Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and, as much as possible, automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as James Webb Space Telescope (JWST), James Webb Space Telescope (LSST), and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of the observed multi-wavelength and multi-dimensional data or ad hoc simulated data to improve and optimize the photometric redshifts prediction and statistical characterization based on both Spectral Energy Distribution (SED) template fitting and machine learning methodologies. They also provided a new impetus in the investigation of hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, which are particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context, we summarize what was learned and proposed in more than a decade of research

Improving the reliability of photometric redshift with machine learning

In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics

COSMOS2015 dataset machine learning photo-z

In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec<1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics

New Pool of Cortical Interneuron Precursors in the Early Postnatal Dorsal White Matter

The migration of cortical γ-aminobutyric acidergic interneurons has been extensively studied in rodent embryos, whereas few studies have documented their postnatal migration. Combining in vivo analysis together with time-lapse imaging on cortical slices, we explored the origin and migration of cortical interneurons during the first weeks of postnatal life. Strikingly, we observed that a large pool of GAD65-GFP-positive cells accumulate in the dorsal white matter region during the first postnatal week. Part of these cells divides and expresses the transcription factor paired box 6 indicating the presence of local transient amplifying precursors. The vast majority of these cells are immature interneurons expressing the neuronal marker doublecortin and partly the calcium-binding protein calretinin. Time-lapse imaging reveals that GAD65-GFP-positive neurons migrate from the white matter pool into the overlying anterior cingulate cortex (aCC). Some interneurons in the postnatal aCC express the same immature neuronal markers suggesting ongoing migration of calretinin-positive interneurons. Finally, bromodeoxyuridine incorporation experiments confirm that a small fraction of interneurons located in the aCC are generated during the early postnatal period. These results altogether reveal that at postnatal ages, the dorsal white matter contains a pool of interneuron precursors that divide and migrate into the aC

New pool of cortical interneuron precursors in the early postnatal dorsal white matter.

The migration of cortical γ-aminobutyric acidergic interneurons has been extensively studied in rodent embryos, whereas few studies have documented their postnatal migration. Combining in vivo analysis together with time-lapse imaging on cortical slices, we explored the origin and migration of cortical interneurons during the first weeks of postnatal life. Strikingly, we observed that a large pool of GAD65-GFP-positive cells accumulate in the dorsal white matter region during the first postnatal week. Part of these cells divides and expresses the transcription factor paired box 6 indicating the presence of local transient amplifying precursors. The vast majority of these cells are immature interneurons expressing the neuronal marker doublecortin and partly the calcium-binding protein calretinin. Time-lapse imaging reveals that GAD65-GFP-positive neurons migrate from the white matter pool into the overlying anterior cingulate cortex (aCC). Some interneurons in the postnatal aCC express the same immature neuronal markers suggesting ongoing migration of calretinin-positive interneurons. Finally, bromodeoxyuridine incorporation experiments confirm that a small fraction of interneurons located in the aCC are generated during the early postnatal period. These results altogether reveal that at postnatal ages, the dorsal white matter contains a pool of interneuron precursors that divide and migrate into the aCC

Dark Matter Search in the Edelweiss Experiment

Preliminary results obtained with 320g bolometers with simultaneous ionization and heat measurements are described. After a few weeks of data taking, data accumulated with one of these detectors are beginning to exclude the upper part of the DAMA region. Prospects for the present run and the second stage of the experiment, EDELWEISS-II, using an innovative reversed cryostat allowing data taking with 100 detectors, are briefly described.Comment: IDM 2000, 3rd International Workshop on the Identification of Dark Matter, York (GB), 18-22/09/2000, v2.0 minor modification

Cysteine synthases CYSL-1 and CYSL-2 mediate C. elegans heritable adaptation to P. vranovensis infection

Abstract: Parental exposure to pathogens can prime offspring immunity in diverse organisms. The mechanisms by which this heritable priming occurs are largely unknown. Here we report that the soil bacteria Pseudomonas vranovensis is a natural pathogen of the nematode Caenorhabditis elegans and that parental exposure of animals to P. vranovensis promotes offspring resistance to infection. Furthermore, we demonstrate a multigenerational enhancement of progeny survival when three consecutive generations of animals are exposed to P. vranovensis. By investigating the mechanisms by which animals heritably adapt to P. vranovensis infection, we found that parental infection by P. vranovensis results in increased expression of the cysteine synthases cysl-1 and cysl-2 and the regulator of hypoxia inducible factor rhy-1 in progeny, and that these three genes are required for adaptation to P. vranovensis. These observations establish a CYSL-1, CYSL-2, and RHY-1 dependent mechanism by which animals heritably adapt to infection