history, physical examination, ultrasound exams, and a review of hormone analysis [including hCG, Tumor markers, Follicle-stimulating hormone (FSH), Luteinizing hormone (LH), Anti-Mullerian Hormone (AMH), Estradiol (E2) levels], we confirmed the diagnosis of premature ovarian insufficiency and pituitary hCG production. Conclusions: In women, serum levels of hCG may increase with age, and are not always an indicator of pregnancy. Therefore, it is imperative to interpret false-positive test results and rule out the extraplacental production of hCG. This will help prevent unnecessary surgical procedures and treatment, including chemotherapy

Being tolerated and being discriminated against:Links to psychological well-being through threatened social identity needs

We investigated whether and how the experience of being tolerated and of being discriminated against are associated with psychological well‐being in three correlational studies among three stigmatized groups in Turkey (LGBTI group members, people with disabilities, and ethnic Kurds, total N = 862). Perceived threat to social identity needs (esteem, meaning, belonging, efficacy, and continuity) was examined as a mediator in these associations. Structural equation models showed evidence for the detrimental role of both toleration and discrimination experiences on positive and negative psychological well‐being through higher levels of threatened social identity needs. A mini‐meta analysis showed small to moderate effect sizes and toleration was associated with lower positive well‐being through threatened needs among all three stigmatized groups

Sex-related differences in aging rate are associated with sex chromosome system in amphibians

Sex-related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture-recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex-specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the "unguarded X/Z effect") or repeat-rich Y/W chromosome (the "toxic Y/W effect") could accelerate aging in the heterogametic sex in some vertebrate clades.Peer reviewe

Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection

The next generation of telescopes will yield a substantial increase in the availability of high-quality spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast, and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (local outlier factor and one-class support vector machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use receiver operating characteristic curves to quantify and compare the performance of the two ML techniques

Identifying the group-theoretic structure of machine-learned symmetries

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building

Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles

We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators. The constructed loss functions ensure that the applied transformations are symmetries and the corresponding set of generators forms a closed (sub)algebra. Our procedure is validated with several examples illustrating different types of conserved quantities preserved by symmetry. In the process of deriving the full set of symmetries, we analyze the complete subgroup structure of the rotation groups SO (2), SO (3), and SO (4), and of the Lorentz group $SO(1,3)$ . Other examples include squeeze mapping, piecewise discontinuous labels, and SO (10), demonstrating that our method is completely general, with many possible applications in physics and data science. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties