QSO photometric redshifts using machine learning and neural networks

ABSTRACT The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here, we compare the accuracy of the predicted photometric redshifts obtained from deep learning (DL) with the k-nearest neighbour (kNN) and the decision tree regression (DTR) algorithms. We find using a combination of near-infrared, visible, and ultraviolet magnitudes, trained upon a sample of Sloan Digital Sky Survey quasi-stellar objects, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σΔz = 0.24 (σΔz(norm) = 0.11). Testing on various subsamples, we find that the DL algorithm generally has lower values of σΔz, in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with neither filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δz < 0.1 up to z ∼ 2.5. Applying the DL algorithm trained on our 70 000 strong sample to other independent (radio-selected) data sets, we find σΔz ≤ 0.36 (σΔz(norm) ≤ 0.17) over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array.

Managing Substance Use Disorder

This practitioner guide reviews screening, assessment, and treatment of substance use disorders (SUDs). It is designed to accompany Managing Your Substance Use Disorder: Client Workbook and A Family Guide to Coping with Substance Use Disorders. The latter guide was added because each person with a SUD affects the family and concerned significant others. The information and strategies that the authors present can be used with clients who have any type of SUD. The guide focuses on strategies to reduce or stop substance use and change behaviors that challenge recovery. The information presented is derived from research, clinical, and recovery literature and from the authors’ extensive experience developing and managing a large continuum of clinical services, providing direct care, conducting quality improvement initiatives, participating in clinical trials, and teaching all disciplines in a large medical center and the community. This guide discusses professional approaches and attitudes toward individuals with SUDs, assessment, diagnostic formulation, psychosocial and pharmacotherapeutic treatments, and mutual support programs. It provides an overview of the recovery and relapse processes and practical strategies to address issues associated with SUDs. This guide is for practitioners from any discipline who encounter individuals with SUDs in addiction, mental health, psychiatric, private practice, or other settings such as social services and the criminal justice system. Even medical practitioners who do not specialize in addiction treatment can benefit from the information in this guide because individuals with SUDs are found in all types of healthcare settings.

THE HARRINGTON–SHELAH MODEL WITH LARGE CONTINUUM

We prove from the existence of a Mahlo cardinal the consistency of the statement that 2ω = ω3 holds and every stationary subset of ${\omega _2}\mathop \cap \nolimits {\rm{cof}}\left( \omega \right)$ reflects to an ordinal less than ω2 with cofinality ω1.

A CO-ANALYTIC COHEN-INDESTRUCTIBLE MAXIMAL COFINITARY GROUP

Assuming that every set is constructible, we find a ${\text{\Pi }}_1^1 $ maximal cofinitary group of permutations of $\mathbb{N}$ which is indestructible by Cohen forcing. Thus we show that the existence of such groups is consistent with arbitrarily large continuum. Our method also gives a new proof, inspired by the forcing method, of Kastermans’ result that there exists a ${\text{\Pi }}_1^1 $ maximal cofinitary group in L.