Breaking the color-reddening degeneracy in type Ia supernovae

A new method to study the intrinsic color and luminosity of type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis (PCA) on spectral series SNe Ia between -12.5 and +17.5 days from B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of color excess due to dust-extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic color variability. Such metric space is a good predictor of the epoch when the maximum in the B-V color curve is reached. Multivariate Partial Least Square (PLS) regression predicts the intrinsic B band light-curve and the intrinsic B-V color curve up to a month after maximum. This allows to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV in the host-galaxy of SNe Ia is found, on average, to be consistent with typical Milky-Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with large sample publicly available. Future automated surveys as LSST will provide a large number of light curves. The analysis shows that observing accompaning spectra for a significative number of SNe will be important even in the case of "normal" SNe Ia.Comment: 11 pages, 11 figure

Luminosity distributions of Type Ia Supernovae

We have assembled a dataset of 165 low redshift, $z<$0.06, publicly available type Ia supernovae (SNe Ia). We produce maximum light magnitude ($M_{B}$ and $M_{V}$) distributions of SNe Ia to explore the diversity of parameter space that they can fill. Before correction for host galaxy extinction we find that the mean $M_{B}$ and $M_{V}$ of SNe Ia are $-18.58\pm0.07$mag and $-18.72\pm0.05$mag respectively. Host galaxy extinction is corrected using a new method based on the SN spectrum. After correction, the mean values of $M_{B}$ and $M_{V}$ of SNe Ia are $-19.10\pm0.06$ and $-19.10\pm0.05$mag respectively. After correction for host galaxy extinction, `normal' SNeIa ($\Delta m_{15}(B)<1.6$mag) fill a larger parameter space in the Width-Luminosity Relation (WLR) than previously suggested, and there is evidence for luminous SNe Ia with large $\Delta m_{15}(B)$. We find a bimodal distribution in $\Delta m_{15}(B)$, with a pronounced lack of transitional events at $\Delta m_{15}(B)$=1.6 mag. We confirm that faster, low-luminosity SNe tend to come from passive galaxies. Dividing the sample by host galaxy type, SNe Ia from star-forming (S-F) galaxies have a mean $M_{B}=-19.20 \pm 0.05$ mag, while SNe Ia from passive galaxies have a mean $M_{B}=-18.57 \pm 0.24$ mag. Even excluding fast declining SNe, `normal' ($M_{B}<-18$ mag) SNe Ia from S-F and passive galaxies are distinct. In the $V$-band, there is a difference of 0.4$\pm$0.13 mag between the median ($M_{V}$) values of the `normal' SN Ia population from passive and S-F galaxies. This is consistent with ($\sim 15 \pm$10)% of `normal' SNe Ia from S-F galaxies coming from an old stellar population

Screening for Bipolar Disorder Symptoms in Depressed Primary Care Attenders: Comparison between Mood Disorder Questionnaire and Hypomania Checklist (HCL-32)

Objective. To describe the prevalence of patients who screen positive for bipolar disorder (BD) symptoms in primary care comparing two screening instruments: Mood Disorders Questionnaire (MDQ) and Hypomania Checklist (HCL-32). Participants. Adult patients presenting to their primary care practitioners for any cause and reporting current depression symptoms or a depressive episode in the last 6 months. Methods. Subjects completed MDQ and HCL-32, and clinical diagnosis was assessed by a psychiatrist following DSM-IV criteria. Depressive symptoms were evaluated in a subgroup with the Patient Health Questionnaire (PHQ-9). Results. A total of 94 patients were approached to participate and 93 completed the survey. Among these, 8.9% screened positive with MDQ and 43.0% with HCL-32. MDQ positive had more likely features associated with BD: panic disorder and smoking habit (P < .05). The best test accuracy was performed by cut-off 5 for MDQ (sensitivity = .91; specificity = .67) and 15 for HCL-32 (sensitivity = .64; specificity = .57). Higher total score of PHQ-9 was related to higher total scores at the screening tests (P < .001). Conclusion. There is a significant prevalence of bipolar symptoms in primary care depressed patients. MDQ seems to have better accuracy and feasibility than HCL-32, features that fit well in the busy setting of primary care

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA

Breaking the color-reddening degeneracy in type Ia supernovae

A new method to study the intrinsic color and luminosity of type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis (PCA) on spectral series SNe Ia between -12.5 and +17.5 days from B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of color excess due to dust-extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic color variability. Such metric space is a good predictor of the epoch when the maximum in the B-V color curve is reached. Multivariate Partial Least Square (PLS) regression predicts the intrinsic B band light-curve and the intrinsic B-V color curve up to a month after maximum. This allows to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV in the host-galaxy of SNe Ia is found, on average, to be consistent with typical Milky-Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with large sample publicly available. Future automated surveys as LSST will provide a large number of light curves. The analysis shows that observing accompaning spectra for a significative number of SNe will be important even in the case of "normal" SNe Ia

Globally optimal shape and spin pole determination with lightcurve inversion

Light-curve inversion is an established technique in determining the shape and spin states of an asteroid. However, the front part of the processing pipeline, which recovers the spin pole and area of each facet, is a non-convex optimization problem. Hence, an y local iterative optimization scheme can only promise a locally optimal solution. Apart from the obvious downsides of getting a non-optimal solution and the need for an initialization scheme, another major implication is that it creates an ambiguous scenario –which is to be blamed for the remaining residual? The inaccuracy of the modelling, the integrity of the data, or the non-global algorithm? We address the last uncertainty in this paper by embedding the spin pole and area vector determination module in a deterministic global optimization framework. To the best of our knowledge, this is the first attempt to solve these parameters globally . Specifically , given calibrated light-curve data, a scattering model for the object, and spin period, our method outputs the globally optimal spin pole and area vector solutions. One theoretical contribution of this paper is the introduction of a lower bound error function that is derived based on (1) the geometric relationship between the incident and scattered light on a surface and (2) the uncertainty of the gap between the observed and estimated brightness at a particular epoch in a light curve. We validated our method’s ability in achieving global minimum with both simulated and real light-curve data. We also tested our method on the real light curves of four asteroids.Chee-Kheng Chng, Michele Sasdelli, and Tat-Jun Chi

Rapid classification of TESS planet candidates with convolutional neural networks

Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increases. This is especially true for NASA's TESS mission which generates thousands of new candidates each month. Here we created the first deep learning model capable of classifying TESS planet candidates. We adapted the neural network model of Ansdell et al. (2018) to TESS data. We then trained and tested this updated model on 4 sectors of high-fidelity, pixel-level simulations data created using the Lilith simulator and processed using the full TESS SPOC pipeline. We find our model performs very well on our simulated data, with 97% average precision and 92% accuracy on planets in the 2-class model. This accuracy is also boosted by another ~4% if planets found at the wrong periods are included. We also performed 3- and 4-class classification of planets, blended & target eclipsing binaries, and non-astrophysical false positives, which have slightly lower average precision and planet accuracies, but are useful for follow-up decisions. When applied to real TESS data, 61% of TCEs coincident with currently published TOIs are recovered as planets, 4% more are suggested to be EBs, and we propose a further 200 TCEs as planet candidates

Anger and depressive ruminations as predictors of dysregulated behaviours in borderline personality disorder.

BACKGROUND: Anger and depressive ruminations have recently received empirical attention as processes related to borderline personality disorder (BPD). The Emotional Cascade Model (Selby, Anestis, & Joiner, 2008) suggests that negative affect (such as anger and sadness) may trigger rumination, which in turn may increase the duration and extent of negative affect, leading to dysregulated behaviours aimed at reducing such intense and unpleasant emotions. AIM: The aim of this study is to explore the relationships between emotional dysregulation, anger and depressive ruminations, and their role in predicting dysregulated behaviours (such as aggression and self-harm) in a clinical sample of patients with BPD. METHODS: Ninety-one patients with a diagnosis of BPD were recruited from three outpatient community mental health centres and asked to complete a comprehensive assessment for personality disorder symptoms, emotion dysregulation, anger and depressive ruminations, aggression, and self-harm. RESULTS: Anger and depressive ruminations were found to be significantly associated to, respectively, self-harm and aggression, beyond the variance accounted by emotional dysregulation. CONCLUSIONS: Rumination may act as a mediator between emotional dysregulation and dysregulated behaviours in BPD. Future research should examine whether clinical techniques aimed at reducing rumination may be helpful in reducing dysregulated behaviours in patients with BPD. "This is the pre-peer reviewed version of the following article: Martino, F., Caselli, G., Di Tommaso, J., Sassaroli, S., Spada, M.M ., Valenti, B., Berardi, D., Sasdelli, A and Menchetti, M (2017) Anger and depressive ruminations as predictors of dysregulated behaviours in borderline personality disorder. Clinical Psychology and Psychotherapy., which has been published in final form at 10.1002/cpp.2152 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.