Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers

no code implementations • 7 Jun 2021 • A. I. Parkes, J. Camilleri, D. A. Hudson, A. J. Sobey

This paper explores the use of the Fit to Median Error measure in machine learning regression automation, using evolutionary computation in order to improve the approximation of the ground truth.

BIG-bench Machine Learning Interpretable Machine Learning +1

Search for Double-Beta Decay of $\mathrm{^{130}Te}$ to the $0^+$ States of $\mathrm{^{130}Xe}$ with CUORE

no code implementations • 26 Jan 2021 • CUORE Collaboration, D. Q. Adams, C. Alduino, K. Alfonso, F. T. Avignone III, O. Azzolini, G. Bari, F. Bellini, G. Benato, M. Biassoni, A. Branca, C. Brofferio, C. Bucci, J. Camilleri, A. Caminata, A. Campani, L. Canonica, X. G. Cao, S. Capelli, L. Cappelli, L. Cardani, P. Carniti, N. Casali, E. Celi, D. Chiesa, M. Clemenza, S. Copello, C. Cosmelli, O. Cremonesi, R. J. Creswick, A. D'Addabbo, I. Dafinei, C. J. Davis, S. Dell'Oro, S. Di Domizio, V. Dompè, D. Q. Fang, G. Fantini, M. Faverzani, E. Ferri, F. Ferroni, E. Fiorini, M. A. Franceschi, S. J. Freedman, S. H. Fu, B. K. Fujikawa, A. Giachero, L. Gironi, A. Giuliani, P. Gorla, C. Gotti, T. D. Gutierrez, K. Han, K. M. Heeger, R. G. Huang, H. Z. Huang, J. Johnston, G. Keppel, Yu. G. Kolomensky, C. Ligi, L. Ma, Y. G. Ma, L. Marini, R. H. Maruyama, D. Mayer, Y. Mei, N. Moggi, S. Morganti, T. Napolitano, M. Nastasi, J. Nikkel, C. Nones, E. B. Norman, A. Nucciotti, I. Nutini, T. O'Donnell, J. L. Ouellet, S. Pagan, C. E. Pagliarone, L. Pagnanini, M. Pallavicini, L. Pattavina, M. Pavan, G. Pessina, V. Pettinacci, C. Pira, S. Pirro, S. Pozzi, E. Previtali, A. Puiu, C. Rosenfeld, C. Rusconi, M. Sakai, S. Sangiorgio, B. Schmidt, N. D. Scielzo, V. Sharma, V. Singh, M. Sisti, D. Speller, P. T. Surukuchi, L. Taffarello, F. Terranova, C. Tomei, K. J. Vetter, M. Vignati, S. L. Wagaarachchi, B. S. Wang, B. Welliver, J. Wilson, K. Wilson, L. A. Winslow, S. Zimmermann, S. Zucchelli

In this work we present the latest results on two searches for the double beta decay (DBD) of $\mathrm{^{130}Te}$ to the first $0^{+}_2$ excited state of $\mathrm{^{130}Xe}$: the $0\nu\beta\beta$ decay and the Standard Model-allowed two-neutrinos double beta decay ($2\nu\beta\beta$).

Nuclear Experiment

Performance of a Large Area Photon Detector For Rare Event Search Applications

no code implementations • 29 Sep 2020 • CPD Collaboration, C. W. Fink, S. L. Watkins, T. Aramaki, P. L. Brink, J. Camilleri, X. Defay, S. Ganjam, Yu. G. Kolomensky, R. Mahapatra, N. Mirabolfathi, W. A. Page, R. Partridge, M. Platt, M. Pyle, B. Sadoulet, B. Serfass, S. Zuber

We present the design and characterization of a large-area Cryogenic PhotoDetector (CPD) designed for active particle identification in rare event searches, such as neutrinoless double beta decay and dark matter experiments.

Instrumentation and Detectors High Energy Physics - Experiment